version (#1099)

* with docs

* nit

.circleci/config.yml

@@ -31,7 +31,7 @@ jobs:
           name: Install dependencies
           command: |
             pip install --upgrade cmake
-            pip install git+https://github.com/wjakob/nanobind.git@4148debcf91f5ccab0c3b8d67b5c3cabd61f407f
+            pip install git+https://github.com/wjakob/nanobind.git@2f04eac452a6d9142dedb957701bdb20125561e4
             pip install numpy
             sudo apt-get update
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
@@ -44,7 +44,7 @@ jobs:
           name: Generate package stubs
           command: |
             echo "stubs"
-            python -m nanobind.stubgen -m mlx.core -r -O python
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
@@ -80,7 +80,7 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install git+https://github.com/wjakob/nanobind.git@4148debcf91f5ccab0c3b8d67b5c3cabd61f407f
+            pip install git+https://github.com/wjakob/nanobind.git@2f04eac452a6d9142dedb957701bdb20125561e4
             pip install numpy
             pip install torch
             pip install tensorflow
@@ -94,7 +94,7 @@ jobs:
           name: Generate package stubs
           command: |
             source env/bin/activate
-            python -m nanobind.stubgen -m mlx.core -r -O python
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
@@ -143,7 +143,7 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install git+https://github.com/wjakob/nanobind.git@4148debcf91f5ccab0c3b8d67b5c3cabd61f407f
+            pip install git+https://github.com/wjakob/nanobind.git@2f04eac452a6d9142dedb957701bdb20125561e4
             pip install --upgrade setuptools
             pip install numpy
             pip install twine
@@ -159,7 +159,7 @@ jobs:
           name: Generate package stubs
           command: |
             source env/bin/activate
-            python -m nanobind.stubgen -m mlx.core -r -O python
+            python setup.py generate_stubs 
       - run:
           name: Build Python package
           command: |
@@ -207,7 +207,7 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install git+https://github.com/wjakob/nanobind.git@4148debcf91f5ccab0c3b8d67b5c3cabd61f407f
+            pip install git+https://github.com/wjakob/nanobind.git@2f04eac452a6d9142dedb957701bdb20125561e4
             pip install --upgrade setuptools
             pip install numpy
             pip install auditwheel
@@ -216,7 +216,7 @@ jobs:
             << parameters.extra_env >> \
               CMAKE_BUILD_PARALLEL_LEVEL="" \
               pip install . -v
-            python -m nanobind.stubgen -m mlx.core -r -O python
+            python setup.py generate_stubs 
             << parameters.extra_env >> \
               CMAKE_BUILD_PARALLEL_LEVEL="" \
               python -m build --wheel

.pre-commit-config.yaml

@@ -1,11 +1,11 @@
 repos:
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v17.0.6
+    rev: v18.1.4
     hooks:
     -   id: clang-format
 # Using this mirror lets us use mypyc-compiled black, which is about 2x faster
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.2.0
+    rev: 24.4.2
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort

ACKNOWLEDGMENTS.md

@@ -7,7 +7,7 @@ with a short description of your contribution(s) below. For example:
 
 MLX was developed with contributions from the following individuals:
 
-- Nripesh Niketan: Added `softsign`, `softmax`, `hardswish`, `logsoftmax` activation functions. Added `dropout3d` ops. Added `LogicalAnd` and `LogicalOR` ops.
+- Nripesh Niketan: Added `softsign`, `softmax`, `hardswish`, `logsoftmax` activation functions. Added `dropout3d` ops. Added `LogicalAnd` and `LogicalOR` ops. Added `clip_grad_norm` along with `tree_reduce`.
 - Juarez Bochi: Fixed bug in cross attention.
 - Justin Deschenaux: Sine, Cosine, arange, randint, truncated normal, bernoulli, lion optimizer, Dropout2d, linear and logistic regression python example.
 - Diogo Da Cruz: Added `tri`, `tril`, `triu`, `tensordot`, `inner`, `outer`, `tile`, `StreamContext`, `stream` and safetensor support.
@@ -15,6 +15,8 @@ MLX was developed with contributions from the following individuals:
 - Hinrik Snær Guðmundsson: Added `atleast_1d`, `atleast_2d`, `atleast_3d` ops.
 - Luca Arnaboldi: Added `Ceil` and `Floor` ops; implemented pickling, copy and deepcopy for mlx arrays.
 - Brian Keene & Atila Orhon, with Argmax Inc.: Added `fast.scaled_dot_product_attention`
+- AmirHossein Razlighi: Added chaining support for some of the ops in `nn.Module`. Comparison works for non array objects in `mlx.core.array`. Exception handling for invalid operations in `mlx.core.array`.
+
 <a href="https://github.com/ml-explore/mlx/graphs/contributors">
   <img class="dark-light" src="https://contrib.rocks/image?repo=ml-explore/mlx&anon=0&columns=20&max=100&r=true" />
 </a>

CMakeLists.txt

@@ -15,31 +15,35 @@ option(MLX_BUILD_EXAMPLES "Build examples for mlx" ON)
 option(MLX_BUILD_BENCHMARKS "Build benchmarks for mlx" OFF)
 option(MLX_BUILD_PYTHON_BINDINGS "Build python bindings for mlx" OFF)
 option(MLX_BUILD_METAL "Build metal backend" ON)
+option(MLX_METAL_DEBUG "Enhance metal debug workflow" OFF)
+option(MLX_ENABLE_X64_MAC "Enable building for x64 macOS" OFF)
+option(MLX_BUILD_GGUF "Include support for GGUF format" ON)
+option(MLX_BUILD_SAFETENSORS "Include support for safetensors format" ON)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
 
 if(NOT MLX_VERSION)
-  set(MLX_VERSION 0.8.0)
+  set(MLX_VERSION 0.13.0)
 endif()
 
 # --------------------- Processor tests -------------------------
 
-message(STATUS "Building MLX for ${CMAKE_HOST_SYSTEM_PROCESSOR} processor on ${CMAKE_SYSTEM_NAME}")
+message(STATUS "Building MLX for ${CMAKE_SYSTEM_PROCESSOR} processor on ${CMAKE_SYSTEM_NAME}")
 
 set(MLX_BUILD_ARM OFF)
 
 if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
-  if (${CMAKE_HOST_SYSTEM_PROCESSOR} MATCHES "x86_64" AND ${CMAKE_HOST_APPLE})
-    message(FATAL_ERROR
-      "Building for x86_64 on macOS is not supported."
-      " If you are on an Apple silicon system, check the build"
-      " documentation for possible fixes: "
-      "https://ml-explore.github.io/mlx/build/html/install.html#build-from-source")
-  elseif (${CMAKE_HOST_SYSTEM_PROCESSOR} MATCHES "x86_64")
-    message(WARNING
-      "Building for x86_64 on macOS is not supported."
-      " If you are on an Apple silicon system, "
-      " make sure you are building for arm64.")
-  elseif(${CMAKE_HOST_SYSTEM_PROCESSOR} MATCHES "arm64")
+  if(${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64")
+    if(NOT MLX_ENABLE_X64_MAC)
+      message(FATAL_ERROR
+        "Building for x86_64 on macOS is not supported."
+        " If you are on an Apple silicon system, check the build"
+        " documentation for possible fixes: "
+        "https://ml-explore.github.io/mlx/build/html/install.html#build-from-source")
+    else()
+      message(WARNING "Building for x86_64 arch is not officially supported.")
+    endif()
+    set(MLX_BUILD_METAL OFF)
+  elseif(${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm64")
     set(MLX_BUILD_ARM ON)
   endif()
 
@@ -64,8 +68,14 @@ endif()
 if (MLX_BUILD_METAL AND NOT METAL_LIB)
   message(STATUS "Metal not found. Unable to build GPU")
   set(MLX_BUILD_METAL OFF)
+  set(MLX_METAL_DEBUG OFF)
 elseif (MLX_BUILD_METAL)
   message(STATUS "Building METAL sources")
+
+  if (MLX_METAL_DEBUG)
+    add_compile_definitions(MLX_METAL_DEBUG)
+  endif()
+
   # Throw an error if xcrun not found
   execute_process(COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
                   OUTPUT_VARIABLE MACOS_VERSION
@@ -74,8 +84,10 @@ elseif (MLX_BUILD_METAL)
   message(STATUS "Building with SDK for macOS version ${MACOS_VERSION}")
 
   if (${MACOS_VERSION} GREATER_EQUAL 14.2)
+    set(METAL_CPP_PATCH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/metal.14.2.diff)
     set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS14.2_iOS17.2.zip)
   elseif (${MACOS_VERSION} GREATER_EQUAL 14.0)
+    set(METAL_CPP_PATCH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/metal.14.0.diff)
     set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS14_iOS17-beta.zip)
   else()
     message(FATAL_ERROR "MLX requires macOS SDK >= 14.0 to be built with MLX_BUILD_METAL=ON" )
@@ -84,6 +96,7 @@ elseif (MLX_BUILD_METAL)
   FetchContent_Declare(
     metal_cpp
     URL ${METAL_CPP_URL}
+    PATCH_COMMAND /usr/bin/patch -N -i ${METAL_CPP_PATCH} || true
   )
 
   FetchContent_MakeAvailable(metal_cpp)
@@ -108,7 +121,27 @@ if (MLX_BUILD_ARM AND ACCELERATE_LIBRARY)
 else()
   message(STATUS "Accelerate or arm neon not found, using default backend.")
   set(MLX_BUILD_ACCELERATE OFF)
-  #set(BLA_VENDOR Generic)
+  if(${CMAKE_HOST_APPLE})
+    # The blas shipped in macOS SDK is not supported, search homebrew for
+    # openblas instead.
+    set(BLA_VENDOR OpenBLAS)
+    set(LAPACK_ROOT "${LAPACK_ROOT};$ENV{LAPACK_ROOT};/usr/local/opt/openblas")
+  endif()
+  # Search and link with lapack.
+  find_package(LAPACK REQUIRED)
+  if (NOT LAPACK_FOUND)
+    message(FATAL_ERROR "Must have LAPACK installed")
+  endif()
+  find_path(LAPACK_INCLUDE_DIRS lapacke.h
+    /usr/include
+    /usr/local/include
+    /usr/local/opt/openblas/include)
+  message(STATUS "Lapack lib " ${LAPACK_LIBRARIES})
+  message(STATUS "Lapack include " ${LAPACK_INCLUDE_DIRS})
+  target_include_directories(mlx PRIVATE ${LAPACK_INCLUDE_DIRS})
+  target_link_libraries(mlx ${LAPACK_LIBRARIES})
+  # List blas after lapack otherwise we may accidentally incldue an old version
+  # of lapack.h from the include dirs of blas.
   find_package(BLAS REQUIRED)
   if (NOT BLAS_FOUND)
     message(FATAL_ERROR "Must have BLAS installed")
@@ -122,17 +155,6 @@ else()
   message(STATUS "Blas include " ${BLAS_INCLUDE_DIRS})
   target_include_directories(mlx PRIVATE ${BLAS_INCLUDE_DIRS})
   target_link_libraries(mlx ${BLAS_LIBRARIES})
-  find_package(LAPACK REQUIRED)
-  if (NOT LAPACK_FOUND)
-      message(FATAL_ERROR "Must have LAPACK installed")
-  endif()
-  find_path(LAPACK_INCLUDE_DIRS lapacke.h
-    /usr/include
-    /usr/local/include)
-  message(STATUS "Lapack lib " ${LAPACK_LIBRARIES})
-  message(STATUS "Lapack include " ${LAPACK_INCLUDE_DIRS})
-  target_include_directories(mlx PRIVATE ${LAPACK_INCLUDE_DIRS})
-  target_link_libraries(mlx ${LAPACK_LIBRARIES})
 endif()
 
 add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/mlx)

benchmarks/cpp/time_utils.h

@@ -17,14 +17,13 @@
             << std::setprecision(5) << time_fn(FUNC, ##__VA_ARGS__) << " msec" \
             << std::endl;
 
-#define TIMEM(MSG, FUNC, ...)                                                  \
-  std::cout << "Timing "                                                       \
-            << "(" << MSG << ") " << #FUNC << " ... " << std::flush            \
-            << std::setprecision(5) << time_fn(FUNC, ##__VA_ARGS__) << " msec" \
-            << std::endl;
+#define TIMEM(MSG, FUNC, ...)                                      \
+  std::cout << "Timing " << "(" << MSG << ") " << #FUNC << " ... " \
+            << std::flush << std::setprecision(5)                  \
+            << time_fn(FUNC, ##__VA_ARGS__) << " msec" << std::endl;
 
 template <typename F, typename... Args>
-double time_fn(F fn, Args... args) {
+double time_fn(F fn, Args&&... args) {
   // warmup
   for (int i = 0; i < 5; ++i) {
     eval(fn(std::forward<Args>(args)...));

benchmarks/python/conv1d_bench.py

@@ -0,0 +1,123 @@
+import argparse
+import math
+import os
+import subprocess
+import time
+
+import mlx.core as mx
+import numpy as np
+import torch
+
+device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
+device_name = device_name.decode("utf-8").strip("\n")
+
+N_warmup = 10
+N_iter_bench = 100
+N_iter_func = 5
+
+
+def bench(f, a, b):
+    for i in range(N_warmup):
+        f(a, b)
+    torch.mps.synchronize()
+
+    s = time.perf_counter_ns()
+    for i in range(N_iter_bench):
+        f(a, b)
+    e = time.perf_counter_ns()
+    return (e - s) * 1e-9
+
+
+def make_mx_conv_1D(strides=1, padding=0, groups=1):
+    def mx_conv_1D(a, b):
+        ys = []
+        for _ in range(N_iter_func):
+            y = mx.conv1d(a, b, stride=strides, padding=padding, groups=groups)
+            ys.append(y)
+        mx.eval(ys)
+        return ys
+
+    return mx_conv_1D
+
+
+def make_pt_conv_1D(strides=1, padding=0, groups=1):
+    @torch.no_grad()
+    def pt_conv_1D(a, b):
+        ys = []
+        for _ in range(N_iter_func):
+            y = torch.conv1d(a, b, stride=strides, padding=padding, groups=groups)
+            ys.append(y)
+        torch.mps.synchronize()
+        return ys
+
+    return pt_conv_1D
+
+
+def bench_shape(N, iH, C, wH, O, strides, padding, np_dtype, groups):
+    scale = 1.0 / math.sqrt(wH * C)
+    a_np = np.random.uniform(0, 0.5, (N, iH, C)).astype(np_dtype)
+    b_np = np.random.uniform(-scale, scale, (O, wH, int(C / groups))).astype(np_dtype)
+
+    a_mx = mx.array(a_np)
+    b_mx = mx.array(b_np)
+
+    a_pt = torch.from_numpy(a_np.transpose((0, 2, 1))).to("mps")
+    b_pt = torch.from_numpy(b_np.transpose((0, 2, 1))).to("mps")
+
+    torch.mps.synchronize()
+
+    f_mx = make_mx_conv_1D(strides, padding, groups)
+    f_pt = make_pt_conv_1D(strides, padding, groups)
+
+    time_torch = bench(f_pt, a_pt, b_pt)
+    time_mlx = bench(f_mx, a_mx, b_mx)
+
+    out_mx = mx.conv1d(a_mx, b_mx, stride=strides, padding=padding, groups=groups)
+    out_pt = torch.conv1d(
+        a_pt.to("cpu"), b_pt.to("cpu"), stride=strides, padding=padding, groups=groups
+    )
+    out_pt = torch.permute(out_pt, (0, 2, 1))
+    out_pt = out_pt.numpy(force=True)
+
+    atol = 2e-5 if np_dtype == np.float32 else 1e-4
+
+    if not np.allclose(out_pt, out_mx, atol=atol):
+        print(
+            f"Failed at {(N, iH, C)}, {(O, wH, C)} [strides = {strides}, padding = {padding}, groups = {groups}] with max(|a - b|) = {np.max(np.abs(out_pt - out_mx))}"
+        )
+
+    return time_mlx, time_torch
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run conv benchmarks")
+
+    dtypes = ("float32",)
+    shapes = (
+        (4, 32, 32, 5, 32, 1, 2, 1),
+        (4, 32, 32, 5, 32, 1, 2, 2),
+        (4, 32, 32, 5, 32, 1, 2, 4),
+        (4, 32, 32, 5, 32, 1, 2, 8),
+        (4, 32, 32, 5, 32, 1, 2, 8),
+        (4, 32, 32, 5, 32, 1, 2, 16),
+        (4, 32, 32, 5, 32, 1, 2, 32),
+        (4, 32, 256, 5, 512, 1, 2, 2),
+        (4, 32, 256, 5, 512, 1, 2, 128),
+        (4, 32, 256, 5, 512, 1, 2, 256),
+    )
+
+    for dtype in dtypes:
+        print("(N,  iH,  C),  (O,  wH,  C),   dtype,  stride, pads, groups, diff%")
+        for N, iH, C, wH, O, strides, padding, groups in shapes:
+            np_dtype = getattr(np, dtype)
+            time_mlx, time_torch = bench_shape(
+                N, iH, C, wH, O, strides, padding, np_dtype, groups
+            )
+            diff = time_torch / time_mlx - 1.0
+
+            print(
+                f"({N}, {iH:3d}, {C:3d}), ({O:3d}, {wH:2d}, {C:3d}), {dtype}, {strides:5d}, {padding:4d}, {groups:6d}, {100. * diff:+5.2f}%"
+            )
+
+            if time_mlx >= 2.0 * time_torch:
+                print("ATTENTION ^^^^^^^")

benchmarks/python/fft_bench.py

@@ -0,0 +1,57 @@
+# Copyright © 2024 Apple Inc.
+
+import matplotlib
+import mlx.core as mx
+import numpy as np
+from time_utils import measure_runtime
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+def bandwidth_gb(runtime_ms, system_size):
+    bytes_per_fft = np.dtype(np.complex64).itemsize * 2
+    bytes_per_gb = 1e9
+    ms_per_s = 1e3
+    return system_size * bytes_per_fft / runtime_ms * ms_per_s / bytes_per_gb
+
+
+def run_bench(system_size):
+    def fft(x):
+        out = mx.fft.fft(x)
+        mx.eval(out)
+        return out
+
+    bandwidths = []
+    for k in range(4, 12):
+        n = 2**k
+        x = mx.random.uniform(shape=(system_size // n, n)).astype(mx.float32)
+        x = x.astype(mx.complex64)
+        mx.eval(x)
+        runtime_ms = measure_runtime(fft, x=x)
+        bandwidths.append(bandwidth_gb(runtime_ms, system_size))
+
+    return bandwidths
+
+
+def time_fft():
+
+    with mx.stream(mx.cpu):
+        cpu_bandwidths = run_bench(system_size=int(2**22))
+
+    with mx.stream(mx.gpu):
+        gpu_bandwidths = run_bench(system_size=int(2**29))
+
+    # plot bandwidths
+    x = [2**k for k in range(4, 12)]
+    plt.scatter(x, gpu_bandwidths, color="green", label="GPU")
+    plt.scatter(x, cpu_bandwidths, color="red", label="CPU")
+    plt.title("MLX FFT Benchmark")
+    plt.xlabel("N")
+    plt.ylabel("Bandwidth (GB/s)")
+    plt.legend()
+    plt.savefig("fft_plot.png")
+
+
+if __name__ == "__main__":
+    time_fft()

benchmarks/python/layer_norm_bench.py

@@ -0,0 +1,41 @@
+# Copyright © 2023-2024 Apple Inc.
+
+import mlx.core as mx
+import mlx.nn as nn
+from time_utils import time_fn
+
+
+def layer_norm(x, w, b, eps):
+    ot = x.dtype
+    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
+
+
+def time_layer_norm():
+    f1 = lambda x, w, b, y: (layer_norm(x, w, b, 1e-5) * y).sum()
+    f2 = lambda x, w, b, y: (mx.fast.layer_norm(x, w, b, 1e-5) * y).sum()
+    g1 = mx.grad(f1, argnums=(0, 1, 2))
+    g2 = mx.grad(f2, argnums=(0, 1, 2))
+
+    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    mx.eval(x, w, b, y)
+
+    def layer_norm_loop(g, x, w, b):
+        gx, gw, gb = x, w, b
+        for _ in range(32):
+            gx, gw, gb = g(gx, gw, gb, y)
+        return gx, gw, gb
+
+    time_fn(layer_norm_loop, g1, x, w, b)
+    time_fn(layer_norm_loop, g2, x, w, b)
+    time_fn(layer_norm_loop, mx.compile(g1), x, w, b)
+    time_fn(layer_norm_loop, mx.compile(g2), x, w, b)
+
+
+if __name__ == "__main__":
+    time_layer_norm()

benchmarks/python/rms_norm_bench.py

@@ -0,0 +1,39 @@
+# Copyright © 2023-2024 Apple Inc.
+
+import mlx.core as mx
+import mlx.nn as nn
+from time_utils import time_fn
+
+
+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
+
+
+def time_rms_norm():
+    f1 = lambda x, w, y: (rms_norm(x, w, 1e-5) * y).sum()
+    f2 = lambda x, w, y: (mx.fast.rms_norm(x, w, 1e-5) * y).sum()
+    g1 = mx.grad(f1, argnums=(0, 1))
+    g2 = mx.grad(f2, argnums=(0, 1))
+
+    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, w):
+        gx, gw = x, w
+        for _ in range(32):
+            gx, gw = g(gx, gw, y)
+        return gx, gw
+
+    time_fn(rms_norm_loop, g1, x, w)
+    time_fn(rms_norm_loop, g2, x, w)
+    time_fn(rms_norm_loop, mx.compile(g1), x, w)
+    time_fn(rms_norm_loop, mx.compile(g2), x, w)
+
+
+if __name__ == "__main__":
+    time_rms_norm()

benchmarks/python/rope_bench.py

@@ -6,21 +6,21 @@ from time_utils import time_fn
 
 
 def time_rope():
-    rope = nn.RoPE(4096)
+    rope = nn.RoPE(64)
 
     # vec
-    x = mx.random.uniform(shape=(1, 4096)).astype(mx.float16)
+    x = mx.random.uniform(shape=(1, 32, 1, 128)).astype(mx.float16)
     mx.eval(x)
 
     def rope_vec(x):
         for _ in range(32):
-            x = rope(x)
+            x = rope(x, offset=100)
         return x
 
     time_fn(rope_vec, x)
 
     # matrix
-    x = mx.random.uniform(shape=(1024, 4096)).astype(mx.float16)
+    x = mx.random.uniform(shape=(1, 32, 1024, 128)).astype(mx.float16)
     mx.eval(x)
 
     def rope_mat(x):

cmake/metal.14.0.diff

@@ -0,0 +1,36 @@
+diff -ur Metal/MTLEvent.hpp MetalNew/MTLEvent.hpp
+--- Metal/MTLEvent.hpp	2023-06-01 12:18:26
++++ MetalNew/MTLEvent.hpp	2024-04-15 07:36:59
+@@ -62,6 +62,7 @@
+ 
+     uint64_t                 signaledValue() const;
+     void                     setSignaledValue(uint64_t signaledValue);
++    bool                     waitUntilSignaledValue(uint64_t signaledValue, uint64_t timeoutMS);
+ };
+ 
+ class SharedEventHandle : public NS::SecureCoding<SharedEventHandle>
+@@ -138,6 +139,11 @@
+ _MTL_INLINE void MTL::SharedEvent::setSignaledValue(uint64_t signaledValue)
+ {
+     Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setSignaledValue_), signaledValue);
++}
++
++// method: waitUntilSignaledValue
++_MTL_INLINE bool MTL::SharedEvent::waitUntilSignaledValue(uint64_t signaledValue, uint64_t timeoutMS) {
++    return Object::sendMessage<bool>(this, _MTL_PRIVATE_SEL(waitUntilSignaledValue_timeoutMS_), signaledValue, timeoutMS);
+ }
+ 
+ // static method: alloc
+diff -ur Metal/MTLHeaderBridge.hpp MetalNew/MTLHeaderBridge.hpp
+--- Metal/MTLHeaderBridge.hpp	2023-06-01 12:18:26
++++ MetalNew/MTLHeaderBridge.hpp	2024-04-15 07:37:29
+@@ -1906,6 +1906,9 @@
+     "setShouldMaximizeConcurrentCompilation:");
+ _MTL_PRIVATE_DEF_SEL(setSignaledValue_,
+     "setSignaledValue:");
++_MTL_PRIVATE_DEF_SEL(
++    waitUntilSignaledValue_timeoutMS_,
++    "waitUntilSignaledValue:timeoutMS:");
+ _MTL_PRIVATE_DEF_SEL(setSize_,
+     "setSize:");
+ _MTL_PRIVATE_DEF_SEL(setSlice_,

cmake/metal.14.2.diff

@@ -0,0 +1,36 @@
+diff -ur Metal/MTLEvent.hpp MetalNew/MTLEvent.hpp
+--- Metal/MTLEvent.hpp	2024-04-15 07:12:10
++++ MetalNew/MTLEvent.hpp	2024-04-15 07:15:50
+@@ -62,6 +62,7 @@
+ 
+     uint64_t                 signaledValue() const;
+     void                     setSignaledValue(uint64_t signaledValue);
++    bool                     waitUntilSignaledValue(uint64_t signaledValue, uint64_t timeoutMS);
+ };
+ 
+ class SharedEventHandle : public NS::SecureCoding<SharedEventHandle>
+@@ -138,6 +139,11 @@
+ _MTL_INLINE void MTL::SharedEvent::setSignaledValue(uint64_t signaledValue)
+ {
+     Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setSignaledValue_), signaledValue);
++}
++
++// method: waitUntilSignaledValue
++_MTL_INLINE bool MTL::SharedEvent::waitUntilSignaledValue(uint64_t signaledValue, uint64_t timeoutMS) {
++    return Object::sendMessage<bool>(this, _MTL_PRIVATE_SEL(waitUntilSignaledValue_timeoutMS_), signaledValue, timeoutMS);
+ }
+ 
+ // static method: alloc
+diff -ur Metal/MTLHeaderBridge.hpp MetalNew/MTLHeaderBridge.hpp
+--- Metal/MTLHeaderBridge.hpp	2024-04-15 07:12:10
++++ MetalNew/MTLHeaderBridge.hpp	2024-04-15 07:16:15
+@@ -1918,6 +1918,9 @@
+     "setShouldMaximizeConcurrentCompilation:");
+ _MTL_PRIVATE_DEF_SEL(setSignaledValue_,
+     "setSignaledValue:");
++_MTL_PRIVATE_DEF_SEL(
++    waitUntilSignaledValue_timeoutMS_,
++    "waitUntilSignaledValue:timeoutMS:");
+ _MTL_PRIVATE_DEF_SEL(setSize_,
+     "setSize:");
+ _MTL_PRIVATE_DEF_SEL(setSlice_,

docs/Doxyfile

@@ -0,0 +1,50 @@
+################################################################################
+# Primary project setup.                                                       #
+################################################################################
+
+PROJECT_NAME           = "MLX"
+OUTPUT_DIRECTORY       = build
+XML_OUTPUT             = xml
+HTML_OUTPUT            = html
+STRIP_FROM_PATH        = ../
+INPUT                  = ../mlx
+FILE_PATTERNS          = *.h
+EXCLUDE_PATTERNS       = */private/*
+CREATE_SUBDIRS         = NO
+FULL_PATH_NAMES        = YES
+RECURSIVE              = YES
+GENERATE_HTML          = YES
+GENERATE_LATEX         = NO
+GENERATE_XML           = YES
+XML_PROGRAMLISTING     = YES
+
+################################################################################
+# Doxygen preprocessor / parser control.                                       #
+################################################################################
+
+ENABLE_PREPROCESSING   = YES
+MACRO_EXPANSION        = YES
+EXPAND_ONLY_PREDEF     = NO
+SKIP_FUNCTION_MACROS   = NO
+
+################################################################################
+# Compound extraction control.                                                 #
+################################################################################
+
+EXTRACT_ALL            = YES
+EXTRACT_PACKAGE        = YES
+EXTRACT_STATIC         = YES
+CASE_SENSE_NAMES       = NO
+
+################################################################################
+# Docstring control / customization.                                           #
+################################################################################
+
+JAVADOC_AUTOBRIEF      = YES
+
+################################################################################
+# Warning suppression.                                                         #
+################################################################################
+
+QUIET                  = YES
+WARN_IF_UNDOCUMENTED   = NO

docs/README.md

@@ -2,12 +2,16 @@
 
 ### Setup (do once)
 
-Install [sphinx](https://www.sphinx-doc.org/en/master/usage/installation.html)
-for example with `conda`:
+Install Doxygen:
 
 ```
-conda install sphinx
-pip install sphinx-book-theme
+brew install doxygen
+```
+
+Install Python packages:
+
+```
+pip install -r requirements.txt
 ```
 
 ### Build
@@ -15,7 +19,7 @@ pip install sphinx-book-theme
 Build the docs from `mlx/docs/`
 
 ```
-make html
+doxygen && make html
 ```
 
 View the docs by running a server in `mlx/docs/build/html/`:

docs/requirements.txt

@@ -0,0 +1,3 @@
+sphinx
+breathe
+sphinx-book-theme

docs/src/_templates/nn-module-template.rst

@@ -0,0 +1,20 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+   {% block methods %}
+
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      {%- if item not in inherited_members and item != "__init__" %}
+         ~{{ name }}.{{ item }}
+      {%- endif %}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+

docs/src/conf.py

@@ -22,6 +22,7 @@ extensions = [
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",
     "sphinx.ext.napoleon",
+    "breathe",
 ]
 
 python_use_unqualified_type_names = True
@@ -33,12 +34,16 @@ intersphinx_mapping = {
     "numpy": ("https://numpy.org/doc/stable/", None),
 }
 
+breathe_projects = {"mlx": "../build/xml"}
+breathe_default_project = "mlx"
+
 templates_path = ["_templates"]
 html_static_path = ["_static"]
 source_suffix = ".rst"
-master_doc = "index"
+main_doc = "index"
 highlight_language = "python"
 pygments_style = "sphinx"
+add_module_names = False
 
 # -- Options for HTML output -------------------------------------------------
 
@@ -62,11 +67,19 @@ htmlhelp_basename = "mlx_doc"
 
 
 def setup(app):
-    wrapped = app.registry.documenters["function"].can_document_member
+    from sphinx.util import inspect
 
-    def nanobind_function_patch(member: Any, *args, **kwargs) -> bool:
-        return "nanobind.nb_func" in str(type(member)) or wrapped(
-            member, *args, **kwargs
-        )
+    wrapped_isfunc = inspect.isfunction
 
-    app.registry.documenters["function"].can_document_member = nanobind_function_patch
+    def isfunc(obj):
+        type_name = str(type(obj))
+        if "nanobind.nb_method" in type_name or "nanobind.nb_func" in type_name:
+            return True
+        return wrapped_isfunc(obj)
+
+    inspect.isfunction = isfunc
+
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_documents = [(main_doc, "MLX.tex", "MLX Documentation", author, "manual")]

docs/src/cpp/ops.rst

@@ -3,4 +3,5 @@
 Operations
 ==========
 
-
+.. doxygengroup:: ops
+   :content-only:

docs/src/dev/extensions.rst

@@ -1,24 +1,16 @@
 Developer Documentation
 =======================
 
-MLX provides a open and flexible backend to which users may add operations 
-and specialized implementations without much hassle. While the library supplies
-efficient operations that can be used and composed for any number of 
-applications, there may arise cases where new functionalities or highly 
-optimized implementations are needed. For such cases, you may design and 
-implement your own operations that link to and build on top of :mod:`mlx.core`.
-We will introduce the inner-workings of MLX and go over a simple example to 
-learn the steps involved in adding new operations to MLX with your own CPU 
-and GPU implementations. 
+You can extend MLX with custom operations on the CPU or GPU. This guide
+explains how to do that with a simple example.
 
 Introducing the Example
 -----------------------
 
-Let's say that you would like an operation that takes in two arrays, 
-``x`` and ``y``, scales them both by some coefficients ``alpha`` and ``beta``
-respectively, and then adds them together to get the result 
-``z = alpha * x + beta * y``. Well, you can very easily do that by just 
-writing out a function as follows:
+Let's say you would like an operation that takes in two arrays, ``x`` and
+``y``, scales them both by coefficients ``alpha`` and ``beta`` respectively,
+and then adds them together to get the result ``z = alpha * x + beta * y``.
+You can do that in MLX directly:
 
 .. code-block:: python
 
@@ -27,44 +19,35 @@ writing out a function as follows:
     def simple_axpby(x: mx.array, y: mx.array, alpha: float, beta: float) -> mx.array:
         return alpha * x + beta * y
 
-This function performs that operation while leaving the implementations and 
-differentiation to MLX. 
+This function performs that operation while leaving the implementation and
+function transformations to MLX.
 
-However, you work with vector math libraries often and realize that the 
-``axpby`` routine defines the same operation ``Y = (alpha * X) + (beta * Y)``. 
-You would really like the part of your applications that does this operation 
-on the CPU to be very fast - so you decide that you want it to rely on the 
-``axpby`` routine provided by the Accelerate_ framework. Continuing to impose 
-our assumptions on to you, let's also assume that you want to learn how to add 
-your own implementation for the gradients of your new operation while going 
-over the ins-and-outs of the MLX framework. 
+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:
 
-Well, what a coincidence! You are in the right place. Over the course of this 
-example, we will learn:
-
-* The structure of the MLX library from the frontend API to the backend implementations.
-* How to implement your own CPU backend that redirects to Accelerate_ when appropriate (and a fallback if needed).
-* How to implement your own GPU implementation using metal.
-* How to add your own ``vjp`` and ``jvp``.
-* How to build your implementations, link them to MLX, and bind them to python.
+* The structure of the MLX library.
+* Implementing a CPU operation that redirects to Accelerate_ when appropriate.
+* Implementing a GPU operation using metal.
+* Adding the ``vjp`` and ``jvp`` function transformation.
+* Building a custom extension and binding it to python.
 
 Operations and Primitives
 -------------------------
 
-In one sentence, operations in MLX build the computation graph, and primitives 
-provide the rules for evaluation and transformations of said graph. Let's start 
-by discussing operations in more detail. 
+Operations in MLX build the computation graph. Primitives provide the rules for
+evaluating and transforming the graph. Let's start by discussing operations in
+more detail.
 
 Operations
 ^^^^^^^^^^^
 
-Operations are the frontend functions that operate on arrays. They are defined 
-in the C++ API (:ref:`cpp_ops`) and then we provide bindings to these 
-operations in the Python API (:ref:`ops`). 
+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 ``y``,
-and two scalars, ``alpha`` and ``beta``. This is how we would define it in the 
-C++ API:
+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++:
 
 .. code-block:: C++
 
@@ -83,10 +66,7 @@ C++ API:
         StreamOrDevice s = {} // Stream on which to schedule the operation
     );
 
-
-This operation itself can call other operations within it if needed. So, the 
-simplest way to go about implementing this operation would be do so in terms 
-of existing operations. 
+The simplest way to this operation is in terms of existing operations:
 
 .. code-block:: C++
 
@@ -100,25 +80,23 @@ of existing operations.
         // Scale x and y on the provided stream
         auto ax = multiply(array(alpha), x, s);
         auto by = multiply(array(beta), y, s);
-        
+
         // Add and return
         return add(ax, by, s);
     }
 
-However, as we discussed earlier, this is not our goal. The operations themselves 
-do not contain the implementations that act on the data, nor do they contain the
-rules of transformations. Rather, they are an easy to use interface that build 
-on top of the building blocks we call :class:`Primitive`. 
+The operations themselves do not contain the implementations that act on the
+data, nor do they contain the rules of transformations. Rather, they are an
+easy to use interface that use :class:`Primitive` building blocks.
 
 Primitives
 ^^^^^^^^^^^
 
-A :class:`Primitive` is part of the computation graph of an :class:`array`. It 
-defines how to create an output given a set of input :class:`array` . Further,
-a :class:`Primitive` is a class that contains rules on how it is evaluated 
-on the CPU or GPU, and how it acts under transformations such as ``vjp`` and 
-``jvp``. These words on their own can be a bit abstract, so lets take a step 
-back and go to our example to give ourselves a more concrete image. 
+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
+: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
+more concrete:
 
 .. code-block:: C++
 
@@ -134,11 +112,15 @@ back and go to our example to give ourselves a more concrete image.
         * To avoid unnecessary allocations, the evaluation function
         * is responsible for allocating space for the array.
         */
-        void eval_cpu(const std::vector<array>& inputs, array& out) override;
-        void eval_gpu(const std::vector<array>& inputs, array& out) override;
+        void eval_cpu(
+            const std::vector<array>& inputs,
+            std::vector<array>& outputs) override;
+        void eval_gpu(
+            const std::vector<array>& inputs,
+            std::vector<array>& outputs) override;
 
         /** The Jacobian-vector product. */
-        array jvp(
+        std::vector<array> jvp(
             const std::vector<array>& primals,
             const std::vector<array>& tangents,
             const std::vector<int>& argnums) override;
@@ -147,7 +129,8 @@ back and go to our example to give ourselves a more concrete image.
         std::vector<array> vjp(
             const std::vector<array>& primals,
             const array& cotan,
-            const std::vector<int>& argnums) override;
+            const std::vector<int>& argnums,
+            const std::vector<array>& outputs) override;
 
         /**
         * The primitive must know how to vectorize itself across
@@ -155,7 +138,7 @@ back and go to our example to give ourselves a more concrete image.
         * representing the vectorized computation and the axis which
         * corresponds to the output vectorized dimension.
         */
-        std::pair<array, int> vmap(
+        virtual std::pair<std::vector<array>, std::vector<int>> vmap(
             const std::vector<array>& inputs,
             const std::vector<int>& axes) override;
 
@@ -175,22 +158,22 @@ back and go to our example to give ourselves a more concrete image.
         void eval(const std::vector<array>& inputs, array& out);
     };
 
-The :class:`Axpby` class derives from the base :class:`Primitive` class and 
-follows the above demonstrated interface. :class:`Axpby` treats ``alpha`` and 
-``beta`` as parameters. It then provides implementations of how the array ``out`` 
-is produced given ``inputs`` through :meth:`Axpby::eval_cpu` and 
-:meth:`Axpby::eval_gpu`. Further, it provides rules of transformations in 
-:meth:`Axpby::jvp`, :meth:`Axpby::vjp`, and :meth:`Axpby::vmap`. 
+The :class:`Axpby` class derives from the base :class:`Primitive` class. The
+:class:`Axpby` treats ``alpha`` and ``beta`` as parameters. It then provides
+implementations of how the output array is produced given the inputs through
+:meth:`Axpby::eval_cpu` and :meth:`Axpby::eval_gpu`. It also provides rules
+of transformations in :meth:`Axpby::jvp`, :meth:`Axpby::vjp`, and
+:meth:`Axpby::vmap`.
 
-Using the Primitives
-^^^^^^^^^^^^^^^^^^^^^
+Using the Primitive
+^^^^^^^^^^^^^^^^^^^
 
-Operations can use this :class:`Primitive` to add a new :class:`array` to 
-the computation graph. An :class:`array` can be constructed by providing its 
-data type, shape, the :class:`Primitive` that computes it, and the 
-:class:`array` inputs that are passed to the primitive.
+Operations can use this :class:`Primitive` to add a new :class:`array` to the
+computation graph. An :class:`array` can be constructed by providing its data
+type, shape, the :class:`Primitive` that computes it, and the :class:`array`
+inputs that are passed to the primitive.
 
-Let's re-implement our operation now in terms of our :class:`Axpby` primitive.
+Let's reimplement our operation now in terms of our :class:`Axpby` primitive.
 
 .. code-block:: C++
 
@@ -223,7 +206,7 @@ Let's re-implement our operation now in terms of our :class:`Axpby` primitive.
             /* const std::vector<int>& shape = */ out_shape,
             /* Dtype dtype = */ out_dtype,
             /* std::unique_ptr<Primitive> primitive = */
-            std::make_unique<Axpby>(to_stream(s), alpha, beta),
+            std::make_shared<Axpby>(to_stream(s), alpha, beta),
             /* const std::vector<array>& inputs = */ broadcasted_inputs);
     }
 
@@ -238,27 +221,26 @@ This operation now handles the following:
 Implementing the Primitive
 --------------------------
 
-No computation happens when we call the operation alone. In effect, the 
-operation only builds the computation graph. When we evaluate the output 
-array, MLX schedules the execution of the computation graph, and calls
-:meth:`Axpby::eval_cpu` or :meth:`Axpby::eval_gpu` depending on the 
-stream/device specified by the user. 
+No computation happens when we call the operation alone. The operation only
+builds the computation graph. When we evaluate the output array, MLX schedules
+the execution of the computation graph, and calls :meth:`Axpby::eval_cpu` or
+:meth:`Axpby::eval_gpu` depending on the stream/device specified by the user.
 
 .. warning::
     When :meth:`Primitive::eval_cpu` or :meth:`Primitive::eval_gpu` are called,
     no memory has been allocated for the output array. It falls on the implementation
-    of these functions to allocate memory as needed
+    of these functions to allocate memory as needed.
 
-Implementing the CPU Backend
+Implementing the CPU Back-end
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Let's start by trying to implement 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 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`.
 
-Our naive method will go over each element of the output array, find the 
-corresponding input elements of ``x`` and ``y`` and perform the operation 
-pointwise. This is captured in the templated function :meth:`axpby_impl`. 
+Our naive 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++
 
@@ -296,19 +278,19 @@ pointwise. This is captured in the templated function :meth:`axpby_impl`.
         }
     }
 
-Now, we would like our implementation to be able to do this pointwise operation 
-for all incoming floating point arrays. Accordingly, we add dispatches for 
-``float32``, ``float16``, ``bfloat16`` and ``complex64``. We throw an error 
-if we encounter an unexpected type.
+Our implementation should work for all incoming floating point arrays.
+Accordingly, we add dispatches for ``float32``, ``float16``, ``bfloat16`` and
+``complex64``. We throw an error if we encounter an unexpected type.
 
 .. code-block:: C++
 
     /** Fall back implementation for evaluation on CPU */
-    void Axpby::eval(const std::vector<array>& inputs, array& out) {
-        // Check the inputs (registered in the op while constructing the out array)
-        assert(inputs.size() == 2);
+    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) {
@@ -321,28 +303,26 @@ if we encounter an unexpected type.
             return axpby_impl<complex64_t>(x, y, out, alpha_, beta_);
         } else {
             throw std::runtime_error(
-                "Axpby is only supported for floating point types.");
+                "[Axpby] Only supports floating point types.");
         }
     }
 
-We have a fallback implementation! Now, to do what we are really here to do. 
-Remember we wanted to use the ``axpby`` routine provided by the Accelerate_
-framework? Well, there are 3 complications to keep in mind:
+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 direct to it for ``float32`` types 
-#.  Accelerate assumes the inputs ``x`` and ``y`` are contiguous and all elements
-    have fixed strides between them. Possibly due to broadcasts and transposes, 
-    we aren't guaranteed that the inputs fit this requirement. We can 
-    only direct to Accelerate if both ``x`` and ``y`` are row contiguous or 
-    column contiguous. 
-#.  Accelerate performs the routine ``Y = (alpha * X) + (beta * Y)`` inplace. 
-    MLX expects to write out the answer to a new array. We must copy the elements 
-    of ``y`` into the output array and use that as an input to ``axpby``
+    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 out an implementation that uses Accelerate in the right conditions. 
-It must simply allocate data for the output, copy elements of ``y`` into it, 
-and then call the :meth:`catlas_saxpby` from accelerate. 
+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++
 
@@ -356,17 +336,7 @@ and then call the :meth:`catlas_saxpby` from accelerate.
         // Accelerate library provides catlas_saxpby which does
         // Y = (alpha * X) + (beta * Y) in place
         // To use it, we first copy the data in y over to the output array
-
-        // This specialization requires both x and y be contiguous in the same mode
-        // i.e: corresponding linear indices in both point to corresponding elements
-        // The data in the output array is allocated to match the strides in y
-        // such that x, y, and out are contiguous in the same mode and
-        // no transposition is needed
-        out.set_data(
-            allocator::malloc_or_wait(y.data_size() * out.itemsize()),
-            y.data_size(),
-            y.strides(),
-            y.flags());
+        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);
@@ -389,18 +359,20 @@ and then call the :meth:`catlas_saxpby` from accelerate.
             /* INCY = */ 1);
     }
 
-Great! But what about the inputs that do not fit the criteria for accelerate?
-Luckily, we can always just direct back to :meth:`Axpby::eval`.
-
-With this in mind, lets finally implement our :meth:`Axpby::eval_cpu`.
+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, array& out) {
+    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];
 
         // Accelerate specialization for contiguous single precision float arrays
         if (out.dtype() == float32 &&
@@ -410,35 +382,33 @@ With this in mind, lets finally implement our :meth:`Axpby::eval_cpu`.
             return;
         }
 
-        // Fall back to common backend if specializations are not available
-        eval(inputs, out);
+        // Fall back to common back-end if specializations are not available
+        eval(inputs, outputs);
     }
 
-We have now hit a milestone! Just this much is enough to run the operation 
-:meth:`axpby` on a CPU stream! 
+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.
 
-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. 
-
-Implementing the GPU Backend
+Implementing the GPU Back-end
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Apple silicon devices address their GPUs using the Metal_ shading language, and 
-all GPU kernels in MLX are written using metal. 
+Apple silicon devices address their GPUs using the Metal_ shading language, and
+GPU kernels in MLX are written using Metal.
 
 .. note::
 
-    Here are some helpful resources if you are new to metal!
+    Here are some helpful resources if you are new to Metal:
 
     * A walkthrough of the metal compute pipeline: `Metal Example`_
     * Documentation for metal shading language: `Metal Specification`_
     * Using metal from C++: `Metal-cpp`_
 
-Let's keep the GPU algorithm simple. We will launch exactly as many threads 
-as there are elements in the output. Each thread will pick the element it needs 
-from ``x`` and ``y``, do the pointwise operation, and then update its assigned 
-element in the output. 
+Let's keep the GPU kernel simple. We will launch exactly as many threads as
+there are elements in the output. Each thread will pick the element it needs
+from ``x`` and ``y``, do the point-wise operation, and update its assigned
+element in the output.
 
 .. code-block:: C++
 
@@ -457,15 +427,14 @@ element in the output.
         // Convert linear indices to offsets in array
         auto x_offset = elem_to_loc(index, shape, x_strides, ndim);
         auto y_offset = elem_to_loc(index, shape, y_strides, ndim);
-        
+
         // Do the operation and update the output
-        out[index] = 
+        out[index] =
             static_cast<T>(alpha) * x[x_offset] + static_cast<T>(beta) * y[y_offset];
     }
 
 We then need to instantiate this template for all floating point types and give
-each instantiation a unique host name so we can identify the right kernel for 
-each data type. 
+each instantiation a unique host name so we can identify it.
 
 .. code-block:: C++
 
@@ -488,29 +457,21 @@ each data type.
     instantiate_axpby(bfloat16, bfloat16_t);
     instantiate_axpby(complex64, complex64_t);
 
-This kernel will be compiled into a metal library ``mlx_ext.metallib`` as we 
-will see later in :ref:`Building with CMake`. In the following example, we 
-assume that the library ``mlx_ext.metallib`` will always be co-located with 
-the executable/ shared-library calling the :meth:`register_library` function. 
-The :meth:`register_library` function takes the library's name and potential 
-path (or in this case, a function that can produce the path of the metal 
-library) and tries to load that library if it hasn't already been registered 
-by the relevant static :class:`mlx::core::metal::Device` object. This is why, 
-it is important to package your C++ library with the metal library. We will 
-go over this process in more detail later. 
-
-The logic to determine the kernel, set the inputs, resolve the grid dimensions 
-and dispatch it to the GPU are contained in :meth:`Axpby::eval_gpu` as shown 
+The logic to determine the kernel, set the inputs, resolve the grid dimensions,
+and dispatch to the GPU are contained in :meth:`Axpby::eval_gpu` as shown
 below.
 
 .. code-block:: C++
 
     /** Evaluate primitive on GPU */
-    void Axpby::eval_gpu(const std::vector<array>& inputs, array& out) {
+    void Axpby::eval_gpu(
+      const std::vector<array>& inputs,
+      std::vector<array>& outputs) {
         // Prepare inputs
         assert(inputs.size() == 2);
         auto& x = inputs[0];
         auto& y = inputs[1];
+        auto& out = outputs[0];
 
         // Each primitive carries the stream it should execute on
         // and each stream carries its device identifiers
@@ -518,10 +479,10 @@ below.
         // We get the needed metal device using the stream
         auto& d = metal::device(s.device);
 
-        // Allocate output memory 
+        // Allocate output memory
         out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
-        // Resolve name of kernel (corresponds to axpby.metal)
+        // Resolve name of kernel
         std::ostringstream kname;
         kname << "axpby_" << "general_" << type_to_name(out);
 
@@ -552,7 +513,7 @@ below.
         compute_encoder->setBytes(&alpha_, sizeof(float), 3);
         compute_encoder->setBytes(&beta_, sizeof(float), 4);
 
-        // Encode shape, strides and ndim 
+        // Encode shape, strides and ndim
         compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5);
         compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6);
         compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7);
@@ -575,28 +536,25 @@ below.
 
 We can now call the :meth:`axpby` operation on both the CPU and the GPU!
 
-A few things to note about MLX and metal before moving on. MLX keeps track 
-of the active ``compute_encoder``. We rely on :meth:`d.get_command_encoder` 
-to give us the active metal compute command encoder instead of building a 
-new one and calling :meth:`compute_encoder->end_encoding` at the end. 
-MLX keeps adding kernels (compute pipelines) to the active command encoder 
-until some specified limit is hit or the compute encoder needs to be flushed 
-for synchronization. MLX also handles enqueuing and committing the associated 
-command buffers as needed. We suggest taking a deeper dive into 
-:class:`metal::Device` if you would like to study this routine further.
+A few things to note about MLX and Metal before moving on. MLX keeps track of
+the active ``command_buffer`` and the ``MTLCommandBuffer`` to which it is
+associated. We rely on :meth:`d.get_command_encoder` to give us the active
+metal compute command encoder instead of building a new one and calling
+:meth:`compute_encoder->end_encoding` at the end. MLX adds kernels (compute
+pipelines) to the active command buffer until some specified limit is hit or
+the command buffer needs to be flushed for synchronization.
 
 Primitive Transforms
 ^^^^^^^^^^^^^^^^^^^^^
 
-Now that we have come this far, let's also learn how to add implementations to 
-transformations in a :class:`Primitive`. These transformations can be built on 
-top of our operations, including the one we just defined now. Which then gives 
-us the following :meth:`Axpby::jvp` and :meth:`Axpby::vjp` implementations.
+Next, let's add implementations for transformations in a :class:`Primitive`.
+These transformations can be built on top of other operations, including the
+one we just defined:
 
 .. code-block:: C++
 
     /** The Jacobian-vector product. */
-    array Axpby::jvp(
+    std::vector<array> Axpby::jvp(
             const std::vector<array>& primals,
             const std::vector<array>& tangents,
             const std::vector<int>& argnums) {
@@ -611,12 +569,12 @@ us the following :meth:`Axpby::jvp` and :meth:`Axpby::vjp` implementations.
         if (argnums.size() > 1) {
             auto scale = argnums[0] == 0 ? alpha_ : beta_;
             auto scale_arr = array(scale, tangents[0].dtype());
-            return multiply(scale_arr, tangents[0], stream());
+            return {multiply(scale_arr, tangents[0], stream())};
         }
         // If, argnums = {0, 1}, we take contributions from both
         // which gives us jvp = tangent_x * alpha + tangent_y * beta
         else {
-            return axpby(tangents[0], tangents[1], alpha_, beta_, stream());
+            return {axpby(tangents[0], tangents[1], alpha_, beta_, stream())};
         }
     }
 
@@ -625,34 +583,35 @@ us the following :meth:`Axpby::jvp` and :meth:`Axpby::vjp` implementations.
     /** The vector-Jacobian product. */
     std::vector<array> Axpby::vjp(
             const std::vector<array>& primals,
-            const array& cotan,
-            const std::vector<int>& argnums) {
+            const std::vector<array>& cotangents,
+            const std::vector<int>& argnums,
+            const std::vector<int>& /* unused */) {
         // Reverse mode diff
         std::vector<array> vjps;
         for (auto arg : argnums) {
             auto scale = arg == 0 ? alpha_ : beta_;
-            auto scale_arr = array(scale, cotan.dtype());
-            vjps.push_back(multiply(scale_arr, cotan, stream()));
+            auto scale_arr = array(scale, cotangents[0].dtype());
+            vjps.push_back(multiply(scale_arr, cotangents[0], stream()));
         }
         return vjps;
     }
 
-Finally, you need not have a transformation fully defined to start using your 
-own :class:`Primitive`.
+Note, a transformation does not need to be fully defined to start using
+the :class:`Primitive`.
 
 .. code-block:: C++
 
     /** Vectorize primitive along given axis */
-    std::pair<array, int> Axpby::vmap(
+    std::pair<std::vector<array>, std::vector<int>> Axpby::vmap(
             const std::vector<array>& inputs,
             const std::vector<int>& axes) {
-        throw std::runtime_error("Axpby has no vmap implementation.");
+        throw std::runtime_error("[Axpby] vmap not implemented.");
     }
 
 Building and Binding
 --------------------
 
-Let's look at the overall directory structure first. 
+Let's look at the overall directory structure first.
 
 | extensions
 | ├── axpby
@@ -666,40 +625,39 @@ Let's look at the overall directory structure first.
 | └── setup.py
 
 * ``extensions/axpby/`` defines the C++ extension library
-* ``extensions/mlx_sample_extensions`` sets out the structure for the 
-  associated python package
-* ``extensions/bindings.cpp`` provides python bindings for our operation
-* ``extensions/CMakeLists.txt`` holds CMake rules to build the library and 
-  python bindings
+* ``extensions/mlx_sample_extensions`` sets out the structure for the
+  associated Python package
+* ``extensions/bindings.cpp`` provides Python bindings for our operation
+* ``extensions/CMakeLists.txt`` holds CMake rules to build the library and
+  Python bindings
 * ``extensions/setup.py`` holds the ``setuptools`` rules to build and install
-  the python package
+  the Python package
 
 Binding to Python
 ^^^^^^^^^^^^^^^^^^
 
-We use PyBind11_ to build a Python API for the C++ library. Since bindings for
+We use nanobind_ to build a Python API for the C++ library. Since bindings for
 components such as :class:`mlx.core.array`, :class:`mlx.core.stream`, etc. are
-already provided, adding our :meth:`axpby` is simple!
+already provided, adding our :meth:`axpby` is simple.
 
 .. code-block:: C++
 
-    PYBIND11_MODULE(mlx_sample_extensions, m) {
-        m.doc() = "Sample C++ and metal extensions for MLX";
+   NB_MODULE(_ext, m) {
+        m.doc() = "Sample extension for MLX";
 
         m.def(
             "axpby",
             &axpby,
             "x"_a,
             "y"_a,
-            py::pos_only(),
             "alpha"_a,
             "beta"_a,
-            py::kw_only(),
-            "stream"_a = py::none(),
-            R"pbdoc(
+            nb::kw_only(),
+            "stream"_a = nb::none(),
+            R"(
                 Scale and sum two vectors element-wise
                 ``z = alpha * x + beta * y``
-                
+
                 Follows numpy style broadcasting between ``x`` and ``y``
                 Inputs are upcasted to floats if needed
 
@@ -711,17 +669,17 @@ already provided, adding our :meth:`axpby` is simple!
 
                 Returns:
                     array: ``alpha * x + beta * y``
-            )pbdoc");
+            )");
     }
 
-Most of the complexity in the above example comes from additional bells and 
+Most of the complexity in the above example comes from additional bells and
 whistles such as the literal names and doc-strings.
 
 .. warning::
 
-    :mod:`mlx.core` needs to be imported before importing 
-    :mod:`mlx_sample_extensions` as defined by the pybind11 module above to 
-    ensure that the casters for :mod:`mlx.core` components like 
+    :mod:`mlx.core` must be imported before importing
+    :mod:`mlx_sample_extensions` as defined by the nanobind module above to
+    ensure that the casters for :mod:`mlx.core` components like
     :class:`mlx.core.array` are available.
 
 .. _Building with CMake:
@@ -729,8 +687,8 @@ whistles such as the literal names and doc-strings.
 Building with CMake
 ^^^^^^^^^^^^^^^^^^^^
 
-Building the C++ extension library itself is simple, it only requires that you 
-``find_package(MLX CONFIG)`` and then link it to your library. 
+Building the C++ extension library only requires that you ``find_package(MLX
+CONFIG)`` and then link it to your library.
 
 .. code-block:: cmake
 
@@ -752,12 +710,12 @@ Building the C++ extension library itself is simple, it only requires that you
     # Link to mlx
     target_link_libraries(mlx_ext PUBLIC mlx)
 
-We also need to build the attached metal library. For convenience, we provide a 
-:meth:`mlx_build_metallib` function that builds a ``.metallib`` target given 
-sources, headers, destinations, etc. (defined in ``cmake/extension.cmake`` and 
-automatically imported with MLX package). 
+We also need to build the attached Metal library. For convenience, we provide a
+:meth:`mlx_build_metallib` function that builds a ``.metallib`` target given
+sources, headers, destinations, etc. (defined in ``cmake/extension.cmake`` and
+automatically imported with MLX package).
 
-Here is what that looks like in practice!
+Here is what that looks like in practice:
 
 .. code-block:: cmake
 
@@ -779,27 +737,29 @@ Here is what that looks like in practice!
 
     endif()
 
-Finally, we build the Pybind11_ bindings
+Finally, we build the nanobind_ bindings
 
 .. code-block:: cmake
 
-    pybind11_add_module(
-        mlx_sample_extensions
-        ${CMAKE_CURRENT_LIST_DIR}/bindings.cpp
+    nanobind_add_module(
+      _ext
+      NB_STATIC STABLE_ABI LTO NOMINSIZE
+      NB_DOMAIN mlx
+      ${CMAKE_CURRENT_LIST_DIR}/bindings.cpp
     )
-    target_link_libraries(mlx_sample_extensions PRIVATE mlx_ext)
+    target_link_libraries(_ext PRIVATE mlx_ext)
 
     if(BUILD_SHARED_LIBS)
-        target_link_options(mlx_sample_extensions PRIVATE -Wl,-rpath,@loader_path)
+      target_link_options(_ext PRIVATE -Wl,-rpath,@loader_path)
     endif()
 
 Building with ``setuptools``
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Once we have set out the CMake build rules as described above, we can use the
-build utilities defined in :mod:`mlx.extension` for a simple build process. 
+build utilities defined in :mod:`mlx.extension`:
 
-.. code-block:: python 
+.. code-block:: python
 
     from mlx import extension
     from setuptools import setup
@@ -809,48 +769,50 @@ build utilities defined in :mod:`mlx.extension` for a simple build process.
             name="mlx_sample_extensions",
             version="0.0.0",
             description="Sample C++ and Metal extensions for MLX primitives.",
-            ext_modules=[extension.CMakeExtension("mlx_sample_extensions")],
+            ext_modules=[extension.CMakeExtension("mlx_sample_extensions._ext")],
             cmdclass={"build_ext": extension.CMakeBuild},
-            packages = ["mlx_sample_extensions"],
-            package_dir = {"": "mlx_sample_extensions"},
-            package_data = {"mlx_sample_extensions" : ["*.so", "*.dylib", "*.metallib"]},
+            packages=["mlx_sample_extensions"],
+            package_data={"mlx_sample_extensions": ["*.so", "*.dylib", "*.metallib"]},
+            extras_require={"dev":[]},
             zip_safe=False,
-            python_requires=">=3.7",
+            python_requires=">=3.8",
         )
 
 .. note::
     We treat ``extensions/mlx_sample_extensions`` as the package directory
     even though it only contains a ``__init__.py`` to ensure the following:
-    
-    * :mod:`mlx.core` is always imported before importing  :mod:`mlx_sample_extensions`
-    * The C++ extension library and the metal library are co-located with the python 
-      bindings and copied together if the package is installed 
 
-You can build inplace for development using
+    * :mod:`mlx.core` must be imported before importing :mod:`_ext`
+    * The C++ extension library and the metal library are co-located with the python
+      bindings and copied together if the package is installed
+
+To build the package, first install the build dependencies with ``pip install
+-r requirements.txt``.  You can then build inplace for development using
 ``python setup.py build_ext -j8 --inplace`` (in ``extensions/``)
 
-This will result in a directory structure as follows:
+This results in the directory structure:
 
 | extensions
 | ├── mlx_sample_extensions
 | │   ├── __init__.py
 | │   ├── libmlx_ext.dylib # C++ extension library
 | │   ├── mlx_ext.metallib # Metal library
-| │   └── mlx_sample_extensions.cpython-3x-darwin.so # Python Binding
+| │   └── _ext.cpython-3x-darwin.so # Python Binding
 | ...
 
-When you try to install using the command ``python -m pip install .`` 
-(in ``extensions/``), the package will be installed with the same structure as 
-``extensions/mlx_sample_extensions`` and the C++ and metal library will be 
-copied along with the python binding since they are specified as ``package_data``.
+When you try to install using the command ``python -m pip install .`` (in
+``extensions/``), the package will be installed with the same structure as
+``extensions/mlx_sample_extensions`` and the C++ and Metal library will be
+copied along with the Python binding since they are specified as
+``package_data``.
 
 Usage
 -----
 
-After installing the extension as described above, you should be able to simply 
-import the python package and play with it as you would any other MLX operation!
+After installing the extension as described above, you should be able to simply
+import the Python package and play with it as you would any other MLX operation.
 
-Let's looks at a simple script and it's results!
+Let's look at a simple script and its results:
 
 .. code-block:: python
 
@@ -874,12 +836,12 @@ Output:
     c correctness: True
 
 Results
-^^^^^^^^^^^^^^^^
+^^^^^^^
 
-Let's run a quick benchmark and see how our new ``axpby`` operation compares 
-with the naive :meth:`simple_axpby` we defined at first on the CPU. 
+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.
 
-.. code-block:: python 
+.. code-block:: python
 
     import mlx.core as mx
     from mlx_sample_extensions import axpby
@@ -898,7 +860,7 @@ with the naive :meth:`simple_axpby` we defined at first on the CPU.
     alpha = 4.0
     beta = 2.0
 
-    mx.eval((x, y))
+    mx.eval(x, y)
 
     def bench(f):
         # Warm up
@@ -919,30 +881,23 @@ with the naive :meth:`simple_axpby` we defined at first on the CPU.
 
     print(f"Simple axpby: {simple_time:.3f} s | Custom axpby: {custom_time:.3f} s")
 
-Results:
-
-.. code-block::
-
-    Simple axpby: 0.114 s | Custom axpby: 0.109 s
-
-We see some modest improvements right away! 
+The results are ``Simple axpby: 0.114 s | Custom axpby: 0.109 s``. We see
+modest improvements right away!
 
 This operation is now good to be used to build other operations, in
 :class:`mlx.nn.Module` calls, and also as a part of graph transformations like
-:meth:`grad`!
+:meth:`grad`.
 
 Scripts
 -------
 
 .. admonition:: Download the code
 
-   The full example code is available in `mlx <code>`_.
-
-.. code: `https://github.com/ml-explore/mlx/tree/main/examples/extensions/`_
+   The full example code is available in `mlx <https://github.com/ml-explore/mlx/tree/main/examples/extensions/>`_.
 
 .. _Accelerate: https://developer.apple.com/documentation/accelerate/blas?language=objc
 .. _Metal: https://developer.apple.com/documentation/metal?language=objc
 .. _Metal-cpp: https://developer.apple.com/metal/cpp/
 .. _`Metal Specification`: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
 .. _`Metal Example`: https://developer.apple.com/documentation/metal/performing_calculations_on_a_gpu?language=objc
-.. _PyBind11: https://pybind11.readthedocs.io/en/stable/
+.. _nanobind: https://nanobind.readthedocs.io/en/latest/

docs/src/dev/metal_debugger.rst

@@ -0,0 +1,68 @@
+Metal Debugger
+==============
+
+.. currentmodule:: mlx.core
+
+Profiling is a key step for performance optimization. You can build MLX with
+the ``MLX_METAL_DEBUG`` option to improve the Metal debugging and
+optimization workflow. The ``MLX_METAL_DEBUG`` debug option:
+
+* Records source during Metal compilation, for later inspection while
+  debugging.
+* Labels Metal objects such as command queues, improving capture readability.
+
+To build with debugging enabled in Python prepend
+``CMAKE_ARGS="-DMLX_METAL_DEBUG=ON"`` to the build call.
+
+The :func:`metal.start_capture` function initiates a capture of all MLX GPU
+work.
+
+.. note::
+
+   To capture a GPU trace you must run the application with
+   ``MTL_CAPTURE_ENABLED=1``.
+
+.. code-block:: python
+
+    import mlx.core as mx
+
+    a = mx.random.uniform(shape=(512, 512))
+    b = mx.random.uniform(shape=(512, 512))
+    mx.eval(a, b)
+
+    trace_file = "mlx_trace.gputrace"
+
+    # Make sure to run with MTL_CAPTURE_ENABLED=1 and
+    # that the path trace_file does not already exist.
+    mx.metal.start_capture(trace_file)
+
+    for _ in range(10):
+      mx.eval(mx.add(a, b))
+
+    mx.metal.stop_capture()
+
+You can open and replay the GPU trace in Xcode. The ``Dependencies`` view
+has a great overview of all operations. Checkout the `Metal debugger
+documentation`_ for more information.
+
+.. image:: ../_static/metal_debugger/capture.png
+    :class: dark-light
+
+Xcode Workflow
+--------------
+
+You can skip saving to a path by running within Xcode. First, generate an
+Xcode project using CMake.
+
+.. code-block::
+
+    mkdir build && cd build
+    cmake .. -DMLX_METAL_DEBUG=ON -G Xcode
+    open mlx.xcodeproj
+
+Select the ``metal_capture`` example schema and run.
+
+.. image:: ../_static/metal_debugger/schema.png
+    :class: dark-light
+
+.. _`Metal debugger documentation`: https://developer.apple.com/documentation/xcode/metal-debugger

docs/src/index.rst

@@ -58,10 +58,12 @@ are the CPU and GPU.
    :maxdepth: 1
 
    python/array
+   python/data_types
    python/devices_and_streams
    python/ops
    python/random
    python/transforms
+   python/fast
    python/fft
    python/linalg
    python/metal
@@ -80,3 +82,4 @@ are the CPU and GPU.
    :maxdepth: 1
 
    dev/extensions
+   dev/metal_debugger

docs/src/install.rst

@@ -74,7 +74,7 @@ Install `nanobind <https://nanobind.readthedocs.io/en/latest/>`_ with:
 
 .. code-block:: shell
 
-    pip install git+https://github.com/wjakob/nanobind.git
+    pip install git+https://github.com/wjakob/nanobind.git@2f04eac452a6d9142dedb957701bdb20125561e4
 
 Then simply build and install MLX using pip:
 
@@ -120,7 +120,7 @@ Create a build directory and run CMake and make:
 .. code-block:: shell
 
    mkdir -p build && cd build
-   cmake .. && make -j 
+   cmake .. && make -j
 
 Run tests with:
 
@@ -139,7 +139,7 @@ directory as the executable statically linked to ``libmlx.a`` or the
 preprocessor constant ``METAL_PATH`` should be defined at build time and it
 should point to the path to the built metal library.
 
-.. list-table:: Build Options 
+.. list-table:: Build Options
    :widths: 25 8
    :header-rows: 1
 
@@ -155,19 +155,24 @@ should point to the path to the built metal library.
      - ON
    * - MLX_BUILD_PYTHON_BINDINGS
      - OFF
-
+   * - MLX_METAL_DEBUG
+     - OFF
+   * - MLX_BUILD_SAFETENSORS
+     - ON
+   * - MLX_BUILD_GGUF
+     - ON
 
 .. note::
 
-    If you have multiple Xcode installations and wish to use 
-    a specific one while building, you can do so by adding the 
-    following environment variable before building 
+    If you have multiple Xcode installations and wish to use
+    a specific one while building, you can do so by adding the
+    following environment variable before building
 
     .. code-block:: shell
 
       export DEVELOPER_DIR="/path/to/Xcode.app/Contents/Developer/"
 
-    Further, you can use the following command to find out which 
+    Further, you can use the following command to find out which
     macOS SDK will be used
 
     .. code-block:: shell
@@ -199,7 +204,7 @@ Then set the active developer directory:
 
   sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer
 
-x86 Shell 
+x86 Shell
 ~~~~~~~~~
 
 .. _build shell:

docs/src/python/array.rst

@@ -10,27 +10,38 @@ Array
 
     array
     array.astype
+    array.at
     array.item
     array.tolist
     array.dtype
+    array.itemsize
+    array.nbytes
     array.ndim
     array.shape
     array.size
-    Dtype
     array.abs
     array.all
     array.any
     array.argmax
     array.argmin
     array.cos
-    array.dtype
+    array.cummax
+    array.cummin
+    array.cumprod
+    array.cumsum
+    array.diag
+    array.diagonal
     array.exp
+    array.flatten
     array.log
+    array.log10
     array.log1p
+    array.log2
     array.logsumexp
     array.max
     array.mean
     array.min
+    array.moveaxis
     array.prod
     array.reciprocal
     array.reshape
@@ -40,6 +51,8 @@ Array
     array.split
     array.sqrt
     array.square
+    array.squeeze
+    array.swapaxes
     array.sum
     array.transpose
     array.T

docs/src/python/data_types.rst

@@ -1,7 +1,5 @@
 .. _data_types:
 
-:orphan:
-
 Data Types
 ==========
 
@@ -44,9 +42,27 @@ The default floating point type is ``float32`` and the default integer type is
    * - ``int64``
      - 8 
      - 64-bit signed integer 
+   * - ``bfloat16``
+     - 2 
+     - 16-bit brain float (e8, m7)
    * - ``float16``
      - 2 
-     - 16-bit float, only available with `ARM C language extensions <https://developer.arm.com/documentation/101028/0012/3--C-language-extensions?lang=en>`_
+     - 16-bit IEEE float (e5, m10)
    * - ``float32``
      - 4 
      - 32-bit float
+   * - ``complex64``
+     - 8 
+     - 64-bit complex float
+
+
+Data type are aranged in a hierarchy. See the :obj:`DtypeCategory` object
+documentation for more information. Use :func:`issubdtype` to determine if one
+``dtype`` (or category) is a subtype of another category.
+
+.. autosummary::
+   :toctree: _autosummary
+
+   Dtype
+   DtypeCategory
+   issubdtype

docs/src/python/devices_and_streams.rst

@@ -16,3 +16,4 @@ Devices and Streams
    new_stream
    set_default_stream
    stream
+   synchronize

docs/src/python/fast.rst

@@ -0,0 +1,14 @@
+.. _fast:
+
+Fast
+====
+
+.. currentmodule:: mlx.core.fast
+
+.. autosummary:: 
+  :toctree: _autosummary
+
+  rms_norm
+  layer_norm
+  rope
+  scaled_dot_product_attention

docs/src/python/metal.rst

@@ -3,12 +3,17 @@ Metal
 
 .. currentmodule:: mlx.core.metal
 
-.. autosummary:: 
+.. autosummary::
   :toctree: _autosummary
 
   is_available
+  device_info
   get_active_memory
   get_peak_memory
+  reset_peak_memory
   get_cache_memory
   set_memory_limit
   set_cache_limit
+  clear_cache
+  start_capture
+  stop_capture

docs/src/python/nn.rst

@@ -173,6 +173,7 @@ In detail:
    :toctree: _autosummary
 
    value_and_grad
+   quantize
 
 .. toctree::
 

docs/src/python/nn/layers.rst

@@ -31,6 +31,7 @@ Layers
    Mish
    MultiHeadAttention
    PReLU
+   QuantizedEmbedding
    QuantizedLinear
    RMSNorm
    ReLU
@@ -43,4 +44,4 @@ Layers
    Softshrink
    Step
    Transformer
-   Upsample
+   Upsample

docs/src/python/nn/module.rst

@@ -30,6 +30,7 @@ Module
       Module.named_modules
       Module.parameters
       Module.save_weights
+      Module.set_dtype
       Module.train
       Module.trainable_parameters
       Module.unfreeze

docs/src/python/ops.rst

@@ -5,13 +5,13 @@ Operations
 
 .. currentmodule:: mlx.core
 
-.. autosummary:: 
+.. autosummary::
   :toctree: _autosummary
 
    abs
    add
    all
-   allclose 
+   allclose
    any
    arange
    arccos
@@ -19,6 +19,7 @@ Operations
    arcsin
    arcsinh
    arctan
+   arctan2
    arctanh
    argmax
    argmin
@@ -28,6 +29,11 @@ Operations
    atleast_1d
    atleast_2d
    atleast_3d
+   bitwise_and
+   bitwise_or
+   bitwise_xor
+   block_masked_mm
+   block_sparse_mm
    broadcast_to
    ceil
    clip
@@ -38,6 +44,11 @@ Operations
    conv_general
    cos
    cosh
+   cummax
+   cummin
+   cumprod
+   cumsum
+   degrees
    dequantize
    diag
    diagonal
@@ -47,6 +58,7 @@ Operations
    erf
    erfinv
    exp
+   expm1
    expand_dims
    eye
    flatten
@@ -58,10 +70,11 @@ Operations
    identity
    inner
    isclose
-   isnan
-   isposinf
-   isneginf
    isinf
+   isnan
+   isneginf
+   isposinf
+   left_shift
    less
    less_equal
    linspace
@@ -79,11 +92,13 @@ Operations
    max
    maximum
    mean
+   meshgrid
    min
    minimum
    moveaxis
    multiply
    negative
+   not_equal
    ones
    ones_like
    outer
@@ -92,9 +107,11 @@ Operations
    prod
    quantize
    quantized_matmul
+   radians
    reciprocal
    repeat
    reshape
+   right_shift
    round
    rsqrt
    save
@@ -113,6 +130,7 @@ Operations
    square
    squeeze
    stack
+   std
    stop_gradient
    subtract
    sum

docs/src/python/optimizers.rst

@@ -1,5 +1,7 @@
 .. _optimizers:
 
+.. currentmodule:: mlx.optimizers
+
 Optimizers
 ==========
 
@@ -34,3 +36,8 @@ model's parameters and the **optimizer state**.
    optimizers/optimizer
    optimizers/common_optimizers
    optimizers/schedulers
+
+.. autosummary::
+   :toctree: _autosummary
+
+   clip_grad_norm

docs/src/python/random.rst

@@ -38,6 +38,7 @@ we use a splittable version of Threefry, which is a counter-based PRNG.
    gumbel
    key
    normal
+   multivariate_normal
    randint
    seed
    split

docs/src/python/tree_utils.rst

@@ -19,3 +19,5 @@ return python trees will be using the default python ``dict``, ``list`` and
    tree_flatten
    tree_unflatten
    tree_map
+   tree_map_with_path
+   tree_reduce

docs/src/usage/lazy_evaluation.rst

@@ -18,7 +18,7 @@ describe below.
 Transforming Compute Graphs
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Lazy evaluation let's us record a compute graph without actually doing any
+Lazy evaluation lets us record a compute graph without actually doing any
 computations. This is useful for function transformations like :func:`grad` and
 :func:`vmap` and graph optimizations.
 

docs/src/usage/saving_and_loading.rst

@@ -49,7 +49,7 @@ it will be added. You can load the array with:
 
 .. code-block:: shell
 
-   >>> mx.load("array.npy", a)
+   >>> mx.load("array.npy")
    array([1], dtype=float32)
 
 Here's an example of saving several arrays to a single file:

examples/cpp/CMakeLists.txt

@@ -8,3 +8,4 @@ endfunction(build_example)
 build_example(tutorial.cpp)
 build_example(linear_regression.cpp)
 build_example(logistic_regression.cpp)
+build_example(metal_capture.cpp)

examples/cpp/metal_capture.cpp

@@ -0,0 +1,31 @@
+// Copyright © 2024 Apple Inc.
+
+#include <cassert>
+#include <iostream>
+
+#include "mlx/mlx.h"
+
+using namespace mlx::core;
+
+int main() {
+  // To use Metal debugging and profiling:
+  // 1. Build with the MLX_METAL_DEBUG CMake option (i.e. -DMLX_METAL_DEBUG=ON).
+  // 2. Run with MTL_CAPTURE_ENABLED=1.
+  metal::start_capture("mlx_trace.gputrace");
+
+  // Start at index two because the default GPU and CPU streams have indices
+  // zero and one, respectively. This naming matches the label assigned to each
+  // stream's command queue.
+  auto s2 = new_stream(Device::gpu);
+  auto s3 = new_stream(Device::gpu);
+
+  auto a = arange(1.f, 10.f, 1.f, float32, s2);
+  auto b = arange(1.f, 10.f, 1.f, float32, s3);
+  auto x = add(a, a, s2);
+  auto y = add(b, b, s3);
+
+  // The multiply will happen on the default stream.
+  std::cout << multiply(x, y) << std::endl;
+
+  metal::stop_capture();
+}

examples/extensions/CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required(VERSION 3.27)
 
-project(mlx_sample_extensions LANGUAGES CXX)
+project(_ext LANGUAGES CXX)
 
 # ----------------------------- Setup -----------------------------
 set(CMAKE_CXX_STANDARD 17)
@@ -11,8 +11,12 @@ option(BUILD_SHARED_LIBS "Build extensions as a shared library" ON)
 
 # ----------------------------- Dependencies -----------------------------
 find_package(MLX CONFIG REQUIRED)
-find_package(Python COMPONENTS Interpreter Development)
-find_package(pybind11 CONFIG REQUIRED)
+find_package(Python 3.8 COMPONENTS Interpreter Development.Module REQUIRED)
+execute_process(
+  COMMAND "${Python_EXECUTABLE}" -m nanobind --cmake_dir
+  OUTPUT_STRIP_TRAILING_WHITESPACE OUTPUT_VARIABLE NB_DIR)
+list(APPEND CMAKE_PREFIX_PATH "${NB_DIR}")
+find_package(nanobind CONFIG REQUIRED)
 
 # ----------------------------- Extensions -----------------------------
 
@@ -38,7 +42,6 @@ target_link_libraries(mlx_ext PUBLIC mlx)
 
 # Build metallib
 if(MLX_BUILD_METAL)
-
   mlx_build_metallib(
     TARGET mlx_ext_metallib
     TITLE mlx_ext
@@ -54,13 +57,15 @@ if(MLX_BUILD_METAL)
 
 endif()
 
-# ----------------------------- Pybind -----------------------------
-pybind11_add_module(
-  mlx_sample_extensions
+# ----------------------------- Python Bindings -----------------------------
+nanobind_add_module(
+  _ext
+  NB_STATIC STABLE_ABI LTO NOMINSIZE
+  NB_DOMAIN mlx 
   ${CMAKE_CURRENT_LIST_DIR}/bindings.cpp
 )
-target_link_libraries(mlx_sample_extensions PRIVATE mlx_ext)
+target_link_libraries(_ext PRIVATE mlx_ext)
 
 if(BUILD_SHARED_LIBS)
-  target_link_options(mlx_sample_extensions PRIVATE -Wl,-rpath,@loader_path)
+  target_link_options(_ext PRIVATE -Wl,-rpath,@loader_path)
 endif()

examples/extensions/README.md

@@ -0,0 +1,18 @@
+
+## Build the extensions
+
+```
+pip install -e .
+```
+
+For faster builds during development, you can also pre-install the requirements:
+
+```
+pip install -r requirements.txt
+```
+
+And then run:
+
+```
+python setup.py build_ext -j8 --inplace
+```

examples/extensions/axpby/axpby.cpp

@@ -1,4 +1,4 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
 #include <cassert>
 #include <iostream>
@@ -43,7 +43,7 @@ array axpby(
   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);
 
@@ -61,7 +61,7 @@ array axpby(
       /* const std::vector<int>& shape = */ out_shape,
       /* Dtype dtype = */ out_dtype,
       /* std::unique_ptr<Primitive> primitive = */
-      std::make_unique<Axpby>(to_stream(s), alpha, beta),
+      std::make_shared<Axpby>(to_stream(s), alpha, beta),
       /* const std::vector<array>& inputs = */ broadcasted_inputs);
 }
 
@@ -106,12 +106,12 @@ void axpby_impl(
 /** Fall back implementation for evaluation on CPU */
 void Axpby::eval(
     const std::vector<array>& inputs,
-    std::vector<array>& out_arr) {
-  auto out = out_arr[0];
+    std::vector<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() == float32) {
@@ -150,11 +150,7 @@ void axpby_impl_accelerate(
   // The data in the output array is allocated to match the strides in y
   // such that x, y, and out are contiguous in the same mode and
   // no transposition is needed
-  out.set_data(
-      allocator::malloc_or_wait(y.data_size() * out.itemsize()),
-      y.data_size(),
-      y.strides(),
-      y.flags());
+  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);
@@ -180,11 +176,11 @@ void axpby_impl_accelerate(
 /** Evaluate primitive on CPU using accelerate specializations */
 void Axpby::eval_cpu(
     const std::vector<array>& inputs,
-    std::vector<array>& outarr) {
-  auto out = outarr[0];
+    std::vector<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() == float32 &&
@@ -195,7 +191,7 @@ void Axpby::eval_cpu(
   }
 
   // Fall back to common backend if specializations are not available
-  eval(inputs, outarr);
+  eval(inputs, outputs);
 }
 
 #else // Accelerate not available
@@ -203,8 +199,8 @@ void Axpby::eval_cpu(
 /** Evaluate primitive on CPU falling back to common backend */
 void Axpby::eval_cpu(
     const std::vector<array>& inputs,
-    std::vector<array>& out) {
-  eval(inputs, out);
+    const std::vector<array>& outputs) {
+  eval(inputs, outputs);
 }
 
 #endif
@@ -218,12 +214,12 @@ void Axpby::eval_cpu(
 /** Evaluate primitive on GPU */
 void Axpby::eval_gpu(
     const std::vector<array>& inputs,
-    std::vector<array>& outarr) {
+    std::vector<array>& outputs) {
   // Prepare inputs
-  auto out = outarr[0];
   assert(inputs.size() == 2);
   auto& x = inputs[0];
   auto& y = inputs[1];
+  auto& out = outputs[0];
 
   // Each primitive carries the stream it should execute on
   // and each stream carries its device identifiers
@@ -372,4 +368,4 @@ bool Axpby::is_equivalent(const Primitive& other) const {
   return alpha_ == r_other.alpha_ && beta_ == r_other.beta_;
 }
 
-} // namespace mlx::core
+} // namespace mlx::core

examples/extensions/axpby/axpby.h

@@ -33,7 +33,7 @@ array axpby(
 class Axpby : public Primitive {
  public:
   explicit Axpby(Stream stream, float alpha, float beta)
-      : Primitive(stream), alpha_(alpha), beta_(beta){};
+      : Primitive(stream), alpha_(alpha), beta_(beta) {};
 
   /**
    * A primitive must know how to evaluate itself on the CPU/GPU
@@ -42,9 +42,9 @@ class Axpby : public Primitive {
    * To avoid unnecessary allocations, the evaluation function
    * is responsible for allocating space for the array.
    */
-  void eval_cpu(const std::vector<array>& inputs, std::vector<array>& out)
+  void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override;
-  void eval_gpu(const std::vector<array>& inputs, std::vector<array>& out)
+  void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override;
 
   /** The Jacobian-vector product. */
@@ -83,7 +83,7 @@ class Axpby : public Primitive {
   float beta_;
 
   /** Fall back implementation for evaluation on CPU */
-  void eval(const std::vector<array>& inputs, std::vector<array>& out);
+  void eval(const std::vector<array>& inputs, std::vector<array>& outputs);
 };
 
-} // namespace mlx::core
+} // namespace mlx::core

examples/extensions/axpby/axpby.metal

@@ -19,7 +19,7 @@ template <typename T>
     uint index [[thread_position_in_grid]]) {
   auto x_offset = elem_to_loc(index, shape, x_strides, ndim);
   auto y_offset = elem_to_loc(index, shape, y_strides, ndim);
-  out[index] = 
+  out[index] =
       static_cast<T>(alpha) * x[x_offset] + static_cast<T>(beta) * y[y_offset];
 }
 
@@ -31,30 +31,30 @@ template <typename T>
     constant const float& alpha [[buffer(3)]],
     constant const float& beta [[buffer(4)]],
     uint index [[thread_position_in_grid]]) {
-  out[index] = 
+  out[index] =
       static_cast<T>(alpha) * x[index] + static_cast<T>(beta) * y[index];
 }
 
-#define instantiate_axpby(type_name, type)            \
-  template [[host_name("axpby_general_" #type_name)]] \
-  [[kernel]] void axpby_general<type>(                \
-      device const type* x [[buffer(0)]],             \
-      device const type* y [[buffer(1)]],             \
-      device type* out [[buffer(2)]],                 \
-      constant const float& alpha [[buffer(3)]],      \
-      constant const float& beta [[buffer(4)]],       \
-      constant const int* shape [[buffer(5)]],        \
-      constant const size_t* x_strides [[buffer(6)]], \
-      constant const size_t* y_strides [[buffer(7)]], \
-      constant const int& ndim [[buffer(8)]],         \
-      uint index [[thread_position_in_grid]]);        \
-  template [[host_name("axpby_contiguous_" #type_name)]] \
-  [[kernel]] void axpby_contiguous<type>(                \
-      device const type* x [[buffer(0)]],                \
-      device const type* y [[buffer(1)]],                \
-      device type* out [[buffer(2)]],                    \
-      constant const float& alpha [[buffer(3)]],         \
-      constant const float& beta [[buffer(4)]],          \
+#define instantiate_axpby(type_name, type)                               \
+  template [[host_name("axpby_general_" #type_name)]] [[kernel]] void    \
+  axpby_general<type>(                                                   \
+      device const type* x [[buffer(0)]],                                \
+      device const type* y [[buffer(1)]],                                \
+      device type* out [[buffer(2)]],                                    \
+      constant const float& alpha [[buffer(3)]],                         \
+      constant const float& beta [[buffer(4)]],                          \
+      constant const int* shape [[buffer(5)]],                           \
+      constant const size_t* x_strides [[buffer(6)]],                    \
+      constant const size_t* y_strides [[buffer(7)]],                    \
+      constant const int& ndim [[buffer(8)]],                            \
+      uint index [[thread_position_in_grid]]);                           \
+  template [[host_name("axpby_contiguous_" #type_name)]] [[kernel]] void \
+  axpby_contiguous<type>(                                                \
+      device const type* x [[buffer(0)]],                                \
+      device const type* y [[buffer(1)]],                                \
+      device type* out [[buffer(2)]],                                    \
+      constant const float& alpha [[buffer(3)]],                         \
+      constant const float& beta [[buffer(4)]],                          \
       uint index [[thread_position_in_grid]]);
 
 instantiate_axpby(float32, float);

examples/extensions/bindings.cpp

@@ -1,31 +1,31 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
-#include <pybind11/pybind11.h>
-#include <pybind11/stl.h>
+#include <nanobind/nanobind.h>
+#include <nanobind/stl/variant.h>
 
 #include "axpby/axpby.h"
 
-namespace py = pybind11;
-using namespace py::literals;
+namespace nb = nanobind;
+using namespace nb::literals;
+
 using namespace mlx::core;
 
-PYBIND11_MODULE(mlx_sample_extensions, m) {
-  m.doc() = "Sample C++ and metal extensions for MLX";
+NB_MODULE(_ext, m) {
+  m.doc() = "Sample extension for MLX";
 
   m.def(
       "axpby",
       &axpby,
       "x"_a,
       "y"_a,
-      py::pos_only(),
       "alpha"_a,
       "beta"_a,
-      py::kw_only(),
-      "stream"_a = py::none(),
-      R"pbdoc(
+      nb::kw_only(),
+      "stream"_a = nb::none(),
+      R"(
         Scale and sum two vectors element-wise
         ``z = alpha * x + beta * y``
-        
+
         Follows numpy style broadcasting between ``x`` and ``y``
         Inputs are upcasted to floats if needed
 
@@ -37,5 +37,5 @@ PYBIND11_MODULE(mlx_sample_extensions, m) {
 
         Returns:
             array: ``alpha * x + beta * y``
-      )pbdoc");
-}
+      )");
+}

examples/extensions/pyproject.toml

@@ -1,3 +1,8 @@
 [build-system]
-requires = ["setuptools>=42", "pybind11>=2.10", "cmake>=3.24", "mlx @ git+https://github.com/mlx-explore/mlx@main"]
-build-backend = "setuptools.build_meta"
+requires = [
+  "setuptools>=42",
+  "cmake>=3.24",
+  "mlx>=0.9.0",
+  "nanobind@git+https://github.com/wjakob/nanobind.git@2f04eac452a6d9142dedb957701bdb20125561e4",
+]
+build-backend = "setuptools.build_meta"

examples/extensions/requirements.txt

@@ -0,0 +1,4 @@
+setuptools>=42
+cmake>=3.24
+mlx>=0.9.0
+nanobind@git+https://github.com/wjakob/nanobind.git#egg=4148debcf91f5ccab0c3b8d67b5c3cabd61f407f

examples/extensions/setup.py

@@ -1,4 +1,4 @@
-# Copyright © 2023 Apple Inc.
+# Copyright © 2023-2024 Apple Inc.
 
 from setuptools import setup
 
@@ -9,11 +9,11 @@ if __name__ == "__main__":
         name="mlx_sample_extensions",
         version="0.0.0",
         description="Sample C++ and Metal extensions for MLX primitives.",
-        ext_modules=[extension.CMakeExtension("mlx_sample_extensions")],
+        ext_modules=[extension.CMakeExtension("mlx_sample_extensions._ext")],
         cmdclass={"build_ext": extension.CMakeBuild},
         packages=["mlx_sample_extensions"],
-        package_dir={"": "."},
         package_data={"mlx_sample_extensions": ["*.so", "*.dylib", "*.metallib"]},
+        extras_require={"dev": []},
         zip_safe=False,
         python_requires=">=3.8",
     )

mlx/allocator.h

@@ -14,7 +14,7 @@ class Buffer {
   void* ptr_;
 
  public:
-  Buffer(void* ptr) : ptr_(ptr){};
+  Buffer(void* ptr) : ptr_(ptr) {};
 
   // Get the raw data pointer from the buffer
   void* raw_ptr();

mlx/array.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2023-2024 Apple Inc.
-
 #include <functional>
 
 #include "mlx/array.h"
@@ -12,16 +11,6 @@ namespace mlx::core {
 
 namespace {
 
-std::pair<size_t, std::vector<size_t>> cum_prod(const std::vector<int>& shape) {
-  std::vector<size_t> strides(shape.size());
-  size_t cum_prod = 1;
-  for (int i = shape.size() - 1; i >= 0; --i) {
-    strides[i] = cum_prod;
-    cum_prod *= shape[i];
-  }
-  return {cum_prod, strides};
-}
-
 /** Return true if we are currently performing a function transformation in
  * order to keep the graph when evaluating tracer arrays. */
 bool in_tracing() {
@@ -48,15 +37,16 @@ array::array(
           std::move(inputs))) {}
 
 std::vector<array> array::make_arrays(
-    const std::vector<std::vector<int>>& shapes,
+    std::vector<std::vector<int>> shapes,
     const std::vector<Dtype>& dtypes,
-    std::shared_ptr<Primitive> primitive,
+    const std::shared_ptr<Primitive>& primitive,
     const std::vector<array>& inputs) {
   std::vector<array> outputs;
-  for (int i = 0; i < shapes.size(); ++i) {
-    outputs.push_back(array(shapes[i], dtypes[i], primitive, inputs));
+  for (size_t i = 0; i < shapes.size(); ++i) {
+    outputs.emplace_back(std::move(shapes[i]), dtypes[i], primitive, inputs);
   }
-  for (int i = 0; i < outputs.size(); ++i) {
+  // For each node in |outputs|, its siblings are the other nodes.
+  for (size_t i = 0; i < outputs.size(); ++i) {
     auto siblings = outputs;
     siblings.erase(siblings.begin() + i);
     outputs[i].set_siblings(std::move(siblings), i);
@@ -102,7 +92,13 @@ void array::detach() {
 }
 
 void array::eval() {
-  mlx::core::eval({*this});
+  // Ensure the array is ready to be read
+  if (status() == Status::scheduled) {
+    event().wait();
+    set_status(Status::available);
+  } else if (status() == Status::unscheduled) {
+    mlx::core::eval({*this});
+  }
 }
 
 bool array::is_tracer() const {
@@ -170,9 +166,54 @@ 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 will be detached
+  if (status() != array::Status::unscheduled) {
+    return;
+  }
+  // Break circular reference for non-detached arrays with siblings
+  if (auto n = siblings().size(); n > 0) {
+    bool do_detach = true;
+    // If all siblings have siblings.size() references except
+    // the one we are currently destroying (which has siblings.size() + 1)
+    // then there are no more external references
+    do_detach &= (array_desc_.use_count() == (n + 1));
+    for (auto& s : siblings()) {
+      do_detach &= (s.array_desc_.use_count() == n);
+      if (!do_detach) {
+        break;
+      }
+    }
+    if (do_detach) {
+      for (auto& s : siblings()) {
+        for (auto& ss : s.siblings()) {
+          ss.array_desc_ = nullptr;
+        }
+        s.array_desc_->siblings.clear();
+      }
+    }
+  }
+}
+
+void array::ArrayDesc::init() {
+  strides.resize(shape.size());
+  size = 1;
+  for (int i = shape.size() - 1; i >= 0; --i) {
+    strides[i] = size;
+    size *= shape[i];
+  }
+  for (auto& in : inputs) {
+    is_tracer |= in.is_tracer();
+  }
+}
+
 array::ArrayDesc::ArrayDesc(std::vector<int> shape, Dtype dtype)
-    : shape(std::move(shape)), dtype(dtype) {
-  std::tie(size, strides) = cum_prod(this->shape);
+    : shape(std::move(shape)), dtype(dtype), status(Status::available) {
+  init();
 }
 
 array::ArrayDesc::ArrayDesc(
@@ -182,11 +223,39 @@ array::ArrayDesc::ArrayDesc(
     std::vector<array> inputs)
     : shape(std::move(shape)),
       dtype(dtype),
+      status(Status::unscheduled),
       primitive(std::move(primitive)),
       inputs(std::move(inputs)) {
-  std::tie(size, strides) = cum_prod(this->shape);
-  for (auto& in : this->inputs) {
-    is_tracer |= in.is_tracer();
+  init();
+}
+
+array::ArrayDesc::~ArrayDesc() {
+  // When an array description is destroyed it will delete a bunch of arrays
+  // that may also destory their corresponding descriptions and so on and so
+  // forth.
+  //
+  // This calls recursively the destructor and can result in stack overflow, we
+  // instead put them in a vector and destroy them one at a time resulting in a
+  // max stack depth of 2.
+  std::vector<std::shared_ptr<ArrayDesc>> for_deletion;
+
+  for (array& a : inputs) {
+    if (a.array_desc_.use_count() == 1) {
+      for_deletion.push_back(std::move(a.array_desc_));
+    }
+  }
+
+  while (!for_deletion.empty()) {
+    // top is going to be deleted at the end of the block *after* the arrays
+    // with inputs have been moved into the vector
+    auto top = std::move(for_deletion.back());
+    for_deletion.pop_back();
+
+    for (array& a : top->inputs) {
+      if (a.array_desc_.use_count() == 1) {
+        for_deletion.push_back(std::move(a.array_desc_));
+      }
+    }
   }
 }
 

mlx/array.h

@@ -9,6 +9,7 @@
 
 #include "mlx/allocator.h"
 #include "mlx/dtype.h"
+#include "mlx/event.h"
 
 namespace mlx::core {
 
@@ -113,6 +114,15 @@ class array {
     return array_desc_->strides;
   };
 
+  /**
+   *  Get the stride of the corresponding dimension.
+   *
+   *  This function supports negative indexing and provides
+   *  bounds checking. */
+  size_t strides(int dim) const {
+    return strides().at(dim < 0 ? dim + ndim() : dim);
+  };
+
   /** Get the arrays data type. */
   Dtype dtype() const {
     return array_desc_->dtype;
@@ -180,9 +190,9 @@ class array {
       std::vector<array> inputs);
 
   static std::vector<array> make_arrays(
-      const std::vector<std::vector<int>>& shapes,
+      std::vector<std::vector<int>> shapes,
       const std::vector<Dtype>& dtypes,
-      std::shared_ptr<Primitive> primitive,
+      const std::shared_ptr<Primitive>& primitive,
       const std::vector<array>& inputs);
 
   /** A unique identifier for an array. */
@@ -199,7 +209,7 @@ class array {
     allocator::Buffer buffer;
     deleter_t d;
     Data(allocator::Buffer buffer, deleter_t d = allocator::free)
-        : buffer(buffer), d(d){};
+        : buffer(buffer), d(d) {};
     // Not copyable
     Data(const Data& d) = delete;
     Data& operator=(const Data& d) = delete;
@@ -251,6 +261,11 @@ class array {
     return array_desc_->siblings;
   };
 
+  /** The array's siblings. */
+  std::vector<array>& siblings() {
+    return array_desc_->siblings;
+  };
+
   void set_siblings(std::vector<array> siblings, uint16_t position) {
     array_desc_->siblings = std::move(siblings);
     array_desc_->position = position;
@@ -304,9 +319,27 @@ class array {
     return static_cast<T*>(array_desc_->data_ptr);
   };
 
-  // Check if the array has been evaluated
-  bool is_evaled() const {
-    return array_desc_->data != nullptr;
+  enum Status { unscheduled, scheduled, available };
+
+  bool is_available() const {
+    return status() == Status::available;
+  }
+  const Status status() const {
+    return array_desc_->status;
+  }
+
+  void set_status(Status s) const {
+    array_desc_->status = s;
+  }
+
+  // Get the array's shared event
+  Event& event() const {
+    return array_desc_->event;
+  }
+
+  // Attach an event to a not yet evaluated array
+  void attach_event(Event e) const {
+    array_desc_->event = std::move(e);
   }
 
   // Mark the array as a tracer array (true) or not.
@@ -347,6 +380,8 @@ class array {
     array_desc_ = other.array_desc_;
   }
 
+  ~array();
+
  private:
   // Initialize the arrays data
   template <typename It>
@@ -359,6 +394,11 @@ class array {
     Dtype dtype;
     std::shared_ptr<Primitive> primitive;
 
+    Status status;
+
+    // An event on the array used for synchronization
+    Event event;
+
     // Indicates an array is being used in a graph transform
     // and should not be detached from the graph
     bool is_tracer{false};
@@ -392,6 +432,12 @@ class array {
         Dtype dtype,
         std::shared_ptr<Primitive> primitive,
         std::vector<array> inputs);
+
+    ~ArrayDesc();
+
+   private:
+    // Initialize size, strides, and other metadata
+    void init();
   };
 
   // The ArrayDesc contains the details of the materialized array including the
@@ -453,10 +499,11 @@ T array::item() const {
   if (size() != 1) {
     throw std::invalid_argument("item can only be called on arrays of size 1.");
   }
-  if (!is_evaled()) {
+  if (status() == Status::unscheduled) {
     throw std::invalid_argument(
         "item() const can only be called on evaled arrays");
   }
+  const_cast<array*>(this)->eval();
   return *data<T>();
 }
 
@@ -506,4 +553,15 @@ void array::init(It src) {
   }
 }
 
+/* Utilities for determining whether a template parameter is array. */
+template <typename T>
+inline constexpr bool is_array_v =
+    std::is_same_v<std::remove_cv_t<std::remove_reference_t<T>>, array>;
+
+template <typename... T>
+inline constexpr bool is_arrays_v = (is_array_v<T> && ...);
+
+template <typename... T>
+using enable_for_arrays_t = typename std::enable_if_t<is_arrays_v<T...>>;
+
 } // namespace mlx::core

mlx/backend/accelerate/matmul.cpp

@@ -1,4 +1,4 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
 #include <cassert>
 
@@ -196,6 +196,40 @@ inline void matmul_bnns(const array& a_pre, const array& b_pre, array& out) {
   return matmul_bnns_general(a_pre, b_pre, out);
 }
 
+template <typename T>
+inline void mask_matrix(
+    T* data,
+    const bool* mask,
+    int tile_size,
+    const int X,
+    const int Y,
+    const size_t X_data_str,
+    const size_t Y_data_str,
+    const size_t X_mask_str,
+    const size_t Y_mask_str) {
+  int tX = (X + tile_size - 1) / tile_size;
+  int tY = (Y + tile_size - 1) / tile_size;
+
+  for (int i = 0; i < tX; i++) {
+    for (int j = 0; j < tY; j++) {
+      bool do_mask = mask[i * X_mask_str + j * Y_mask_str];
+      if (!do_mask) {
+        int loc_x = i * tile_size;
+        int loc_y = j * tile_size;
+        T* data_block = data + loc_x * X_data_str + loc_y * Y_data_str;
+
+        int size_x = std::min(tile_size, X - loc_x);
+        int size_y = std::min(tile_size, Y - loc_y);
+        for (int ii = 0; ii < size_x; ii++) {
+          for (int jj = 0; jj < size_y; jj++) {
+            data_block[ii * X_data_str + jj * Y_data_str] = T(0.);
+          }
+        }
+      }
+    }
+  }
+}
+
 } // namespace
 
 void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {

mlx/backend/accelerate/primitives.cpp

@@ -31,6 +31,8 @@ DEFAULT(ArgPartition)
 DEFAULT(ArgReduce)
 DEFAULT(ArgSort)
 DEFAULT(AsStrided)
+DEFAULT(BlockMaskedMM)
+DEFAULT(BlockSparseMM)
 DEFAULT(Broadcast)
 DEFAULT(Ceil)
 DEFAULT(Concatenate)
@@ -191,6 +193,26 @@ void ArcTan::eval_cpu(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void ArcTan2::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  if (out.dtype() == float32 && a.flags().row_contiguous &&
+      b.flags().row_contiguous) {
+    if (a.is_donatable()) {
+      out.copy_shared_buffer(a);
+    } else if (b.is_donatable()) {
+      out.copy_shared_buffer(b);
+    } else {
+      out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    }
+    int size = a.data_size();
+    vvatan2f(out.data<float>(), a.data<float>(), b.data<float>(), &size);
+  } else {
+    eval(inputs, out);
+  }
+}
+
 void ArcTanh::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
@@ -301,7 +323,7 @@ void Exp::eval_cpu(const std::vector<array>& inputs, array& out) {
     set_unary_output_data(in, out);
     auto size = in.data_size();
     vvexpf(out.data<float>(), in.data<float>(), reinterpret_cast<int*>(&size));
-  } else if (is_floating_point(out.dtype())) {
+  } else if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, [](auto x) { return std::exp(x); });
   } else {
     throw std::invalid_argument(
@@ -310,6 +332,19 @@ void Exp::eval_cpu(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void Expm1::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  const auto& in = inputs[0];
+  if (out.dtype() == float32 && in.flags().contiguous) {
+    set_unary_output_data(in, out);
+    auto size = in.data_size();
+    vvexpm1f(
+        out.data<float>(), in.data<float>(), reinterpret_cast<int*>(&size));
+  } else {
+    eval(inputs, out);
+  }
+}
+
 void Full::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
@@ -355,7 +390,7 @@ void Log1p::eval_cpu(const std::vector<array>& inputs, array& out) {
     auto size = in.data_size();
     vvlog1pf(
         out.data<float>(), in.data<float>(), reinterpret_cast<int*>(&size));
-  } else if (is_floating_point(out.dtype())) {
+  } else if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, [](auto x) { return std::log1p(x); });
   } else {
     throw std::invalid_argument(

mlx/backend/accelerate/softmax.cpp

@@ -1,4 +1,4 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
 #include <cassert>
 #include <limits>
@@ -201,7 +201,7 @@ struct NeonFp16SimdOps {
   }
 };
 
-template <typename T, typename VT, typename Ops, int N>
+template <typename T, typename AccT, typename VT, typename Ops, int N>
 void softmax(const array& in, array& out) {
   Ops ops;
 
@@ -218,13 +218,21 @@ void softmax(const array& in, array& out) {
     VT vmaximum = ops.init(-std::numeric_limits<float>::infinity());
     size_t s = M;
     while (s >= N) {
-      vmaximum = ops.max(ops.load(current_in_ptr), vmaximum);
+      VT vals;
+      if constexpr (std::is_same<T, AccT>::value) {
+        vals = ops.load(current_in_ptr);
+      } else {
+        for (int i = 0; i < N; ++i) {
+          vals[i] = static_cast<AccT>(current_in_ptr[i]);
+        }
+      }
+      vmaximum = ops.max(vals, vmaximum);
       current_in_ptr += N;
       s -= N;
     }
-    T maximum = ops.reduce_max(vmaximum);
+    AccT maximum = ops.reduce_max(vmaximum);
     while (s-- > 0) {
-      maximum = std::max(maximum, *current_in_ptr);
+      maximum = std::max(maximum, static_cast<AccT>(*current_in_ptr));
       current_in_ptr++;
     }
 
@@ -234,18 +242,29 @@ void softmax(const array& in, array& out) {
     current_in_ptr = in_ptr;
     s = M;
     while (s >= N) {
-      VT vexp = ops.exp(ops.sub(*(VT*)current_in_ptr, maximum));
-      ops.store(current_out_ptr, vexp);
-      *(VT*)current_out_ptr = vexp;
+      VT vexp;
+      if constexpr (std::is_same<T, AccT>::value) {
+        vexp = ops.load(current_in_ptr);
+      } else {
+        for (int i = 0; i < N; ++i) {
+          vexp[i] = static_cast<AccT>(current_in_ptr[i]);
+        }
+      }
+      vexp = ops.exp(ops.sub(vexp, maximum));
+      if constexpr (std::is_same<T, AccT>::value) {
+        ops.store(current_out_ptr, vexp);
+      }
       vnormalizer = ops.add(vnormalizer, vexp);
       current_in_ptr += N;
       current_out_ptr += N;
       s -= N;
     }
-    T normalizer = ops.reduce_add(vnormalizer);
+    AccT normalizer = ops.reduce_add(vnormalizer);
     while (s-- > 0) {
-      T _exp = std::exp(*current_in_ptr - maximum);
-      *current_out_ptr = _exp;
+      AccT _exp = std::exp(*current_in_ptr - maximum);
+      if (std::is_same<T, AccT>::value) {
+        *current_out_ptr = _exp;
+      }
       normalizer += _exp;
       current_in_ptr++;
       current_out_ptr++;
@@ -254,14 +273,33 @@ void softmax(const array& in, array& out) {
 
     // Normalize
     current_out_ptr = out_ptr;
+    current_in_ptr = in_ptr;
     s = M;
     while (s >= N) {
-      ops.store(current_out_ptr, ops.mul(*(VT*)current_out_ptr, normalizer));
+      if constexpr (std::is_same<T, AccT>::value) {
+        ops.store(current_out_ptr, ops.mul(*(VT*)current_out_ptr, normalizer));
+      } else {
+        VT vexp;
+        for (int i = 0; i < N; ++i) {
+          vexp[i] = static_cast<AccT>(current_in_ptr[i]);
+        }
+        vexp = ops.mul(ops.exp(ops.sub(vexp, maximum)), normalizer);
+        for (int i = 0; i < N; ++i) {
+          current_out_ptr[i] = vexp[i];
+        }
+        current_in_ptr += N;
+      }
       current_out_ptr += N;
       s -= N;
     }
     while (s-- > 0) {
-      *current_out_ptr *= normalizer;
+      if constexpr (std::is_same<T, AccT>::value) {
+        *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++;
     }
   }
@@ -308,15 +346,29 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
           "Softmax is defined only for floating point types");
       break;
     case float32:
-      softmax<float, simd_float16, AccelerateSimdOps<float, simd_float16>, 16>(
-          in, out);
+      softmax<
+          float,
+          float,
+          simd_float16,
+          AccelerateSimdOps<float, simd_float16>,
+          16>(in, out);
       break;
     case float16:
-      softmax<
-          float16_t,
-          float16x8_t,
-          NeonFp16SimdOps<float16_t, float16x8_t>,
-          8>(in, out);
+      if (precise_) {
+        softmax<
+            float16_t,
+            float,
+            simd_float16,
+            AccelerateSimdOps<float, simd_float16>,
+            16>(in, out);
+      } else {
+        softmax<
+            float16_t,
+            float16_t,
+            float16x8_t,
+            NeonFp16SimdOps<float16_t, float16x8_t>,
+            8>(in, out);
+      }
       break;
     case bfloat16:
       eval(inputs, out);

mlx/backend/common/CMakeLists.txt

@@ -41,6 +41,7 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/erf.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/masked_mm.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp

mlx/backend/common/binary.cpp

@@ -179,18 +179,16 @@ void LogAddExp::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  if (is_floating_point(out.dtype())) {
-    if (out.dtype() == float32) {
-      binary_op<float>(a, b, out, detail::LogAddExp());
-    } else if (out.dtype() == float16) {
-      binary_op<float16_t>(a, b, out, detail::LogAddExp());
-    } else if (out.dtype() == bfloat16) {
-      binary_op<bfloat16_t>(a, b, out, detail::LogAddExp());
-    } else {
-      std::ostringstream err;
-      err << "[logaddexp] Does not support " << out.dtype();
-      throw std::invalid_argument(err.str());
-    }
+  if (out.dtype() == float32) {
+    binary_op<float>(a, b, out, detail::LogAddExp());
+  } else if (out.dtype() == float16) {
+    binary_op<float16_t>(a, b, out, detail::LogAddExp());
+  } else if (out.dtype() == bfloat16) {
+    binary_op<bfloat16_t>(a, b, out, detail::LogAddExp());
+  } else if (issubdtype(out.dtype(), inexact)) {
+    std::ostringstream err;
+    err << "[logaddexp] Does not support " << out.dtype();
+    throw std::invalid_argument(err.str());
   } else {
     throw std::invalid_argument(
         "[logaddexp] Cannot compute logaddexp for arrays with"
@@ -238,4 +236,82 @@ void Subtract::eval(const std::vector<array>& inputs, array& out) {
   binary(a, b, out, detail::Subtract());
 }
 
+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());
+      break;
+    case BitwiseBinary::Or:
+      dispatch_type(detail::BitwiseOr());
+      break;
+    case BitwiseBinary::Xor:
+      dispatch_type(detail::BitwiseXor());
+      break;
+    case BitwiseBinary::LeftShift:
+      dispatch_type(detail::LeftShift());
+      break;
+    case BitwiseBinary::RightShift:
+      dispatch_type(detail::RightShift());
+      break;
+  }
+}
+
+void ArcTan2::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  const auto& a = inputs[0];
+  const auto& b = inputs[1];
+  if (out.dtype() == float32) {
+    binary_op<float>(a, b, out, detail::ArcTan2());
+  } else if (out.dtype() == float16) {
+    binary_op<float16_t>(a, b, out, detail::ArcTan2());
+  } else if (out.dtype() == bfloat16) {
+    binary_op<bfloat16_t>(a, b, out, detail::ArcTan2());
+  } else if (issubdtype(out.dtype(), inexact)) {
+    std::ostringstream err;
+    err << "[arctan2] Does not support " << out.dtype();
+    throw std::invalid_argument(err.str());
+  } else {
+    throw std::invalid_argument(
+        "[arctan2] Cannot compute inverse tangent for arrays"
+        " with non floating point type.");
+  }
+}
+
 } // namespace mlx::core

mlx/backend/common/compiled.cpp

@@ -126,4 +126,102 @@ std::string build_lib_name(
   return os.str();
 }
 
+bool compiled_check_contiguity(
+    const std::vector<array>& inputs,
+    const std::vector<int>& shape) {
+  bool contiguous = true;
+  bool all_contig = true;
+  bool all_row_contig = true;
+  bool all_col_contig = true;
+  int non_scalar_inputs = 0;
+  for (const auto& x : inputs) {
+    if (is_scalar(x)) {
+      continue;
+    }
+    non_scalar_inputs++;
+    bool shape_eq = x.shape() == shape;
+    all_contig &= (x.flags().contiguous && shape_eq);
+    all_row_contig &= (x.flags().row_contiguous && shape_eq);
+    all_col_contig &= (x.flags().col_contiguous && shape_eq);
+  }
+  if (non_scalar_inputs > 1 && !all_row_contig && !all_col_contig) {
+    contiguous = false;
+  } else if (non_scalar_inputs == 1 && !all_contig) {
+    contiguous = false;
+  } else if (non_scalar_inputs == 0 && !shape.empty()) {
+    contiguous = false;
+  }
+  return contiguous;
+}
+
+void compiled_allocate_outputs(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs,
+    const std::vector<array>& inputs_,
+    const std::unordered_set<uintptr_t>& constant_ids_,
+    bool contiguous,
+    bool move_buffers /* = false */) {
+  if (contiguous) {
+    int o = 0;
+    std::vector<size_t> strides;
+    size_t data_size;
+    array::Flags flags;
+    for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {
+      auto& in = inputs[i];
+      // Conditions for donation
+      // - Correct size
+      // - Not a scalar
+      // - Donatable
+      // - Not a constant
+      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);
+        }
+      }
+      // Get representative input flags to properly set non-donated outputs
+      if (strides.empty() && in.size() == outputs[0].size()) {
+        strides = in.strides();
+        flags = in.flags();
+        data_size = in.data_size();
+      }
+    }
+    for (; o < outputs.size(); ++o) {
+      outputs[o].set_data(
+          allocator::malloc_or_wait(data_size * outputs[o].itemsize()),
+          data_size,
+          strides,
+          flags);
+    }
+  } else {
+    int o = 0;
+    for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {
+      auto& in = inputs[i];
+      // Conditions for donation
+      // - Row contiguous
+      // - Donatable
+      // - Correct size
+      // - Not a constant
+      if (in.flags().row_contiguous && in.nbytes() == outputs[o].nbytes() &&
+          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());
+        }
+        o++;
+      }
+    }
+    for (; o < outputs.size(); ++o) {
+      outputs[o].set_data(allocator::malloc_or_wait(outputs[o].nbytes()));
+    }
+  }
+}
+
 } // namespace mlx::core

mlx/backend/common/compiled.h

@@ -53,4 +53,18 @@ inline bool is_scalar(const array& x) {
   return x.ndim() == 0;
 }
 
+// Check if we can use a contiguous operation given inputs and the output shape
+bool compiled_check_contiguity(
+    const std::vector<array>& inputs,
+    const std::vector<int>& shape);
+
+// Allocate space for the outputs possibly with input donation
+void compiled_allocate_outputs(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs,
+    const std::vector<array>& inputs_,
+    const std::unordered_set<uintptr_t>& constant_ids_,
+    bool contiguous,
+    bool move_buffers = false);
+
 } // namespace mlx::core

mlx/backend/common/compiled_cpu.cpp

@@ -52,8 +52,25 @@ void* compile(
     return nullptr;
   }
 
+  std::string kernel_file_name;
+
+  // Deal with long kernel names. Maximum length for files on macOS is 255
+  // characters. Clip file name with a little extra room and append a 16
+  // character hash.
+  constexpr int max_file_name_length = 245;
+  if (kernel_name.size() > max_file_name_length) {
+    std::ostringstream file_name;
+    file_name
+        << std::string_view(kernel_name).substr(0, max_file_name_length - 16);
+    auto file_id = std::hash<std::string>{}(kernel_name);
+    file_name << "_" << std::hex << std::setw(16) << file_id << std::dec;
+    kernel_file_name = file_name.str();
+  } else {
+    kernel_file_name = kernel_name;
+  }
+
   std::ostringstream shared_lib_name;
-  shared_lib_name << "lib" << kernel_name << ".so";
+  shared_lib_name << "lib" << kernel_file_name << ".so";
   auto shared_lib_path = get_temp_file(shared_lib_name.str());
   bool lib_exists = false;
   {
@@ -64,7 +81,7 @@ void* compile(
   if (!lib_exists) {
     // Open source file and write source code to it
     std::ostringstream source_file_name;
-    source_file_name << kernel_name << ".cpp";
+    source_file_name << kernel_file_name << ".cpp";
     auto source_file_path = get_temp_file(source_file_name.str());
 
     std::ofstream source_file(source_file_path);
@@ -248,28 +265,7 @@ void Compiled::eval_cpu(
 
   // Figure out which kernel we are using
   auto& shape = outputs[0].shape();
-  bool contiguous = true;
-  {
-    bool all_contig = true;
-    bool all_row_contig = true;
-    bool all_col_contig = true;
-    int non_scalar_inputs = 0;
-    for (auto& x : inputs) {
-      if (is_scalar(x)) {
-        continue;
-      }
-      non_scalar_inputs++;
-      bool shape_eq = x.shape() == shape;
-      all_contig &= (x.flags().contiguous && shape_eq);
-      all_row_contig &= (x.flags().row_contiguous && shape_eq);
-      all_col_contig &= (x.flags().col_contiguous && shape_eq);
-    }
-    if (non_scalar_inputs > 1 && !all_row_contig && !all_col_contig) {
-      contiguous = false;
-    } else if (non_scalar_inputs == 1 && !all_contig) {
-      contiguous = false;
-    }
-  }
+  bool contiguous = compiled_check_contiguity(inputs, shape);
 
   // Handle all broadcasting and collect function input arguments
   std::vector<void*> args;
@@ -342,58 +338,8 @@ void Compiled::eval_cpu(
     fn_ptr = compile(kernel_name, kernel.str());
   }
 
-  // Allocate space for the outputs possibly with input donation
-  if (contiguous) {
-    int o = 0;
-    std::vector<size_t> strides;
-    size_t data_size;
-    array::Flags flags;
-    for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {
-      auto& in = inputs[i];
-      // Conditions for donation
-      // - Contiguous
-      // - Donatable
-      // - Correct size
-      // - Not a constant
-      if (in.flags().contiguous && !is_scalar(in) && in.is_donatable() &&
-          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
-        outputs[o++].copy_shared_buffer(in);
-      }
-      // Get representative input flags to properly set non-donated outputs
-      if (strides.empty() && in.size() == outputs[0].size()) {
-        strides = in.strides();
-        flags = in.flags();
-        data_size = in.data_size();
-      }
-    }
-    for (; o < outputs.size(); ++o) {
-      outputs[o].set_data(
-          allocator::malloc_or_wait(data_size * outputs[o].itemsize()),
-          data_size,
-          strides,
-          flags);
-    }
-  } else {
-    int o = 0;
-    for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {
-      auto& in = inputs[i];
-      // Conditions for donation
-      // - Row contiguous
-      // - Donatable
-      // - Correct size
-      // - Not a constant
-      if (in.flags().row_contiguous && in.nbytes() == outputs[o].nbytes() &&
-          in.is_donatable() &&
-          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
-        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()));
-    }
-  }
+  compiled_allocate_outputs(
+      inputs, outputs, inputs_, constant_ids_, contiguous, false);
 
   for (auto& x : outputs) {
     args.push_back(x.data<void>());

mlx/backend/common/conv.cpp

@@ -38,11 +38,15 @@ void slow_conv_1D(
 
   const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
   const int iH = 1 + in_dilation[0] * (in.shape(1) - 1); // Input spatial dim
+  const int C = in.shape(2); // Input channels
   const int oH = out.shape(1); // Output spatial dim
   const int O = wt.shape(0); // Out channels
-  const int C = wt.shape(2); // In channels
   const int wH = wt.shape(1); // Weight spatial dim
 
+  const int groups = C / wt.shape(2);
+  const int C_per_group = wt.shape(2);
+  const int O_per_group = O / groups;
+
   const size_t in_stride_N = in.strides()[0];
   const size_t in_stride_H = in.strides()[1];
   const size_t in_stride_C = in.strides()[2];
@@ -57,35 +61,36 @@ void slow_conv_1D(
 
   for (int n = 0; n < N; ++n) {
     for (int oh = 0; oh < oH; ++oh) {
-      for (int o = 0; o < O; ++o) {
-        const T* filter_wt_ptr = start_wt_ptr + o * wt_stride_O;
-        float r = 0.;
+      for (int g = 0; g < groups; ++g) {
+        for (int o = g * O_per_group; o < (g + 1) * O_per_group; ++o) {
+          const T* filter_wt_ptr = start_wt_ptr + o * wt_stride_O;
+          float r = 0.;
 
-        for (int wh = 0; wh < wH; ++wh) {
-          const T* wt_ptr = filter_wt_ptr + wh * wt_stride_H;
+          for (int wh = 0; wh < wH; ++wh) {
+            const T* wt_ptr = filter_wt_ptr + wh * wt_stride_H;
 
-          int wh_flip = flip ? (wH - wh - 1) : wh;
-          int ih = oh * wt_strides[0] - padding[0] + wh_flip * wt_dilation[0];
+            int wh_flip = flip ? (wH - wh - 1) : wh;
+            int ih = oh * wt_strides[0] - padding[0] + wh_flip * wt_dilation[0];
 
-          auto ih_div = std::div(ih, in_dilation[0]);
+            auto ih_div = std::div(ih, in_dilation[0]);
 
-          if (ih >= 0 && ih < iH && ih_div.rem == 0) {
-            for (int c = 0; c < C; ++c) {
-              r += static_cast<float>(
-                       in_ptr[ih_div.quot * in_stride_H + c * in_stride_C]) *
-                  static_cast<float>(wt_ptr[c * wt_stride_C]);
-            } // c
+            if (ih >= 0 && ih < iH && ih_div.rem == 0) {
+              for (int c = g * C_per_group; c < (g + 1) * C_per_group; ++c) {
+                r += static_cast<float>(
+                         in_ptr[ih_div.quot * in_stride_H + c * in_stride_C]) *
+                    static_cast<float>(wt_ptr[(c % C_per_group) * wt_stride_C]);
+              } // c
 
-          } // ih check
-        } // wh
+            } // ih check
+          } // wh
 
-        out_ptr[oh * out_stride_H + o * out_stride_O] = static_cast<T>(r);
-      } // o
+          out_ptr[oh * out_stride_H + o * out_stride_O] = static_cast<T>(r);
+        } // o
+      } // g
     } // oh
 
     in_ptr += in_stride_N;
     out_ptr += out_stride_N;
-
   } // n
 }
 
@@ -366,11 +371,15 @@ void explicit_gemm_conv_1D_cpu(
     const std::vector<int>& wt_dilation) {
   const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
   const int iH = in.shape(1); // Input spatial dim
+  const int C = in.shape(2); // Input channels
   const int oH = out.shape(1); // Output spatial dim
   const int O = wt.shape(0); // Out channels
-  const int C = wt.shape(2); // In channels
   const int wH = wt.shape(1); // Weight spatial dim
 
+  const int groups = C / wt.shape(2);
+  const int C_per_group = wt.shape(2);
+  const int O_per_group = O / groups;
+
   auto conv_dtype = float32;
 
   // Pad input
@@ -402,6 +411,11 @@ void explicit_gemm_conv_1D_cpu(
       in_padded.strides()[1],
       in_padded.strides()[2]};
   auto flags = in_padded.flags();
+  if (groups > 1) {
+    // Transpose the last two dimensions for grouped convolutions
+    std::swap(strided_shape[2], strided_shape[3]);
+    std::swap(strided_strides[2], strided_strides[3]);
+  }
 
   array in_strided_view(strided_shape, in_padded.dtype(), nullptr, {});
   in_strided_view.copy_shared_buffer(
@@ -416,7 +430,19 @@ void explicit_gemm_conv_1D_cpu(
   auto gemm_wt = wt;
   auto gemm_out = out;
 
-  if (wt.dtype() != float32 || !wt.flags().row_contiguous) {
+  if (groups > 1) {
+    // Transpose the last two dimensions for grouped convolutions
+    array wt_transpose(
+        {wt.shape(0), wt.shape(2), wt.shape(1)}, wt.dtype(), nullptr, {});
+    wt_transpose.copy_shared_buffer(
+        wt,
+        {wt.strides(0), wt.strides(2), wt.strides(1)},
+        wt.flags(),
+        wt.size(),
+        0);
+    gemm_wt = array(wt_transpose.shape(), float32, nullptr, {});
+    copy(wt_transpose, gemm_wt, CopyType::General);
+  } else if (wt.dtype() != float32 || !wt.flags().row_contiguous) {
     auto ctype =
         wt.flags().row_contiguous ? CopyType::Vector : CopyType::General;
     gemm_wt = array(wt.shape(), float32, nullptr, {});
@@ -428,27 +454,29 @@ void explicit_gemm_conv_1D_cpu(
     gemm_out.set_data(allocator::malloc_or_wait(gemm_out.nbytes()));
   }
 
-  // Perform gemm
-  cblas_sgemm(
-      CblasRowMajor,
-      CblasNoTrans, // no trans A
-      CblasTrans, // transB
-      strided_reshape[0], // M
-      O, // N
-      strided_reshape[1], // K
-      1.0f, // alpha
-      in_strided.data<float>(),
-      strided_reshape[1], // lda
-      gemm_wt.data<float>(),
-      strided_reshape[1], // ldb
-      0.0f, // beta
-      gemm_out.data<float>(),
-      O // ldc
-  );
+  for (int g = 0; g < groups; ++g) {
+    // Perform gemm
+    cblas_sgemm(
+        CblasRowMajor,
+        CblasNoTrans, // no trans A
+        CblasTrans, // transB
+        strided_reshape[0], // M
+        O_per_group, // N
+        C_per_group * wH, // K
+        1.0f, // alpha
+        in_strided.data<float>() + g * C_per_group * wH, // A
+        wH * C, // lda
+        gemm_wt.data<float>() + g * O_per_group * C_per_group * wH, // B
+        wH * C_per_group, // ldb
+        0.0f, // beta
+        gemm_out.data<float>() + g * O_per_group, // C
+        O // ldc
+    );
 
-  // Copy results if needed
-  if (out.dtype() != float32) {
-    copy(gemm_out, out, CopyType::Vector);
+    // Copy results if needed
+    if (out.dtype() != float32) {
+      copy(gemm_out, out, CopyType::Vector);
+    }
   }
 }
 

mlx/backend/common/copy.cpp

@@ -272,7 +272,7 @@ inline void copy_general_general(const array& src, array& dst) {
 }
 
 template <typename SrcT, typename DstT, typename... Args>
-void copy(const array& src, array& dst, CopyType ctype, Args... args) {
+void copy(const array& src, array& dst, CopyType ctype, Args&&... args) {
   switch (ctype) {
     case CopyType::Scalar:
       copy_single<SrcT, DstT>(src, dst);
@@ -281,54 +281,54 @@ void copy(const array& src, array& dst, CopyType ctype, Args... args) {
       copy_vector<SrcT, DstT>(src, dst);
       return;
     case CopyType::General:
-      copy_general<SrcT, DstT>(src, dst, args...);
+      copy_general<SrcT, DstT>(src, dst, std::forward<Args>(args)...);
       return;
     case CopyType::GeneralGeneral:
-      copy_general_general<SrcT, DstT>(src, dst, args...);
+      copy_general_general<SrcT, DstT>(src, dst, std::forward<Args>(args)...);
   }
 }
 
 template <typename SrcT, typename... Args>
-void copy(const array& src, array& dst, CopyType ctype, Args... args) {
+void copy(const array& src, array& dst, CopyType ctype, Args&&... args) {
   switch (dst.dtype()) {
     case bool_:
-      copy<SrcT, bool>(src, dst, ctype, args...);
+      copy<SrcT, bool>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint8:
-      copy<SrcT, uint8_t>(src, dst, ctype, args...);
+      copy<SrcT, uint8_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint16:
-      copy<SrcT, uint16_t>(src, dst, ctype, args...);
+      copy<SrcT, uint16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint32:
-      copy<SrcT, uint32_t>(src, dst, ctype, args...);
+      copy<SrcT, uint32_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint64:
-      copy<SrcT, uint64_t>(src, dst, ctype, args...);
+      copy<SrcT, uint64_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int8:
-      copy<SrcT, int8_t>(src, dst, ctype, args...);
+      copy<SrcT, int8_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int16:
-      copy<SrcT, int16_t>(src, dst, ctype, args...);
+      copy<SrcT, int16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int32:
-      copy<SrcT, int32_t>(src, dst, ctype, args...);
+      copy<SrcT, int32_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int64:
-      copy<SrcT, int64_t>(src, dst, ctype, args...);
+      copy<SrcT, int64_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case float16:
-      copy<SrcT, float16_t>(src, dst, ctype, args...);
+      copy<SrcT, float16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case float32:
-      copy<SrcT, float>(src, dst, ctype, args...);
+      copy<SrcT, float>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case bfloat16:
-      copy<SrcT, bfloat16_t>(src, dst, ctype, args...);
+      copy<SrcT, bfloat16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case complex64:
-      copy<SrcT, complex64_t>(src, dst, ctype, args...);
+      copy<SrcT, complex64_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
   }
 }
@@ -338,46 +338,46 @@ inline void copy_inplace_dispatch(
     const array& src,
     array& dst,
     CopyType ctype,
-    Args... args) {
+    Args&&... args) {
   switch (src.dtype()) {
     case bool_:
-      copy<bool>(src, dst, ctype, args...);
+      copy<bool>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint8:
-      copy<uint8_t>(src, dst, ctype, args...);
+      copy<uint8_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint16:
-      copy<uint16_t>(src, dst, ctype, args...);
+      copy<uint16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint32:
-      copy<uint32_t>(src, dst, ctype, args...);
+      copy<uint32_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case uint64:
-      copy<uint64_t>(src, dst, ctype, args...);
+      copy<uint64_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int8:
-      copy<int8_t>(src, dst, ctype, args...);
+      copy<int8_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int16:
-      copy<int16_t>(src, dst, ctype, args...);
+      copy<int16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int32:
-      copy<int32_t>(src, dst, ctype, args...);
+      copy<int32_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case int64:
-      copy<int64_t>(src, dst, ctype, args...);
+      copy<int64_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case float16:
-      copy<float16_t>(src, dst, ctype, args...);
+      copy<float16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case float32:
-      copy<float>(src, dst, ctype, args...);
+      copy<float>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case bfloat16:
-      copy<bfloat16_t>(src, dst, ctype, args...);
+      copy<bfloat16_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
     case complex64:
-      copy<complex64_t>(src, dst, ctype, args...);
+      copy<complex64_t>(src, dst, ctype, std::forward<Args>(args)...);
       break;
   }
 }

mlx/backend/common/default_primitives.cpp

@@ -34,6 +34,7 @@ DEFAULT(ArcCosh)
 DEFAULT(ArcSin)
 DEFAULT(ArcSinh)
 DEFAULT(ArcTan)
+DEFAULT(ArcTan2)
 DEFAULT(ArcTanh)
 DEFAULT(ArgPartition)
 DEFAULT(ArgReduce)
@@ -41,6 +42,8 @@ DEFAULT(ArgSort)
 DEFAULT(AsType)
 DEFAULT(AsStrided)
 DEFAULT(Broadcast)
+DEFAULT(BlockMaskedMM)
+DEFAULT(BlockSparseMM)
 DEFAULT_MULTI(DivMod)
 DEFAULT(Ceil)
 DEFAULT(Concatenate)
@@ -57,6 +60,7 @@ DEFAULT(Equal)
 DEFAULT(Erf)
 DEFAULT(ErfInv)
 DEFAULT(Exp)
+DEFAULT(Expm1)
 DEFAULT(FFT)
 DEFAULT(Floor)
 DEFAULT(Full)

mlx/backend/common/make_compiled_preamble.sh

@@ -11,7 +11,7 @@ GCC=$2
 SRCDIR=$3
 CLANG=$4
 
-if [ $CLANG = "TRUE" ]; then
+if [ "$CLANG" = "TRUE" ]; then
   read -r -d '' INCLUDES <<- EOM
   #include <cmath>
   #include <complex>

mlx/backend/common/masked_mm.cpp

@@ -0,0 +1,280 @@
+// Copyright © 2024 Apple Inc.
+
+#ifdef ACCELERATE_NEW_LAPACK
+#include <Accelerate/Accelerate.h>
+#else
+#include <cblas.h>
+#endif
+
+#include <cstring>
+
+#include "mlx/array.h"
+#include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/utils.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+namespace {
+
+template <typename T>
+inline void mask_matrix(
+    T* data,
+    const bool* mask,
+    int block_size,
+    const int X,
+    const int Y,
+    const size_t X_data_str,
+    const size_t Y_data_str,
+    const size_t X_mask_str,
+    const size_t Y_mask_str) {
+  int tX = (X + block_size - 1) / block_size;
+  int tY = (Y + block_size - 1) / block_size;
+
+  for (int i = 0; i < tX; i++) {
+    for (int j = 0; j < tY; j++) {
+      bool do_mask = mask[i * X_mask_str + j * Y_mask_str];
+      if (!do_mask) {
+        int loc_x = i * block_size;
+        int loc_y = j * block_size;
+        T* data_block = data + loc_x * X_data_str + loc_y * Y_data_str;
+
+        int size_x = std::min(block_size, X - loc_x);
+        int size_y = std::min(block_size, Y - loc_y);
+        for (int ii = 0; ii < size_x; ii++) {
+          for (int jj = 0; jj < size_y; jj++) {
+            data_block[ii * X_data_str + jj * Y_data_str] = T(0.);
+          }
+        }
+      }
+    }
+  }
+}
+
+} // namespace
+
+void BlockMaskedMM::eval(const std::vector<array>& inputs, array& out) {
+  if (out.dtype() != float32) {
+    throw std::runtime_error(
+        "[BlockMaskedMM::eval] Currently only supports float32.");
+  }
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
+  auto& a_pre = inputs[0];
+  auto& b_pre = inputs[1];
+  auto& out_mask = inputs[2];
+
+  auto check_transpose = [](const array& arr, bool do_copy) {
+    auto stx = arr.strides()[arr.ndim() - 2];
+    auto sty = arr.strides()[arr.ndim() - 1];
+    if (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);
+      }
+      return std::make_tuple(false, stx, arr);
+    } else if (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);
+      }
+      return std::make_tuple(true, sty, arr);
+    } else {
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy(arr, arr_copy, CopyType::General);
+      size_t stx = arr.shape(-1);
+      return std::make_tuple(false, stx, arr_copy);
+    }
+  };
+
+  bool has_op_mask = inputs.size() > 3;
+  auto [a_transposed, lda, a] = check_transpose(a_pre, has_op_mask);
+  auto [b_transposed, ldb, b] = check_transpose(b_pre, has_op_mask);
+
+  size_t M = a.shape(-2);
+  size_t N = b.shape(-1);
+  size_t K = a.shape(-1);
+
+  if (M == 0 || N == 0) {
+    return;
+  }
+
+  if (K == 0) {
+    std::memset(static_cast<void*>(out.data<float>()), 0, out.nbytes());
+    return;
+  }
+
+  auto mask_array = [](const array& mask,
+                       float* data,
+                       int block_size,
+                       int batch_idx,
+                       int X,
+                       int Y,
+                       size_t X_data_str,
+                       size_t Y_data_str) {
+    const bool* mask_ptr = mask.data<bool>() +
+        elem_to_loc(mask.shape(-1) * mask.shape(-2) * batch_idx,
+                    mask.shape(),
+                    mask.strides());
+
+    size_t X_mask_str = mask.strides()[mask.ndim() - 2];
+    size_t Y_mask_str = mask.strides()[mask.ndim() - 1];
+
+    return mask_matrix(
+        data,
+        mask_ptr,
+        block_size,
+        X,
+        Y,
+        X_data_str,
+        Y_data_str,
+        X_mask_str,
+        Y_mask_str);
+  };
+
+  for (int i = 0; i < (a.size() / (M * K)); ++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;
+
+    // Zero out blocks in a and b if needed
+    if (has_op_mask) {
+      auto& a_mask = inputs[3];
+      mask_array(
+          a_mask,
+          ai,
+          block_size_,
+          i,
+          M,
+          K,
+          a_transposed ? 1 : lda,
+          a_transposed ? lda : 1);
+
+      auto& b_mask = inputs[4];
+      mask_array(
+          b_mask,
+          bi,
+          block_size_,
+          i,
+          K,
+          N,
+          b_transposed ? 1 : ldb,
+          b_transposed ? ldb : 1);
+    }
+
+    // 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
+    mask_array(out_mask, ci, block_size_, i, M, N, N, 1);
+  }
+}
+
+void BlockSparseMM::eval(const std::vector<array>& inputs, array& out) {
+  if (out.dtype() != float32) {
+    throw std::runtime_error(
+        "[BlockSparseMM::eval] Currently only supports float32.");
+  }
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
+  auto& a_pre = inputs[0];
+  auto& b_pre = inputs[1];
+
+  auto check_transpose = [](const array& arr) {
+    auto stx = arr.strides()[arr.ndim() - 2];
+    auto sty = arr.strides()[arr.ndim() - 1];
+    if (stx == arr.shape(-1) && sty == 1) {
+      return std::make_tuple(false, stx, arr);
+    } else if (stx == 1 && sty == arr.shape(-2)) {
+      return std::make_tuple(true, sty, arr);
+    } else {
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy(arr, arr_copy, CopyType::General);
+      size_t stx = arr.shape(-1);
+      return std::make_tuple(false, stx, arr_copy);
+    }
+  };
+
+  auto [a_transposed, lda, a] = check_transpose(a_pre);
+  auto [b_transposed, ldb, b] = check_transpose(b_pre);
+
+  size_t M = a.shape(-2);
+  size_t N = b.shape(-1);
+  size_t K = a.shape(-1);
+
+  if (M == 0 || N == 0) {
+    return;
+  }
+
+  if (K == 0) {
+    std::memset(static_cast<void*>(out.data<float>()), 0, out.nbytes());
+    return;
+  }
+
+  // Get batch dims
+  auto batch_size_out = out.size() / (M * N);
+  size_t matrix_stride_out = M * N;
+
+  auto get_batch_dims = [](const auto& v) {
+    return decltype(v){v.begin(), v.end() - 2};
+  };
+
+  auto& lhs_indices = inputs[2];
+  auto& rhs_indices = inputs[3];
+
+  std::vector<int> batch_shape = get_batch_dims(out.shape());
+  int batch_ndim = batch_shape.size();
+
+  std::vector<int> batch_shape_A = get_batch_dims(a.shape());
+  std::vector<size_t> batch_strides_A = get_batch_dims(a.strides());
+  std::vector<int> batch_shape_B = get_batch_dims(b.shape());
+  std::vector<size_t> batch_strides_B = get_batch_dims(b.strides());
+
+  const uint32_t* lhs_indices_ptr = lhs_indices.data<uint32_t>();
+  const uint32_t* rhs_indices_ptr = rhs_indices.data<uint32_t>();
+
+  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)];
+
+    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
+    );
+  }
+}
+
+} // namespace mlx::core

mlx/backend/common/ops.h

@@ -161,6 +161,13 @@ struct ArcTan {
   };
 };
 
+struct ArcTan2 {
+  template <typename T>
+  T operator()(T y, T x) {
+    return std::atan2(y, x);
+  };
+};
+
 struct ArcTanh {
   template <typename T>
   T operator()(T x) {
@@ -241,6 +248,13 @@ struct Exp {
   }
 };
 
+struct Expm1 {
+  template <typename T>
+  T operator()(T x) {
+    return expm1(x);
+  };
+};
+
 struct Floor {
   template <typename T>
   T operator()(T x) {
@@ -599,4 +613,39 @@ struct Select {
   }
 };
 
+struct BitwiseAnd {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x & y;
+  };
+};
+
+struct BitwiseOr {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x | y;
+  };
+};
+
+struct BitwiseXor {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x ^ y;
+  };
+};
+
+struct LeftShift {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x << y;
+  };
+};
+
+struct RightShift {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x >> y;
+  };
+};
+
 } // namespace mlx::core::detail

mlx/backend/common/primitives.cpp

@@ -22,7 +22,7 @@ namespace mlx::core {
 void Abs::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  if (is_unsigned(in.dtype())) {
+  if (issubdtype(in.dtype(), unsignedinteger)) {
     // No-op for unsigned types
     out.copy_shared_buffer(in);
   } else {
@@ -37,7 +37,7 @@ void Arange::eval(const std::vector<array>& inputs, array& out) {
 void ArcCos::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::ArcCos());
   } else {
     throw std::invalid_argument(
@@ -49,7 +49,7 @@ void ArcCos::eval(const std::vector<array>& inputs, array& out) {
 void ArcCosh::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::ArcCosh());
   } else {
     throw std::invalid_argument(
@@ -61,7 +61,7 @@ void ArcCosh::eval(const std::vector<array>& inputs, array& out) {
 void ArcSin::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::ArcSin());
   } else {
     throw std::invalid_argument(
@@ -73,7 +73,7 @@ void ArcSin::eval(const std::vector<array>& inputs, array& out) {
 void ArcSinh::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::ArcSinh());
   } else {
     throw std::invalid_argument(
@@ -85,7 +85,7 @@ void ArcSinh::eval(const std::vector<array>& inputs, array& out) {
 void ArcTan::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::ArcTan());
   } else {
     throw std::invalid_argument(
@@ -97,7 +97,7 @@ void ArcTan::eval(const std::vector<array>& inputs, array& out) {
 void ArcTanh::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::ArcTanh());
   } else {
     throw std::invalid_argument(
@@ -171,7 +171,7 @@ void Broadcast::eval(const std::vector<array>& inputs, array& out) {
 void Ceil::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  if (not is_integral(in.dtype())) {
+  if (issubdtype(in.dtype(), inexact)) {
     unary_fp(in, out, detail::Ceil());
   } else {
     // No-op integer types
@@ -211,7 +211,7 @@ void Copy::eval(const std::vector<array>& inputs, array& out) {
 void Cos::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Cos());
   } else {
     throw std::invalid_argument(
@@ -223,7 +223,7 @@ void Cos::eval(const std::vector<array>& inputs, array& out) {
 void Cosh::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Cosh());
   } else {
     throw std::invalid_argument(
@@ -350,7 +350,7 @@ void ErfInv::eval(const std::vector<array>& inputs, array& out) {
 void Exp::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Exp());
   } else {
     throw std::invalid_argument(
@@ -359,10 +359,22 @@ void Exp::eval(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void Expm1::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  const auto& in = inputs[0];
+  if (issubdtype(out.dtype(), inexact)) {
+    unary_fp(in, out, detail::Expm1());
+  } else {
+    throw std::invalid_argument(
+        "[expm1] Cannot exponentiate elements in array"
+        " with non floating point type.");
+  }
+}
+
 void Floor::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  if (not is_integral(in.dtype())) {
+  if (issubdtype(in.dtype(), inexact)) {
     unary_fp(in, out, detail::Floor());
   } else {
     // No-op integer types
@@ -388,7 +400,7 @@ void Full::eval(const std::vector<array>& inputs, array& out) {
 void Log::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     switch (base_) {
       case Base::e:
         unary_fp(in, out, detail::Log());
@@ -410,7 +422,7 @@ void Log::eval(const std::vector<array>& inputs, array& out) {
 void Log1p::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Log1p());
   } else {
     throw std::invalid_argument(
@@ -597,7 +609,7 @@ void Reshape::eval(const std::vector<array>& inputs, array& out) {
 void Round::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  if (not is_integral(in.dtype())) {
+  if (issubdtype(in.dtype(), inexact)) {
     unary_fp(in, out, detail::Round());
   } else {
     // No-op integer types
@@ -608,7 +620,7 @@ void Round::eval(const std::vector<array>& inputs, array& out) {
 void Sigmoid::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Sigmoid());
   } else {
     throw std::invalid_argument(
@@ -630,7 +642,7 @@ void Sign::eval(const std::vector<array>& inputs, array& out) {
 void Sin::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Sin());
   } else {
     throw std::invalid_argument(
@@ -642,7 +654,7 @@ void Sin::eval(const std::vector<array>& inputs, array& out) {
 void Sinh::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Sinh());
   } else {
     throw std::invalid_argument(
@@ -850,7 +862,7 @@ void StopGradient::eval(const std::vector<array>& inputs, array& out) {
 void Tan::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Tan());
   } else {
     throw std::invalid_argument(
@@ -862,7 +874,7 @@ void Tan::eval(const std::vector<array>& inputs, array& out) {
 void Tanh::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  if (is_floating_point(out.dtype())) {
+  if (issubdtype(out.dtype(), inexact)) {
     unary_fp(in, out, detail::Tanh());
   } else {
     throw std::invalid_argument(

mlx/backend/common/reduce.h

@@ -6,8 +6,6 @@
 
 namespace mlx::core {
 
-namespace {
-
 enum ReductionOpType {
   // Self-explanatory. Read everything and produce 1 output.
   ContiguousAllReduce,
@@ -38,6 +36,21 @@ enum ReductionOpType {
   GeneralReduce
 };
 
+struct ReductionPlan {
+  ReductionOpType type;
+  std::vector<int> shape;
+  std::vector<size_t> strides;
+
+  ReductionPlan(
+      ReductionOpType type_,
+      std::vector<int> shape_,
+      std::vector<size_t> strides_)
+      : type(type_), shape(std::move(shape_)), strides(std::move(strides_)) {}
+  ReductionPlan(ReductionOpType type_) : type(type_) {}
+};
+
+namespace {
+
 // Helper for the ndimensional strided loop
 // Should this be in utils?
 inline void nd_loop(
@@ -110,19 +123,6 @@ struct DefaultContiguousReduce {
   }
 };
 
-struct ReductionPlan {
-  ReductionOpType type;
-  std::vector<int> shape;
-  std::vector<size_t> strides;
-
-  ReductionPlan(
-      ReductionOpType type_,
-      std::vector<int> shape_,
-      std::vector<size_t> strides_)
-      : type(type_), shape(std::move(shape_)), strides(std::move(strides_)) {}
-  ReductionPlan(ReductionOpType type_) : type(type_) {}
-};
-
 ReductionPlan get_reduction_plan(const array& x, const std::vector<int> axes) {
   // The data is all there and we are reducing over everything
   if (x.size() == x.data_size() && axes.size() == x.ndim() &&

mlx/backend/common/scan.cpp

@@ -222,7 +222,7 @@ void scan_dispatch(
     }
     case Scan::Min: {
       auto op = [](U* o, const U* y, const T* x) { *o = (*x < *y) ? *x : *y; };
-      auto init = (is_floating_point(input.dtype()))
+      auto init = (issubdtype(input.dtype(), floating))
           ? static_cast<U>(std::numeric_limits<float>::infinity())
           : std::numeric_limits<U>::max();
       auto opcs = DefaultContiguousScan<T, U, decltype(op)>(op, init);
@@ -232,7 +232,7 @@ void scan_dispatch(
     }
     case Scan::Max: {
       auto op = [](U* o, const U* y, const T* x) { *o = (*x < *y) ? *y : *x; };
-      auto init = (is_floating_point(input.dtype()))
+      auto init = (issubdtype(input.dtype(), floating))
           ? static_cast<U>(-std::numeric_limits<float>::infinity())
           : std::numeric_limits<U>::max();
       auto opcs = DefaultContiguousScan<T, U, decltype(op)>(op, init);

mlx/backend/common/softmax.cpp

@@ -1,4 +1,4 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
 #include <cassert>
 #include <cmath>
@@ -10,7 +10,7 @@ namespace mlx::core {
 
 namespace {
 
-template <typename T>
+template <typename T, typename AccT>
 void softmax(const array& in, array& out) {
   const T* in_ptr = in.data<T>();
   T* out_ptr = out.data<T>();
@@ -22,26 +22,36 @@ void softmax(const array& in, array& out) {
   for (int i = 0; i < M; i++, in_ptr += N, out_ptr += N) {
     // Find the maximum
     current_in_ptr = in_ptr;
-    T maximum = *current_in_ptr;
+    AccT maximum = *current_in_ptr;
     for (int j = 0; j < N; j++, current_in_ptr++) {
-      maximum = (maximum < *current_in_ptr) ? *current_in_ptr : maximum;
+      maximum = (maximum < *current_in_ptr) ? static_cast<AccT>(*current_in_ptr)
+                                            : maximum;
     }
 
     // Compute the normalizer and the exponentials
-    T normalizer = 0;
+    AccT normalizer = 0;
     current_out_ptr = out_ptr;
     current_in_ptr = in_ptr;
     for (int j = 0; j < N; j++, current_out_ptr++, current_in_ptr++) {
-      T expv = std::exp(*current_in_ptr - maximum);
+      AccT expv = std::exp(*current_in_ptr - maximum);
       normalizer += expv;
-      *current_out_ptr = expv;
+      if constexpr (std::is_same<T, AccT>::value) {
+        *current_out_ptr = expv;
+      }
     }
     normalizer = 1 / normalizer;
 
     // Normalize
+    current_in_ptr = in_ptr;
     current_out_ptr = out_ptr;
     for (int j = 0; j < N; j++, current_out_ptr++) {
-      *current_out_ptr *= normalizer;
+      if constexpr (std::is_same<T, AccT>::value) {
+        *current_out_ptr *= normalizer;
+      } else {
+        auto v = std::exp(*current_in_ptr - maximum);
+        *current_out_ptr = static_cast<T>(v * normalizer);
+        current_in_ptr++;
+      }
     }
   }
 }
@@ -91,13 +101,21 @@ void Softmax::eval(const std::vector<array>& inputs, array& out) {
           "Softmax is defined only for floating point types");
       break;
     case float32:
-      softmax<float>(in, out);
+      softmax<float, float>(in, out);
       break;
     case float16:
-      softmax<float16_t>(in, out);
+      if (precise_) {
+        softmax<float16_t, float>(in, out);
+      } else {
+        softmax<float16_t, float16_t>(in, out);
+      }
       break;
     case bfloat16:
-      softmax<bfloat16_t>(in, out);
+      if (precise_) {
+        softmax<bfloat16_t, float>(in, out);
+      } else {
+        softmax<bfloat16_t, bfloat16_t>(in, out);
+      }
       break;
     case complex64:
       throw std::invalid_argument(

mlx/backend/common/utils.h

@@ -89,9 +89,8 @@ collapse_contiguous_dims(const std::vector<array>& xs) {
   return collapse_contiguous_dims(xs[0].shape(), strides);
 }
 
-template <typename... Arrays>
-inline std::tuple<std::vector<int>, std::vector<std::vector<size_t>>>
-collapse_contiguous_dims(Arrays... xs) {
+template <typename... Arrays, typename = enable_for_arrays_t<Arrays...>>
+inline auto collapse_contiguous_dims(Arrays&&... xs) {
   return collapse_contiguous_dims(
       std::vector<array>{std::forward<Arrays>(xs)...});
 }

mlx/backend/metal/CMakeLists.txt

@@ -26,6 +26,7 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/event.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp

mlx/backend/metal/allocator.cpp

@@ -1,6 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 #include "mlx/backend/metal/allocator.h"
 #include "mlx/backend/metal/metal.h"
+#include "mlx/backend/metal/metal_impl.h"
 
 #include <mach/vm_page_size.h>
 #include <unistd.h>
@@ -139,10 +140,15 @@ void BufferCache::remove_from_list(BufferCache::BufferHolder* to_remove) {
 
 MetalAllocator::MetalAllocator()
     : device_(device(mlx::core::Device::gpu).mtl_device()),
-      buffer_cache_(device_),
-      block_limit_(1.5 * device_->recommendedMaxWorkingSetSize()),
-      gc_limit_(0.95 * device_->recommendedMaxWorkingSetSize()),
-      max_pool_size_(block_limit_) {}
+      buffer_cache_(device_) {
+  auto memsize = std::get<size_t>(device_info()["memory_size"]);
+  block_limit_ =
+      std::min(1.5 * device_->recommendedMaxWorkingSetSize(), 0.95 * memsize);
+  gc_limit_ = std::min(
+      static_cast<size_t>(0.95 * device_->recommendedMaxWorkingSetSize()),
+      block_limit_);
+  max_pool_size_ = block_limit_;
+}
 
 size_t MetalAllocator::set_cache_limit(size_t limit) {
   std::swap(limit, max_pool_size_);
@@ -164,6 +170,15 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
     return Buffer{nullptr};
   }
 
+  // More helpful message if maximum buffer length is exceeded
+  if (size > device_->maxBufferLength()) {
+    std::ostringstream msg;
+    msg << "Attempting to allocate " << size << " bytes which is greater than"
+        << " the maximum allowed buffer size of " << device_->maxBufferLength()
+        << " bytes.";
+    throw std::runtime_error(msg.str());
+  }
+
   // Align up memory
   if (size > vm_page_size) {
     size = vm_page_size * ((size + vm_page_size - 1) / vm_page_size);
@@ -208,6 +223,11 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
   return Buffer{static_cast<void*>(buf)};
 }
 
+void MetalAllocator::clear_cache() {
+  std::unique_lock lk(mutex_);
+  buffer_cache_.clear();
+}
+
 void MetalAllocator::free(Buffer buffer) {
   auto buf = static_cast<MTL::Buffer*>(buffer.ptr());
   std::unique_lock lk(mutex_);
@@ -238,9 +258,15 @@ size_t get_active_memory() {
 size_t get_peak_memory() {
   return allocator().get_peak_memory();
 }
+void reset_peak_memory() {
+  allocator().reset_peak_memory();
+}
 size_t get_cache_memory() {
   return allocator().get_cache_memory();
 }
+void clear_cache() {
+  return allocator().clear_cache();
+}
 
 } // namespace metal
 

mlx/backend/metal/allocator.h

@@ -26,6 +26,7 @@ class BufferCache {
   size_t cache_size() {
     return pool_size_;
   }
+  void clear();
 
  private:
   struct BufferHolder {
@@ -37,7 +38,6 @@ class BufferCache {
     MTL::Buffer* buf;
   };
 
-  void clear();
   void add_at_head(BufferHolder* to_add);
   void remove_from_list(BufferHolder* to_remove);
 
@@ -62,11 +62,16 @@ class MetalAllocator : public allocator::Allocator {
   size_t get_peak_memory() {
     return peak_memory_;
   };
+  void reset_peak_memory() {
+    std::unique_lock lk(mutex_);
+    peak_memory_ = 0;
+  };
   size_t get_cache_memory() {
     return buffer_cache_.cache_size();
   };
   size_t set_cache_limit(size_t limit);
   size_t set_memory_limit(size_t limit, bool relaxed);
+  void clear_cache();
 
  private:
   MTL::Device* device_;

mlx/backend/metal/compiled.cpp

@@ -229,14 +229,7 @@ void Compiled::eval_gpu(
 
   // Figure out which kernel we are using
   auto& output_shape = outputs[0].shape();
-  bool contiguous = true;
-  for (auto& x : inputs) {
-    if ((!x.flags().row_contiguous || x.shape() != output_shape) &&
-        !is_scalar(x)) {
-      contiguous = false;
-      break;
-    }
-  }
+  bool contiguous = compiled_check_contiguity(inputs, output_shape);
 
   // Collapse contiguous dims to route to a faster kernel if possible. Also
   // handle all broadcasting.
@@ -296,7 +289,7 @@ void Compiled::eval_gpu(
     }
   }
   auto kernel = d.get_kernel(kernel_name, lib);
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   compute_encoder->setComputePipelineState(kernel);
 
   // Put the inputs in
@@ -307,7 +300,7 @@ void Compiled::eval_gpu(
       continue;
     }
     auto& x = inputs[i];
-    set_array_buffer(compute_encoder, x, cnt++);
+    compute_encoder.set_input_array(x, cnt++);
     if (!contiguous && !is_scalar(x)) {
       compute_encoder->setBytes(
           strides[stride_idx].data(),
@@ -317,32 +310,12 @@ void Compiled::eval_gpu(
     }
   }
 
-  // Allocate space for the outputs possibly with input donation
-  {
-    int o = 0;
-    for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {
-      auto& in = inputs[i];
-      // Conditions for donation
-      // - Row contiguous
-      // - Donatable
-      // - Correct size
-      // - Not a constant
-      if (in.flags().row_contiguous && in.nbytes() == outputs[o].nbytes() &&
-          in.is_donatable() &&
-          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
-        outputs[o].move_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()));
-    }
-  }
+  compiled_allocate_outputs(
+      inputs, outputs, inputs_, constant_ids_, contiguous, true);
 
   // Put the outputs in
   for (auto& x : outputs) {
-    set_array_buffer(compute_encoder, x, cnt++);
+    compute_encoder.set_output_array(x, cnt++);
   }
 
   // Put the output shape and strides in
@@ -363,7 +336,7 @@ void Compiled::eval_gpu(
     MTL::Size grid_dims(nthreads, 1, 1);
     MTL::Size group_dims(
         std::min(nthreads, kernel->maxTotalThreadsPerThreadgroup()), 1, 1);
-    compute_encoder->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   } else {
     size_t dim0 = ndim > 0 ? shape[ndim - 1] : 1;
     size_t dim1 = ndim > 1 ? shape[ndim - 2] : 1;
@@ -374,7 +347,7 @@ void Compiled::eval_gpu(
     }
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   }
 }
 

mlx/backend/metal/conv.cpp

@@ -41,12 +41,12 @@ void explicit_gemm_conv_ND_gpu(
   // Prepare unfolding kernel
   std::ostringstream kname;
   kname << "naive_unfold_nd_" << type_to_name(in_unfolded) << "_" << N;
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
   compute_encoder->setComputePipelineState(kernel);
 
-  set_array_buffer(compute_encoder, in, 0);
-  set_array_buffer(compute_encoder, in_unfolded, 1);
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_output_array(in_unfolded, 1);
 
   compute_encoder->setBytes(&conv_params, sizeof(conv_params), 2);
 
@@ -59,7 +59,7 @@ void explicit_gemm_conv_ND_gpu(
   MTL::Size grid_dims = MTL::Size(
       conv_params.C, unfolded_shape[1] / conv_params.C, unfolded_shape[0]);
 
-  compute_encoder->dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 
   // Reshape weight
   std::vector<int> wt_reshape{implicit_K, implicit_N};
@@ -89,6 +89,90 @@ void explicit_gemm_conv_ND_gpu(
       /*copies = */ copies);
 }
 
+template <int N>
+void explicit_gemm_conv_group_ND_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<N>& conv_params) {
+  const int groups = conv_params.groups;
+  const int C_per_group = conv_params.C / conv_params.groups;
+  const int O_per_group = conv_params.O / conv_params.groups;
+  // Get gemm shapes
+  const int implicit_M = out.size() / conv_params.O;
+  const int implicit_K = wt.size() / conv_params.O;
+  const int implicit_N = O_per_group;
+
+  int kernel_size = 1;
+  for (int i = 0; i < N; ++i) {
+    kernel_size *= conv_params.wS[i];
+  }
+
+  // Prepare unfolding array
+  std::vector<int> 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()));
+
+  // Prepare unfolding kernel
+  std::ostringstream kname;
+  kname << "naive_unfold_transpose_nd_" << type_to_name(in_unfolded) << "_"
+        << N;
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(kname.str());
+  compute_encoder->setComputePipelineState(kernel);
+
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_output_array(in_unfolded, 1);
+
+  compute_encoder->setBytes(&conv_params, sizeof(conv_params), 2);
+
+  // Launch unfolding kernel
+  int tgp_x = std::min(conv_params.C, 64);
+  tgp_x = 32 * ((tgp_x + 32 - 1) / 32);
+  int tgp_y = 256 / tgp_x;
+
+  MTL::Size group_dims = MTL::Size(tgp_x, tgp_y, 1);
+  MTL::Size grid_dims = MTL::Size(
+      conv_params.C, unfolded_shape[1] / conv_params.C, unfolded_shape[0]);
+
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
+
+  // Transpose kernel weights so that we can slice them by contiguous chunks
+  // of channel groups.
+  array wt_view(
+      {wt.shape(0), C_per_group, kernel_size}, wt.dtype(), nullptr, {});
+  wt_view.copy_shared_buffer(
+      wt,
+      {wt.strides(0), 1, static_cast<size_t>(C_per_group)},
+      wt.flags(),
+      wt.size());
+
+  // Materialize
+  auto wt_transpose = array(wt_view.shape(), wt_view.dtype(), nullptr, {});
+  copy_gpu(wt_view, wt_transpose, CopyType::General, s);
+
+  // Perform gemm
+  std::vector<array> copies = {in_unfolded, wt_view, wt_transpose};
+  return steel_matmul_conv_groups(
+      s,
+      d,
+      /*a = */ in_unfolded,
+      /*b = */ wt_transpose,
+      /*c = */ out,
+      /*M = */ implicit_M,
+      /*N = */ implicit_N,
+      /*K = */ implicit_K,
+      /*a_cols = */ implicit_K * groups,
+      /*b_cols = */ implicit_K,
+      /*out_cols = */ implicit_N * groups,
+      /*a_transposed = */ false,
+      /*b_transposed = */ true,
+      /* groups = */ groups,
+      /*copies = */ copies);
+}
+
 void conv_1D_gpu(
     const Stream& s,
     metal::Device& d,
@@ -99,6 +183,7 @@ void conv_1D_gpu(
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
+    int groups,
     bool flip) {
   // Make conv params
   MLXConvParams<1> conv_params{
@@ -118,11 +203,15 @@ void conv_1D_gpu(
       {wt.strides()[0], wt.strides()[1], wt.strides()[2]},
       /* const size_t out_strides[NDIM + 2] = */
       {out.strides()[0], out.strides()[1], out.strides()[2]},
-      /* const int groups = */ 1,
+      /* const int groups = */ groups,
       /* const bool flip = */ flip};
 
   // Direct to explicit gemm conv
-  return explicit_gemm_conv_ND_gpu(s, d, in, wt, out, conv_params);
+  if (groups > 1) {
+    return explicit_gemm_conv_group_ND_gpu(s, d, in, wt, out, conv_params);
+  } else {
+    return explicit_gemm_conv_ND_gpu(s, d, in, wt, out, conv_params);
+  }
 }
 
 void slow_conv_2D_gpu(
@@ -140,7 +229,7 @@ void slow_conv_2D_gpu(
         << "_tm" << tm << "_tn" << tn;
 
   // Encode and dispatch kernel
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
   compute_encoder->setComputePipelineState(kernel);
 
@@ -153,12 +242,12 @@ void slow_conv_2D_gpu(
   MTL::Size group_dims = MTL::Size(bm, bn, 1);
   MTL::Size grid_dims = MTL::Size(grid_dim_x, grid_dim_y, grid_dim_z);
 
-  set_array_buffer(compute_encoder, in, 0);
-  set_array_buffer(compute_encoder, wt, 1);
-  set_array_buffer(compute_encoder, out, 2);
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_input_array(wt, 1);
+  compute_encoder.set_output_array(out, 2);
 
   compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
-  compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
 }
 
 void implicit_gemm_conv_2D_gpu(
@@ -241,7 +330,7 @@ void implicit_gemm_conv_2D_gpu(
         << "_filter_" << (small_filter ? 's' : 'l');
 
   // Encode and dispatch kernel
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
   compute_encoder->setComputePipelineState(kernel);
 
@@ -254,16 +343,16 @@ void implicit_gemm_conv_2D_gpu(
   MTL::Size grid_dims = MTL::Size(grid_dim_x, grid_dim_y, 1);
 
   // Encode arrays
-  set_array_buffer(compute_encoder, in, 0);
-  set_array_buffer(compute_encoder, wt, 1);
-  set_array_buffer(compute_encoder, out, 2);
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_input_array(wt, 1);
+  compute_encoder.set_output_array(out, 2);
 
   // Encode params
   compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
   compute_encoder->setBytes(&gemm_params, sizeof(ImplicitGemmConv2DParams), 4);
 
   // Launch kernel
-  compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
 }
 
 void implicit_gemm_conv_2D_general_gpu(
@@ -394,7 +483,7 @@ void implicit_gemm_conv_2D_general_gpu(
         << "_bn" << bn << "_bk" << bk << "_wm" << wm << "_wn" << wn;
 
   // Encode and dispatch kernel
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
   compute_encoder->setComputePipelineState(kernel);
 
@@ -408,9 +497,9 @@ void implicit_gemm_conv_2D_general_gpu(
   MTL::Size grid_dims = MTL::Size(grid_dim_x, grid_dim_y, grid_dim_z);
 
   // Encode arrays
-  set_array_buffer(compute_encoder, in, 0);
-  set_array_buffer(compute_encoder, wt, 1);
-  set_array_buffer(compute_encoder, out, 2);
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_input_array(wt, 1);
+  compute_encoder.set_output_array(out, 2);
 
   // Encode params
   compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
@@ -423,7 +512,7 @@ void implicit_gemm_conv_2D_general_gpu(
       base_w.data(), sizeof(Conv2DGeneralBaseInfo) * base_w.size(), 7);
 
   // Launch kernel
-  compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
 }
 
 void winograd_conv_2D_gpu(
@@ -511,12 +600,12 @@ void winograd_conv_2D_gpu(
     std::ostringstream kname;
     kname << "winograd_conv_2d_weight_transform_" << type_to_name(out) << "_bc"
           << bc;
-    auto compute_encoder = d.get_command_encoder(s.index);
+    auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
     compute_encoder->setComputePipelineState(kernel);
 
-    set_array_buffer(compute_encoder, wt, 0);
-    set_array_buffer(compute_encoder, filt_wg, 1);
+    compute_encoder.set_input_array(wt, 0);
+    compute_encoder.set_output_array(filt_wg, 1);
 
     compute_encoder->setBytes(&C_c, sizeof(int), 2);
     compute_encoder->setBytes(&O_c, sizeof(int), 3);
@@ -524,7 +613,7 @@ void winograd_conv_2D_gpu(
     MTL::Size group_dims = MTL::Size(32, bo, 1);
     MTL::Size grid_dims = MTL::Size(O_c / bo, 1, 1);
 
-    compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
   }
 
   // Do input transform
@@ -539,12 +628,12 @@ void winograd_conv_2D_gpu(
     std::ostringstream kname;
     kname << "winograd_conv_2d_input_transform_" << type_to_name(out) << "_bc"
           << bc;
-    auto compute_encoder = d.get_command_encoder(s.index);
+    auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
     compute_encoder->setComputePipelineState(kernel);
 
-    set_array_buffer(compute_encoder, in_padded, 0);
-    set_array_buffer(compute_encoder, inp_wg, 1);
+    compute_encoder.set_input_array(in_padded, 0);
+    compute_encoder.set_output_array(inp_wg, 1);
 
     compute_encoder->setBytes(
         &conv_params_updated, sizeof(MLXConvParams<2>), 2);
@@ -552,7 +641,7 @@ void winograd_conv_2D_gpu(
     MTL::Size group_dims = MTL::Size(32, wn, wm);
     MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
 
-    compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
   }
 
   // Do batched gemm
@@ -587,12 +676,12 @@ void winograd_conv_2D_gpu(
     std::ostringstream kname;
     kname << "winograd_conv_2d_output_transform_" << type_to_name(out) << "_bo"
           << bc;
-    auto compute_encoder = d.get_command_encoder(s.index);
+    auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
     compute_encoder->setComputePipelineState(kernel);
 
-    set_array_buffer(compute_encoder, out_wg, 0);
-    set_array_buffer(compute_encoder, out, 1);
+    compute_encoder.set_input_array(out_wg, 0);
+    compute_encoder.set_output_array(out, 1);
 
     compute_encoder->setBytes(
         &conv_params_updated, sizeof(MLXConvParams<2>), 2);
@@ -600,7 +689,7 @@ void winograd_conv_2D_gpu(
     MTL::Size group_dims = MTL::Size(32, wn, wm);
     MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
 
-    compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
   }
 }
 
@@ -721,6 +810,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
+        groups_,
         flip_);
   }
   // Throw error

mlx/backend/metal/copy.cpp

@@ -12,8 +12,15 @@ namespace mlx::core {
 
 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()),
@@ -76,15 +83,15 @@ void copy_gpu_inplace(
     kname << "_" << shape.size();
   }
   auto kernel = d.get_kernel(kname.str());
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   compute_encoder->setComputePipelineState(kernel);
   bool donate_in = in.data_shared_ptr() == nullptr;
 
   inp_offset *= size_of(in.dtype());
   out_offset *= size_of(out.dtype());
 
-  set_array_buffer(compute_encoder, donate_in ? out : in, inp_offset, 0);
-  set_array_buffer(compute_encoder, out, out_offset, 1);
+  compute_encoder.set_input_array(donate_in ? out : in, 0, inp_offset);
+  compute_encoder.set_output_array(out, 1, out_offset);
 
   if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
     int ndim = shape.size();
@@ -119,7 +126,7 @@ void copy_gpu_inplace(
 
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   } else {
     size_t nthreads = out.data_size();
     MTL::Size grid_dims = MTL::Size(nthreads, 1, 1);
@@ -128,7 +135,7 @@ void copy_gpu_inplace(
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    compute_encoder->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   }
 }
 

mlx/backend/metal/device.cpp

@@ -1,17 +1,21 @@
-// Copyright © 2023-24 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
 #include <dlfcn.h>
 #include <cstdlib>
 #include <filesystem>
 #include <sstream>
 
+#include <sys/sysctl.h>
+
 #define NS_PRIVATE_IMPLEMENTATION
 #define CA_PRIVATE_IMPLEMENTATION
 #define MTL_PRIVATE_IMPLEMENTATION
 
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/metal.h"
+#include "mlx/backend/metal/metal_impl.h"
 #include "mlx/backend/metal/mps/gemm.h"
+#include "mlx/backend/metal/utils.h"
 
 namespace fs = std::filesystem;
 
@@ -21,6 +25,7 @@ namespace {
 
 // TODO nicer way to set this or possibly expose as an environment variable
 constexpr int MAX_BUFFERS_PER_QUEUE = 12;
+constexpr int MAX_DISPATCHES_PER_ENCODER = 2;
 
 constexpr const char* default_mtllib_path = METAL_PATH;
 
@@ -33,7 +38,6 @@ auto load_device() {
   }
   return device;
 }
-
 std::pair<MTL::Library*, NS::Error*> load_library_from_path(
     MTL::Device* device,
     const char* path) {
@@ -112,6 +116,33 @@ MTL::Library* load_library(
 
 } // namespace
 
+void CommandEncoder::dispatchThreadgroups(
+    MTL::Size grid_dims,
+    MTL::Size group_dims) {
+  num_dispatches++;
+  enc->dispatchThreadgroups(grid_dims, group_dims);
+  maybe_split();
+}
+
+void CommandEncoder::dispatchThreads(
+    MTL::Size grid_dims,
+    MTL::Size group_dims) {
+  num_dispatches++;
+  enc->dispatchThreads(grid_dims, group_dims);
+  maybe_split();
+}
+
+void CommandEncoder::maybe_split() {
+  if (num_dispatches > MAX_DISPATCHES_PER_ENCODER && !concurrent) {
+    enc->endEncoding();
+    enc->release();
+    num_dispatches = 0;
+    outputs.clear();
+    enc = cbuf->computeCommandEncoder(MTL::DispatchTypeConcurrent);
+    enc->retain();
+  }
+}
+
 Device::Device() {
   auto pool = new_scoped_memory_pool();
   device_ = load_device();
@@ -126,9 +157,6 @@ Device::~Device() {
   for (auto& b : buffer_map_) {
     b.second.second->release();
   }
-  for (auto& e : encoder_map_) {
-    e.second->release();
-  }
   for (auto& k : kernel_map_) {
     k.second->release();
   }
@@ -145,6 +173,7 @@ void Device::new_queue(int index) {
   // We lock this as a critical section for safety
   const std::lock_guard<std::mutex> lock(mtx_);
   auto q = device_->newCommandQueue(MAX_BUFFERS_PER_QUEUE);
+  debug_set_stream_queue_label(q, index);
   if (!q) {
     throw std::runtime_error(
         "[metal::Device] Failed to make new command queue.");
@@ -164,27 +193,26 @@ void Device::increment_command_buffer_ops(int index) {
 
 MTL::CommandBuffer* Device::get_command_buffer(int index) {
   auto bit = buffer_map_.find(index);
-  return (bit == buffer_map_.end()) ? nullptr : bit->second.second;
-}
+  if (bit == buffer_map_.end()) {
+    auto qit = queue_map_.find(index);
+    if (qit == queue_map_.end()) {
+      throw std::runtime_error(
+          "[metal::Device] Attempting to get command buffer for invalid queue.");
+    }
 
-MTL::CommandBuffer* Device::new_command_buffer(int index) {
-  auto qit = queue_map_.find(index);
-  if (qit == queue_map_.end()) {
-    throw std::runtime_error(
-        "[metal::Device] Attempting to get command buffer for invalid queue.");
+    auto cb = qit->second->commandBufferWithUnretainedReferences();
+
+    if (!cb) {
+      throw std::runtime_error(
+          "[metal::Device] Unable to create new command buffer");
+    }
+
+    // Increment ref count so the buffer is not garbage collected
+    cb->retain();
+
+    bit = buffer_map_.insert({index, {0, cb}}).first;
   }
-
-  auto cb = qit->second->commandBufferWithUnretainedReferences();
-
-  if (!cb) {
-    throw std::runtime_error(
-        "[metal::Device] Unable to create new command buffer");
-  }
-
-  // Increment ref count so the buffer is not garbage collected
-  cb->retain();
-
-  return buffer_map_.insert({index, {0, cb}}).first->second.second;
+  return bit->second.second;
 }
 
 void Device::commit_command_buffer(int index) {
@@ -195,24 +223,17 @@ void Device::commit_command_buffer(int index) {
 }
 
 void Device::end_encoding(int index) {
-  auto eit = encoder_map_.find(index);
-  if (eit != encoder_map_.end()) {
-    eit->second->endEncoding();
-    eit->second->release();
-    encoder_map_.erase(eit);
-  }
+  encoder_map_.erase(index);
 }
 
-MTL::ComputeCommandEncoder* Device::get_command_encoder(int index) {
+CommandEncoder& Device::get_command_encoder(int index) {
   auto eit = encoder_map_.find(index);
   if (eit == encoder_map_.end()) {
     auto cb = get_command_buffer(index);
-    auto compute_encoder = cb->computeCommandEncoder();
-    // Increment ref count so the buffer is not garbage collected
-    compute_encoder->retain();
-    eit = encoder_map_.insert({index, compute_encoder}).first;
+    eit =
+        encoder_map_.emplace(index, std::make_unique<CommandEncoder>(cb)).first;
   }
-  return eit->second;
+  return *(eit->second);
 }
 
 void Device::register_library(
@@ -259,8 +280,7 @@ MTL::Library* Device::get_library_(const std::string& source_string) {
   // Throw error if unable to compile library
   if (!mtl_lib) {
     std::ostringstream msg;
-    msg << "[metal::Device] Unable to load build metal library from source"
-        << "\n";
+    msg << "[metal::Device] Unable to build metal library from source" << "\n";
     if (error) {
       msg << error->localizedDescription()->utf8String() << "\n";
     }
@@ -279,8 +299,7 @@ MTL::Library* Device::get_library_(const MTL::StitchedLibraryDescriptor* desc) {
   // Throw error if unable to compile library
   if (!mtl_lib) {
     std::ostringstream msg;
-    msg << "[metal::Device] Unable to load build stitched metal library"
-        << "\n";
+    msg << "[metal::Device] Unable to build stitched metal library" << "\n";
     if (error) {
       msg << error->localizedDescription()->utf8String() << "\n";
     }
@@ -538,11 +557,12 @@ Device& device(mlx::core::Device) {
   return metal_device;
 }
 
-std::shared_ptr<void> new_scoped_memory_pool() {
+std::unique_ptr<void, std::function<void(void*)>> new_scoped_memory_pool() {
   auto dtor = [](void* ptr) {
     static_cast<NS::AutoreleasePool*>(ptr)->release();
   };
-  return std::shared_ptr<void>(NS::AutoreleasePool::alloc()->init(), dtor);
+  return std::unique_ptr<void, std::function<void(void*)>>(
+      NS::AutoreleasePool::alloc()->init(), dtor);
 }
 
 void new_stream(Stream stream) {
@@ -551,4 +571,23 @@ void new_stream(Stream stream) {
   }
 }
 
+std::unordered_map<std::string, std::variant<std::string, size_t>>
+device_info() {
+  auto raw_device = device(default_device()).mtl_device();
+  auto arch = std::string(raw_device->architecture()->name()->utf8String());
+
+  int mib[] = {CTL_HW, HW_MEMSIZE};
+  size_t memsize = 0;
+  size_t length = sizeof(memsize);
+
+  sysctl(mib, 2, &memsize, &length, NULL, 0);
+
+  return {
+      {"architecture", arch},
+      {"max_buffer_length", raw_device->maxBufferLength()},
+      {"max_recommended_working_set_size",
+       raw_device->recommendedMaxWorkingSetSize()},
+      {"memory_size", memsize}};
+}
+
 } // namespace mlx::core::metal

mlx/backend/metal/device.h

@@ -1,4 +1,4 @@
-// Copyright © 2023-24 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
 #pragma once
 
@@ -7,10 +7,12 @@
 #include <mutex>
 #include <string>
 #include <unordered_map>
+#include <unordered_set>
 
 #include <dlfcn.h>
 #include <filesystem>
 
+#include "mlx/array.h"
 #include "mlx/device.h"
 
 namespace fs = std::filesystem;
@@ -34,6 +36,84 @@ inline std::string get_colocated_mtllib_path(const std::string& lib_name) {
 using MTLFCList =
     std::vector<std::tuple<const void*, MTL::DataType, NS::UInteger>>;
 
+struct CommandEncoder {
+  CommandEncoder(MTL::CommandBuffer* cbuf) : cbuf(cbuf) {
+    enc = cbuf->computeCommandEncoder(MTL::DispatchTypeConcurrent);
+    enc->retain();
+  };
+  CommandEncoder(const CommandEncoder&) = delete;
+  CommandEncoder& operator=(const CommandEncoder&) = delete;
+
+  struct ConcurrentContext {
+    ConcurrentContext(CommandEncoder& enc) : enc(enc) {
+      enc.concurrent = true;
+    }
+    ~ConcurrentContext() {
+      enc.concurrent = false;
+      enc.outputs.insert(
+          enc.concurrent_outputs.begin(), enc.concurrent_outputs.end());
+      enc.concurrent_outputs.clear();
+    }
+
+   private:
+    CommandEncoder& enc;
+  };
+
+  MTL::ComputeCommandEncoder* operator->() {
+    return enc;
+  }
+
+  void set_input_array(const array& a, int idx, int offset = 0) {
+    auto r_buf =
+        static_cast<MTL::Resource*>(const_cast<void*>(a.buffer().ptr()));
+    if (auto it = outputs.find(r_buf); it != outputs.end()) {
+      // Insert a barrier
+      enc->memoryBarrier(&r_buf, 1);
+
+      // Remove the output
+      outputs.erase(it);
+    }
+    auto a_buf = static_cast<const MTL::Buffer*>(a.buffer().ptr());
+    auto base_offset = a.data<char>() -
+        static_cast<char*>(const_cast<MTL::Buffer*>(a_buf)->contents());
+    base_offset += offset;
+    enc->setBuffer(a_buf, base_offset, idx);
+  }
+
+  void set_output_array(array& a, int idx, int offset = 0) {
+    // Add barriers before adding the output to the output set
+    set_input_array(a, idx, offset);
+    auto buf = static_cast<MTL::Resource*>(a.buffer().ptr());
+    if (concurrent) {
+      concurrent_outputs.insert(buf);
+    } else {
+      outputs.insert(buf);
+    }
+  }
+
+  void dispatchThreadgroups(MTL::Size grid_dims, MTL::Size group_dims);
+  void dispatchThreads(MTL::Size grid_dims, MTL::Size group_dims);
+
+  ConcurrentContext start_concurrent() {
+    return ConcurrentContext(*this);
+  }
+
+  ~CommandEncoder() {
+    enc->endEncoding();
+    enc->release();
+  }
+
+ private:
+  void maybe_split();
+
+  int num_dispatches{0};
+  MTL::CommandBuffer* cbuf;
+  MTL::ComputeCommandEncoder* enc;
+  bool concurrent{false};
+  std::unordered_set<MTL::Resource*> outputs;
+  std::unordered_set<MTL::Resource*> concurrent_outputs;
+};
+
 class Device {
  public:
   Device();
@@ -46,12 +126,11 @@ class Device {
   };
 
   void new_queue(int index);
-  MTL::CommandBuffer* new_command_buffer(int index);
   MTL::CommandBuffer* get_command_buffer(int index);
   int get_command_buffer_ops(int index);
   void increment_command_buffer_ops(int index);
   void commit_command_buffer(int index);
-  MTL::ComputeCommandEncoder* get_command_encoder(int index);
+  CommandEncoder& get_command_encoder(int index);
   void end_encoding(int index);
 
   void register_library(
@@ -132,7 +211,7 @@ class Device {
   MTL::Device* device_;
   std::unordered_map<int32_t, MTL::CommandQueue*> queue_map_;
   std::unordered_map<int32_t, std::pair<int, MTL::CommandBuffer*>> buffer_map_;
-  std::unordered_map<int32_t, MTL::ComputeCommandEncoder*> encoder_map_;
+  std::unordered_map<int32_t, std::unique_ptr<CommandEncoder>> encoder_map_;
   std::unordered_map<std::string, MTL::ComputePipelineState*> kernel_map_;
   std::unordered_map<std::string, MTL::Library*> library_map_;
   std::mutex mtx_;

mlx/backend/metal/event.cpp

@@ -0,0 +1,30 @@
+// Copyright © 2024 Apple Inc.
+
+#include "mlx/event.h"
+#include "mlx/backend/metal/device.h"
+#include "mlx/backend/metal/metal_impl.h"
+
+namespace mlx::core {
+
+Event::Event(const Stream& stream) : stream_(stream) {
+  auto dtor = [](void* ptr) {
+    auto p = metal::new_scoped_memory_pool();
+    static_cast<MTL::SharedEvent*>(ptr)->release();
+  };
+  auto p = metal::new_scoped_memory_pool();
+  event_ = std::shared_ptr<void>(
+      metal::device(stream.device).mtl_device()->newSharedEvent(), dtor);
+}
+
+void Event::wait() {
+  if (!static_cast<MTL::SharedEvent*>(raw_event().get())
+           ->waitUntilSignaledValue(value(), -1)) {
+    throw std::runtime_error("[Event::wait] Timed out");
+  }
+}
+
+void Event::signal() {
+  static_cast<MTL::SharedEvent*>(raw_event().get())->setSignaledValue(value());
+}
+
+} // namespace mlx::core

mlx/backend/metal/fft.cpp

@@ -1,12 +1,106 @@
 // Copyright © 2023 Apple Inc.
-
+#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/metal/utils.h"
+#include "mlx/mlx.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
 void FFT::eval_gpu(const std::vector<array>& inputs, array& out) {
+  auto& s = out.primitive().stream();
+  auto& d = metal::device(s.device);
+
   auto& in = inputs[0];
-  throw std::runtime_error("[FFT] NYI for Metal backend.");
+
+  if (axes_.size() == 0 || axes_.size() > 1 || inverse_ ||
+      in.dtype() != complex64 || out.dtype() != complex64) {
+    // Could also fallback to CPU implementation here.
+    throw std::runtime_error(
+        "GPU FFT is only implemented for 1D, forward, complex FFTs.");
+  }
+
+  size_t n = in.shape(axes_[0]);
+
+  if (!is_power_of_2(n) || n > 2048 || n < 4) {
+    throw std::runtime_error(
+        "GPU FFT is only implemented for the powers of 2 from 4 -> 2048");
+  }
+
+  // Make sure that the array is contiguous and has stride 1 in the FFT dim
+  std::vector<array> copies;
+  auto check_input = [this, &copies, &s](const array& x) {
+    // TODO: Pass the strides to the kernel so
+    // we can avoid the copy when x is not contiguous.
+    bool no_copy = x.strides()[axes_[0]] == 1 && x.flags().row_contiguous ||
+        x.flags().col_contiguous;
+    if (no_copy) {
+      return x;
+    } else {
+      array x_copy(x.shape(), x.dtype(), nullptr, {});
+      std::vector<size_t> strides;
+      size_t cur_stride = x.shape(axes_[0]);
+      for (int axis = 0; axis < x.ndim(); axis++) {
+        if (axis == axes_[0]) {
+          strides.push_back(1);
+        } else {
+          strides.push_back(cur_stride);
+          cur_stride *= x.shape(axis);
+        }
+      }
+
+      auto flags = x.flags();
+      size_t f_stride = 1;
+      size_t b_stride = 1;
+      flags.col_contiguous = true;
+      flags.row_contiguous = true;
+      for (int i = 0, ri = x.ndim() - 1; i < x.ndim(); ++i, --ri) {
+        flags.col_contiguous &= (strides[i] == f_stride || x.shape(i) == 1);
+        f_stride *= x.shape(i);
+        flags.row_contiguous &= (strides[ri] == b_stride || x.shape(ri) == 1);
+        b_stride *= x.shape(ri);
+      }
+      // This is probably over-conservative
+      flags.contiguous = false;
+
+      x_copy.set_data(
+          allocator::malloc_or_wait(x.nbytes()), x.data_size(), strides, flags);
+      copy_gpu_inplace(x, x_copy, CopyType::GeneralGeneral, s);
+      copies.push_back(x_copy);
+      return x_copy;
+    }
+  };
+  const array& in_contiguous = check_input(inputs[0]);
+
+  // TODO: allow donation here
+  out.set_data(
+      allocator::malloc_or_wait(out.nbytes()),
+      in_contiguous.data_size(),
+      in_contiguous.strides(),
+      in_contiguous.flags());
+
+  // We use n / 4 threads by default since radix-4
+  // is the largest single threaded radix butterfly
+  // we currently implement.
+  size_t m = n / 4;
+  size_t batch = in.size() / in.shape(axes_[0]);
+
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  {
+    std::ostringstream kname;
+    kname << "fft_" << n;
+    auto kernel = d.get_kernel(kname.str());
+
+    bool donated = in.data_shared_ptr() == nullptr;
+    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_input_array(in_contiguous, 0);
+    compute_encoder.set_output_array(out, 1);
+
+    auto group_dims = MTL::Size(1, m, 1);
+    auto grid_dims = MTL::Size(batch, m, 1);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
+  }
+  d.get_command_buffer(s.index)->addCompletedHandler(
+      [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
 }
 
 } // namespace mlx::core

mlx/backend/metal/indexing.cpp

@@ -49,7 +49,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
     kname << "_" << idx_ndim;
   }
 
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
   compute_encoder->setComputePipelineState(kernel);
 
@@ -81,8 +81,8 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 
   // Set all the buffers
-  set_array_buffer(compute_encoder, src, 0);
-  set_array_buffer(compute_encoder, out, 1);
+  compute_encoder.set_input_array(src, 0);
+  compute_encoder.set_output_array(out, 1);
 
   // Set source info
   compute_encoder->setBytes(src.shape().data(), ndim * sizeof(int), 2);
@@ -103,11 +103,11 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   // Set index buffers
   for (int i = 1; i < nidx + 1; ++i) {
-    set_array_buffer(compute_encoder, inputs[i], 20 + i);
+    compute_encoder.set_input_array(inputs[i], 20 + i);
   }
 
   // Launch grid
-  compute_encoder->dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 }
 
 void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -183,7 +183,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   kname << "_" << nidx;
 
-  auto compute_encoder = d.get_command_encoder(s.index);
+  auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
 
   auto& upd = inputs.back();
@@ -192,8 +192,8 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder->setComputePipelineState(kernel);
 
   // Set all the buffers
-  set_array_buffer(compute_encoder, upd, 1);
-  set_array_buffer(compute_encoder, out, 2);
+  compute_encoder.set_input_array(upd, 1);
+  compute_encoder.set_output_array(out, 2);
 
   // Set update info
   uint upd_ndim = upd.ndim();
@@ -210,13 +210,13 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
 
     // Set index buffers
     for (int i = 1; i < nidx + 1; ++i) {
-      set_array_buffer(compute_encoder, inputs[i], 20 + i);
+      compute_encoder.set_input_array(inputs[i], 20 + i);
     }
 
     // Launch grid
     MTL::Size grid_dims = MTL::Size(upd_size, nthreads / upd_size, 1);
     MTL::Size group_dims = get_block_dims(upd_size, nthreads / upd_size, 1);
-    compute_encoder->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
 
   } else {
     // Collect all idx shapes and strides into one place
@@ -280,13 +280,13 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
 
     // Set index buffers
     for (int i = 1; i < nidx + 1; ++i) {
-      set_array_buffer(compute_encoder, inputs[i], 20 + i);
+      compute_encoder.set_input_array(inputs[i], 20 + i);
     }
 
     // Launch grid
     MTL::Size grid_dims = MTL::Size(upd_size, nthreads / upd_size, 1);
     MTL::Size group_dims = get_block_dims(upd_size, nthreads / upd_size, 1);
-    compute_encoder->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   }
 }
 

mlx/backend/metal/kernels/CMakeLists.txt

@@ -7,6 +7,7 @@ set(
   ${CMAKE_CURRENT_SOURCE_DIR}/complex.h
   ${CMAKE_CURRENT_SOURCE_DIR}/defines.h
   ${CMAKE_CURRENT_SOURCE_DIR}/erf.h
+  ${CMAKE_CURRENT_SOURCE_DIR}/expm1f.h
   ${CMAKE_CURRENT_SOURCE_DIR}/indexing.h
   ${CMAKE_CURRENT_SOURCE_DIR}/unary.h
   ${CMAKE_CURRENT_SOURCE_DIR}/utils.h
@@ -20,6 +21,7 @@ set(
   "binary_two"
   "conv"
   "copy"
+  "fft"
   "gemv"
   "quantized"
   "random"
@@ -37,11 +39,17 @@ set(
 )
 
 function(build_kernel_base TARGET SRCFILE DEPS)
+  set(METAL_FLAGS -Wall -Wextra -fno-fast-math)
+  if(MLX_METAL_DEBUG)
+    set(METAL_FLAGS ${METAL_FLAGS}
+        -gline-tables-only
+        -frecord-sources)
+  endif()
   add_custom_command(
-    COMMAND xcrun -sdk macosx metal -Wall -Wextra
-                  -fno-fast-math
-                  -c ${SRCFILE} 
-                  -I${PROJECT_SOURCE_DIR} 
+    COMMAND xcrun -sdk macosx metal
+                  ${METAL_FLAGS}
+                  -c ${SRCFILE}
+                  -I${PROJECT_SOURCE_DIR}
                   -o ${TARGET}.air
     DEPENDS ${SRCFILE} ${DEPS}
     OUTPUT ${TARGET}.air

mlx/backend/metal/kernels/arange.metal

@@ -11,22 +11,22 @@ template <typename T>
   out[index] = start + index * step;
 }
 
-#define instantiate_arange(tname, type) \
-  template [[host_name("arange" #tname)]] \
-  [[kernel]] void arange<type>( \
-    constant const type& start, \
-    constant const type& step, \
-    device type* out, \
-    uint index [[thread_position_in_grid]]);
+#define instantiate_arange(tname, type)                                 \
+  template [[host_name("arange" #tname)]] [[kernel]] void arange<type>( \
+      constant const type& start,                                       \
+      constant const type& step,                                        \
+      device type* out,                                                 \
+      uint index [[thread_position_in_grid]]);
 
-instantiate_arange(uint8, uint8_t)
+// clang-format off
+instantiate_arange(uint8, uint8_t) 
 instantiate_arange(uint16, uint16_t)
-instantiate_arange(uint32, uint32_t)
+instantiate_arange(uint32, uint32_t) 
 instantiate_arange(uint64, uint64_t)
-instantiate_arange(int8, int8_t)
+instantiate_arange(int8, int8_t) 
 instantiate_arange(int16, int16_t)
 instantiate_arange(int32, int32_t)
 instantiate_arange(int64, int64_t)
 instantiate_arange(float16, half)
 instantiate_arange(float32, float)
-instantiate_arange(bfloat16, bfloat16_t)
+instantiate_arange(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/arg_reduce.metal

@@ -18,7 +18,8 @@ struct ArgMin {
   static constexpr constant U init = Limits<U>::max;
 
   IndexValPair<U> reduce(IndexValPair<U> best, IndexValPair<U> current) {
-    if (best.val > current.val || (best.val == current.val && best.index > current.index)) {
+    if (best.val > current.val ||
+        (best.val == current.val && best.index > current.index)) {
       return current;
     } else {
       return best;
@@ -26,11 +27,12 @@ struct ArgMin {
   }
 
   template <int N>
-  IndexValPair<U> reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
-    for (int i=0; i<N; i++) {
+  IndexValPair<U>
+  reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+    for (int i = 0; i < N; i++) {
       if (vals[i] < best.val) {
         best.val = vals[i];
-        best.index = offset+i;
+        best.index = offset + i;
       }
     }
     return best;
@@ -42,7 +44,8 @@ struct ArgMax {
   static constexpr constant U init = Limits<U>::min;
 
   IndexValPair<U> reduce(IndexValPair<U> best, IndexValPair<U> current) {
-    if (best.val < current.val || (best.val == current.val && best.index > current.index)) {
+    if (best.val < current.val ||
+        (best.val == current.val && best.index > current.index)) {
       return current;
     } else {
       return best;
@@ -50,11 +53,12 @@ struct ArgMax {
   }
 
   template <int N>
-  IndexValPair<U> reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
-    for (int i=0; i<N; i++) {
+  IndexValPair<U>
+  reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+    for (int i = 0; i < N; i++) {
       if (vals[i] > best.val) {
         best.val = vals[i];
-        best.index = offset+i;
+        best.index = offset + i;
       }
     }
     return best;
@@ -64,19 +68,16 @@ struct ArgMax {
 template <typename U>
 IndexValPair<U> simd_shuffle_down(IndexValPair<U> data, uint16_t delta) {
   return IndexValPair<U>{
-    simd_shuffle_down(data.index, delta),
-    simd_shuffle_down(data.val, delta)
-  };
+      simd_shuffle_down(data.index, delta), simd_shuffle_down(data.val, delta)};
 }
 
-
 template <typename T, typename Op, int N_READS>
 [[kernel]] void arg_reduce_general(
-    const device T *in [[buffer(0)]],
-    device uint32_t *out [[buffer(1)]],
-    const device int *shape [[buffer(2)]],
-    const device size_t *in_strides [[buffer(3)]],
-    const device size_t *out_strides [[buffer(4)]],
+    const device T* in [[buffer(0)]],
+    device uint32_t* out [[buffer(1)]],
+    const device int* shape [[buffer(2)]],
+    const device size_t* in_strides [[buffer(3)]],
+    const device size_t* out_strides [[buffer(4)]],
     const device size_t& ndim [[buffer(5)]],
     const device size_t& axis_stride [[buffer(6)]],
     const device size_t& axis_size [[buffer(7)]],
@@ -86,7 +87,6 @@ template <typename T, typename Op, int N_READS>
     uint simd_size [[threads_per_simdgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-
   // Shapes and strides *do not* contain the reduction axis. The reduction size
   // and stride are provided in axis_stride and axis_size.
   //
@@ -113,13 +113,13 @@ template <typename T, typename Op, int N_READS>
   threadgroup IndexValPair<T> local_data[32];
 
   // Loop over the reduction axis in lsize*N_READS buckets
-  for (uint r=0; r < ceildiv(axis_size, N_READS*lsize); r++) {
+  for (uint r = 0; r < ceildiv(axis_size, N_READS * lsize); r++) {
     // Read the current value
-    uint32_t current_index = r*lsize*N_READS + lid*N_READS;
+    uint32_t current_index = r * lsize * N_READS + lid * N_READS;
     uint32_t offset = current_index;
-    const device T * current_in = in + in_idx + current_index * axis_stride;
+    const device T* current_in = in + in_idx + current_index * axis_stride;
     T vals[N_READS];
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       vals[i] = (current_index < axis_size) ? *current_in : T(Op::init);
       current_index++;
       current_in += axis_stride;
@@ -130,7 +130,7 @@ template <typename T, typename Op, int N_READS>
   // need to reduce across the thread group.
 
   // First per simd reduction.
-  for (uint offset=simd_size/2; offset>0; offset/=2) {
+  for (uint offset = simd_size / 2; offset > 0; offset /= 2) {
     IndexValPair<T> neighbor = simd_shuffle_down(best, offset);
     best = op.reduce(best, neighbor);
   }
@@ -149,7 +149,7 @@ template <typename T, typename Op, int N_READS>
   if (simd_lane_id < simd_groups) {
     best = local_data[simd_lane_id];
   }
-  for (uint offset=simd_size/2; offset>0; offset/=2) {
+  for (uint offset = simd_size / 2; offset > 0; offset /= 2) {
     IndexValPair<T> neighbor = simd_shuffle_down(best, offset);
     best = op.reduce(best, neighbor);
   }
@@ -161,24 +161,25 @@ template <typename T, typename Op, int N_READS>
 }
 
 #define instantiate_arg_reduce_helper(name, itype, op) \
-  template [[host_name(name)]] \
-  [[kernel]] void arg_reduce_general<itype, op<itype>, 4>( \
-      const device itype *in [[buffer(0)]], \
-      device uint32_t * out [[buffer(1)]], \
-      const device int *shape [[buffer(2)]], \
-      const device size_t *in_strides [[buffer(3)]], \
-      const device size_t *out_strides [[buffer(4)]], \
-      const device size_t& ndim [[buffer(5)]], \
-      const device size_t& axis_stride [[buffer(6)]], \
-      const device size_t& axis_size [[buffer(7)]], \
-      uint gid [[thread_position_in_grid]], \
-      uint lid [[thread_position_in_threadgroup]], \
-      uint lsize [[threads_per_threadgroup]], \
-      uint simd_size [[threads_per_simdgroup]], \
+  template [[host_name(name)]] [[kernel]] void         \
+  arg_reduce_general<itype, op<itype>, 4>(             \
+      const device itype* in [[buffer(0)]],            \
+      device uint32_t* out [[buffer(1)]],              \
+      const device int* shape [[buffer(2)]],           \
+      const device size_t* in_strides [[buffer(3)]],   \
+      const device size_t* out_strides [[buffer(4)]],  \
+      const device size_t& ndim [[buffer(5)]],         \
+      const device size_t& axis_stride [[buffer(6)]],  \
+      const device size_t& axis_size [[buffer(7)]],    \
+      uint gid [[thread_position_in_grid]],            \
+      uint lid [[thread_position_in_threadgroup]],     \
+      uint lsize [[threads_per_threadgroup]],          \
+      uint simd_size [[threads_per_simdgroup]],        \
       uint simd_lane_id [[thread_index_in_simdgroup]], \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
-#define instantiate_arg_reduce(name, itype) \
+// clang-format off
+#define instantiate_arg_reduce(name, itype)                      \
   instantiate_arg_reduce_helper("argmin_" #name , itype, ArgMin) \
   instantiate_arg_reduce_helper("argmax_" #name , itype, ArgMax)
 
@@ -193,4 +194,4 @@ instantiate_arg_reduce(int32, int32_t)
 instantiate_arg_reduce(int64, int64_t)
 instantiate_arg_reduce(float16, half)
 instantiate_arg_reduce(float32, float)
-instantiate_arg_reduce(bfloat16, bfloat16_t)
+instantiate_arg_reduce(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/binary.h

@@ -229,3 +229,45 @@ struct LogicalOr {
     return x || y;
   };
 };
+
+struct BitwiseAnd {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x & y;
+  };
+};
+
+struct BitwiseOr {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x | y;
+  };
+};
+
+struct BitwiseXor {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x ^ y;
+  };
+};
+
+struct LeftShift {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x << y;
+  };
+};
+
+struct RightShift {
+  template <typename T>
+  T operator()(T x, T y) {
+    return x >> y;
+  };
+};
+
+struct ArcTan2 {
+  template <typename T>
+  T operator()(T y, T x) {
+    return metal::precise::atan2(y, x);
+  }
+};

mlx/backend/metal/kernels/binary.metal

@@ -77,7 +77,8 @@ template <typename T, typename U, typename Op>
     uint3 grid_dim [[threads_per_grid]]) {
   auto a_idx = elem_to_loc_3(index, a_strides);
   auto b_idx = elem_to_loc_3(index, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op()(a[a_idx], b[b_idx]);
 }
 
@@ -92,7 +93,8 @@ template <typename T, typename U, typename Op, int DIM>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto idx = elem_to_loc_2_nd<DIM>(index, shape, a_strides, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op()(a[idx.x], b[idx.y]);
 }
 
@@ -112,111 +114,118 @@ template <typename T, typename U, typename Op>
   c[out_idx] = Op()(a[idx.x], b[idx.y]);
 }
 
-#define instantiate_binary(name, itype, otype, op, bopt) \
-  template [[host_name(name)]] \
-  [[kernel]] void binary_op_##bopt<itype, otype, op>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      uint index [[thread_position_in_grid]]);
+#define instantiate_binary(name, itype, otype, op, bopt)                      \
+  template                                                                    \
+      [[host_name(name)]] [[kernel]] void binary_op_##bopt<itype, otype, op>( \
+          device const itype* a,                                              \
+          device const itype* b,                                              \
+          device otype* c,                                                    \
+          uint index [[thread_position_in_grid]]);
 
 #define instantiate_binary_g_dim(name, itype, otype, op, dims) \
-  template [[host_name(name "_" #dims)]] \
-  [[kernel]] void binary_op_g_nd<itype, otype, op, dims>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      constant const int shape[dims], \
-      constant const size_t a_strides[dims], \
-      constant const size_t b_strides[dims], \
-      uint3 index [[thread_position_in_grid]], \
+  template [[host_name(name "_" #dims)]] [[kernel]] void       \
+  binary_op_g_nd<itype, otype, op, dims>(                      \
+      device const itype* a,                                   \
+      device const itype* b,                                   \
+      device otype* c,                                         \
+      constant const int shape[dims],                          \
+      constant const size_t a_strides[dims],                   \
+      constant const size_t b_strides[dims],                   \
+      uint3 index [[thread_position_in_grid]],                 \
       uint3 grid_dim [[threads_per_grid]]);
 
 #define instantiate_binary_g_nd(name, itype, otype, op) \
-  template [[host_name(name "_1")]] \
-  [[kernel]] void binary_op_g_nd1<itype, otype, op>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      constant const size_t& a_stride, \
-      constant const size_t& b_stride, \
-      uint index [[thread_position_in_grid]]); \
-  template [[host_name(name "_2")]] \
-  [[kernel]] void binary_op_g_nd2<itype, otype, op>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      constant const size_t a_strides[2], \
-      constant const size_t b_strides[2], \
-      uint2 index [[thread_position_in_grid]], \
-      uint2 grid_dim [[threads_per_grid]]); \
-  template [[host_name(name "_3")]] \
-  [[kernel]] void binary_op_g_nd3<itype, otype, op>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      constant const size_t a_strides[3], \
-      constant const size_t b_strides[3], \
-      uint3 index [[thread_position_in_grid]], \
-      uint3 grid_dim [[threads_per_grid]]); \
-  instantiate_binary_g_dim(name, itype, otype, op, 4) \
-  instantiate_binary_g_dim(name, itype, otype, op, 5)
+  template [[host_name(name "_1")]] [[kernel]] void     \
+  binary_op_g_nd1<itype, otype, op>(                    \
+      device const itype* a,                            \
+      device const itype* b,                            \
+      device otype* c,                                  \
+      constant const size_t& a_stride,                  \
+      constant const size_t& b_stride,                  \
+      uint index [[thread_position_in_grid]]);          \
+  template [[host_name(name "_2")]] [[kernel]] void     \
+  binary_op_g_nd2<itype, otype, op>(                    \
+      device const itype* a,                            \
+      device const itype* b,                            \
+      device otype* c,                                  \
+      constant const size_t a_strides[2],               \
+      constant const size_t b_strides[2],               \
+      uint2 index [[thread_position_in_grid]],          \
+      uint2 grid_dim [[threads_per_grid]]);             \
+  template [[host_name(name "_3")]] [[kernel]] void     \
+  binary_op_g_nd3<itype, otype, op>(                    \
+      device const itype* a,                            \
+      device const itype* b,                            \
+      device otype* c,                                  \
+      constant const size_t a_strides[3],               \
+      constant const size_t b_strides[3],               \
+      uint3 index [[thread_position_in_grid]],          \
+      uint3 grid_dim [[threads_per_grid]]);             \
+  instantiate_binary_g_dim(name, itype, otype, op, 4)   \
+      instantiate_binary_g_dim(name, itype, otype, op, 5)
 
-
-#define instantiate_binary_g(name, itype, otype, op) \
-  template [[host_name(name)]] \
-  [[kernel]] void binary_op_g<itype, otype, op>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      constant const int* shape, \
-      constant const size_t* a_strides, \
-      constant const size_t* b_strides, \
-      constant const int& ndim, \
-      uint3 index [[thread_position_in_grid]], \
+#define instantiate_binary_g(name, itype, otype, op)                          \
+  template [[host_name(name)]] [[kernel]] void binary_op_g<itype, otype, op>( \
+      device const itype* a,                                                  \
+      device const itype* b,                                                  \
+      device otype* c,                                                        \
+      constant const int* shape,                                              \
+      constant const size_t* a_strides,                                       \
+      constant const size_t* b_strides,                                       \
+      constant const int& ndim,                                               \
+      uint3 index [[thread_position_in_grid]],                                \
       uint3 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_binary_all(name, tname, itype, otype, op) \
   instantiate_binary("ss" #name #tname, itype, otype, op, ss) \
   instantiate_binary("sv" #name #tname, itype, otype, op, sv) \
   instantiate_binary("vs" #name #tname, itype, otype, op, vs) \
   instantiate_binary("vv" #name #tname, itype, otype, op, vv) \
-  instantiate_binary_g("g" #name #tname, itype, otype, op) \
-  instantiate_binary_g_nd("g" #name #tname, itype, otype, op)
+  instantiate_binary_g("g" #name #tname, itype, otype, op)    \
+  instantiate_binary_g_nd("g" #name #tname, itype, otype, op) // clang-format on
 
-#define instantiate_binary_float(name, op) \
-  instantiate_binary_all(name, float16, half, half, op) \
-  instantiate_binary_all(name, float32, float, float, op) \
-  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op)
-
-#define instantiate_binary_types(name, op) \
-  instantiate_binary_all(name, bool_, bool, bool, op) \
-  instantiate_binary_all(name, uint8, uint8_t, uint8_t, op) \
+// clang-format off
+#define instantiate_binary_integer(name, op)                   \
+  instantiate_binary_all(name, uint8, uint8_t, uint8_t, op)    \
   instantiate_binary_all(name, uint16, uint16_t, uint16_t, op) \
   instantiate_binary_all(name, uint32, uint32_t, uint32_t, op) \
   instantiate_binary_all(name, uint64, uint64_t, uint64_t, op) \
-  instantiate_binary_all(name, int8, int8_t, int8_t, op) \
-  instantiate_binary_all(name, int16, int16_t, int16_t, op) \
-  instantiate_binary_all(name, int32, int32_t, int32_t, op) \
-  instantiate_binary_all(name, int64, int64_t, int64_t, op) \
+  instantiate_binary_all(name, int8, int8_t, int8_t, op)       \
+  instantiate_binary_all(name, int16, int16_t, int16_t, op)    \
+  instantiate_binary_all(name, int32, int32_t, int32_t, op)    \
+  instantiate_binary_all(name, int64, int64_t, int64_t, op) // clang-format on
+
+// clang-format off
+#define instantiate_binary_float(name, op)                \
+  instantiate_binary_all(name, float16, half, half, op)   \
+  instantiate_binary_all(name, float32, float, float, op) \
+  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op) // clang-format on
+
+// clang-format off
+#define instantiate_binary_types(name, op)                              \
+  instantiate_binary_all(name, bool_, bool, bool, op)                   \
+  instantiate_binary_integer(name, op)                                  \
   instantiate_binary_all(name, complex64, complex64_t, complex64_t, op) \
-  instantiate_binary_float(name, op)
+  instantiate_binary_float(name, op) // clang-format on
 
-#define instantiate_binary_types_bool(name, op) \
-  instantiate_binary_all(name, bool_, bool, bool, op) \
-  instantiate_binary_all(name, uint8, uint8_t, bool, op) \
-  instantiate_binary_all(name, uint16, uint16_t, bool, op) \
-  instantiate_binary_all(name, uint32, uint32_t, bool, op) \
-  instantiate_binary_all(name, uint64, uint64_t, bool, op) \
-  instantiate_binary_all(name, int8, int8_t, bool, op) \
-  instantiate_binary_all(name, int16, int16_t, bool, op) \
-  instantiate_binary_all(name, int32, int32_t, bool, op) \
-  instantiate_binary_all(name, int64, int64_t, bool, op) \
-  instantiate_binary_all(name, float16, half, bool, op) \
-  instantiate_binary_all(name, float32, float, bool, op) \
+// clang-format off
+#define instantiate_binary_types_bool(name, op)                \
+  instantiate_binary_all(name, bool_, bool, bool, op)          \
+  instantiate_binary_all(name, uint8, uint8_t, bool, op)       \
+  instantiate_binary_all(name, uint16, uint16_t, bool, op)     \
+  instantiate_binary_all(name, uint32, uint32_t, bool, op)     \
+  instantiate_binary_all(name, uint64, uint64_t, bool, op)     \
+  instantiate_binary_all(name, int8, int8_t, bool, op)         \
+  instantiate_binary_all(name, int16, int16_t, bool, op)       \
+  instantiate_binary_all(name, int32, int32_t, bool, op)       \
+  instantiate_binary_all(name, int64, int64_t, bool, op)       \
+  instantiate_binary_all(name, float16, half, bool, op)        \
+  instantiate_binary_all(name, float32, float, bool, op)       \
   instantiate_binary_all(name, bfloat16, bfloat16_t, bool, op) \
-  instantiate_binary_all(name, complex64, complex64_t, bool, op)
+  instantiate_binary_all(name, complex64, complex64_t, bool, op) // clang-format on
 
+// clang-format off
 instantiate_binary_types(add, Add)
 instantiate_binary_types(div, Divide)
 instantiate_binary_types_bool(eq, Equal)
@@ -232,6 +241,7 @@ instantiate_binary_types(mul, Multiply)
 instantiate_binary_types(sub, Subtract)
 instantiate_binary_types(pow, Power)
 instantiate_binary_types(rem, Remainder)
+instantiate_binary_float(arctan2, ArcTan2)
 
 // NaNEqual only needed for floating point types with boolean output
 instantiate_binary_all(naneq, float16, half, bool, NaNEqual)
@@ -241,3 +251,13 @@ instantiate_binary_all(naneq, complex64, complex64_t, bool, NaNEqual)
 
 instantiate_binary_all(lor, bool_, bool, bool, LogicalOr)
 instantiate_binary_all(land, bool_, bool, bool, LogicalAnd)
+
+// Bitwise ops only need integer types and bool (except for l/r shift)
+instantiate_binary_integer(bitwise_and, BitwiseAnd)
+instantiate_binary_all(bitwise_and, bool_, bool, bool, BitwiseAnd)
+instantiate_binary_integer(bitwise_or, BitwiseOr)
+instantiate_binary_all(bitwise_or, bool_, bool, bool, BitwiseOr)
+instantiate_binary_integer(bitwise_xor, BitwiseXor)
+instantiate_binary_all(bitwise_xor, bool_, bool, bool, BitwiseXor)
+instantiate_binary_integer(left_shift, LeftShift)
+instantiate_binary_integer(right_shift, RightShift) // clang-format on

mlx/backend/metal/kernels/binary_two.metal

@@ -3,28 +3,42 @@
 #include <metal_integer>
 #include <metal_math>
 
-#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/utils.h"
 
 struct FloorDivide {
-  template <typename T> T operator()(T x, T y) { return x / y; }
-  template <> float operator()(float x, float y) { return trunc(x / y); }
-  template <> half operator()(half x, half y) { return trunc(x / y); }
-  template <> bfloat16_t operator()(bfloat16_t x, bfloat16_t y) { return trunc(x / y); }
+  template <typename T>
+  T operator()(T x, T y) {
+    return x / y;
+  }
+  template <>
+  float operator()(float x, float y) {
+    return trunc(x / y);
+  }
+  template <>
+  half operator()(half x, half y) {
+    return trunc(x / y);
+  }
+  template <>
+  bfloat16_t operator()(bfloat16_t x, bfloat16_t y) {
+    return trunc(x / y);
+  }
 };
 
 struct Remainder {
   template <typename T>
-  metal::enable_if_t<metal::is_integral_v<T> & !metal::is_signed_v<T>, T> operator()(T x, T y) {
+  metal::enable_if_t<metal::is_integral_v<T> & !metal::is_signed_v<T>, T>
+  operator()(T x, T y) {
     return x % y;
   }
   template <typename T>
-  metal::enable_if_t<metal::is_integral_v<T> & metal::is_signed_v<T>, T> operator()(T x, T y) {
+  metal::enable_if_t<metal::is_integral_v<T> & metal::is_signed_v<T>, T>
+  operator()(T x, T y) {
     auto r = x % y;
     if (r != 0 && (r < 0 != y < 0)) {
       r += y;
     }
-    return r; 
+    return r;
   }
   template <typename T>
   metal::enable_if_t<!metal::is_integral_v<T>, T> operator()(T x, T y) {
@@ -32,10 +46,11 @@ struct Remainder {
     if (r != 0 && (r < 0 != y < 0)) {
       r += y;
     }
-    return r; 
+    return r;
   }
-  template <> complex64_t operator()(complex64_t x, complex64_t y) {
-    return x % y; 
+  template <>
+  complex64_t operator()(complex64_t x, complex64_t y) {
+    return x % y;
   }
 };
 
@@ -50,7 +65,6 @@ template <typename T, typename U, typename Op1, typename Op2>
   d[index] = Op2()(a[0], b[0]);
 }
 
-
 template <typename T, typename U, typename Op1, typename Op2>
 [[kernel]] void binary_op_ss(
     device const T* a,
@@ -139,7 +153,8 @@ template <typename T, typename U, typename Op1, typename Op2>
     uint3 grid_dim [[threads_per_grid]]) {
   auto a_idx = elem_to_loc_3(index, a_strides);
   auto b_idx = elem_to_loc_3(index, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op1()(a[a_idx], b[b_idx]);
   d[out_idx] = Op2()(a[a_idx], b[b_idx]);
 }
@@ -156,7 +171,8 @@ template <typename T, typename U, typename Op1, typename Op2, int DIM>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto idx = elem_to_loc_2_nd<DIM>(index, shape, a_strides, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op1()(a[idx.x], b[idx.y]);
   d[out_idx] = Op2()(a[idx.x], b[idx.y]);
 }
@@ -180,99 +196,102 @@ template <typename T, typename U, typename Op1, typename Op2>
 }
 
 #define instantiate_binary(name, itype, otype, op1, op2, bopt) \
-  template [[host_name(name)]] \
-  [[kernel]] void binary_op_##bopt<itype, otype, op1, op2>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      device otype* d, \
-      uint index [[thread_position_in_grid]]);
+  template [[host_name(name)]] [[kernel]] void                 \
+      binary_op_##bopt<itype, otype, op1, op2>(                \
+          device const itype* a,                               \
+          device const itype* b,                               \
+          device otype* c,                                     \
+          device otype* d,                                     \
+          uint index [[thread_position_in_grid]]);
 
 #define instantiate_binary_g_dim(name, itype, otype, op1, op2, dims) \
-  template [[host_name(name "_" #dims)]] \
-  [[kernel]] void binary_op_g_nd<itype, otype, op1, op2, dims>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      device otype* d, \
-      constant const int shape[dims], \
-      constant const size_t a_strides[dims], \
-      constant const size_t b_strides[dims], \
-      uint3 index [[thread_position_in_grid]], \
+  template [[host_name(name "_" #dims)]] [[kernel]] void             \
+  binary_op_g_nd<itype, otype, op1, op2, dims>(                      \
+      device const itype* a,                                         \
+      device const itype* b,                                         \
+      device otype* c,                                               \
+      device otype* d,                                               \
+      constant const int shape[dims],                                \
+      constant const size_t a_strides[dims],                         \
+      constant const size_t b_strides[dims],                         \
+      uint3 index [[thread_position_in_grid]],                       \
       uint3 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_binary_g_nd(name, itype, otype, op1, op2) \
-  template [[host_name(name "_1")]] \
-  [[kernel]] void binary_op_g_nd1<itype, otype, op1, op2>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      device otype* d, \
-      constant const size_t& a_stride, \
-      constant const size_t& b_stride, \
-      uint index [[thread_position_in_grid]]); \
-  template [[host_name(name "_2")]] \
-  [[kernel]] void binary_op_g_nd2<itype, otype, op1, op2>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      device otype* d, \
-      constant const size_t a_strides[2], \
-      constant const size_t b_strides[2], \
-      uint2 index [[thread_position_in_grid]], \
-      uint2 grid_dim [[threads_per_grid]]); \
-  template [[host_name(name "_3")]] \
-  [[kernel]] void binary_op_g_nd3<itype, otype, op1, op2>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      device otype* d, \
-      constant const size_t a_strides[3], \
-      constant const size_t b_strides[3], \
-      uint3 index [[thread_position_in_grid]], \
-      uint3 grid_dim [[threads_per_grid]]); \
-  instantiate_binary_g_dim(name, itype, otype, op1, op2, 4) \
-  instantiate_binary_g_dim(name, itype, otype, op1, op2, 5)
-
+  template [[host_name(name "_1")]] [[kernel]] void           \
+  binary_op_g_nd1<itype, otype, op1, op2>(                    \
+      device const itype* a,                                  \
+      device const itype* b,                                  \
+      device otype* c,                                        \
+      device otype* d,                                        \
+      constant const size_t& a_stride,                        \
+      constant const size_t& b_stride,                        \
+      uint index [[thread_position_in_grid]]);                \
+  template [[host_name(name "_2")]] [[kernel]] void           \
+  binary_op_g_nd2<itype, otype, op1, op2>(                    \
+      device const itype* a,                                  \
+      device const itype* b,                                  \
+      device otype* c,                                        \
+      device otype* d,                                        \
+      constant const size_t a_strides[2],                     \
+      constant const size_t b_strides[2],                     \
+      uint2 index [[thread_position_in_grid]],                \
+      uint2 grid_dim [[threads_per_grid]]);                   \
+  template [[host_name(name "_3")]] [[kernel]] void           \
+  binary_op_g_nd3<itype, otype, op1, op2>(                    \
+      device const itype* a,                                  \
+      device const itype* b,                                  \
+      device otype* c,                                        \
+      device otype* d,                                        \
+      constant const size_t a_strides[3],                     \
+      constant const size_t b_strides[3],                     \
+      uint3 index [[thread_position_in_grid]],                \
+      uint3 grid_dim [[threads_per_grid]]);                   \
+  instantiate_binary_g_dim(name, itype, otype, op1, op2, 4)   \
+  instantiate_binary_g_dim(name, itype, otype, op1, op2, 5) // clang-format on
 
 #define instantiate_binary_g(name, itype, otype, op1, op2) \
-  template [[host_name(name)]] \
-  [[kernel]] void binary_op_g<itype, otype, op2, op2>( \
-      device const itype* a, \
-      device const itype* b, \
-      device otype* c, \
-      device otype* d, \
-      constant const int* shape, \
-      constant const size_t* a_strides, \
-      constant const size_t* b_strides, \
-      constant const int& ndim, \
-      uint3 index [[thread_position_in_grid]], \
+  template [[host_name(name)]] [[kernel]] void             \
+  binary_op_g<itype, otype, op2, op2>(                     \
+      device const itype* a,                               \
+      device const itype* b,                               \
+      device otype* c,                                     \
+      device otype* d,                                     \
+      constant const int* shape,                           \
+      constant const size_t* a_strides,                    \
+      constant const size_t* b_strides,                    \
+      constant const int& ndim,                            \
+      uint3 index [[thread_position_in_grid]],             \
       uint3 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_binary_all(name, tname, itype, otype, op1, op2) \
   instantiate_binary("ss" #name #tname, itype, otype, op1, op2, ss) \
   instantiate_binary("sv" #name #tname, itype, otype, op1, op2, sv) \
   instantiate_binary("vs" #name #tname, itype, otype, op1, op2, vs) \
   instantiate_binary("vv" #name #tname, itype, otype, op1, op2, vv) \
-  instantiate_binary_g("g" #name #tname, itype, otype, op1, op2) \
-  instantiate_binary_g_nd("g" #name #tname, itype, otype, op1, op2)
+  instantiate_binary_g("g" #name #tname, itype, otype, op1, op2)    \
+  instantiate_binary_g_nd("g" #name #tname, itype, otype, op1, op2) // clang-format on
 
-#define instantiate_binary_float(name, op1, op2) \
-  instantiate_binary_all(name, float16, half, half, op1, op2) \
+// clang-format off
+#define instantiate_binary_float(name, op1, op2)                \
+  instantiate_binary_all(name, float16, half, half, op1, op2)   \
   instantiate_binary_all(name, float32, float, float, op1, op2) \
-  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op1, op2)
+  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op1, op2) // clang-format on
 
-#define instantiate_binary_types(name, op1, op2) \
-  instantiate_binary_all(name, bool_, bool, bool, op1, op2) \
-  instantiate_binary_all(name, uint8, uint8_t, uint8_t, op1, op2) \
-  instantiate_binary_all(name, uint16, uint16_t, uint16_t, op1, op2) \
-  instantiate_binary_all(name, uint32, uint32_t, uint32_t, op1, op2) \
-  instantiate_binary_all(name, uint64, uint64_t, uint64_t, op1, op2) \
-  instantiate_binary_all(name, int8, int8_t, int8_t, op1, op2) \
-  instantiate_binary_all(name, int16, int16_t, int16_t, op1, op2) \
-  instantiate_binary_all(name, int32, int32_t, int32_t, op1, op2) \
-  instantiate_binary_all(name, int64, int64_t, int64_t, op1, op2) \
+// clang-format off
+#define instantiate_binary_types(name, op1, op2)                              \
+  instantiate_binary_all(name, bool_, bool, bool, op1, op2)                   \
+  instantiate_binary_all(name, uint8, uint8_t, uint8_t, op1, op2)             \
+  instantiate_binary_all(name, uint16, uint16_t, uint16_t, op1, op2)          \
+  instantiate_binary_all(name, uint32, uint32_t, uint32_t, op1, op2)          \
+  instantiate_binary_all(name, uint64, uint64_t, uint64_t, op1, op2)          \
+  instantiate_binary_all(name, int8, int8_t, int8_t, op1, op2)                \
+  instantiate_binary_all(name, int16, int16_t, int16_t, op1, op2)             \
+  instantiate_binary_all(name, int32, int32_t, int32_t, op1, op2)             \
+  instantiate_binary_all(name, int64, int64_t, int64_t, op1, op2)             \
   instantiate_binary_all(name, complex64, complex64_t, complex64_t, op1, op2) \
   instantiate_binary_float(name, op1, op2)
 
-instantiate_binary_types(divmod, FloorDivide, Remainder)
+instantiate_binary_types(divmod, FloorDivide, Remainder) // clang-format on

mlx/backend/metal/kernels/complex.h

@@ -22,7 +22,7 @@ struct complex64_t {
   float imag;
 
   // Constructors
-  constexpr complex64_t(float real, float imag) : real(real), imag(imag){};
+  constexpr complex64_t(float real, float imag) : real(real), imag(imag) {};
 
   // Conversions to complex64_t
   template <

mlx/backend/metal/kernels/conv.metal

@@ -1,13 +1,11 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <metal_stdlib>
 #include <metal_simdgroup>
 #include <metal_simdgroup_matrix>
 #include <metal_stdlib>
 
-
-#include "mlx/backend/metal/kernels/steel/conv/params.h"
 #include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/steel/conv/params.h"
 
 #define MLX_MTL_CONST static constant constexpr const
 
@@ -23,17 +21,18 @@ template <typename T, int N>
     device T* out [[buffer(1)]],
     const constant MLXConvParams<N>* params [[buffer(2)]],
     uint3 gid [[thread_position_in_grid]]) {
-
   int filter_size = params->C;
-  for(short i = 0; i < N; i++) filter_size *= params->wS[i];
+  for (short i = 0; i < N; i++)
+    filter_size *= params->wS[i];
 
   int out_pixels = 1;
-  for(short i = 0; i < N; i++) out_pixels *= params->oS[i];
+  for (short i = 0; i < N; i++)
+    out_pixels *= params->oS[i];
 
-  // Set out 
+  // Set out
   out += gid.z * filter_size + gid.y * (params->C);
 
-  // Corrdinates in input
+  // Coordinates in input
   int is[N] = {0};
 
   // gid.z: N oS (Batch and row in unfolded output)
@@ -46,11 +45,11 @@ template <typename T, int N>
 
   bool valid = n < params->N;
 
-  // Unroll dimensions 
+  // Unroll dimensions
   for (int i = N - 1; i >= 0; --i) {
     int os_ = (oS % params->oS[i]);
     int ws_ = (wS % params->wS[i]);
-    
+
     ws_ = params->flip ? params->wS[i] - ws_ - 1 : ws_;
 
     int is_ = os_ * params->str[i] - params->pad[i] + ws_ * params->kdil[i];
@@ -64,10 +63,10 @@ template <typename T, int N>
     wS /= params->wS[i];
   }
 
-  if(valid) {
+  if (valid) {
     size_t in_offset = n * params->in_strides[0];
 
-    for(int i = 0; i < N; ++i) {
+    for (int i = 0; i < N; ++i) {
       in_offset += is[i] * params->in_strides[i + 1];
     }
 
@@ -75,21 +74,91 @@ template <typename T, int N>
   } else {
     out[gid.x] = T(0);
   }
-
 }
 
-#define instantiate_naive_unfold_nd(name, itype, n) \
-  template [[host_name("naive_unfold_nd_" #name "_" #n)]] \
-  [[kernel]] void naive_unfold_Nd( \
-      const device itype* in [[buffer(0)]], \
-      device itype* out [[buffer(1)]], \
-      const constant MLXConvParams<n>* params [[buffer(2)]], \
-      uint3 gid [[thread_position_in_grid]]);
+// This kernel unfolds the input array of size (N, *spatial_dims, C)
+// into an array of size (N x *spatial_dims, C x *kernel_dims).
+template <typename T, int N>
+[[kernel]] void naive_unfold_transpose_Nd(
+    const device T* in [[buffer(0)]],
+    device T* out [[buffer(1)]],
+    const constant MLXConvParams<N>* params [[buffer(2)]],
+    uint3 gid [[thread_position_in_grid]]) {
+  int filter_size = params->C;
+  for (short i = 0; i < N; i++)
+    filter_size *= params->wS[i];
 
-#define instantiate_naive_unfold_nd_dims(name, itype) \
-  instantiate_naive_unfold_nd(name, itype, 1) \
-  instantiate_naive_unfold_nd(name, itype, 2) \
-  instantiate_naive_unfold_nd(name, itype, 3)
+  int out_pixels = 1;
+  for (short i = 0; i < N; i++)
+    out_pixels *= params->oS[i];
+
+  // Set out
+  out += gid.z * filter_size + gid.x * (filter_size / params->C);
+
+  // Coordinates in input
+  int is[N] = {0};
+
+  // gid.z: N oS (Batch and row in unfolded output)
+  // gid.y: wS (Filter location to unfold input)
+  // gid.x: C (channel)
+
+  int n = (gid.z) / out_pixels;
+  int oS = (gid.z) % out_pixels;
+  int wS = gid.y;
+
+  bool valid = n < params->N;
+
+  // Unroll dimensions
+  for (int i = N - 1; i >= 0; --i) {
+    int os_ = (oS % params->oS[i]);
+    int ws_ = (wS % params->wS[i]);
+
+    ws_ = params->flip ? params->wS[i] - ws_ - 1 : ws_;
+
+    int is_ = os_ * params->str[i] - params->pad[i] + ws_ * params->kdil[i];
+    int is_max = 1 + params->idil[i] * (params->iS[i] - 1);
+
+    valid &= is_ >= 0 && is_ < is_max && (is_ % params->idil[i] == 0);
+
+    is[i] = is_ / params->idil[i];
+
+    oS /= params->oS[i];
+    wS /= params->wS[i];
+
+    out += ws_ * params->str[i];
+  }
+
+  if (valid) {
+    size_t in_offset = n * params->in_strides[0];
+
+    for (int i = 0; i < N; ++i) {
+      in_offset += is[i] * params->in_strides[i + 1];
+    }
+
+    out[0] = in[in_offset + gid.x];
+  } else {
+    out[0] = T(0);
+  }
+}
+
+#define instantiate_naive_unfold_nd(name, itype, n)                            \
+  template [[host_name("naive_unfold_nd_" #name "_" #n)]] [[kernel]] void      \
+  naive_unfold_Nd(                                                             \
+      const device itype* in [[buffer(0)]],                                    \
+      device itype* out [[buffer(1)]],                                         \
+      const constant MLXConvParams<n>* params [[buffer(2)]],                   \
+      uint3 gid [[thread_position_in_grid]]);                                  \
+  template                                                                     \
+      [[host_name("naive_unfold_transpose_nd_" #name "_" #n)]] [[kernel]] void \
+      naive_unfold_transpose_Nd(                                               \
+          const device itype* in [[buffer(0)]],                                \
+          device itype* out [[buffer(1)]],                                     \
+          const constant MLXConvParams<n>* params [[buffer(2)]],               \
+          uint3 gid [[thread_position_in_grid]]);
+
+#define instantiate_naive_unfold_nd_dims(name, itype)                      \
+  instantiate_naive_unfold_nd(name, itype, 1) instantiate_naive_unfold_nd( \
+      name, itype, 2) instantiate_naive_unfold_nd(name, itype, 3)
 
 instantiate_naive_unfold_nd_dims(float32, float);
 instantiate_naive_unfold_nd_dims(float16, half);
@@ -99,12 +168,13 @@ instantiate_naive_unfold_nd_dims(bfloat16, bfloat16_t);
 /// Slow and naive conv2d kernels
 ///////////////////////////////////////////////////////////////////////////////
 
-template <typename T, 
-          const int BM, /* Threadgroup rows (in threads) */
-          const int BN, /* Threadgroup cols (in threads) */
-          const int TM, /* Thread rows (in elements) */
-          const int TN, /* Thread cols (in elements) */
-          const int BC = 16>
+template <
+    typename T,
+    const int BM, /* Threadgroup rows (in threads) */
+    const int BN, /* Threadgroup cols (in threads) */
+    const int TM, /* Thread rows (in elements) */
+    const int TN, /* Thread cols (in elements) */
+    const int BC = 16>
 [[kernel]] void naive_conv_2d(
     const device T* in [[buffer(0)]],
     const device T* wt [[buffer(1)]],
@@ -114,7 +184,6 @@ template <typename T,
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-  
   (void)simd_gid;
   (void)simd_lid;
 
@@ -123,80 +192,82 @@ template <typename T,
 
   int out_o = tid.y * BN * TN + lid.y * TN;
   int out_hw = tid.x * BM * TM + lid.x * TM;
-    
+
   int out_h[TM];
   int out_w[TN];
 
-  for(int m = 0; m < TM; ++m) {
+  for (int m = 0; m < TM; ++m) {
     int mm = (out_hw + m);
     out_h[m] = mm / params.oS[1];
     out_w[m] = mm % params.oS[1];
   }
 
-
   T in_local[TM];
   T wt_local[TN];
   T out_local[TM * TN] = {T(0)};
 
-  for(int h = 0; h < params.wS[0]; ++h) {
-    for(int w = 0; w < params.wS[1]; ++w) {
-      for(int c = 0; c < params.C; ++c) {
-
+  for (int h = 0; h < params.wS[0]; ++h) {
+    for (int w = 0; w < params.wS[1]; ++w) {
+      for (int c = 0; c < params.C; ++c) {
         // Local in
-        for(int m = 0; m < TM; m++) {
+        for (int m = 0; m < TM; m++) {
           int i = out_h[m] * params.str[0] - params.pad[0] + h * params.kdil[0];
           int j = out_w[m] * params.str[1] - params.pad[1] + w * params.kdil[1];
 
           bool valid = i >= 0 && i < params.iS[0] && j >= 0 && j < params.iS[1];
-          in_local[m] = valid ? in[i * params.in_strides[1] + j * params.in_strides[2] + c] : T(0);
+          in_local[m] = valid
+              ? in[i * params.in_strides[1] + j * params.in_strides[2] + c]
+              : T(0);
         }
 
         // Load weight
         for (int n = 0; n < TN; ++n) {
           int o = out_o + n;
-          wt_local[n] = o < params.O ? wt[o * params.wt_strides[0] + 
-                                          h * params.wt_strides[1] + 
-                                          w * params.wt_strides[2] + c] : T(0);
+          wt_local[n] = o < params.O
+              ? wt[o * params.wt_strides[0] + h * params.wt_strides[1] +
+                   w * params.wt_strides[2] + c]
+              : T(0);
         }
 
         // Accumulate
-        for(int m = 0; m < TM; ++m) {
-          for(int n = 0; n < TN; ++n) {
+        for (int m = 0; m < TM; ++m) {
+          for (int n = 0; n < TN; ++n) {
             out_local[m * TN + n] += in_local[m] * wt_local[n];
           }
         }
-
       }
     }
   }
 
-  for(int m = 0; m < TM; ++m) {
-    for(int n = 0; n < TN; ++n) {
-      if(out_h[m] < params.oS[0] && out_w[m] < params.oS[1] && (out_o + n) < params.O)
-      out[out_h[m] * params.out_strides[1] +
-          out_w[m] * params.out_strides[2] + out_o + n] = out_local[m * TN + n];
+  for (int m = 0; m < TM; ++m) {
+    for (int n = 0; n < TN; ++n) {
+      if (out_h[m] < params.oS[0] && out_w[m] < params.oS[1] &&
+          (out_o + n) < params.O)
+        out[out_h[m] * params.out_strides[1] +
+            out_w[m] * params.out_strides[2] + out_o + n] =
+            out_local[m * TN + n];
     }
   }
-
 }
 
 // Instantiations
 
-#define instantiate_naive_conv_2d(name, itype, bm, bn, tm, tn) \
-  template [[host_name("naive_conv_2d_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn)]] \
-  [[kernel]] void naive_conv_2d<itype, bm, bn, tm, tn>( \
-      const device itype* in [[buffer(0)]], \
-      const device itype* wt [[buffer(1)]], \
-      device itype* out [[buffer(2)]], \
-      const constant MLXConvParams<2>& params [[buffer(3)]], \
-      uint3 tid [[threadgroup_position_in_grid]], \
-      uint3 lid [[thread_position_in_threadgroup]], \
-      uint simd_gid [[simdgroup_index_in_threadgroup]], \
+#define instantiate_naive_conv_2d(name, itype, bm, bn, tm, tn)              \
+  template [[host_name("naive_conv_2d_" #name "_bm" #bm "_bn" #bn "_tm" #tm \
+                       "_tn" #tn)]] [[kernel]] void                         \
+  naive_conv_2d<itype, bm, bn, tm, tn>(                                     \
+      const device itype* in [[buffer(0)]],                                 \
+      const device itype* wt [[buffer(1)]],                                 \
+      device itype* out [[buffer(2)]],                                      \
+      const constant MLXConvParams<2>& params [[buffer(3)]],                \
+      uint3 tid [[threadgroup_position_in_grid]],                           \
+      uint3 lid [[thread_position_in_threadgroup]],                         \
+      uint simd_gid [[simdgroup_index_in_threadgroup]],                     \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
 #define instantiate_naive_conv_2d_blocks(name, itype) \
-    instantiate_naive_conv_2d(name, itype, 16, 8, 4, 4) \
-    instantiate_naive_conv_2d(name, itype, 16, 8, 2, 4) 
+  instantiate_naive_conv_2d(name, itype, 16, 8, 4, 4) \
+      instantiate_naive_conv_2d(name, itype, 16, 8, 2, 4)
 
 instantiate_naive_conv_2d_blocks(float32, float);
 instantiate_naive_conv_2d_blocks(float16, half);
@@ -207,9 +278,7 @@ instantiate_naive_conv_2d_blocks(bfloat16, bfloat16_t);
 ///////////////////////////////////////////////////////////////////////////////
 
 template <int M, int R, int S>
-struct WinogradTransforms {
-
-};
+struct WinogradTransforms {};
 
 template <>
 struct WinogradTransforms<6, 3, 8> {
@@ -218,36 +287,36 @@ struct WinogradTransforms<6, 3, 8> {
   MLX_MTL_CONST int IN_TILE_SIZE = OUT_TILE_SIZE + FILTER_SIZE - 1;
   MLX_MTL_CONST int SIMD_MATRIX_SIZE = 8;
   MLX_MTL_CONST float in_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
-    { 1.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f},
-    { 0.00f,  1.00f, -1.00f,  0.50f, -0.50f,  2.00f, -2.00f, -1.00f},
-    {-5.25f,  1.00f,  1.00f,  0.25f,  0.25f,  4.00f,  4.00f,  0.00f},
-    { 0.00f, -4.25f,  4.25f, -2.50f,  2.50f, -2.50f,  2.50f,  5.25f},
-    { 5.25f, -4.25f, -4.25f, -1.25f, -1.25f, -5.00f, -5.00f,  0.00f},
-    { 0.00f,  1.00f, -1.00f,  2.00f, -2.00f,  0.50f, -0.50f, -5.25f},
-    {-1.00f,  1.00f,  1.00f,  1.00f,  1.00f,  1.00f,  1.00f,  0.00f},
-    { 0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  1.00f},
+      {1.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f},
+      {0.00f, 1.00f, -1.00f, 0.50f, -0.50f, 2.00f, -2.00f, -1.00f},
+      {-5.25f, 1.00f, 1.00f, 0.25f, 0.25f, 4.00f, 4.00f, 0.00f},
+      {0.00f, -4.25f, 4.25f, -2.50f, 2.50f, -2.50f, 2.50f, 5.25f},
+      {5.25f, -4.25f, -4.25f, -1.25f, -1.25f, -5.00f, -5.00f, 0.00f},
+      {0.00f, 1.00f, -1.00f, 2.00f, -2.00f, 0.50f, -0.50f, -5.25f},
+      {-1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 0.00f},
+      {0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 1.00f},
   };
 
   MLX_MTL_CONST float out_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
-    { 1.00f,  0.00f,  0.00f,   0.00f,    0.00f,    0.00f},
-    { 1.00f,  1.00f,  1.00f,   1.00f,    1.00f,    1.00f},
-    { 1.00f, -1.00f,  1.00f,  -1.00f,    1.00f,   -1.00f},
-    { 1.00f,  2.00f,  4.00f,   8.00f,   16.00f,   32.00f},
-    { 1.00f, -2.00f,  4.00f,  -8.00f,   16.00f,  -32.00f},
-    { 1.00f,  0.50f,  0.25f,  0.125f,   0.0625f,   0.03125f},
-    { 1.00f, -0.50f,  0.25f, -0.125f,   0.0625f,  -0.03125f},
-    { 0.00f,  0.00f,  0.00f,  0.00f,      0.00f,   1.00f},
+      {1.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f},
+      {1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f},
+      {1.00f, -1.00f, 1.00f, -1.00f, 1.00f, -1.00f},
+      {1.00f, 2.00f, 4.00f, 8.00f, 16.00f, 32.00f},
+      {1.00f, -2.00f, 4.00f, -8.00f, 16.00f, -32.00f},
+      {1.00f, 0.50f, 0.25f, 0.125f, 0.0625f, 0.03125f},
+      {1.00f, -0.50f, 0.25f, -0.125f, 0.0625f, -0.03125f},
+      {0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 1.00f},
   };
 
   MLX_MTL_CONST float wt_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
-    {      1.00,       0.00,       0.00},
-    { -2.0/9.00,  -2.0/9.00,  -2.0/9.00},
-    { -2.0/9.00,   2.0/9.00,  -2.0/9.00},
-    {  1.0/90.0,   1.0/45.0,   2.0/45.0},
-    {  1.0/90.0,  -1.0/45.0,   2.0/45.0},
-    { 32.0/45.0,  16.0/45.0,   8.0/45.0},
-    { 32.0/45.0, -16.0/45.0,   8.0/45.0},
-    {      0.00,       0.00,       1.00},
+      {1.00, 0.00, 0.00},
+      {-2.0 / 9.00, -2.0 / 9.00, -2.0 / 9.00},
+      {-2.0 / 9.00, 2.0 / 9.00, -2.0 / 9.00},
+      {1.0 / 90.0, 1.0 / 45.0, 2.0 / 45.0},
+      {1.0 / 90.0, -1.0 / 45.0, 2.0 / 45.0},
+      {32.0 / 45.0, 16.0 / 45.0, 8.0 / 45.0},
+      {32.0 / 45.0, -16.0 / 45.0, 8.0 / 45.0},
+      {0.00, 0.00, 1.00},
   };
 };
 
@@ -255,12 +324,9 @@ constant constexpr const float WinogradTransforms<6, 3, 8>::wt_transform[8][8];
 constant constexpr const float WinogradTransforms<6, 3, 8>::in_transform[8][8];
 constant constexpr const float WinogradTransforms<6, 3, 8>::out_transform[8][8];
 
-template <typename T,
-          int BC = 32,
-          int BO = 4,
-          int M = 6,
-          int R = 3>
-[[kernel, max_total_threads_per_threadgroup(BO * 32)]] void winograd_conv_2d_weight_transform(
+template <typename T, int BC = 32, int BO = 4, int M = 6, int R = 3>
+[[kernel, max_total_threads_per_threadgroup(BO * 32)]] void
+winograd_conv_2d_weight_transform(
     const device T* wt_in [[buffer(0)]],
     device T* wt_out [[buffer(1)]],
     const constant int& C [[buffer(2)]],
@@ -268,7 +334,6 @@ template <typename T,
     uint tid [[threadgroup_position_in_grid]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]]) {
-
   using WGT = WinogradTransforms<M, R, 8>;
 
   // Get lane position in simdgroup
@@ -288,35 +353,37 @@ template <typename T,
 
   // Move to the correct output filter
   size_t ko = BO * tid + simd_group_id;
-  wt_in  += ko * R * R * C;
+  wt_in += ko * R * R * C;
 
   // wt_out is stored transposed (A x A x C x O)
   short ohw_0 = sm * 8 + sn;
   short ohw_1 = sm * 8 + sn + 1;
   device T* wt_out_0 = wt_out + ohw_0 * C * O + ko;
-  device T* wt_out_1 = wt_out + ohw_1 * C * O + ko; 
+  device T* wt_out_1 = wt_out + ohw_1 * C * O + ko;
 
   // Prepare shared memory
   threadgroup T Ws[BO][R][R][BC];
 
   // Loop over C
-  for(int bc = 0; bc < C; bc += BC) {
+  for (int bc = 0; bc < C; bc += BC) {
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Read into shared memory
-    for(int kh = 0; kh < R; ++kh) {
-      for(int kw = 0; kw < R; ++kw) {
-        for(int kc = simd_lane_id; kc < BC; kc += 32) {
+    for (int kh = 0; kh < R; ++kh) {
+      for (int kw = 0; kw < R; ++kw) {
+        for (int kc = simd_lane_id; kc < BC; kc += 32) {
           Ws[simd_group_id][kh][kw][kc] = wt_in[kh * R * C + kw * C + kc];
         }
       }
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
-    // Do transform and store the result 
-    for(int c = 0; c < BC; ++c) {
+    // Do transform and store the result
+    for (int c = 0; c < BC; ++c) {
       simdgroup_matrix<T, 8, 8> g;
-      g.thread_elements()[0] = sm < R && sn < R ? Ws[simd_group_id][sm][sn][c] : T(0);
-      g.thread_elements()[1] = sm < R && sn + 1 < R ? Ws[simd_group_id][sm][sn + 1][c] : T(0);
+      g.thread_elements()[0] =
+          sm < R && sn < R ? Ws[simd_group_id][sm][sn][c] : T(0);
+      g.thread_elements()[1] =
+          sm < R && sn + 1 < R ? Ws[simd_group_id][sm][sn + 1][c] : T(0);
 
       simdgroup_matrix<T, 8, 8> g_out = (G * g) * Gt;
       wt_out_0[c * O] = g_out.thread_elements()[0];
@@ -327,27 +394,23 @@ template <typename T,
     wt_out_0 += BC * O;
     wt_out_1 += BC * O;
   }
-
 }
 
 #define instantiate_winograd_conv_2d_weight_transform_base(name, itype, bc) \
-  template [[host_name("winograd_conv_2d_weight_transform_" #name "_bc" #bc)]]\
-  [[kernel]] void winograd_conv_2d_weight_transform<itype, bc>(\
-      const device itype* wt_in [[buffer(0)]],\
-      device itype* wt_out [[buffer(1)]],\
-      const constant int& C [[buffer(2)]],\
-      const constant int& O [[buffer(3)]],\
-      uint tid [[threadgroup_position_in_grid]],\
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+  template [[host_name("winograd_conv_2d_weight_transform_" #name           \
+                       "_bc" #bc)]] [[kernel]] void                         \
+  winograd_conv_2d_weight_transform<itype, bc>(                             \
+      const device itype* wt_in [[buffer(0)]],                              \
+      device itype* wt_out [[buffer(1)]],                                   \
+      const constant int& C [[buffer(2)]],                                  \
+      const constant int& O [[buffer(3)]],                                  \
+      uint tid [[threadgroup_position_in_grid]],                            \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],                \
       uint simd_lane_id [[thread_index_in_simdgroup]]);
 
-template <typename T,
-          int BC,
-          int WM,
-          int WN,
-          int M = 6,
-          int R = 3>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void winograd_conv_2d_input_transform(
+template <typename T, int BC, int WM, int WN, int M = 6, int R = 3>
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
+winograd_conv_2d_input_transform(
     const device T* inp_in [[buffer(0)]],
     device T* inp_out [[buffer(1)]],
     const constant MLXConvParams<2>& params [[buffer(2)]],
@@ -356,7 +419,6 @@ template <typename T,
     uint3 tgp_per_grid [[threadgroups_per_grid]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]]) {
-
   (void)lid;
 
   using WGT = WinogradTransforms<M, R, 8>;
@@ -387,46 +449,48 @@ template <typename T,
   int bw = M * tid.x + kw;
 
   // Move to the correct input tile
-  inp_in += tid.z * params.in_strides[0] 
-             + bh * params.in_strides[1]
-             + bw * params.in_strides[2];
+  inp_in += tid.z * params.in_strides[0] + bh * params.in_strides[1] +
+      bw * params.in_strides[2];
 
-  // Pre compute strides 
+  // Pre compute strides
   int jump_in[TH][TW];
 
-  for(int h = 0; h < TH; h++) {
-    for(int w = 0; w < TW; w++) {
-       jump_in[h][w] = h * params.in_strides[1] + w * params.in_strides[2];
+  for (int h = 0; h < TH; h++) {
+    for (int w = 0; w < TW; w++) {
+      jump_in[h][w] = h * params.in_strides[1] + w * params.in_strides[2];
     }
   }
 
   // inp_out is stored interleaved (A x A x tiles x C)
   size_t N_TILES = tgp_per_grid.x * tgp_per_grid.y * tgp_per_grid.z;
-  size_t tile_id = tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
+  size_t tile_id =
+      tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
   size_t ohw_0 = sm * 8 + sn;
   size_t ohw_1 = sm * 8 + sn + 1;
-  device T* inp_out_0 = inp_out + ohw_0 * N_TILES * params.C + tile_id * params.C;
-  device T* inp_out_1 = inp_out + ohw_1 * N_TILES * params.C + tile_id * params.C;
+  device T* inp_out_0 =
+      inp_out + ohw_0 * N_TILES * params.C + tile_id * params.C;
+  device T* inp_out_1 =
+      inp_out + ohw_1 * N_TILES * params.C + tile_id * params.C;
 
   // Prepare shared memory
   threadgroup T Is[A][A][BC];
 
   // Loop over C
-  for(int bc = 0; bc < params.C; bc += BC) {
+  for (int bc = 0; bc < params.C; bc += BC) {
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Read into shared memory
-    for(int h = 0; h < TH; h++) {
-      for(int w = 0; w < TW; w++) {
+    for (int h = 0; h < TH; h++) {
+      for (int w = 0; w < TW; w++) {
         const device T* in_ptr = inp_in + jump_in[h][w];
-        for(int c = simd_lane_id; c < BC; c += 32) {
+        for (int c = simd_lane_id; c < BC; c += 32) {
           Is[kh + h][kw + w][c] = in_ptr[c];
         }
       }
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
-    // Do transform and store the result 
-    for(int c = simd_group_id; c < BC; c += N_SIMD_GROUPS) {
+    // Do transform and store the result
+    for (int c = simd_group_id; c < BC; c += N_SIMD_GROUPS) {
       simdgroup_matrix<T, 8, 8> I;
       I.thread_elements()[0] = Is[sm][sn][c];
       I.thread_elements()[1] = Is[sm][sn + 1][c];
@@ -440,28 +504,24 @@ template <typename T,
     inp_out_0 += BC;
     inp_out_1 += BC;
   }
-
 }
 
 #define instantiate_winograd_conv_2d_input_transform(name, itype, bc) \
-  template [[host_name("winograd_conv_2d_input_transform_" #name "_bc" #bc)]]\
-  [[kernel]] void winograd_conv_2d_input_transform<itype, bc, 2, 2>(\
-      const device itype* inp_in [[buffer(0)]],\
-      device itype* inp_out [[buffer(1)]],\
-      const constant MLXConvParams<2>& params [[buffer(2)]],\
-      uint3 tid [[threadgroup_position_in_grid]],\
-      uint3 lid [[thread_position_in_threadgroup]],\
-      uint3 tgp_per_grid [[threadgroups_per_grid]],\
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+  template [[host_name("winograd_conv_2d_input_transform_" #name      \
+                       "_bc" #bc)]] [[kernel]] void                   \
+  winograd_conv_2d_input_transform<itype, bc, 2, 2>(                  \
+      const device itype* inp_in [[buffer(0)]],                       \
+      device itype* inp_out [[buffer(1)]],                            \
+      const constant MLXConvParams<2>& params [[buffer(2)]],          \
+      uint3 tid [[threadgroup_position_in_grid]],                     \
+      uint3 lid [[thread_position_in_threadgroup]],                   \
+      uint3 tgp_per_grid [[threadgroups_per_grid]],                   \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],          \
       uint simd_lane_id [[thread_index_in_simdgroup]]);
 
-template <typename T,
-          int BO,
-          int WM,
-          int WN,
-          int M = 6,
-          int R = 3>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void winograd_conv_2d_output_transform(
+template <typename T, int BO, int WM, int WN, int M = 6, int R = 3>
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
+winograd_conv_2d_output_transform(
     const device T* out_in [[buffer(0)]],
     device T* out_out [[buffer(1)]],
     const constant MLXConvParams<2>& params [[buffer(2)]],
@@ -470,7 +530,6 @@ template <typename T,
     uint3 tgp_per_grid [[threadgroups_per_grid]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]]) {
-
   (void)lid;
 
   using WGT = WinogradTransforms<M, R, 8>;
@@ -503,57 +562,59 @@ template <typename T,
   int bw = M * tid.x + kw;
 
   // Move to the correct input tile
-  out_out += tid.z * params.out_strides[0] 
-              + bh * params.out_strides[1]
-              + bw * params.out_strides[2];
+  out_out += tid.z * params.out_strides[0] + bh * params.out_strides[1] +
+      bw * params.out_strides[2];
 
-  // Pre compute strides 
+  // Pre compute strides
   int jump_in[TH][TW];
 
-  for(int h = 0; h < TH; h++) {
-    for(int w = 0; w < TW; w++) {
+  for (int h = 0; h < TH; h++) {
+    for (int w = 0; w < TW; w++) {
       bool valid = ((bh + h) < params.oS[0]) && ((bw + w) < params.oS[1]);
-      jump_in[h][w] = valid ? h * params.out_strides[1] + w * params.out_strides[2] : -1;
+      jump_in[h][w] =
+          valid ? h * params.out_strides[1] + w * params.out_strides[2] : -1;
     }
   }
 
   // out_in is stored interleaved (A x A x tiles x O)
   size_t N_TILES = tgp_per_grid.x * tgp_per_grid.y * tgp_per_grid.z;
-  size_t tile_id = tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
+  size_t tile_id =
+      tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
   size_t ohw_0 = sm * 8 + sn;
   size_t ohw_1 = sm * 8 + sn + 1;
-  const device T* out_in_0 = out_in + ohw_0 * N_TILES * params.O + tile_id * params.O;
-  const device T* out_in_1 = out_in + ohw_1 * N_TILES * params.O + tile_id * params.O;
+  const device T* out_in_0 =
+      out_in + ohw_0 * N_TILES * params.O + tile_id * params.O;
+  const device T* out_in_1 =
+      out_in + ohw_1 * N_TILES * params.O + tile_id * params.O;
 
   // Prepare shared memory
   threadgroup T Os[M][M][BO];
 
   // Loop over O
-  for(int bo = 0; bo < params.O; bo += BO) {
-
+  for (int bo = 0; bo < params.O; bo += BO) {
     threadgroup_barrier(mem_flags::mem_threadgroup);
-    // Do transform and store the result 
-    for(int c = simd_group_id; c < BO; c += N_SIMD_GROUPS) {
+    // Do transform and store the result
+    for (int c = simd_group_id; c < BO; c += N_SIMD_GROUPS) {
       simdgroup_matrix<T, 8, 8> O_mat;
       O_mat.thread_elements()[0] = out_in_0[c];
       O_mat.thread_elements()[1] = out_in_1[c];
 
       simdgroup_matrix<T, 8, 8> O_out = (Bt * (O_mat * B));
-      if((sm < M) && (sn < M)) {
+      if ((sm < M) && (sn < M)) {
         Os[sm][sn][c] = O_out.thread_elements()[0];
       }
-      if((sm < M) && ((sn + 1) < M)) {
+      if ((sm < M) && ((sn + 1) < M)) {
         Os[sm][sn + 1][c] = O_out.thread_elements()[1];
       }
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Read out from shared memory
-    for(int h = 0; h < TH; h++) {
-      for(int w = 0; w < TW; w++) {
-        if(jump_in[h][w] >= 0) {
+    for (int h = 0; h < TH; h++) {
+      for (int w = 0; w < TW; w++) {
+        if (jump_in[h][w] >= 0) {
           device T* out_ptr = out_out + jump_in[h][w];
-          for(int c = simd_lane_id; c < BO; c += 32) {
+          for (int c = simd_lane_id; c < BO; c += 32) {
             out_ptr[c] = Os[kh + h][kw + w][c];
           }
         }
@@ -564,25 +625,27 @@ template <typename T,
     out_in_0 += BO;
     out_in_1 += BO;
   }
-
 }
 
 #define instantiate_winograd_conv_2d_output_transform(name, itype, bo) \
-  template [[host_name("winograd_conv_2d_output_transform_" #name "_bo" #bo)]]\
-  [[kernel]] void winograd_conv_2d_output_transform<itype, bo, 2, 2>(\
-      const device itype* out_in [[buffer(0)]],\
-      device itype* out_out [[buffer(1)]],\
-      const constant MLXConvParams<2>& params [[buffer(2)]],\
-      uint3 tid [[threadgroup_position_in_grid]],\
-      uint3 lid [[thread_position_in_threadgroup]],\
-      uint3 tgp_per_grid [[threadgroups_per_grid]],\
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+  template [[host_name("winograd_conv_2d_output_transform_" #name      \
+                       "_bo" #bo)]] [[kernel]] void                    \
+  winograd_conv_2d_output_transform<itype, bo, 2, 2>(                  \
+      const device itype* out_in [[buffer(0)]],                        \
+      device itype* out_out [[buffer(1)]],                             \
+      const constant MLXConvParams<2>& params [[buffer(2)]],           \
+      uint3 tid [[threadgroup_position_in_grid]],                      \
+      uint3 lid [[thread_position_in_threadgroup]],                    \
+      uint3 tgp_per_grid [[threadgroups_per_grid]],                    \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],           \
       uint simd_lane_id [[thread_index_in_simdgroup]]);
 
-#define instantiate_winograd_conv_2d(name, itype) \
+// clang-format off
+#define instantiate_winograd_conv_2d(name, itype)                     \
   instantiate_winograd_conv_2d_weight_transform_base(name, itype, 32) \
-  instantiate_winograd_conv_2d_input_transform(name, itype, 32) \
-  instantiate_winograd_conv_2d_output_transform(name, itype, 32)
+  instantiate_winograd_conv_2d_input_transform(name, itype, 32)       \
+  instantiate_winograd_conv_2d_output_transform(name, itype, 32) // clang-format on
 
+// clang-format off
 instantiate_winograd_conv_2d(float32, float);
-instantiate_winograd_conv_2d(float16, half);
+instantiate_winograd_conv_2d(float16, half); // clang-format on

mlx/backend/metal/kernels/copy.metal

@@ -49,7 +49,8 @@ template <typename T, typename U>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto src_idx = elem_to_loc_3(index, src_strides);
-  int64_t dst_idx = index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
+  int64_t dst_idx =
+      index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
@@ -62,7 +63,8 @@ template <typename T, typename U, int DIM>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto src_idx = elem_to_loc_nd<DIM>(index, src_shape, src_strides);
-  int64_t dst_idx = index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
+  int64_t dst_idx =
+      index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
@@ -76,7 +78,8 @@ template <typename T, typename U>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto src_idx = elem_to_loc(index, src_shape, src_strides, ndim);
-  int64_t dst_idx = index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
+  int64_t dst_idx =
+      index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
@@ -143,116 +146,110 @@ template <typename T, typename U>
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
-#define instantiate_copy(name, itype, otype, ctype) \
-  template [[host_name(name)]] \
-  [[kernel]] void copy_##ctype<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
+#define instantiate_copy(name, itype, otype, ctype)                        \
+  template [[host_name(name)]] [[kernel]] void copy_##ctype<itype, otype>( \
+      device const itype* src [[buffer(0)]],                               \
+      device otype* dst [[buffer(1)]],                                     \
       uint index [[thread_position_in_grid]]);
 
-#define instantiate_copy_g_dim(name, itype, otype, dims) \
-  template [[host_name(name "_" #dims)]] \
-  [[kernel]] void copy_g_nd<itype, otype, dims>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int* src_shape [[buffer(2)]], \
-      constant const int64_t* src_strides [[buffer(3)]], \
-      uint3 index [[thread_position_in_grid]], \
-      uint3 grid_dim [[threads_per_grid]]); \
-  template [[host_name("g" name "_" #dims)]] \
-  [[kernel]] void copy_gg_nd<itype, otype, dims>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int* src_shape [[buffer(2)]], \
-      constant const int64_t* src_strides [[buffer(3)]], \
-      constant const int64_t* dst_strides [[buffer(4)]], \
+#define instantiate_copy_g_dim(name, itype, otype, dims)     \
+  template [[host_name(name "_" #dims)]] [[kernel]] void     \
+  copy_g_nd<itype, otype, dims>(                             \
+      device const itype* src [[buffer(0)]],                 \
+      device otype* dst [[buffer(1)]],                       \
+      constant const int* src_shape [[buffer(2)]],           \
+      constant const int64_t* src_strides [[buffer(3)]],     \
+      uint3 index [[thread_position_in_grid]],               \
+      uint3 grid_dim [[threads_per_grid]]);                  \
+  template [[host_name("g" name "_" #dims)]] [[kernel]] void \
+  copy_gg_nd<itype, otype, dims>(                            \
+      device const itype* src [[buffer(0)]],                 \
+      device otype* dst [[buffer(1)]],                       \
+      constant const int* src_shape [[buffer(2)]],           \
+      constant const int64_t* src_strides [[buffer(3)]],     \
+      constant const int64_t* dst_strides [[buffer(4)]],     \
       uint3 index [[thread_position_in_grid]]);
 
+#define instantiate_copy_g_nd(name, itype, otype)                             \
+  template [[host_name(name "_1")]] [[kernel]] void copy_g_nd1<itype, otype>( \
+      device const itype* src [[buffer(0)]],                                  \
+      device otype* dst [[buffer(1)]],                                        \
+      constant const int64_t& src_stride [[buffer(3)]],                       \
+      uint index [[thread_position_in_grid]]);                                \
+  template [[host_name(name "_2")]] [[kernel]] void copy_g_nd2<itype, otype>( \
+      device const itype* src [[buffer(0)]],                                  \
+      device otype* dst [[buffer(1)]],                                        \
+      constant const int64_t* src_strides [[buffer(3)]],                      \
+      uint2 index [[thread_position_in_grid]],                                \
+      uint2 grid_dim [[threads_per_grid]]);                                   \
+  template [[host_name(name "_3")]] [[kernel]] void copy_g_nd3<itype, otype>( \
+      device const itype* src [[buffer(0)]],                                  \
+      device otype* dst [[buffer(1)]],                                        \
+      constant const int64_t* src_strides [[buffer(3)]],                      \
+      uint3 index [[thread_position_in_grid]],                                \
+      uint3 grid_dim [[threads_per_grid]]);                                   \
+  template [[host_name("g" name "_1")]] [[kernel]] void                       \
+  copy_gg_nd1<itype, otype>(                                                  \
+      device const itype* src [[buffer(0)]],                                  \
+      device otype* dst [[buffer(1)]],                                        \
+      constant const int64_t& src_stride [[buffer(3)]],                       \
+      constant const int64_t& dst_stride [[buffer(4)]],                       \
+      uint index [[thread_position_in_grid]]);                                \
+  template [[host_name("g" name "_2")]] [[kernel]] void                       \
+  copy_gg_nd2<itype, otype>(                                                  \
+      device const itype* src [[buffer(0)]],                                  \
+      device otype* dst [[buffer(1)]],                                        \
+      constant const int64_t* src_strides [[buffer(3)]],                      \
+      constant const int64_t* dst_strides [[buffer(4)]],                      \
+      uint2 index [[thread_position_in_grid]]);                               \
+  template [[host_name("g" name "_3")]] [[kernel]] void                       \
+  copy_gg_nd3<itype, otype>(                                                  \
+      device const itype* src [[buffer(0)]],                                  \
+      device otype* dst [[buffer(1)]],                                        \
+      constant const int64_t* src_strides [[buffer(3)]],                      \
+      constant const int64_t* dst_strides [[buffer(4)]],                      \
+      uint3 index [[thread_position_in_grid]]);                               \
+  instantiate_copy_g_dim(name, itype, otype, 4)                               \
+      instantiate_copy_g_dim(name, itype, otype, 5)
 
-#define instantiate_copy_g_nd(name, itype, otype) \
-  template [[host_name(name "_1")]] \
-  [[kernel]] void copy_g_nd1<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int64_t& src_stride [[buffer(3)]], \
-      uint index [[thread_position_in_grid]]); \
-  template [[host_name(name "_2")]] \
-  [[kernel]] void copy_g_nd2<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int64_t* src_strides [[buffer(3)]], \
-      uint2 index [[thread_position_in_grid]], \
-      uint2 grid_dim [[threads_per_grid]]); \
-  template [[host_name(name "_3")]] \
-  [[kernel]] void copy_g_nd3<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int64_t* src_strides [[buffer(3)]], \
-      uint3 index [[thread_position_in_grid]], \
-      uint3 grid_dim [[threads_per_grid]]); \
-  template [[host_name("g" name "_1")]] \
-  [[kernel]] void copy_gg_nd1<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int64_t& src_stride [[buffer(3)]], \
-      constant const int64_t& dst_stride [[buffer(4)]], \
-      uint index [[thread_position_in_grid]]); \
-  template [[host_name("g" name "_2")]] \
-  [[kernel]] void copy_gg_nd2<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int64_t* src_strides [[buffer(3)]], \
-      constant const int64_t* dst_strides [[buffer(4)]], \
-      uint2 index [[thread_position_in_grid]]); \
-  template [[host_name("g" name "_3")]] \
-  [[kernel]] void copy_gg_nd3<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int64_t* src_strides [[buffer(3)]], \
-      constant const int64_t* dst_strides [[buffer(4)]], \
-      uint3 index [[thread_position_in_grid]]); \
-  instantiate_copy_g_dim(name, itype, otype, 4) \
-  instantiate_copy_g_dim(name, itype, otype, 5)
-
-
-#define instantiate_copy_g(name, itype, otype) \
-  template [[host_name(name)]] \
-  [[kernel]] void copy_g<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int* src_shape [[buffer(2)]], \
-      constant const int64_t* src_strides  [[buffer(3)]], \
-      constant const int& ndim [[buffer(5)]], \
-      uint3 index [[thread_position_in_grid]], \
-      uint3 grid_dim [[threads_per_grid]]); \
-  template [[host_name("g" name)]] \
-  [[kernel]] void copy_gg<itype, otype>( \
-      device const itype* src [[buffer(0)]], \
-      device otype* dst [[buffer(1)]], \
-      constant const int* src_shape [[buffer(2)]], \
-      constant const int64_t* src_strides [[buffer(3)]], \
-      constant const int64_t* dst_strides [[buffer(4)]], \
-      constant const int& ndim [[buffer(5)]], \
+#define instantiate_copy_g(name, itype, otype)                            \
+  template [[host_name(name)]] [[kernel]] void copy_g<itype, otype>(      \
+      device const itype* src [[buffer(0)]],                              \
+      device otype* dst [[buffer(1)]],                                    \
+      constant const int* src_shape [[buffer(2)]],                        \
+      constant const int64_t* src_strides [[buffer(3)]],                  \
+      constant const int& ndim [[buffer(5)]],                             \
+      uint3 index [[thread_position_in_grid]],                            \
+      uint3 grid_dim [[threads_per_grid]]);                               \
+  template [[host_name("g" name)]] [[kernel]] void copy_gg<itype, otype>( \
+      device const itype* src [[buffer(0)]],                              \
+      device otype* dst [[buffer(1)]],                                    \
+      constant const int* src_shape [[buffer(2)]],                        \
+      constant const int64_t* src_strides [[buffer(3)]],                  \
+      constant const int64_t* dst_strides [[buffer(4)]],                  \
+      constant const int& ndim [[buffer(5)]],                             \
       uint3 index [[thread_position_in_grid]]);
 
-#define instantiate_copy_all(tname, itype, otype) \
+// clang-format off
+#define instantiate_copy_all(tname, itype, otype)   \
   instantiate_copy("scopy" #tname, itype, otype, s) \
   instantiate_copy("vcopy" #tname, itype, otype, v) \
-  instantiate_copy_g("gcopy" #tname, itype, otype) \
-  instantiate_copy_g_nd("gcopy" #tname, itype, otype)
+  instantiate_copy_g("gcopy" #tname, itype, otype)  \
+  instantiate_copy_g_nd("gcopy" #tname, itype, otype) // clang-format on
 
-#define instantiate_copy_itype(itname, itype) \
-  instantiate_copy_all(itname ##bool_, itype, bool) \
-  instantiate_copy_all(itname ##uint8, itype, uint8_t) \
-  instantiate_copy_all(itname ##uint16, itype, uint16_t) \
-  instantiate_copy_all(itname ##uint32, itype, uint32_t) \
-  instantiate_copy_all(itname ##uint64, itype, uint64_t) \
-  instantiate_copy_all(itname ##int8, itype, int8_t) \
-  instantiate_copy_all(itname ##int16, itype, int16_t) \
-  instantiate_copy_all(itname ##int32, itype, int32_t) \
-  instantiate_copy_all(itname ##int64, itype, int64_t) \
-  instantiate_copy_all(itname ##float16, itype, half) \
-  instantiate_copy_all(itname ##float32, itype, float) \
+// clang-format off
+#define instantiate_copy_itype(itname, itype)                \
+  instantiate_copy_all(itname ##bool_, itype, bool)          \
+  instantiate_copy_all(itname ##uint8, itype, uint8_t)       \
+  instantiate_copy_all(itname ##uint16, itype, uint16_t)     \
+  instantiate_copy_all(itname ##uint32, itype, uint32_t)     \
+  instantiate_copy_all(itname ##uint64, itype, uint64_t)     \
+  instantiate_copy_all(itname ##int8, itype, int8_t)         \
+  instantiate_copy_all(itname ##int16, itype, int16_t)       \
+  instantiate_copy_all(itname ##int32, itype, int32_t)       \
+  instantiate_copy_all(itname ##int64, itype, int64_t)       \
+  instantiate_copy_all(itname ##float16, itype, half)        \
+  instantiate_copy_all(itname ##float32, itype, float)       \
   instantiate_copy_all(itname ##bfloat16, itype, bfloat16_t) \
   instantiate_copy_all(itname ##complex64, itype, complex64_t)
 
@@ -268,4 +265,4 @@ instantiate_copy_itype(int64, int64_t)
 instantiate_copy_itype(float16, half)
 instantiate_copy_itype(float32, float)
 instantiate_copy_itype(bfloat16, bfloat16_t)
-instantiate_copy_itype(complex64, complex64_t)
+instantiate_copy_itype(complex64, complex64_t) // clang-format on

mlx/backend/metal/kernels/expm1f.h

@@ -0,0 +1,89 @@
+// Copyright © 2023 Apple Inc.
+
+#pragma once
+
+#include <metal_math>
+
+// Original license copied below:
+//  Copyright (c) 2015-2023 Norbert Juffa
+//  All rights reserved.
+//
+//  Redistribution and use in source and binary forms, with or without
+//  modification, are permitted provided that the following conditions
+//  are met:
+//
+//  1. Redistributions of source code must retain the above copyright
+//     notice, this list of conditions and the following disclaimer.
+//
+//  2. Redistributions in binary form must reproduce the above copyright
+//     notice, this list of conditions and the following disclaimer in the
+//     documentation and/or other materials provided with the distribution.
+//
+//  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+//  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+//  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+//  A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+//  HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+//  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+//  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+//  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+//  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+//  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+//  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+/* Compute exponential base e minus 1. Maximum ulp error = 0.997458
+
+   i = rint(a/log(2)), f = a-i*log(2). Then expm1(a) = 2**i * (expm1(f)+1) - 1.
+   Compute r = expm1(f). Then expm1(a)= 2 * (0.5 * 2**i * r + 0.5 * 2**i - 0.5).
+   With t = 0.5*2**i, expm1(a) = 2*(r * t + t-0.5). However, for best accuracy,
+   when i == 1, expm1(a)= 2*(r + 0.5), and when i == 0, expm1(a) = r.
+
+   NOTE: Scale factor b is only applied if i < 0 or i > 1 (should be power of 2)
+*/
+float expm1f_scaled_unchecked(float a, float b) {
+  float f, j, r, s, t, u, v, x, y;
+  int i;
+
+  // exp(a) = 2**i * exp(f); i = rintf (a / log(2))
+  j = fma(1.442695f, a, 12582912.f); // 0x1.715476p0, 0x1.8p23
+  j = j - 12582912.0f; // 0x1.8p23
+  i = (int)j;
+  f = fma(j, -6.93145752e-1f, a);
+
+  // approximate r = exp(f)-1 on interval [-log(2)/2, +log(2)/2]
+  s = f * f;
+  if (a == 0.0f)
+    s = a; // ensure -0 is passed through
+  // err = 0.997458  ulp1 = 11081805
+  r = 1.97350979e-4f; // 0x1.9de000p-13
+  r = fma(r, f, 1.39309070e-3f); // 0x1.6d30bcp-10
+  r = fma(r, f, 8.33343994e-3f); // 0x1.1111f6p-7
+  r = fma(r, f, 4.16668020e-2f); // 0x1.55559ep-5
+  r = fma(r, f, 1.66666716e-1f); // 0x1.55555cp-3
+  r = fma(r, f, 4.99999970e-1f); // 0x1.fffffep-2
+  u = (j == 1) ? (f + 0.5f) : f;
+  v = fma(r, s, u);
+  s = 0.5f * b;
+  t = ldexp(s, i);
+  y = t - s;
+  x = (t - y) - s; // double-float canonicalization of difference
+  r = fma(v, t, x) + y;
+  r = r + r;
+  if (j == 0)
+    r = v;
+  if (j == 1)
+    r = v + v;
+  return r;
+}
+
+/* Compute exponential base e minus 1. max ulp err = 0.99746 */
+float expm1f(float a) {
+  float r;
+
+  r = expm1f_scaled_unchecked(a, 1.0f);
+  /* handle severe overflow and underflow */
+  if (abs(a - 1.0f) > 88.0f) {
+    r = fma(r, r, -1.0f);
+  }
+  return r;
+}

mlx/backend/metal/kernels/fft.metal

@@ -0,0 +1,199 @@
+// Copyright © 2024 Apple Inc.
+
+// Metal FFT using Stockham's algorithm
+//
+// References:
+// - VkFFT (https://github.com/DTolm/VkFFT)
+// - Eric Bainville's excellent page (http://www.bealto.com/gpu-fft.html)
+
+#include <metal_common>
+#include <metal_math>
+
+#include "mlx/backend/metal/kernels/defines.h"
+#include "mlx/backend/metal/kernels/utils.h"
+
+using namespace metal;
+
+float2 complex_mul(float2 a, float2 b) {
+  float2 c;
+  c.x = a.x * b.x - a.y * b.y;
+  c.y = a.x * b.y + a.y * b.x;
+  return c;
+}
+
+float2 get_twiddle(int k, int p) {
+  float theta = -1.0f * k * M_PI_F / (2 * p);
+
+  float2 twiddle;
+  twiddle.x = metal::fast::cos(theta);
+  twiddle.y = metal::fast::sin(theta);
+  return twiddle;
+}
+
+// single threaded radix2 implemetation
+void radix2(
+    int i,
+    int p,
+    int m,
+    threadgroup float2* read_buf,
+    threadgroup float2* write_buf) {
+  float2 x_0 = read_buf[i];
+  float2 x_1 = read_buf[i + m];
+
+  // The index within this sub-DFT
+  int k = i & (p - 1);
+
+  float2 twiddle = get_twiddle(k, p);
+
+  float2 z = complex_mul(x_1, twiddle);
+
+  float2 y_0 = x_0 + z;
+  float2 y_1 = x_0 - z;
+
+  int j = (i << 1) - k;
+
+  write_buf[j] = y_0;
+  write_buf[j + p] = y_1;
+}
+
+// single threaded radix4 implemetation
+void radix4(
+    int i,
+    int p,
+    int m,
+    threadgroup float2* read_buf,
+    threadgroup float2* write_buf) {
+  float2 x_0 = read_buf[i];
+  float2 x_1 = read_buf[i + m];
+  float2 x_2 = read_buf[i + 2 * m];
+  float2 x_3 = read_buf[i + 3 * m];
+
+  // The index within this sub-DFT
+  int k = i & (p - 1);
+
+  float2 twiddle = get_twiddle(k, p);
+  // e^a * e^b = e^(a + b)
+  float2 twiddle_2 = complex_mul(twiddle, twiddle);
+  float2 twiddle_3 = complex_mul(twiddle, twiddle_2);
+
+  x_1 = complex_mul(x_1, twiddle);
+  x_2 = complex_mul(x_2, twiddle_2);
+  x_3 = complex_mul(x_3, twiddle_3);
+
+  float2 minus_i;
+  minus_i.x = 0;
+  minus_i.y = -1;
+
+  // Hard coded twiddle factors for DFT4
+  float2 z_0 = x_0 + x_2;
+  float2 z_1 = x_0 - x_2;
+  float2 z_2 = x_1 + x_3;
+  float2 z_3 = complex_mul(x_1 - x_3, minus_i);
+
+  float2 y_0 = z_0 + z_2;
+  float2 y_1 = z_1 + z_3;
+  float2 y_2 = z_0 - z_2;
+  float2 y_3 = z_1 - z_3;
+
+  int j = ((i - k) << 2) + k;
+
+  write_buf[j] = y_0;
+  write_buf[j + p] = y_1;
+  write_buf[j + 2 * p] = y_2;
+  write_buf[j + 3 * p] = y_3;
+}
+
+// Each FFT is computed entirely in shared GPU memory.
+//
+// N is decomposed into radix-2 and radix-4 DFTs:
+// e.g. 128 = 2 * 4 * 4 * 4
+//
+// At each step we use n / 4 threads, each performing
+// a single-threaded radix-4 or radix-2 DFT.
+//
+// We provide the number of radix-2 and radix-4
+// steps at compile time for a ~20% performance boost.
+template <size_t n, size_t radix_2_steps, size_t radix_4_steps>
+[[kernel]] void fft(
+    const device float2* in [[buffer(0)]],
+    device float2* out [[buffer(1)]],
+    uint3 thread_position_in_grid [[thread_position_in_grid]],
+    uint3 threads_per_grid [[threads_per_grid]]) {
+  // Index of the DFT in batch
+  int batch_idx = thread_position_in_grid.x * n;
+  // The index in the DFT we're working on
+  int i = thread_position_in_grid.y;
+  // The number of the threads we're using for each DFT
+  int m = threads_per_grid.y;
+
+  // Allocate 2 shared memory buffers for Stockham.
+  // We alternate reading from one and writing to the other at each radix step.
+  threadgroup float2 shared_in[n];
+  threadgroup float2 shared_out[n];
+
+  // Pointers to facilitate Stockham buffer swapping
+  threadgroup float2* read_buf = shared_in;
+  threadgroup float2* write_buf = shared_out;
+  threadgroup float2* tmp;
+
+  // Copy input into shared memory
+  shared_in[i] = in[batch_idx + i];
+  shared_in[i + m] = in[batch_idx + i + m];
+  shared_in[i + 2 * m] = in[batch_idx + i + 2 * m];
+  shared_in[i + 3 * m] = in[batch_idx + i + 3 * m];
+
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  int p = 1;
+
+  for (size_t r = 0; r < radix_2_steps; r++) {
+    radix2(i, p, m * 2, read_buf, write_buf);
+    radix2(i + m, p, m * 2, read_buf, write_buf);
+    p *= 2;
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Stockham switch of buffers
+    tmp = write_buf;
+    write_buf = read_buf;
+    read_buf = tmp;
+  }
+
+  for (size_t r = 0; r < radix_4_steps; r++) {
+    radix4(i, p, m, read_buf, write_buf);
+    p *= 4;
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Stockham switch of buffers
+    tmp = write_buf;
+    write_buf = read_buf;
+    read_buf = tmp;
+  }
+
+  // Copy shared memory to output
+  out[batch_idx + i] = read_buf[i];
+  out[batch_idx + i + m] = read_buf[i + m];
+  out[batch_idx + i + 2 * m] = read_buf[i + 2 * m];
+  out[batch_idx + i + 3 * m] = read_buf[i + 3 * m];
+}
+
+#define instantiate_fft(name, n, radix_2_steps, radix_4_steps)   \
+  template [[host_name("fft_" #name)]] [[kernel]] void           \
+  fft<n, radix_2_steps, radix_4_steps>(                          \
+      const device float2* in [[buffer(0)]],                     \
+      device float2* out [[buffer(1)]],                          \
+      uint3 thread_position_in_grid [[thread_position_in_grid]], \
+      uint3 threads_per_grid [[threads_per_grid]]);
+
+// Explicitly define kernels for each power of 2.
+// clang-format off
+instantiate_fft(4, /* n= */ 4, /* radix_2_steps= */ 0, /* radix_4_steps= */ 1)
+instantiate_fft(8, 8, 1, 1) instantiate_fft(16, 16, 0, 2)
+instantiate_fft(32, 32, 1, 2) instantiate_fft(64, 64, 0, 3)
+instantiate_fft(128, 128, 1, 3) instantiate_fft(256, 256, 0, 4)
+instantiate_fft(512, 512, 1, 4)
+instantiate_fft(1024, 1024, 0, 5)
+// 2048 is the max that will fit into 32KB of threadgroup memory.
+// TODO: implement 4 step FFT for larger n.
+instantiate_fft(2048, 2048, 1, 5) // clang-format on

mlx/backend/metal/kernels/gather.metal

@@ -14,17 +14,16 @@ using namespace metal;
 
 template <typename T, typename IdxT, int NIDX, int IDX_NDIM>
 METAL_FUNC void gather_impl(
-    const device T *src [[buffer(0)]],
-    device T *out [[buffer(1)]],
-    const constant int *src_shape [[buffer(2)]],
-    const constant size_t *src_strides [[buffer(3)]],
+    const device T* src [[buffer(0)]],
+    device T* out [[buffer(1)]],
+    const constant int* src_shape [[buffer(2)]],
+    const constant size_t* src_strides [[buffer(3)]],
     const constant size_t& src_ndim [[buffer(4)]],
-    const constant int *slice_sizes [[buffer(5)]],
-    const constant int *axes [[buffer(6)]],
+    const constant int* slice_sizes [[buffer(5)]],
+    const constant int* axes [[buffer(6)]],
     const thread Indices<IdxT, NIDX>& indices,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-
   auto ind_idx = index.x;
   auto ind_offset = index.y;
 
@@ -43,93 +42,78 @@ METAL_FUNC void gather_impl(
           indices.ndim);
     }
     auto ax = axes[i];
-    auto idx_val = offset_neg_idx(
-        indices.buffers[i][idx_loc], src_shape[ax]);
+    auto idx_val = offset_neg_idx(indices.buffers[i][idx_loc], src_shape[ax]);
     src_idx += idx_val * src_strides[ax];
   }
 
-  auto src_offset = elem_to_loc(
-      ind_offset, slice_sizes, src_strides, src_ndim);
+  auto src_offset = elem_to_loc(ind_offset, slice_sizes, src_strides, src_ndim);
 
   size_t out_idx = index.y + static_cast<size_t>(grid_dim.y) * index.x;
   out[out_idx] = src[src_offset + src_idx];
-
 }
 
-#define make_gather_impl(IDX_ARG, IDX_ARR) \
-template <typename T, typename IdxT, int NIDX, int IDX_NDIM>  \
-[[kernel]] void gather( \
-    const device T *src [[buffer(0)]], \
-    device T *out [[buffer(1)]], \
-    const constant int *src_shape [[buffer(2)]], \
-    const constant size_t *src_strides [[buffer(3)]], \
-    const constant size_t& src_ndim [[buffer(4)]], \
-    const constant int *slice_sizes [[buffer(5)]], \
-    const constant int *axes [[buffer(6)]], \
-    const constant int *idx_shapes [[buffer(7)]], \
-    const constant size_t *idx_strides [[buffer(8)]], \
-    const constant int& idx_ndim [[buffer(9)]], \
-    IDX_ARG(IdxT) \
-    uint2 index [[thread_position_in_grid]], \
-    uint2 grid_dim [[threads_per_grid]]) { \
- \
-  Indices<IdxT, NIDX> idxs{ \
-      {{IDX_ARR()}}, \
-      idx_shapes, \
-      idx_strides, \
-      idx_ndim}; \
- \
-  return gather_impl<T, IdxT, NIDX, IDX_NDIM>( \
-      src, \
-      out, \
-      src_shape, \
-      src_strides, \
-      src_ndim, \
-      slice_sizes, \
-      axes, \
-      idxs, \
-      index, \
-      grid_dim); \
-} 
+#define make_gather_impl(IDX_ARG, IDX_ARR)                     \
+  template <typename T, typename IdxT, int NIDX, int IDX_NDIM> \
+  [[kernel]] void gather(                                      \
+      const device T* src [[buffer(0)]],                       \
+      device T* out [[buffer(1)]],                             \
+      const constant int* src_shape [[buffer(2)]],             \
+      const constant size_t* src_strides [[buffer(3)]],        \
+      const constant size_t& src_ndim [[buffer(4)]],           \
+      const constant int* slice_sizes [[buffer(5)]],           \
+      const constant int* axes [[buffer(6)]],                  \
+      const constant int* idx_shapes [[buffer(7)]],            \
+      const constant size_t* idx_strides [[buffer(8)]],        \
+      const constant int& idx_ndim [[buffer(9)]],              \
+      IDX_ARG(IdxT) uint2 index [[thread_position_in_grid]],   \
+      uint2 grid_dim [[threads_per_grid]]) {                   \
+    Indices<IdxT, NIDX> idxs{                                  \
+        {{IDX_ARR()}}, idx_shapes, idx_strides, idx_ndim};     \
+                                                               \
+    return gather_impl<T, IdxT, NIDX, IDX_NDIM>(               \
+        src,                                                   \
+        out,                                                   \
+        src_shape,                                             \
+        src_strides,                                           \
+        src_ndim,                                              \
+        slice_sizes,                                           \
+        axes,                                                  \
+        idxs,                                                  \
+        index,                                                 \
+        grid_dim);                                             \
+  }
 
-#define make_gather(n) make_gather_impl(IDX_ARG_ ##n, IDX_ARR_ ##n)
+#define make_gather(n) make_gather_impl(IDX_ARG_##n, IDX_ARR_##n)
 
-make_gather(0)
-make_gather(1)
-make_gather(2)
-make_gather(3)
-make_gather(4)
-make_gather(5)
-make_gather(6)
-make_gather(7)
-make_gather(8)
-make_gather(9)
-make_gather(10)
+make_gather(0) make_gather(1) make_gather(2) make_gather(3) make_gather(4)
+    make_gather(5) make_gather(6) make_gather(7) make_gather(8) make_gather(9)
+        make_gather(10)
 
 /////////////////////////////////////////////////////////////////////
 // Gather instantiations
 /////////////////////////////////////////////////////////////////////
 
-#define instantiate_gather6(name, src_t, idx_t, nidx, IDX_ARG, nd, nd_name) \
-template [[host_name("gather" name "_" #nidx "" #nd_name)]] \
-[[kernel]] void gather<src_t, idx_t, nidx, nd>( \
-    const device src_t *src [[buffer(0)]], \
-    device src_t *out [[buffer(1)]], \
-    const constant int *src_shape [[buffer(2)]], \
-    const constant size_t *src_strides [[buffer(3)]], \
-    const constant size_t& src_ndim [[buffer(4)]], \
-    const constant int *slice_sizes [[buffer(5)]], \
-    const constant int *axes [[buffer(6)]], \
-    const constant int *idx_shapes [[buffer(7)]], \
-    const constant size_t *idx_strides [[buffer(8)]], \
-    const constant int& idx_ndim [[buffer(9)]], \
-    IDX_ARG(idx_t) \
-    uint2 index [[thread_position_in_grid]], \
-    uint2 grid_dim [[threads_per_grid]]);
+#define instantiate_gather6(name, src_t, idx_t, nidx, IDX_ARG, nd, nd_name)   \
+  template [[host_name("gather" name "_" #nidx "" #nd_name)]] [[kernel]] void \
+  gather<src_t, idx_t, nidx, nd>(                                             \
+      const device src_t* src [[buffer(0)]],                                  \
+      device src_t* out [[buffer(1)]],                                        \
+      const constant int* src_shape [[buffer(2)]],                            \
+      const constant size_t* src_strides [[buffer(3)]],                       \
+      const constant size_t& src_ndim [[buffer(4)]],                          \
+      const constant int* slice_sizes [[buffer(5)]],                          \
+      const constant int* axes [[buffer(6)]],                                 \
+      const constant int* idx_shapes [[buffer(7)]],                           \
+      const constant size_t* idx_strides [[buffer(8)]],                       \
+      const constant int& idx_ndim [[buffer(9)]],                             \
+      IDX_ARG(idx_t) uint2 index [[thread_position_in_grid]],                 \
+      uint2 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_gather5(name, src_t, idx_t, nidx, nd, nd_name) \
-  instantiate_gather6(name, src_t, idx_t, nidx, IDX_ARG_ ##nidx, nd, nd_name)
+  instantiate_gather6(name, src_t, idx_t, nidx, IDX_ARG_ ##nidx, nd, nd_name) // clang-format on
 
+// clang-format off
 #define instantiate_gather4(name, src_t, idx_t, nidx) \
   instantiate_gather5(name, src_t, idx_t, nidx, 0, _0) \
   instantiate_gather5(name, src_t, idx_t, nidx, 1, _1) \
@@ -148,29 +132,31 @@ instantiate_gather4("int32", int32_t, bool, 0)
 instantiate_gather4("int64", int64_t, bool, 0)
 instantiate_gather4("float16", half, bool, 0)
 instantiate_gather4("float32", float, bool, 0)
-instantiate_gather4("bfloat16", bfloat16_t, bool, 0)
+instantiate_gather4("bfloat16", bfloat16_t, bool, 0) // clang-format on
 
+// clang-format off
 #define instantiate_gather3(name, src_type, ind_type) \
-  instantiate_gather4(name, src_type, ind_type, 1) \
-  instantiate_gather4(name, src_type, ind_type, 2) \
-  instantiate_gather4(name, src_type, ind_type, 3) \
-  instantiate_gather4(name, src_type, ind_type, 4) \
-  instantiate_gather4(name, src_type, ind_type, 5) \
-  instantiate_gather4(name, src_type, ind_type, 6) \
-  instantiate_gather4(name, src_type, ind_type, 7) \
-  instantiate_gather4(name, src_type, ind_type, 8) \
-  instantiate_gather4(name, src_type, ind_type, 9) \
-  instantiate_gather4(name, src_type, ind_type, 10)
+  instantiate_gather4(name, src_type, ind_type, 1)    \
+  instantiate_gather4(name, src_type, ind_type, 2)    \
+  instantiate_gather4(name, src_type, ind_type, 3)    \
+  instantiate_gather4(name, src_type, ind_type, 4)    \
+  instantiate_gather4(name, src_type, ind_type, 5)    \
+  instantiate_gather4(name, src_type, ind_type, 6)    \
+  instantiate_gather4(name, src_type, ind_type, 7)    \
+  instantiate_gather4(name, src_type, ind_type, 8)    \
+  instantiate_gather4(name, src_type, ind_type, 9)    \
+  instantiate_gather4(name, src_type, ind_type, 10) // clang-format on
 
-#define instantiate_gather(name, src_type) \
-  instantiate_gather3(#name "bool_", src_type, bool) \
-  instantiate_gather3(#name "uint8", src_type, uint8_t) \
+// clang-format off
+#define instantiate_gather(name, src_type)                \
+  instantiate_gather3(#name "bool_", src_type, bool)      \
+  instantiate_gather3(#name "uint8", src_type, uint8_t)   \
   instantiate_gather3(#name "uint16", src_type, uint16_t) \
   instantiate_gather3(#name "uint32", src_type, uint32_t) \
   instantiate_gather3(#name "uint64", src_type, uint64_t) \
-  instantiate_gather3(#name "int8", src_type, int8_t) \
-  instantiate_gather3(#name "int16", src_type, int16_t) \
-  instantiate_gather3(#name "int32", src_type, int32_t) \
+  instantiate_gather3(#name "int8", src_type, int8_t)     \
+  instantiate_gather3(#name "int16", src_type, int16_t)   \
+  instantiate_gather3(#name "int32", src_type, int32_t)   \
   instantiate_gather3(#name "int64", src_type, int64_t)
 
 instantiate_gather(bool_, bool)
@@ -184,4 +170,4 @@ instantiate_gather(int32, int32_t)
 instantiate_gather(int64, int64_t)
 instantiate_gather(float16, half)
 instantiate_gather(float32, float)
-instantiate_gather(bfloat16, bfloat16_t)
+instantiate_gather(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/gemv.metal

@@ -1,12 +1,14 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <metal_stdlib>
 #include <metal_simdgroup>
+#include <metal_stdlib>
 
 #include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/defines.h"
 #include "mlx/backend/metal/kernels/utils.h"
 
+#include "mlx/backend/metal/kernels/steel/utils.h"
+
 using namespace metal;
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -18,33 +20,34 @@ using namespace metal;
 MLX_MTL_CONST int SIMD_SIZE = 32;
 
 template <
-  typename T, 
-  const int BM, /* Threadgroup rows (in threads) */
-  const int BN, /* Threadgroup cols (in threads) */
-  const int TM, /* Thread rows (in elements) */
-  const int TN , /* Thread cols (in elements) */ 
-  const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
+    typename T,
+    const int BM, /* Threadgroup rows (in threads) */
+    const int BN, /* Threadgroup cols (in threads) */
+    const int TM, /* Thread rows (in elements) */
+    const int TN, /* Thread cols (in elements) */
+    const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
 struct GEMVKernel {
-
   static_assert(BN == SIMD_SIZE, "gemv block must have a width of SIMD_SIZE");
 
-  // - The matrix of size (M = out_vec_size, N = in_vec_size) is divided up 
+  // - The matrix of size (M = out_vec_size, N = in_vec_size) is divided up
   //   into blocks of (BM * TM, BN * TN) divided among threadgroups
   // - Every thread works on a block of (TM, TN)
   // - We assume each thead group is launched with (BN, BM, 1) threads
   //
-  // 1. A thread loads TN elements each from mat along TM contiguous rows 
-  //      and the corresponding scalar from the vector
-  // 2. The thread then multiplies and adds to accumulate its local result for the block
-  // 3. At the end, each thread has accumulated results over all blocks across the rows
-  //      These are then summed up across the threadgroup
+  // 1. A thread loads TN elements each from mat along TM contiguous rows
+  //    and the corresponding scalar from the vector
+  // 2. The thread then multiplies and adds to accumulate its local result for
+  //    the block
+  // 3. At the end, each thread has accumulated results over all blocks across
+  //    the rows. These are then summed up across the threadgroup
   // 4. Each threadgroup writes its accumulated BN * TN outputs
   //
   // Edge case handling:
-  // - The threadgroup with the largest tid will have blocks that exceed the matrix
-  //   * The blocks that start outside the matrix are never read (thread results remain zero)
-  //   * The last thread that partially overlaps with the matrix is shifted inwards 
-  //     such that the thread block fits exactly in the matrix
+  // - The threadgroup with the largest tid has blocks that exceed the matrix
+  //   * The blocks that start outside the matrix are never read (thread results
+  //     remain zero)
+  //   * The last thread that partially overlaps with the matrix is shifted
+  //     inwards such that the thread block fits exactly in the matrix
 
   MLX_MTL_CONST short tgp_mem_size = BN * TN * 2;
 
@@ -52,7 +55,7 @@ struct GEMVKernel {
       const device T* mat [[buffer(0)]],
       const device T* in_vec [[buffer(1)]],
       const device T* bias [[buffer(2)]],
-      device T* out_vec [[buffer(3)]], 
+      device T* out_vec [[buffer(3)]],
       const constant int& in_vec_size [[buffer(4)]],
       const constant int& out_vec_size [[buffer(5)]],
       const constant int& marix_ld [[buffer(6)]],
@@ -64,14 +67,13 @@ struct GEMVKernel {
       uint3 lid [[thread_position_in_threadgroup]],
       uint simd_gid [[simdgroup_index_in_threadgroup]],
       uint simd_lid [[thread_index_in_simdgroup]]) {
-
-    // Appease compiler 
+    // Appease compiler
     (void)lid;
 
     // Threadgroup in_vec cache
     threadgroup T* in_vec_block = tgp_memory + simd_lid * TN * 2;
 
-    // Thread local accumulation results 
+    // Thread local accumulation results
     thread T result[TM] = {0};
     thread T inter[TN];
     thread T v_coeff[TN];
@@ -80,7 +82,7 @@ struct GEMVKernel {
     int out_row = (tid.x * BM + simd_gid) * TM;
 
     // Exit simdgroup if rows out of bound
-    if(out_row >= out_vec_size) 
+    if (out_row >= out_vec_size)
       return;
 
     // Adjust tail simdgroup to ensure in bound reads
@@ -90,89 +92,81 @@ struct GEMVKernel {
     mat += out_row * marix_ld;
 
     // Loop over in_vec in blocks of BN * TN
-    for(int bn = simd_lid * TN; bn < in_vec_size; bn += BN * TN) {
-
+    for (int bn = simd_lid * TN; bn < in_vec_size; bn += BN * TN) {
       threadgroup_barrier(mem_flags::mem_threadgroup);
 
       // Prefetch in_vector for threadgroup use
-      if(simd_gid == 0) {
+      if (simd_gid == 0) {
         // Main load loop
-        if(bn + TN <= in_vec_size) {
-
-          #pragma clang loop unroll(full)
-          for(int tn = 0; tn < TN; tn++) {
+        if (bn + TN <= in_vec_size) {
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tn = 0; tn < TN; tn++) {
             in_vec_block[tn] = in_vec[bn + tn];
           }
 
         } else { // Edgecase
 
-          #pragma clang loop unroll(full)
-          for(int tn = 0; tn < TN; tn++) {
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tn = 0; tn < TN; tn++) {
             in_vec_block[tn] = bn + tn < in_vec_size ? in_vec[bn + tn] : 0;
           }
-
         }
       }
 
       threadgroup_barrier(mem_flags::mem_threadgroup);
 
       // Load for all rows
-      #pragma clang loop unroll(full)
-      for(int tn = 0; tn < TN; tn++) {
+      MLX_MTL_PRAGMA_UNROLL
+      for (int tn = 0; tn < TN; tn++) {
         v_coeff[tn] = in_vec_block[tn];
       }
 
       // Per thread work loop
-      #pragma clang loop unroll(full)
-      for(int tm = 0; tm < TM; tm++) {
-
-        // Load for the row 
-        if(bn + TN <= in_vec_size) {
-          #pragma clang loop unroll(full)
-          for(int tn = 0; tn < TN; tn++) {
+      MLX_MTL_PRAGMA_UNROLL
+      for (int tm = 0; tm < TM; tm++) {
+        // Load for the row
+        if (bn + TN <= in_vec_size) {
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tn = 0; tn < TN; tn++) {
             inter[tn] = mat[tm * marix_ld + bn + tn];
           }
 
         } else { // Edgecase
-          #pragma clang loop unroll(full)
-          for(int tn = 0; tn < TN; tn++) {
-            int col_idx = (bn + tn) < in_vec_size ? (bn + tn) : (in_vec_size - 1);
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tn = 0; tn < TN; tn++) {
+            int col_idx =
+                (bn + tn) < in_vec_size ? (bn + tn) : (in_vec_size - 1);
             inter[tn] = mat[tm * marix_ld + col_idx];
           }
         }
 
         // Accumulate results
-        for(int tn = 0; tn < TN; tn++) {
+        MLX_MTL_PRAGMA_UNROLL
+        for (int tn = 0; tn < TN; tn++) {
           result[tm] += inter[tn] * v_coeff[tn];
         }
-
       }
     }
 
     // Simdgroup accumulations
-    #pragma clang loop unroll(full)
-    for(int tm = 0; tm < TM; tm++) {
+    MLX_MTL_PRAGMA_UNROLL
+    for (int tm = 0; tm < TM; tm++) {
       result[tm] = simd_sum(result[tm]);
     }
 
     // Write outputs
-    if(simd_lid == 0) {
-
-      #pragma clang loop unroll(full)
-      for(int tm = 0; tm < TM; tm++) {
-        if(kDoAxpby) {
-          out_vec[out_row + tm] = 
-              static_cast<T>(alpha) * result[tm] + 
+    if (simd_lid == 0) {
+      MLX_MTL_PRAGMA_UNROLL
+      for (int tm = 0; tm < TM; tm++) {
+        if (kDoAxpby) {
+          out_vec[out_row + tm] = static_cast<T>(alpha) * result[tm] +
               static_cast<T>(beta) * bias[(out_row + tm) * bias_stride];
         } else {
           out_vec[out_row + tm] = result[tm];
         }
       }
-
     }
-
   }
-
 };
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -180,32 +174,31 @@ struct GEMVKernel {
 ///////////////////////////////////////////////////////////////////////////////
 
 template <
-  typename T, 
-  const int BM, /* Threadgroup rows (in threads) */
-  const int BN, /* Threadgroup cols (in threads) */
-  const int TM, /* Thread rows (in elements) */
-  const int TN, /* Thread cols (in elements) */ 
-  const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
+    typename T,
+    const int BM, /* Threadgroup rows (in threads) */
+    const int BN, /* Threadgroup cols (in threads) */
+    const int TM, /* Thread rows (in elements) */
+    const int TN, /* Thread cols (in elements) */
+    const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
 struct GEMVTKernel {
-
-  // - The matrix of size (M = in_vec_size, N = out_vec_size) is divided up 
+  // - The matrix of size (M = in_vec_size, N = out_vec_size) is divided up
   //   into blocks of (BM * TM, BN * TN) divided among threadgroups
   // - Every thread works on a block of (TM, TN)
   // - We assume each thead group is launched with (BN, BM, 1) threads
   //
-  // 1. A thread loads TN elements each from mat along TM contiguous rows 
-  //      and the corresponding scalar from the vector
-  // 2. The thread then multiplies and adds to accumulate its local result for the block
-  // 3. At the end, each thread has accumulated results over all blocks across the rows
-  //      These are then summed up across the threadgroup
+  // 1. A thread loads TN elements each from mat along TM contiguous rows
+  //    and the corresponding scalar from the vector
+  // 2. The thread then accumulates its local result for the block
+  // 3. At the end, each thread has accumulated results over all blocks across
+  //    the rows. These are then summed up across the threadgroup
   // 4. Each threadgroup writes its accumulated BN * TN outputs
   //
   // Edge case handling:
-  // - The threadgroup with the largest tid will have blocks that exceed the matrix
-  //   * The blocks that start outside the matrix are never read (thread results remain zero)
-  //   * The last thread that partially overlaps with the matrix is shifted inwards 
-  //     such that the thread block fits exactly in the matrix
-
+  // - The threadgroup with the largest tid has blocks that exceed the matrix
+  //   * The blocks that start outside the matrix are never read (thread results
+  //     remain zero)
+  //   * The last thread that partially overlaps with the matrix is shifted
+  //     inwards such that the thread block fits exactly in the matrix
 
   MLX_MTL_CONST short tgp_mem_size = BN * BM * TN;
 
@@ -213,7 +206,7 @@ struct GEMVTKernel {
       const device T* mat [[buffer(0)]],
       const device T* in_vec [[buffer(1)]],
       const device T* bias [[buffer(2)]],
-      device T* out_vec [[buffer(3)]], 
+      device T* out_vec [[buffer(3)]],
       const constant int& in_vec_size [[buffer(4)]],
       const constant int& out_vec_size [[buffer(5)]],
       const constant int& marix_ld [[buffer(6)]],
@@ -225,8 +218,7 @@ struct GEMVTKernel {
       uint3 lid [[thread_position_in_threadgroup]],
       uint simd_gid [[simdgroup_index_in_threadgroup]],
       uint simd_lid [[thread_index_in_simdgroup]]) {
-
-    // Appease compiler 
+    // Appease compiler
     (void)simd_gid;
     (void)simd_lid;
 
@@ -243,77 +235,69 @@ struct GEMVTKernel {
 
     // Edgecase handling
     if (out_col < out_vec_size) {
-
       out_col = out_col + TN < out_vec_size ? out_col : out_vec_size - TN;
 
       // Per thread accumulation main loop
       int bm = in_row;
-      for(; bm < in_vec_size; bm += BM * TM) {
+      for (; bm < in_vec_size; bm += BM * TM) {
         // Adding a threadgroup_barrier improves performance slightly
         // This is possibly it may help exploit cache better
         threadgroup_barrier(mem_flags::mem_none);
 
-        if(bm + TM <= in_vec_size) {
-
-          #pragma clang loop unroll(full)
-          for(int tm = 0; tm < TM; tm++) {
+        if (bm + TM <= in_vec_size) {
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tm = 0; tm < TM; tm++) {
             v_coeff[tm] = in_vec[bm + tm];
           }
 
-          #pragma clang loop unroll(full)
-          for(int tm = 0; tm < TM; tm++) {
-            for(int tn = 0; tn < TN; tn++) {
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tm = 0; tm < TM; tm++) {
+            for (int tn = 0; tn < TN; tn++) {
               inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
             }
-            for(int tn = 0; tn < TN; tn++) {
+            for (int tn = 0; tn < TN; tn++) {
               result[tn] += v_coeff[tm] * inter[tn];
             }
           }
-        
+
         } else { // Edgecase handling
-          for(int tm = 0; bm + tm < in_vec_size; tm++) {
+          for (int tm = 0; bm + tm < in_vec_size; tm++) {
             v_coeff[tm] = in_vec[bm + tm];
 
-            for(int tn = 0; tn < TN; tn++) {
+            for (int tn = 0; tn < TN; tn++) {
               inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
             }
-            for(int tn = 0; tn < TN; tn++) {
+            for (int tn = 0; tn < TN; tn++) {
               result[tn] += v_coeff[tm] * inter[tn];
             }
-
           }
         }
       }
-
     }
 
     // Threadgroup collection
 
-    #pragma clang loop unroll(full)
-    for(int i = 0; i < TN; i++) {
+    MLX_MTL_PRAGMA_UNROLL
+    for (int i = 0; i < TN; i++) {
       tgp_results[lid.y * TN + i] = result[i];
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     // Threadgroup accumulation and writing out results
-    if(lid.y == 0 && out_col < out_vec_size) {
-      
-      #pragma clang loop unroll(full)
-      for(int i = 1; i < BM; i++) {
-
-        #pragma clang loop unroll(full)
-        for(int j = 0; j < TN; j++) {
+    if (lid.y == 0 && out_col < out_vec_size) {
+      MLX_MTL_PRAGMA_UNROLL
+      for (int i = 1; i < BM; i++) {
+        MLX_MTL_PRAGMA_UNROLL
+        for (int j = 0; j < TN; j++) {
           result[j] += tgp_results[i * TN + j];
         }
       }
 
-      #pragma clang loop unroll(full)
-      for(int j = 0; j < TN; j++) {
-
-        if(kDoAxpby) {
-          out_vec[out_col + j] = 
-              static_cast<T>(alpha) * result[j] + 
+      MLX_MTL_PRAGMA_UNROLL
+      for (int j = 0; j < TN; j++) {
+        if (kDoAxpby) {
+          out_vec[out_col + j] = static_cast<T>(alpha) * result[j] +
               static_cast<T>(beta) * bias[(out_col + j) * bias_stride];
         } else {
           out_vec[out_col + j] = result[j];
@@ -328,18 +312,18 @@ struct GEMVTKernel {
 ///////////////////////////////////////////////////////////////////////////////
 
 template <
-    typename T, 
+    typename T,
     const int BM, /* Threadgroup rows (in threads) */
     const int BN, /* Threadgroup cols (in threads) */
     const int TM, /* Thread rows (in elements) */
     const int TN, /* Thread cols (in elements) */
     const bool kDoNCBatch, /* Batch ndim > 1 */
     const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
-[[kernel, max_total_threads_per_threadgroup(BM * BN)]] void gemv(
+[[kernel, max_total_threads_per_threadgroup(BM* BN)]] void gemv(
     const device T* mat [[buffer(0)]],
     const device T* in_vec [[buffer(1)]],
     const device T* bias [[buffer(2)]],
-    device T* out_vec [[buffer(3)]], 
+    device T* out_vec [[buffer(3)]],
     const constant int& in_vec_size [[buffer(4)]],
     const constant int& out_vec_size [[buffer(5)]],
     const constant int& marix_ld [[buffer(6)]],
@@ -355,16 +339,15 @@ template <
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   using gemv_kernel = GEMVKernel<T, BM, BN, TM, TN, kDoAxpby>;
   threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
 
   // Update batch offsets
-  if(kDoNCBatch) {
+  if (kDoNCBatch) {
     in_vec += elem_to_loc(tid.z, batch_shape, vector_batch_stride, batch_ndim);
     mat += elem_to_loc(tid.z, batch_shape, matrix_batch_stride, batch_ndim);
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += elem_to_loc(tid.z, batch_shape, bias_batch_stride, batch_ndim);
     }
 
@@ -372,89 +355,215 @@ template <
     in_vec += tid.z * vector_batch_stride[0];
     mat += tid.z * matrix_batch_stride[0];
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += tid.z * bias_batch_stride[0];
     }
   }
 
   out_vec += tid.z * out_vec_size;
 
-  gemv_kernel::run( 
-    mat, 
-    in_vec, 
-    bias,
-    out_vec,
-    in_vec_size,
-    out_vec_size,
-    marix_ld,
-    alpha,
-    beta,
-    bias_stride,
-    tgp_memory,
-    tid,
-    lid,
-    simd_gid,
-    simd_lid
-  );
-
+  gemv_kernel::run(
+      mat,
+      in_vec,
+      bias,
+      out_vec,
+      in_vec_size,
+      out_vec_size,
+      marix_ld,
+      alpha,
+      beta,
+      bias_stride,
+      tgp_memory,
+      tid,
+      lid,
+      simd_gid,
+      simd_lid);
 }
 
+#define instantiate_gemv_helper(name, itype, bm, bn, tm, tn, nc, axpby)      \
+  template [[host_name("gemv_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn \
+                       "_nc" #nc "_axpby" #axpby)]] [[kernel]] void          \
+  gemv<itype, bm, bn, tm, tn, nc, axpby>(                                    \
+      const device itype* mat [[buffer(0)]],                                 \
+      const device itype* in_vec [[buffer(1)]],                              \
+      const device itype* bias [[buffer(2)]],                                \
+      device itype* out_vec [[buffer(3)]],                                   \
+      const constant int& in_vec_size [[buffer(4)]],                         \
+      const constant int& out_vec_size [[buffer(5)]],                        \
+      const constant int& marix_ld [[buffer(6)]],                            \
+      const constant float& alpha [[buffer(7)]],                             \
+      const constant float& beta [[buffer(8)]],                              \
+      const constant int& batch_ndim [[buffer(9)]],                          \
+      const constant int* batch_shape [[buffer(10)]],                        \
+      const constant size_t* vector_batch_stride [[buffer(11)]],             \
+      const constant size_t* matrix_batch_stride [[buffer(12)]],             \
+      const constant size_t* bias_batch_stride [[buffer(13)]],               \
+      const constant int& bias_stride [[buffer(14)]],                        \
+      uint3 tid [[threadgroup_position_in_grid]],                            \
+      uint3 lid [[thread_position_in_threadgroup]],                          \
+      uint simd_gid [[simdgroup_index_in_threadgroup]],                      \
+      uint simd_lid [[thread_index_in_simdgroup]]);
 
-#define instantiate_gemv_helper(name, itype, bm, bn, tm, tn, nc, axpby) \
-  template [[host_name("gemv_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn "_nc" #nc "_axpby" #axpby)]] \
-  [[kernel]] void gemv<itype, bm, bn, tm, tn, nc, axpby>( \
-    const device itype* mat [[buffer(0)]], \
-    const device itype* in_vec [[buffer(1)]], \
-    const device itype* bias [[buffer(2)]], \
-    device itype* out_vec [[buffer(3)]], \
-    const constant int& in_vec_size [[buffer(4)]], \
-    const constant int& out_vec_size [[buffer(5)]], \
-    const constant int& marix_ld [[buffer(6)]], \
-    const constant float& alpha [[buffer(7)]], \
-    const constant float& beta [[buffer(8)]], \
-    const constant int& batch_ndim [[buffer(9)]], \
-    const constant int* batch_shape [[buffer(10)]], \
-    const constant size_t* vector_batch_stride [[buffer(11)]], \
-    const constant size_t* matrix_batch_stride [[buffer(12)]], \
-    const constant size_t* bias_batch_stride [[buffer(13)]], \
-    const constant int& bias_stride [[buffer(14)]], \
-    uint3 tid [[threadgroup_position_in_grid]], \
-    uint3 lid [[thread_position_in_threadgroup]], \
-    uint simd_gid [[simdgroup_index_in_threadgroup]], \
-    uint simd_lid [[thread_index_in_simdgroup]]);
-
-#define instantiate_gemv(name, itype, bm, bn, tm, tn) \
+// clang-format off
+#define instantiate_gemv(name, itype, bm, bn, tm, tn)        \
   instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 0, 0) \
   instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 0, 1) \
   instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 1, 0) \
-  instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 1, 1)
+  instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 1, 1) // clang-format on
 
+// clang-format off
 #define instantiate_gemv_blocks(name, itype) \
   instantiate_gemv(name, itype, 4, 32, 1, 4) \
   instantiate_gemv(name, itype, 4, 32, 4, 4) \
-  instantiate_gemv(name, itype, 8, 32, 4, 4)
+  instantiate_gemv(name, itype, 8, 32, 4, 4) // clang-format on
 
 instantiate_gemv_blocks(float32, float);
 instantiate_gemv_blocks(float16, half);
 instantiate_gemv_blocks(bfloat16, bfloat16_t);
 
+template <
+    typename T,
+    const int BM, /* Threadgroup rows (in threads) */
+    const int BN, /* Threadgroup cols (in threads) */
+    const int TM, /* Thread rows (in elements) */
+    const int TN> /* Thread cols (in elements) */
+[[kernel, max_total_threads_per_threadgroup(BM* BN)]] void gemv_bs(
+    const device T* mat [[buffer(0)]],
+    const device T* in_vec [[buffer(1)]],
+    const device T* bias [[buffer(2)]],
+    device T* out_vec [[buffer(3)]],
+    const constant int& in_vec_size [[buffer(4)]],
+    const constant int& out_vec_size [[buffer(5)]],
+    const constant int& marix_ld [[buffer(6)]],
+    const constant float& alpha [[buffer(7)]],
+    const constant float& beta [[buffer(8)]],
+    const constant int& batch_ndim [[buffer(9)]],
+    const constant int* batch_shape [[buffer(10)]],
+    const constant size_t* index_batch_strides [[buffer(11)]],
+    const constant int& vector_batch_ndim [[buffer(12)]],
+    const constant int* vector_batch_shape [[buffer(13)]],
+    const constant size_t* vector_batch_stride [[buffer(14)]],
+    const constant int& matrix_batch_ndim [[buffer(15)]],
+    const constant int* matrix_batch_shape [[buffer(16)]],
+    const constant size_t* matrix_batch_stride [[buffer(17)]],
+    const constant uint32_t* vec_indices [[buffer(18)]],
+    const constant uint32_t* mat_indices [[buffer(19)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  using gemv_kernel = GEMVKernel<T, BM, BN, TM, TN, false>;
+  threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
+
+  uint32_t indx_vec;
+  uint32_t indx_mat;
+
+  // Update batch offsets
+  if (batch_ndim > 1) {
+    const constant size_t* veci_bstrides = index_batch_strides;
+    const constant size_t* mati_bstrides = index_batch_strides + batch_ndim;
+
+    ulong2 batch_offsets = elem_to_loc_broadcast(
+        tid.z, batch_shape, veci_bstrides, mati_bstrides, batch_ndim);
+
+    indx_vec = vec_indices[batch_offsets.x];
+    indx_mat = mat_indices[batch_offsets.y];
+
+  } else {
+    indx_vec = vec_indices[index_batch_strides[0] * tid.z];
+    indx_mat = mat_indices[index_batch_strides[batch_ndim] * tid.z];
+  }
+
+  if (vector_batch_ndim > 1) {
+    in_vec += elem_to_loc(
+        indx_vec, vector_batch_shape, vector_batch_stride, vector_batch_ndim);
+  } else {
+    in_vec += indx_vec * vector_batch_stride[0];
+  }
+
+  if (matrix_batch_ndim > 1) {
+    mat += elem_to_loc(
+        indx_mat, matrix_batch_shape, matrix_batch_stride, matrix_batch_ndim);
+  } else {
+    mat += indx_mat * matrix_batch_stride[0];
+  }
+
+  out_vec += tid.z * out_vec_size;
+
+  gemv_kernel::run(
+      mat,
+      in_vec,
+      bias,
+      out_vec,
+      in_vec_size,
+      out_vec_size,
+      marix_ld,
+      alpha,
+      beta,
+      batch_ndim, // Not used
+      tgp_memory,
+      tid,
+      lid,
+      simd_gid,
+      simd_lid);
+}
+
+#define instantiate_gemv_bs_helper(nm, itype, bm, bn, tm, tn)       \
+  template [[host_name("gemv_bs_" #nm "_bm" #bm "_bn" #bn "_tm" #tm \
+                       "_tn" #tn)]] [[kernel]] void                 \
+  gemv_bs<itype, bm, bn, tm, tn>(                                   \
+      const device itype* mat [[buffer(0)]],                        \
+      const device itype* in_vec [[buffer(1)]],                     \
+      const device itype* bias [[buffer(2)]],                       \
+      device itype* out_vec [[buffer(3)]],                          \
+      const constant int& in_vec_size [[buffer(4)]],                \
+      const constant int& out_vec_size [[buffer(5)]],               \
+      const constant int& marix_ld [[buffer(6)]],                   \
+      const constant float& alpha [[buffer(7)]],                    \
+      const constant float& beta [[buffer(8)]],                     \
+      const constant int& batch_ndim [[buffer(9)]],                 \
+      const constant int* batch_shape [[buffer(10)]],               \
+      const constant size_t* index_batch_strides [[buffer(11)]],    \
+      const constant int& vector_batch_ndim [[buffer(12)]],         \
+      const constant int* vector_batch_shape [[buffer(13)]],        \
+      const constant size_t* vector_batch_stride [[buffer(14)]],    \
+      const constant int& matrix_batch_ndim [[buffer(15)]],         \
+      const constant int* matrix_batch_shape [[buffer(16)]],        \
+      const constant size_t* matrix_batch_stride [[buffer(17)]],    \
+      const constant uint32_t* vec_indices [[buffer(18)]],          \
+      const constant uint32_t* mat_indices [[buffer(19)]],          \
+      uint3 tid [[threadgroup_position_in_grid]],                   \
+      uint3 lid [[thread_position_in_threadgroup]],                 \
+      uint simd_gid [[simdgroup_index_in_threadgroup]],             \
+      uint simd_lid [[thread_index_in_simdgroup]]);
+
+// clang-format off
+#define instantiate_gemv_bs_blocks(name, itype)        \
+  instantiate_gemv_bs_helper(name, itype, 4, 32, 1, 4) \
+  instantiate_gemv_bs_helper(name, itype, 4, 32, 4, 4) \
+  instantiate_gemv_bs_helper(name, itype, 8, 32, 4, 4) // clang-format on
+
+instantiate_gemv_bs_blocks(float32, float);
+instantiate_gemv_bs_blocks(float16, half);
+instantiate_gemv_bs_blocks(bfloat16, bfloat16_t);
+
 ///////////////////////////////////////////////////////////////////////////////
 /// Vector matrix multiplication
 ///////////////////////////////////////////////////////////////////////////////
 
 template <
-    typename T, 
+    typename T,
     const int BM, /* Threadgroup rows (in threads) */
     const int BN, /* Threadgroup cols (in threads) */
     const int TM, /* Thread rows (in elements) */
     const int TN, /* Thread cols (in elements) */
     const bool kDoNCBatch, /* Batch ndim > 1 */
     const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
-[[kernel, max_total_threads_per_threadgroup(BM * BN)]] void gemv_t(
+[[kernel, max_total_threads_per_threadgroup(BM* BN)]] void gemv_t(
     const device T* mat [[buffer(0)]],
     const device T* in_vec [[buffer(1)]],
     const device T* bias [[buffer(2)]],
-    device T* out_vec [[buffer(3)]], 
+    device T* out_vec [[buffer(3)]],
     const constant int& in_vec_size [[buffer(4)]],
     const constant int& out_vec_size [[buffer(5)]],
     const constant int& marix_ld [[buffer(6)]],
@@ -470,16 +579,15 @@ template <
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   using gemv_kernel = GEMVTKernel<T, BM, BN, TM, TN, kDoAxpby>;
   threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
 
   // Update batch offsets
-  if(kDoNCBatch) {
+  if (kDoNCBatch) {
     in_vec += elem_to_loc(tid.z, batch_shape, vector_batch_stride, batch_ndim);
     mat += elem_to_loc(tid.z, batch_shape, matrix_batch_stride, batch_ndim);
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += elem_to_loc(tid.z, batch_shape, bias_batch_stride, batch_ndim);
     }
 
@@ -487,70 +595,202 @@ template <
     in_vec += tid.z * vector_batch_stride[0];
     mat += tid.z * matrix_batch_stride[0];
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += tid.z * bias_batch_stride[0];
     }
   }
 
   out_vec += tid.z * out_vec_size;
 
-  gemv_kernel::run( 
-    mat, 
-    in_vec, 
-    bias,
-    out_vec,
-    in_vec_size,
-    out_vec_size,
-    marix_ld,
-    alpha,
-    beta,
-    bias_stride,
-    tgp_memory,
-    tid,
-    lid,
-    simd_gid,
-    simd_lid
-  );
-
+  gemv_kernel::run(
+      mat,
+      in_vec,
+      bias,
+      out_vec,
+      in_vec_size,
+      out_vec_size,
+      marix_ld,
+      alpha,
+      beta,
+      bias_stride,
+      tgp_memory,
+      tid,
+      lid,
+      simd_gid,
+      simd_lid);
 }
 
-#define instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, nc, axpby) \
-  template [[host_name("gemv_t_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn "_nc" #nc "_axpby" #axpby)]] \
-  [[kernel]] void gemv_t<itype, bm, bn, tm, tn, nc, axpby>( \
-    const device itype* mat [[buffer(0)]], \
-    const device itype* in_vec [[buffer(1)]], \
-    const device itype* bias [[buffer(2)]], \
-    device itype* out_vec [[buffer(3)]], \
-    const constant int& in_vec_size [[buffer(4)]], \
-    const constant int& out_vec_size [[buffer(5)]], \
-    const constant int& marix_ld [[buffer(6)]], \
-    const constant float& alpha [[buffer(7)]], \
-    const constant float& beta [[buffer(8)]], \
-    const constant int& batch_ndim [[buffer(9)]], \
-    const constant int* batch_shape [[buffer(10)]], \
-    const constant size_t* vector_batch_stride [[buffer(11)]], \
-    const constant size_t* matrix_batch_stride [[buffer(12)]], \
-    const constant size_t* bias_batch_stride [[buffer(13)]], \
-    const constant int& bias_stride [[buffer(14)]], \
-    uint3 tid [[threadgroup_position_in_grid]], \
-    uint3 lid [[thread_position_in_threadgroup]], \
-    uint simd_gid [[simdgroup_index_in_threadgroup]], \
-    uint simd_lid [[thread_index_in_simdgroup]]);
+#define instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, nc, axpby)      \
+  template [[host_name("gemv_t_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn \
+                       "_nc" #nc "_axpby" #axpby)]] [[kernel]] void            \
+  gemv_t<itype, bm, bn, tm, tn, nc, axpby>(                                    \
+      const device itype* mat [[buffer(0)]],                                   \
+      const device itype* in_vec [[buffer(1)]],                                \
+      const device itype* bias [[buffer(2)]],                                  \
+      device itype* out_vec [[buffer(3)]],                                     \
+      const constant int& in_vec_size [[buffer(4)]],                           \
+      const constant int& out_vec_size [[buffer(5)]],                          \
+      const constant int& marix_ld [[buffer(6)]],                              \
+      const constant float& alpha [[buffer(7)]],                               \
+      const constant float& beta [[buffer(8)]],                                \
+      const constant int& batch_ndim [[buffer(9)]],                            \
+      const constant int* batch_shape [[buffer(10)]],                          \
+      const constant size_t* vector_batch_stride [[buffer(11)]],               \
+      const constant size_t* matrix_batch_stride [[buffer(12)]],               \
+      const constant size_t* bias_batch_stride [[buffer(13)]],                 \
+      const constant int& bias_stride [[buffer(14)]],                          \
+      uint3 tid [[threadgroup_position_in_grid]],                              \
+      uint3 lid [[thread_position_in_threadgroup]],                            \
+      uint simd_gid [[simdgroup_index_in_threadgroup]],                        \
+      uint simd_lid [[thread_index_in_simdgroup]]);
 
-#define instantiate_gemv_t(name, itype, bm, bn, tm, tn) \
+// clang-format off
+#define instantiate_gemv_t(name, itype, bm, bn, tm, tn)        \
   instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 0, 0) \
   instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 0, 1) \
   instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 0) \
-  instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 1)
+  instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 1) // clang-format on
 
+// clang-format off
 #define instantiate_gemv_t_blocks(name, itype) \
-  instantiate_gemv_t(name, itype, 8, 8, 4, 1) \
-  instantiate_gemv_t(name, itype, 8, 8, 4, 4) \
+  instantiate_gemv_t(name, itype, 8, 8, 4, 1)  \
+  instantiate_gemv_t(name, itype, 8, 8, 4, 4)  \
   instantiate_gemv_t(name, itype, 8, 16, 4, 4) \
   instantiate_gemv_t(name, itype, 8, 32, 4, 4) \
   instantiate_gemv_t(name, itype, 8, 64, 4, 4) \
-  instantiate_gemv_t(name, itype, 8, 128, 4, 4)
+  instantiate_gemv_t(name, itype, 8, 128, 4, 4) // clang-format on
 
+// clang-format off
 instantiate_gemv_t_blocks(float32, float);
 instantiate_gemv_t_blocks(float16, half);
-instantiate_gemv_t_blocks(bfloat16, bfloat16_t);
+instantiate_gemv_t_blocks(bfloat16, bfloat16_t); // clang-format on
+
+template <
+    typename T,
+    const int BM, /* Threadgroup rows (in threads) */
+    const int BN, /* Threadgroup cols (in threads) */
+    const int TM, /* Thread rows (in elements) */
+    const int TN> /* Thread cols (in elements) */
+[[kernel, max_total_threads_per_threadgroup(BM* BN)]] void gemv_t_bs(
+    const device T* mat [[buffer(0)]],
+    const device T* in_vec [[buffer(1)]],
+    const device T* bias [[buffer(2)]],
+    device T* out_vec [[buffer(3)]],
+    const constant int& in_vec_size [[buffer(4)]],
+    const constant int& out_vec_size [[buffer(5)]],
+    const constant int& marix_ld [[buffer(6)]],
+    const constant float& alpha [[buffer(7)]],
+    const constant float& beta [[buffer(8)]],
+    const constant int& batch_ndim [[buffer(9)]],
+    const constant int* batch_shape [[buffer(10)]],
+    const constant size_t* index_batch_strides [[buffer(11)]],
+    const constant int& vector_batch_ndim [[buffer(12)]],
+    const constant int* vector_batch_shape [[buffer(13)]],
+    const constant size_t* vector_batch_stride [[buffer(14)]],
+    const constant int& matrix_batch_ndim [[buffer(15)]],
+    const constant int* matrix_batch_shape [[buffer(16)]],
+    const constant size_t* matrix_batch_stride [[buffer(17)]],
+    const constant uint32_t* vec_indices [[buffer(18)]],
+    const constant uint32_t* mat_indices [[buffer(19)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  using gemv_kernel = GEMVTKernel<T, BM, BN, TM, TN, false>;
+  threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
+
+  uint32_t indx_vec;
+  uint32_t indx_mat;
+
+  // Update batch offsets
+  if (batch_ndim > 1) {
+    const constant size_t* veci_bstrides = index_batch_strides;
+    const constant size_t* mati_bstrides = index_batch_strides + batch_ndim;
+
+    ulong2 batch_offsets = elem_to_loc_broadcast(
+        tid.z, batch_shape, veci_bstrides, mati_bstrides, batch_ndim);
+
+    indx_vec = vec_indices[batch_offsets.x];
+    indx_mat = mat_indices[batch_offsets.y];
+
+  } else {
+    indx_vec = vec_indices[index_batch_strides[0] * tid.z];
+    indx_mat = mat_indices[index_batch_strides[batch_ndim] * tid.z];
+  }
+
+  if (vector_batch_ndim > 1) {
+    in_vec += elem_to_loc(
+        indx_vec, vector_batch_shape, vector_batch_stride, vector_batch_ndim);
+  } else {
+    in_vec += indx_vec * vector_batch_stride[0];
+  }
+
+  if (matrix_batch_ndim > 1) {
+    mat += elem_to_loc(
+        indx_mat, matrix_batch_shape, matrix_batch_stride, matrix_batch_ndim);
+  } else {
+    mat += indx_mat * matrix_batch_stride[0];
+  }
+
+  out_vec += tid.z * out_vec_size;
+
+  gemv_kernel::run(
+      mat,
+      in_vec,
+      bias,
+      out_vec,
+      in_vec_size,
+      out_vec_size,
+      marix_ld,
+      alpha,
+      beta,
+      batch_ndim, // Not used,
+      tgp_memory,
+      tid,
+      lid,
+      simd_gid,
+      simd_lid);
+}
+
+#define instantiate_gemv_t_bs_helper(nm, itype, bm, bn, tm, tn)       \
+  template [[host_name("gemv_t_bs_" #nm "_bm" #bm "_bn" #bn "_tm" #tm \
+                       "_tn" #tn)]] [[kernel]] void                   \
+  gemv_t_bs<itype, bm, bn, tm, tn>(                                   \
+      const device itype* mat [[buffer(0)]],                          \
+      const device itype* in_vec [[buffer(1)]],                       \
+      const device itype* bias [[buffer(2)]],                         \
+      device itype* out_vec [[buffer(3)]],                            \
+      const constant int& in_vec_size [[buffer(4)]],                  \
+      const constant int& out_vec_size [[buffer(5)]],                 \
+      const constant int& marix_ld [[buffer(6)]],                     \
+      const constant float& alpha [[buffer(7)]],                      \
+      const constant float& beta [[buffer(8)]],                       \
+      const constant int& batch_ndim [[buffer(9)]],                   \
+      const constant int* batch_shape [[buffer(10)]],                 \
+      const constant size_t* index_batch_strides [[buffer(11)]],      \
+      const constant int& vector_batch_ndim [[buffer(12)]],           \
+      const constant int* vector_batch_shape [[buffer(13)]],          \
+      const constant size_t* vector_batch_stride [[buffer(14)]],      \
+      const constant int& matrix_batch_ndim [[buffer(15)]],           \
+      const constant int* matrix_batch_shape [[buffer(16)]],          \
+      const constant size_t* matrix_batch_stride [[buffer(17)]],      \
+      const constant uint32_t* vec_indices [[buffer(18)]],            \
+      const constant uint32_t* mat_indices [[buffer(19)]],            \
+      uint3 tid [[threadgroup_position_in_grid]],                     \
+      uint3 lid [[thread_position_in_threadgroup]],                   \
+      uint simd_gid [[simdgroup_index_in_threadgroup]],               \
+      uint simd_lid [[thread_index_in_simdgroup]]);
+
+// clang-format off
+#define instantiate_gemv_t_bs_blocks(name, itype) \
+  instantiate_gemv_t_bs_helper(name, itype, 8, 8, 4, 1)  \
+  instantiate_gemv_t_bs_helper(name, itype, 8, 8, 4, 4)  \
+  instantiate_gemv_t_bs_helper(name, itype, 8, 16, 4, 4) \
+  instantiate_gemv_t_bs_helper(name, itype, 8, 32, 4, 4) \
+  instantiate_gemv_t_bs_helper(name, itype, 8, 64, 4, 4) \
+  instantiate_gemv_t_bs_helper(name, itype, 8, 128, 4, 4) // clang-format on
+
+// clang-format off
+instantiate_gemv_t_bs_blocks(float32, float);
+instantiate_gemv_t_bs_blocks(float16, half);
+instantiate_gemv_t_bs_blocks(bfloat16, bfloat16_t); // clang-format on

mlx/backend/metal/kernels/layer_norm.metal

@@ -99,7 +99,8 @@ template <typename T, int N_READS = RMS_N_READS>
     for (int i = 0; i < N_READS; i++) {
       if ((lid * N_READS + i) < axis_size) {
         thread_x[i] = (thread_x[i] - mean) * normalizer;
-        out[i] = w[w_stride * i] * static_cast<T>(thread_x[i]) + b[b_stride * i];
+        out[i] =
+            w[w_stride * i] * static_cast<T>(thread_x[i]) + b[b_stride * i];
       }
     }
   }
@@ -192,52 +193,358 @@ template <typename T, int N_READS = RMS_N_READS>
     if (r + lid * N_READS + N_READS <= axis_size) {
       for (int i = 0; i < N_READS; i++) {
         float xi = (x[r + i] - mean) * normalizer;
-        out[r + i] = w[w_stride * (i + r)] * static_cast<T>(xi) + b[b_stride * (i + r)];
+        out[r + i] =
+            w[w_stride * (i + r)] * static_cast<T>(xi) + b[b_stride * (i + r)];
       }
     } else {
       for (int i = 0; i < N_READS; i++) {
         if ((r + lid * N_READS + i) < axis_size) {
           float xi = (x[r + i] - mean) * normalizer;
-          out[r + i] = w[w_stride * (i + r)] * static_cast<T>(xi) + b[b_stride * (i + r)];
+          out[r + i] = w[w_stride * (i + r)] * static_cast<T>(xi) +
+              b[b_stride * (i + r)];
         }
       }
     }
   }
 }
 
+template <typename T, int N_READS = RMS_N_READS>
+[[kernel]] void vjp_layer_norm_single_row(
+    const device T* x,
+    const device T* w,
+    const device T* g,
+    device T* gx,
+    device T* gw,
+    constant float& eps,
+    constant uint& axis_size,
+    constant uint& w_stride,
+    uint gid [[threadgroup_position_in_grid]],
+    uint lid [[thread_position_in_threadgroup]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  // Advance the input pointers
+  x += gid * axis_size + lid * N_READS;
+  g += gid * axis_size + lid * N_READS;
+  w += w_stride * lid * N_READS;
+
+  // Allocate registers for the computation and accumulators
+  float thread_x[N_READS];
+  float thread_w[N_READS];
+  float thread_g[N_READS];
+  float sumx = 0;
+  float sumx2 = 0;
+  float sumwg = 0;
+  float sumwgx = 0;
+
+  constexpr int SIMD_SIZE = 32;
+
+  threadgroup float local_sumx[SIMD_SIZE];
+  threadgroup float local_sumx2[SIMD_SIZE];
+  threadgroup float local_sumwg[SIMD_SIZE];
+  threadgroup float local_sumwgx[SIMD_SIZE];
+  threadgroup float local_mean[1];
+  threadgroup float local_normalizer[1];
+  threadgroup float local_meanwg[1];
+  threadgroup float local_meanwgx[1];
+
+  if (lid * N_READS + N_READS <= axis_size) {
+    for (int i = 0; i < N_READS; i++) {
+      thread_x[i] = x[i];
+      thread_w[i] = w[i * w_stride];
+      thread_g[i] = g[i];
+      float wg = thread_w[i] * thread_g[i];
+      sumx += thread_x[i];
+      sumx2 += thread_x[i] * thread_x[i];
+      sumwg += wg;
+      sumwgx += wg * thread_x[i];
+    }
+  } else {
+    for (int i = 0; i < N_READS; i++) {
+      if ((lid * N_READS + i) < axis_size) {
+        thread_x[i] = x[i];
+        thread_w[i] = w[i * w_stride];
+        thread_g[i] = g[i];
+        float wg = thread_w[i] * thread_g[i];
+        sumx += thread_x[i];
+        sumx2 += thread_x[i] * thread_x[i];
+        sumwg += wg;
+        sumwgx += wg * thread_x[i];
+      }
+    }
+  }
+
+  sumx = simd_sum(sumx);
+  sumx2 = simd_sum(sumx2);
+  sumwg = simd_sum(sumwg);
+  sumwgx = simd_sum(sumwgx);
+
+  //  Initialize shared memory
+  if (simd_group_id == 0) {
+    local_sumx[simd_lane_id] = 0;
+    local_sumx2[simd_lane_id] = 0;
+    local_sumwg[simd_lane_id] = 0;
+    local_sumwgx[simd_lane_id] = 0;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  // Write simd accumulations into shared memory
+  if (simd_lane_id == 0) {
+    local_sumx[simd_group_id] = sumx;
+    local_sumx2[simd_group_id] = sumx2;
+    local_sumwg[simd_group_id] = sumwg;
+    local_sumwgx[simd_group_id] = sumwgx;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  // Accumulate over simd groups
+  if (simd_group_id == 0) {
+    sumx = simd_sum(local_sumx[simd_lane_id]);
+    sumx2 = simd_sum(local_sumx2[simd_lane_id]);
+    sumwg = simd_sum(local_sumwg[simd_lane_id]);
+    sumwgx = simd_sum(local_sumwgx[simd_lane_id]);
+    if (simd_lane_id == 0) {
+      float mean = sumx / axis_size;
+      float variance = sumx2 / axis_size - mean * mean;
+
+      local_mean[0] = mean;
+      local_normalizer[0] = metal::precise::rsqrt(variance + eps);
+      local_meanwg[0] = sumwg / axis_size;
+      local_meanwgx[0] = sumwgx / axis_size;
+    }
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  float mean = local_mean[0];
+  float normalizer = local_normalizer[0];
+  float meanwg = local_meanwg[0];
+  float meanwgxc = local_meanwgx[0] - meanwg * mean;
+  float normalizer2 = normalizer * normalizer;
+
+  // Write the outputs
+  gx += gid * axis_size + lid * N_READS;
+  gw += gid * axis_size + lid * N_READS;
+  if (lid * N_READS + N_READS <= axis_size) {
+    for (int i = 0; i < N_READS; i++) {
+      thread_x[i] = (thread_x[i] - mean) * normalizer;
+      gx[i] = static_cast<T>(
+          normalizer * (thread_w[i] * thread_g[i] - meanwg) -
+          thread_x[i] * meanwgxc * normalizer2);
+      gw[i] = static_cast<T>(thread_g[i] * thread_x[i]);
+    }
+  } else {
+    for (int i = 0; i < N_READS; i++) {
+      if ((lid * N_READS + i) < axis_size) {
+        thread_x[i] = (thread_x[i] - mean) * normalizer;
+        gx[i] = static_cast<T>(
+            normalizer * (thread_w[i] * thread_g[i] - meanwg) -
+            thread_x[i] * meanwgxc * normalizer2);
+        gw[i] = static_cast<T>(thread_g[i] * thread_x[i]);
+      }
+    }
+  }
+}
+
+template <typename T, int N_READS = RMS_N_READS>
+[[kernel]] void vjp_layer_norm_looped(
+    const device T* x,
+    const device T* w,
+    const device T* g,
+    device T* gx,
+    device T* gw,
+    constant float& eps,
+    constant uint& axis_size,
+    constant uint& w_stride,
+    uint gid [[threadgroup_position_in_grid]],
+    uint lid [[thread_position_in_threadgroup]],
+    uint lsize [[threads_per_threadgroup]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  // Advance the input pointers
+  x += gid * axis_size + lid * N_READS;
+  g += gid * axis_size + lid * N_READS;
+  w += w_stride * lid * N_READS;
+
+  // Allocate registers for the accumulators
+  float sumx = 0;
+  float sumx2 = 0;
+  float sumwg = 0;
+  float sumwgx = 0;
+
+  constexpr int SIMD_SIZE = 32;
+
+  threadgroup float local_sumx[SIMD_SIZE];
+  threadgroup float local_sumx2[SIMD_SIZE];
+  threadgroup float local_sumwg[SIMD_SIZE];
+  threadgroup float local_sumwgx[SIMD_SIZE];
+  threadgroup float local_mean[1];
+  threadgroup float local_normalizer[1];
+  threadgroup float local_meanwg[1];
+  threadgroup float local_meanwgx[1];
+
+  for (uint r = 0; r < axis_size; r += lsize * N_READS) {
+    if (r + lid * N_READS + N_READS <= axis_size) {
+      for (int i = 0; i < N_READS; i++) {
+        float xi = x[i + r];
+        float wi = w[(i + r) * w_stride];
+        float gi = g[i + r];
+        float wg = wi * gi;
+        sumx += xi;
+        sumx2 += xi * xi;
+        sumwg += wg;
+        sumwgx += wg * xi;
+      }
+    } else {
+      for (int i = 0; i < N_READS; i++) {
+        if ((r + lid * N_READS + i) < axis_size) {
+          float xi = x[i + r];
+          float wi = w[(i + r) * w_stride];
+          float gi = g[i + r];
+          float wg = wi * gi;
+          sumx += xi;
+          sumx2 += xi * xi;
+          sumwg += wg;
+          sumwgx += wg * xi;
+        }
+      }
+    }
+  }
+
+  sumx = simd_sum(sumx);
+  sumx2 = simd_sum(sumx2);
+  sumwg = simd_sum(sumwg);
+  sumwgx = simd_sum(sumwgx);
+
+  //  Initialize shared memory
+  if (simd_group_id == 0) {
+    local_sumx[simd_lane_id] = 0;
+    local_sumx2[simd_lane_id] = 0;
+    local_sumwg[simd_lane_id] = 0;
+    local_sumwgx[simd_lane_id] = 0;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  // Write simd accumulations into shared memory
+  if (simd_lane_id == 0) {
+    local_sumx[simd_group_id] = sumx;
+    local_sumx2[simd_group_id] = sumx2;
+    local_sumwg[simd_group_id] = sumwg;
+    local_sumwgx[simd_group_id] = sumwgx;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  // Accumulate over simd groups
+  if (simd_group_id == 0) {
+    sumx = simd_sum(local_sumx[simd_lane_id]);
+    sumx2 = simd_sum(local_sumx2[simd_lane_id]);
+    sumwg = simd_sum(local_sumwg[simd_lane_id]);
+    sumwgx = simd_sum(local_sumwgx[simd_lane_id]);
+    if (simd_lane_id == 0) {
+      float mean = sumx / axis_size;
+      float variance = sumx2 / axis_size - mean * mean;
+
+      local_mean[0] = mean;
+      local_normalizer[0] = metal::precise::rsqrt(variance + eps);
+      local_meanwg[0] = sumwg / axis_size;
+      local_meanwgx[0] = sumwgx / axis_size;
+    }
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  float mean = local_mean[0];
+  float normalizer = local_normalizer[0];
+  float meanwg = local_meanwg[0];
+  float meanwgxc = local_meanwgx[0] - meanwg * mean;
+  float normalizer2 = normalizer * normalizer;
+
+  // Write the outputs
+  gx += gid * axis_size + lid * N_READS;
+  gw += gid * axis_size + lid * N_READS;
+  for (uint r = 0; r < axis_size; r += lsize * N_READS) {
+    if (r + lid * N_READS + N_READS <= axis_size) {
+      for (int i = 0; i < N_READS; i++) {
+        float xi = (x[i + r] - mean) * normalizer;
+        float wi = w[(i + r) * w_stride];
+        float gi = g[i + r];
+        gx[i + r] = static_cast<T>(
+            normalizer * (wi * gi - meanwg) - xi * meanwgxc * normalizer2);
+        gw[i + r] = static_cast<T>(gi * xi);
+      }
+    } else {
+      for (int i = 0; i < N_READS; i++) {
+        if ((r + lid * N_READS + i) < axis_size) {
+          float xi = (x[i + r] - mean) * normalizer;
+          float wi = w[(i + r) * w_stride];
+          float gi = g[i + r];
+          gx[i + r] = static_cast<T>(
+              normalizer * (wi * gi - meanwg) - xi * meanwgxc * normalizer2);
+          gw[i + r] = static_cast<T>(gi * xi);
+        }
+      }
+    }
+  }
+}
 
 // clang-format off
-#define instantiate_layer_norm_single_row(name, itype)        \
-  template [[host_name("layer_norm" #name)]] [[kernel]] void  \
-  layer_norm_single_row<itype>(                               \
-      const device itype* x,                                  \
-      const device itype* w,                                  \
-      const device itype* b,                                  \
-      device itype* out,                                      \
-      constant float& eps,                                    \
-      constant uint& axis_size,                               \
-      constant uint& w_stride,                                \
-      constant uint& b_stride,                                \
-      uint gid [[thread_position_in_grid]],                   \
-      uint lid [[thread_position_in_threadgroup]],            \
-      uint simd_lane_id [[thread_index_in_simdgroup]],        \
+#define instantiate_layer_norm_single_row(name, itype)            \
+  template [[host_name("layer_norm" #name)]] [[kernel]] void      \
+  layer_norm_single_row<itype>(                                   \
+      const device itype* x,                                      \
+      const device itype* w,                                      \
+      const device itype* b,                                      \
+      device itype* out,                                          \
+      constant float& eps,                                        \
+      constant uint& axis_size,                                   \
+      constant uint& w_stride,                                    \
+      constant uint& b_stride,                                    \
+      uint gid [[thread_position_in_grid]],                       \
+      uint lid [[thread_position_in_threadgroup]],                \
+      uint simd_lane_id [[thread_index_in_simdgroup]],            \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]]);     \
+  template [[host_name("vjp_layer_norm" #name)]] [[kernel]] void  \
+  vjp_layer_norm_single_row<itype>(                               \
+      const device itype* x,                                      \
+      const device itype* w,                                      \
+      const device itype* g,                                      \
+      device itype* gx,                                           \
+      device itype* gw,                                           \
+      constant float& eps,                                        \
+      constant uint& axis_size,                                   \
+      constant uint& w_stride,                                    \
+      uint gid [[thread_position_in_grid]],                       \
+      uint lid [[thread_position_in_threadgroup]],                \
+      uint simd_lane_id [[thread_index_in_simdgroup]],            \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
-#define instantiate_layer_norm_looped(name, itype)                   \
-  template [[host_name("layer_norm_looped" #name)]] [[kernel]] void  \
-  layer_norm_looped<itype>(                                                 \
-      const device itype* x,                                         \
-      const device itype* w,                                         \
-      const device itype* b,                                         \
-      device itype* out,                                             \
-      constant float& eps,                                           \
-      constant uint& axis_size,                                      \
-      constant uint& w_stride,                                       \
-      constant uint& b_stride,                                       \
-      uint gid [[thread_position_in_grid]],                          \
-      uint lid [[thread_position_in_threadgroup]],                   \
-      uint lsize [[threads_per_threadgroup]],                        \
-      uint simd_lane_id [[thread_index_in_simdgroup]],               \
+#define instantiate_layer_norm_looped(name, itype)                       \
+  template [[host_name("layer_norm_looped" #name)]] [[kernel]] void      \
+  layer_norm_looped<itype>(                                              \
+      const device itype* x,                                             \
+      const device itype* w,                                             \
+      const device itype* b,                                             \
+      device itype* out,                                                 \
+      constant float& eps,                                               \
+      constant uint& axis_size,                                          \
+      constant uint& w_stride,                                           \
+      constant uint& b_stride,                                           \
+      uint gid [[thread_position_in_grid]],                              \
+      uint lid [[thread_position_in_threadgroup]],                       \
+      uint lsize [[threads_per_threadgroup]],                            \
+      uint simd_lane_id [[thread_index_in_simdgroup]],                   \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]]);            \
+  template [[host_name("vjp_layer_norm_looped" #name)]] [[kernel]] void  \
+  vjp_layer_norm_looped<itype>(                                          \
+      const device itype* x,                                             \
+      const device itype* w,                                             \
+      const device itype* g,                                             \
+      device itype* gx,                                                  \
+      device itype* gb,                                                  \
+      constant float& eps,                                               \
+      constant uint& axis_size,                                          \
+      constant uint& w_stride,                                           \
+      uint gid [[thread_position_in_grid]],                              \
+      uint lid [[thread_position_in_threadgroup]],                       \
+      uint lsize [[threads_per_threadgroup]],                            \
+      uint simd_lane_id [[thread_index_in_simdgroup]],                   \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
 #define instantiate_layer_norm(name, itype)      \
@@ -246,6 +553,4 @@ template <typename T, int N_READS = RMS_N_READS>
 
 instantiate_layer_norm(float32, float)
 instantiate_layer_norm(float16, half)
-instantiate_layer_norm(bfloat16, bfloat16_t)
-    // clang-format on
-
+instantiate_layer_norm(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/random.metal

@@ -3,9 +3,8 @@
 #include "mlx/backend/metal/kernels/utils.h"
 
 static constexpr constant uint32_t rotations[2][4] = {
-  {13, 15, 26, 6},
-  {17, 29, 16, 24}
-};
+    {13, 15, 26, 6},
+    {17, 29, 16, 24}};
 
 union rbits {
   uint2 val;
@@ -13,7 +12,6 @@ union rbits {
 };
 
 rbits threefry2x32_hash(const thread uint2& key, uint2 count) {
-
   uint4 ks = {key.x, key.y, key.x ^ key.y ^ 0x1BD11BDA};
 
   rbits v;
@@ -51,7 +49,7 @@ rbits threefry2x32_hash(const thread uint2& key, uint2 count) {
     out[4 * count.x + i] = bits.bytes[0][i];
   }
   if (!drop_last) {
-    if ((index.y + 1) ==  half_size && (bytes_per_key % 4) > 0) {
+    if ((index.y + 1) == half_size && (bytes_per_key % 4) > 0) {
       int edge_bytes = (bytes_per_key % 4);
       for (int i = 0; i < edge_bytes; ++i) {
         out[4 * count.y + i] = bits.bytes[1][i];
@@ -87,7 +85,7 @@ rbits threefry2x32_hash(const thread uint2& key, uint2 count) {
     out[4 * count.x + i] = bits.bytes[0][i];
   }
   if (!drop_last) {
-    if ((index.y + 1) ==  half_size && (bytes_per_key % 4) > 0) {
+    if ((index.y + 1) == half_size && (bytes_per_key % 4) > 0) {
       int edge_bytes = (bytes_per_key % 4);
       for (int i = 0; i < edge_bytes; ++i) {
         out[4 * count.y + i] = bits.bytes[1][i];

mlx/backend/metal/kernels/reduction/kernels/reduce_all.metal

@@ -1,8 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include "mlx/backend/metal/kernels/reduction/utils.h"
 #include "mlx/backend/metal/kernels/reduction/ops.h"
 #include "mlx/backend/metal/kernels/reduction/reduce_inst.h"
+#include "mlx/backend/metal/kernels/reduction/utils.h"
 
 using namespace metal;
 
@@ -60,14 +60,13 @@ METAL_FUNC U per_thread_all_reduce(
 // All reduce kernel
 ///////////////////////////////////////////////////////////////////////////////
 
-
 // NB: This kernel assumes threads_per_threadgroup is at most
 // 1024. This way with a simd_size of 32, we are guaranteed to
 // complete the reduction in two steps of simd-level reductions.
-template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void all_reduce(
-    const device T *in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
+    const device T* in [[buffer(0)]],
+    device mlx_atomic<U>* out [[buffer(1)]],
     const device size_t& in_size [[buffer(2)]],
     uint gid [[thread_position_in_grid]],
     uint lid [[thread_position_in_threadgroup]],
@@ -75,11 +74,11 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
     uint simd_per_group [[simdgroups_per_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-
   Op op;
   threadgroup U local_vals[simd_size];
 
-  U total_val = per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
+  U total_val =
+      per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
 
   // Reduction within simd group
   total_val = op.simd_reduce(total_val);
@@ -98,10 +97,10 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
   }
 }
 
-template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void all_reduce_no_atomics(
-    const device T *in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    const device T* in [[buffer(0)]],
+    device U* out [[buffer(1)]],
     const device size_t& in_size [[buffer(2)]],
     uint gid [[thread_position_in_grid]],
     uint lid [[thread_position_in_threadgroup]],
@@ -110,14 +109,16 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint thread_group_id [[threadgroup_position_in_grid]]) {
-
   Op op;
   threadgroup U local_vals[simd_size];
 
-  U total_val = per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
+  U total_val =
+      per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
 
-  // Reduction within simd group (simd_add isn't supported for uint64/int64 types)
-  for (uint16_t lane_offset = simd_size/2; lane_offset > 0; lane_offset /= 2) {
+  // Reduction within simd group (simd_add isn't supported for uint64/int64
+  // types)
+  for (uint16_t lane_offset = simd_size / 2; lane_offset > 0;
+       lane_offset /= 2) {
     total_val = op(total_val, simd_shuffle_down(total_val, lane_offset));
   }
   // Write simd group reduction results to local memory
@@ -128,7 +129,8 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 
   // Reduction of simdgroup reduction results within threadgroup.
   total_val = lid < simd_per_group ? local_vals[lid] : op.init;
-  for (uint16_t lane_offset = simd_size/2; lane_offset > 0; lane_offset /= 2) {
+  for (uint16_t lane_offset = simd_size / 2; lane_offset > 0;
+       lane_offset /= 2) {
     total_val = op(total_val, simd_shuffle_down(total_val, lane_offset));
   }
 
@@ -138,31 +140,31 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
   }
 }
 
-#define instantiate_all_reduce(name, itype, otype, op) \
-  template [[host_name("all_reduce_" #name)]] \
-  [[kernel]] void all_reduce<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
-      const device size_t& in_size [[buffer(2)]], \
-      uint gid [[thread_position_in_grid]], \
-      uint lid [[thread_position_in_threadgroup]], \
-      uint grid_size [[threads_per_grid]], \
-      uint simd_per_group [[simdgroups_per_threadgroup]], \
-      uint simd_lane_id [[thread_index_in_simdgroup]], \
+#define instantiate_all_reduce(name, itype, otype, op)        \
+  template [[host_name("all_reduce_" #name)]] [[kernel]] void \
+  all_reduce<itype, otype, op>(                               \
+      const device itype* in [[buffer(0)]],                   \
+      device mlx_atomic<otype>* out [[buffer(1)]],            \
+      const device size_t& in_size [[buffer(2)]],             \
+      uint gid [[thread_position_in_grid]],                   \
+      uint lid [[thread_position_in_threadgroup]],            \
+      uint grid_size [[threads_per_grid]],                    \
+      uint simd_per_group [[simdgroups_per_threadgroup]],     \
+      uint simd_lane_id [[thread_index_in_simdgroup]],        \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
-#define instantiate_all_reduce_no_atomics(name, itype, otype, op) \
-  template [[host_name("all_reduce_no_atomics_" #name)]] \
-  [[kernel]] void all_reduce_no_atomics<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device otype *out [[buffer(1)]], \
-      const device size_t& in_size [[buffer(2)]], \
-      uint gid [[thread_position_in_grid]], \
-      uint lid [[thread_position_in_threadgroup]], \
-      uint grid_size [[threads_per_grid]], \
-      uint simd_per_group [[simdgroups_per_threadgroup]], \
-      uint simd_lane_id [[thread_index_in_simdgroup]], \
-      uint simd_group_id [[simdgroup_index_in_threadgroup]], \
+#define instantiate_all_reduce_no_atomics(name, itype, otype, op)        \
+  template [[host_name("all_reduce_no_atomics_" #name)]] [[kernel]] void \
+  all_reduce_no_atomics<itype, otype, op>(                               \
+      const device itype* in [[buffer(0)]],                              \
+      device otype* out [[buffer(1)]],                                   \
+      const device size_t& in_size [[buffer(2)]],                        \
+      uint gid [[thread_position_in_grid]],                              \
+      uint lid [[thread_position_in_threadgroup]],                       \
+      uint grid_size [[threads_per_grid]],                               \
+      uint simd_per_group [[simdgroups_per_threadgroup]],                \
+      uint simd_lane_id [[thread_index_in_simdgroup]],                   \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],             \
       uint thread_group_id [[threadgroup_position_in_grid]]);
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -170,11 +172,12 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 ///////////////////////////////////////////////////////////////////////////////
 
 #define instantiate_same_all_reduce_helper(name, tname, type, op) \
-  instantiate_all_reduce(name ##tname, type, type, op<type>)
+  instantiate_all_reduce(name##tname, type, type, op<type>)
 
 #define instantiate_same_all_reduce_na_helper(name, tname, type, op) \
-  instantiate_all_reduce_no_atomics(name ##tname, type, type, op<type>)
+  instantiate_all_reduce_no_atomics(name##tname, type, type, op<type>)
 
+// clang-format off
 instantiate_reduce_ops(instantiate_same_all_reduce_helper, instantiate_reduce_helper_types)
 instantiate_reduce_ops(instantiate_same_all_reduce_na_helper, instantiate_reduce_helper_64b)
 
@@ -182,4 +185,4 @@ instantiate_reduce_from_types(instantiate_all_reduce, and, bool, And)
 instantiate_reduce_from_types(instantiate_all_reduce, or, bool, Or)
 
 // special case bool with larger output type
-instantiate_all_reduce(sumbool_, bool, uint32_t, Sum<uint32_t>)
+instantiate_all_reduce(sumbool_, bool, uint32_t, Sum<uint32_t>) // clang-format on

mlx/backend/metal/kernels/reduction/kernels/reduce_col.metal

@@ -1,8 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include "mlx/backend/metal/kernels/reduction/utils.h"
 #include "mlx/backend/metal/kernels/reduction/ops.h"
 #include "mlx/backend/metal/kernels/reduction/reduce_inst.h"
+#include "mlx/backend/metal/kernels/reduction/utils.h"
 
 using namespace metal;
 
@@ -12,8 +12,8 @@ using namespace metal;
 
 template <typename T, typename U, typename Op>
 [[kernel]] void col_reduce_small(
-    const device T *in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    const device T* in [[buffer(0)]],
+    device U* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& reduction_stride [[buffer(3)]],
     const constant size_t& out_size [[buffer(4)]],
@@ -25,7 +25,6 @@ template <typename T, typename U, typename Op>
     const constant size_t* non_col_strides [[buffer(10)]],
     const constant int& non_col_ndim [[buffer(11)]],
     uint tid [[thread_position_in_grid]]) {
-
   // Appease the compiler
   (void)out_size;
 
@@ -35,15 +34,16 @@ template <typename T, typename U, typename Op>
   auto out_idx = tid;
 
   in += elem_to_loc(
-        out_idx,
-        shape + non_col_ndim,
-        strides + non_col_ndim,
-        ndim - non_col_ndim);
+      out_idx,
+      shape + non_col_ndim,
+      strides + non_col_ndim,
+      ndim - non_col_ndim);
 
-  for(uint i = 0; i < non_col_reductions; i++) {
-    size_t in_idx = elem_to_loc(i, non_col_shapes, non_col_strides, non_col_ndim);
+  for (uint i = 0; i < non_col_reductions; i++) {
+    size_t in_idx =
+        elem_to_loc(i, non_col_shapes, non_col_strides, non_col_ndim);
 
-    for(uint j = 0; j < reduction_size; j++, in_idx += reduction_stride) {
+    for (uint j = 0; j < reduction_size; j++, in_idx += reduction_stride) {
       U val = static_cast<U>(in[in_idx]);
       total_val = op(total_val, val);
     }
@@ -52,21 +52,21 @@ template <typename T, typename U, typename Op>
   out[out_idx] = total_val;
 }
 
-#define instantiate_col_reduce_small(name, itype, otype, op) \
-  template [[host_name("col_reduce_small_" #name)]] \
-  [[kernel]] void col_reduce_small<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device otype *out [[buffer(1)]], \
-      const constant size_t& reduction_size [[buffer(2)]], \
-      const constant size_t& reduction_stride [[buffer(3)]], \
-      const constant size_t& out_size [[buffer(4)]], \
-      const constant int* shape [[buffer(5)]],  \
-      const constant size_t* strides [[buffer(6)]],  \
-      const constant int& ndim [[buffer(7)]],  \
-      const constant size_t& non_col_reductions [[buffer(8)]], \
-      const constant int* non_col_shapes [[buffer(9)]], \
-      const constant size_t* non_col_strides [[buffer(10)]], \
-      const constant int& non_col_ndim [[buffer(11)]], \
+#define instantiate_col_reduce_small(name, itype, otype, op)        \
+  template [[host_name("col_reduce_small_" #name)]] [[kernel]] void \
+  col_reduce_small<itype, otype, op>(                               \
+      const device itype* in [[buffer(0)]],                         \
+      device otype* out [[buffer(1)]],                              \
+      const constant size_t& reduction_size [[buffer(2)]],          \
+      const constant size_t& reduction_stride [[buffer(3)]],        \
+      const constant size_t& out_size [[buffer(4)]],                \
+      const constant int* shape [[buffer(5)]],                      \
+      const constant size_t* strides [[buffer(6)]],                 \
+      const constant int& ndim [[buffer(7)]],                       \
+      const constant size_t& non_col_reductions [[buffer(8)]],      \
+      const constant int* non_col_shapes [[buffer(9)]],             \
+      const constant size_t* non_col_strides [[buffer(10)]],        \
+      const constant int& non_col_ndim [[buffer(11)]],              \
       uint tid [[thread_position_in_grid]]);
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -112,39 +112,35 @@ METAL_FUNC U _contiguous_strided_reduce(
 
 template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void col_reduce_general(
-    const device T *in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
+    const device T* in [[buffer(0)]],
+    device mlx_atomic<U>* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& reduction_stride [[buffer(3)]],
     const constant size_t& out_size [[buffer(4)]],
     const constant int* shape [[buffer(5)]],
     const constant size_t* strides [[buffer(6)]],
     const constant int& ndim [[buffer(7)]],
-    threadgroup U *local_data [[threadgroup(0)]],
+    threadgroup U* local_data [[threadgroup(0)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]],
     uint3 lsize [[threads_per_threadgroup]]) {
   auto out_idx = tid.x * lsize.x + lid.x;
-  auto in_idx = elem_to_loc(
-    out_idx + tid.z * out_size,
-    shape,
-    strides,
-    ndim
-  );
+  auto in_idx = elem_to_loc(out_idx + tid.z * out_size, shape, strides, ndim);
 
   Op op;
-  if(out_idx < out_size) {
+  if (out_idx < out_size) {
     U val = _contiguous_strided_reduce<T, U, Op, N_READS>(
-              in,
-              local_data,
-              in_idx,
-              reduction_size,
-              reduction_stride,
-              tid.xy,
-              lid.xy,
-              lsize.xy);
+        in,
+        local_data,
+        in_idx,
+        reduction_size,
+        reduction_stride,
+        tid.xy,
+        lid.xy,
+        lsize.xy);
 
-    // Write out reduction results generated by threadgroups working on specific output element, contiguously.
+    // Write out reduction results generated by threadgroups working on specific
+    // output element, contiguously.
     if (lid.y == 0) {
       op.atomic_update(out, val, out_idx);
     }
@@ -153,40 +149,36 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 
 template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void col_reduce_general_no_atomics(
-    const device T *in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    const device T* in [[buffer(0)]],
+    device U* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& reduction_stride [[buffer(3)]],
     const constant size_t& out_size [[buffer(4)]],
     const constant int* shape [[buffer(5)]],
     const constant size_t* strides [[buffer(6)]],
     const constant int& ndim [[buffer(7)]],
-    threadgroup U *local_data [[threadgroup(0)]],
+    threadgroup U* local_data [[threadgroup(0)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]],
     uint3 gid [[thread_position_in_grid]],
     uint3 lsize [[threads_per_threadgroup]],
     uint3 gsize [[threads_per_grid]]) {
   auto out_idx = tid.x * lsize.x + lid.x;
-  auto in_idx = elem_to_loc(
-    out_idx + tid.z * out_size,
-    shape,
-    strides,
-    ndim
-  );
+  auto in_idx = elem_to_loc(out_idx + tid.z * out_size, shape, strides, ndim);
 
-  if(out_idx < out_size) {
+  if (out_idx < out_size) {
     U val = _contiguous_strided_reduce<T, U, Op, N_READS>(
-              in,
-              local_data,
-              in_idx,
-              reduction_size,
-              reduction_stride,
-              tid.xy,
-              lid.xy,
-              lsize.xy);
+        in,
+        local_data,
+        in_idx,
+        reduction_size,
+        reduction_stride,
+        tid.xy,
+        lid.xy,
+        lsize.xy);
 
-    // Write out reduction results generated by threadgroups working on specific output element, contiguously.
+    // Write out reduction results generated by threadgroups working on specific
+    // output element, contiguously.
     if (lid.y == 0) {
       uint tgsize_y = ceildiv(gsize.y, lsize.y);
       uint tgsize_z = ceildiv(gsize.z, lsize.z);
@@ -195,52 +187,56 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
   }
 }
 
-#define instantiate_col_reduce_general(name, itype, otype, op) \
-  template [[host_name("col_reduce_general_" #name)]] \
-  [[kernel]] void col_reduce_general<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
-      const constant size_t& reduction_size [[buffer(2)]], \
-      const constant size_t& reduction_stride [[buffer(3)]], \
-      const constant size_t& out_size [[buffer(4)]], \
-      const constant int* shape [[buffer(5)]],  \
-      const constant size_t* strides [[buffer(6)]],  \
-      const constant int& ndim [[buffer(7)]],  \
-      threadgroup otype *local_data [[threadgroup(0)]], \
-      uint3 tid [[threadgroup_position_in_grid]], \
-      uint3 lid [[thread_position_in_threadgroup]], \
+#define instantiate_col_reduce_general(name, itype, otype, op)        \
+  template [[host_name("col_reduce_general_" #name)]] [[kernel]] void \
+  col_reduce_general<itype, otype, op>(                               \
+      const device itype* in [[buffer(0)]],                           \
+      device mlx_atomic<otype>* out [[buffer(1)]],                    \
+      const constant size_t& reduction_size [[buffer(2)]],            \
+      const constant size_t& reduction_stride [[buffer(3)]],          \
+      const constant size_t& out_size [[buffer(4)]],                  \
+      const constant int* shape [[buffer(5)]],                        \
+      const constant size_t* strides [[buffer(6)]],                   \
+      const constant int& ndim [[buffer(7)]],                         \
+      threadgroup otype* local_data [[threadgroup(0)]],               \
+      uint3 tid [[threadgroup_position_in_grid]],                     \
+      uint3 lid [[thread_position_in_threadgroup]],                   \
       uint3 lsize [[threads_per_threadgroup]]);
 
-#define instantiate_col_reduce_general_no_atomics(name, itype, otype, op) \
-  template [[host_name("col_reduce_general_no_atomics_" #name)]] \
-  [[kernel]] void col_reduce_general_no_atomics<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device otype *out [[buffer(1)]], \
-      const constant size_t& reduction_size [[buffer(2)]], \
-      const constant size_t& reduction_stride [[buffer(3)]], \
-      const constant size_t& out_size [[buffer(4)]], \
-      const constant int* shape [[buffer(5)]],  \
-      const constant size_t* strides [[buffer(6)]],  \
-      const constant int& ndim [[buffer(7)]],  \
-      threadgroup otype *local_data [[threadgroup(0)]], \
-      uint3 tid [[threadgroup_position_in_grid]], \
-      uint3 lid [[thread_position_in_threadgroup]], \
-      uint3 gid [[thread_position_in_grid]], \
-      uint3 lsize [[threads_per_threadgroup]], \
-      uint3 gsize [[threads_per_grid]]);
+#define instantiate_col_reduce_general_no_atomics(name, itype, otype, op)   \
+  template                                                                  \
+      [[host_name("col_reduce_general_no_atomics_" #name)]] [[kernel]] void \
+      col_reduce_general_no_atomics<itype, otype, op>(                      \
+          const device itype* in [[buffer(0)]],                             \
+          device otype* out [[buffer(1)]],                                  \
+          const constant size_t& reduction_size [[buffer(2)]],              \
+          const constant size_t& reduction_stride [[buffer(3)]],            \
+          const constant size_t& out_size [[buffer(4)]],                    \
+          const constant int* shape [[buffer(5)]],                          \
+          const constant size_t* strides [[buffer(6)]],                     \
+          const constant int& ndim [[buffer(7)]],                           \
+          threadgroup otype* local_data [[threadgroup(0)]],                 \
+          uint3 tid [[threadgroup_position_in_grid]],                       \
+          uint3 lid [[thread_position_in_threadgroup]],                     \
+          uint3 gid [[thread_position_in_grid]],                            \
+          uint3 lsize [[threads_per_threadgroup]],                          \
+          uint3 gsize [[threads_per_grid]]);
 
 ///////////////////////////////////////////////////////////////////////////////
 // Instantiations
 ///////////////////////////////////////////////////////////////////////////////
 
-#define instantiate_same_col_reduce_helper(name, tname, type, op) \
+// clang-format off
+#define instantiate_same_col_reduce_helper(name, tname, type, op)  \
   instantiate_col_reduce_small(name ##tname, type, type, op<type>) \
-  instantiate_col_reduce_general(name ##tname, type, type, op<type>)
+  instantiate_col_reduce_general(name ##tname, type, type, op<type>) // clang-format on
 
+// clang-format off
 #define instantiate_same_col_reduce_na_helper(name, tname, type, op) \
-  instantiate_col_reduce_small(name ##tname, type, type, op<type>) \
-  instantiate_col_reduce_general_no_atomics(name ##tname, type, type, op<type>)
+  instantiate_col_reduce_small(name ##tname, type, type, op<type>)   \
+  instantiate_col_reduce_general_no_atomics(name ##tname, type, type, op<type>) // clang-format on
 
+// clang-format off
 instantiate_reduce_ops(instantiate_same_col_reduce_helper, instantiate_reduce_helper_types)
 instantiate_reduce_ops(instantiate_same_col_reduce_na_helper, instantiate_reduce_helper_64b)
 
@@ -250,4 +246,4 @@ instantiate_reduce_from_types(instantiate_col_reduce_general, or, bool, Or)
 
 instantiate_col_reduce_small(sumbool_, bool, uint32_t, Sum<uint32_t>)
 instantiate_reduce_from_types(instantiate_col_reduce_small, and, bool, And)
-instantiate_reduce_from_types(instantiate_col_reduce_small, or, bool, Or)
+instantiate_reduce_from_types(instantiate_col_reduce_small, or, bool, Or) // clang-format on