Update benchmark output

* Fix stubgen

* add multi optim to docs

.circleci/config.yml

@@ -24,8 +24,8 @@ jobs:
         type: boolean
         default: false
     macos:
-      xcode: "15.2.0"
-    resource_class: macos.m1.medium.gen1
+      xcode: "16.2.0"
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
@@ -89,15 +89,14 @@ jobs:
             pip install numpy
             sudo apt-get update
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
+            sudo apt-get install openmpi-bin openmpi-common libopenmpi-dev
       - run:
           name: Install Python package
           command: |
-            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF
-              CMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
               CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
               python3 setup.py build_ext --inplace
-            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF \
-              CMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
               CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
               python3 setup.py develop
       - run:
@@ -110,6 +109,8 @@ jobs:
           name: Run Python tests
           command: |
             python3 -m unittest discover python/tests -v
+            mpirun --bind-to none -host localhost:8 -np 8 python python/tests/mpi_test_distributed.py
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
       - run:
           name: Build CPP only
           command: |
@@ -124,10 +125,15 @@ jobs:
     parameters:
       xcode_version:
         type: string
-        default: "15.2.0"
+        default: "16.2.0"
+      macosx_deployment_target:
+        type: string
+        default: ""
     macos:
       xcode: << parameters.xcode_version >>
-    resource_class: macos.m1.medium.gen1
+    environment:
+      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
@@ -149,7 +155,7 @@ jobs:
           command: |
             source env/bin/activate
             DEBUG=1 CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
-            CMAKE_ARGS="CMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+            CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
               pip install -e . -v
       - run:
           name: Generate package stubs
@@ -213,13 +219,18 @@ jobs:
         default: "3.9"
       xcode_version:
         type: string
-        default: "15.2.0"
+        default: "16.2.0"
       build_env:
         type: string
         default: ""
+      macosx_deployment_target:
+        type: string
+        default: ""
     macos:
       xcode: << parameters.xcode_version >>
-    resource_class: macos.m1.medium.gen1
+    resource_class: m2pro.medium
+    environment:
+      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
     steps:
       - checkout
       - run:
@@ -240,7 +251,7 @@ jobs:
           name: Install Python package
           command: |
             source env/bin/activate
-            DEV_RELEASE=1 \
+            env -u MACOSX_DEPLOYMENT_TARGET DEV_RELEASE=1 \
               CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
               pip install . -v
       - run:
@@ -335,7 +346,7 @@ workflows:
       - mac_build_and_test:
           matrix:
             parameters:
-              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test
       - build_documentation 
 
@@ -355,8 +366,70 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              xcode_version: ["15.0.0", "15.2.0"]
+              macosx_deployment_target: ["13.5", "14.0", "15.0"]
               build_env: ["PYPI_RELEASE=1"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
       - build_documentation:
           filters:
             tags:
@@ -379,7 +452,7 @@ workflows:
           requires: [ hold ]
           matrix:
             parameters:
-              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test:
           requires: [ hold ]
   nightly_build:
@@ -392,7 +465,54 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              xcode_version: ["15.0.0", "15.2.0"]
+              macosx_deployment_target: ["13.5", "14.0", "15.0"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
   weekly_build:
     when:
       and:
@@ -403,8 +523,70 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              macosx_deployment_target: ["13.5", "14.0", "15.0"]
               build_env: ["DEV_RELEASE=1"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
   linux_test_release:
     when:
       and:

CMakeLists.txt

@@ -212,24 +212,6 @@ else()
   set(MLX_BUILD_ACCELERATE OFF)
 endif()
 
-find_package(MPI)
-if(MPI_FOUND)
-  execute_process(
-    COMMAND zsh "-c" "mpirun --version"
-    OUTPUT_VARIABLE MPI_VERSION
-    ERROR_QUIET)
-  if(${MPI_VERSION} MATCHES ".*Open MPI.*")
-    target_include_directories(mlx PRIVATE ${MPI_INCLUDE_PATH})
-  elseif(MPI_VERSION STREQUAL "")
-    set(MPI_FOUND FALSE)
-    message(
-      WARNING "MPI found but mpirun is not available. Building without MPI.")
-  else()
-    set(MPI_FOUND FALSE)
-    message(WARNING "MPI which is not OpenMPI found. Building without MPI.")
-  endif()
-endif()
-
 message(STATUS "Downloading json")
 FetchContent_Declare(
   json

CONTRIBUTING.md

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

benchmarks/python/blas/bench_gemm.py

@@ -157,7 +157,7 @@ def bench_shape(B, M, N, K, np_dtype, transpose="nn"):
 
 
 def get_gflop_count(B, M, N, K):
-    return float(2.0 * N_iter_bench * N_iter_func * B * M * N * K) / float(1024.0**3)
+    return float(2.0 * N_iter_bench * N_iter_func * B * M * N * K) / float(1000.0**3)
 
 
 if __name__ == "__main__":
@@ -175,6 +175,8 @@ if __name__ == "__main__":
         (1, 4096, 4096, 4096),
     )
 
+    print(f"  B,    M,    N,     K,   dtype,  t, gflops_pt, gflops_mx, diff%")
+
     for dtype in dtypes:
         for transpose in transposes:
             for B, M, N, K in shapes:
@@ -187,7 +189,7 @@ if __name__ == "__main__":
                 diff = gflops_mx / gflops_pt - 1.0
 
                 print(
-                    f"{B:3d}, {M:4d}, {N:4d}, {K:4d}, {dtype}, {transpose}, {gflops_pt:05.3f}, {gflops_mx:05.3f}, {100. * diff:+5.2f}%"
+                    f"{B:3d}, {M:4d}, {N:4d}, {K:5d}, {dtype}, {transpose},  {gflops_pt:8.2f},  {gflops_mx:8.2f}, {100. * diff:+5.2f}%"
                 )
                 if gflops_pt >= 2.0 * gflops_mx:
                     print("ATTENTION ^^^^^^^")

benchmarks/python/gather_mm_bench.py

@@ -0,0 +1,74 @@
+# Copyright © 2023-2024 Apple Inc.
+
+import mlx.core as mx
+from time_utils import time_fn
+
+N = 1024
+D = 1024
+M = 1024
+E = 32
+I = 4
+
+
+def gather_sort(x, indices):
+    N, M = indices.shape
+    indices = indices.flatten()
+    order = mx.argsort(indices)
+    inv_order = mx.argsort(order)
+    return x.flatten(0, -3)[order // M], indices[order], inv_order
+
+
+def scatter_unsort(x, inv_order, shape=None):
+    x = x[inv_order]
+    if shape is not None:
+        x = mx.unflatten(x, 0, shape)
+    return x
+
+
+def gather_mm_simulate(x, w, indices):
+    x, idx, inv_order = gather_sort(x, indices)
+    for i in range(2):
+        y = mx.concatenate([x[i] @ w[j].T for i, j in enumerate(idx.tolist())], axis=0)
+        x = y[:, None]
+    x = scatter_unsort(x, inv_order, indices.shape)
+    return x
+
+
+def time_gather_mm():
+    x = mx.random.normal((N, 1, 1, D)) / 1024**0.5
+    w1 = mx.random.normal((E, M, D)) / 1024**0.5
+    w2 = mx.random.normal((E, D, M)) / 1024**0.5
+    indices = (mx.random.uniform(shape=(N, I)) * E).astype(mx.uint32)
+    sorted_indices = mx.sort(indices.flatten()).reshape(N, I)
+    mx.eval(x, w1, w2, indices, sorted_indices)
+
+    def gather_mm(x, w1, w2, indices, sort):
+        idx = indices
+        inv_order = None
+        if sort:
+            x, idx, inv_order = gather_sort(x, indices)
+        x = mx.gather_mm(x, w1.swapaxes(-1, -2), rhs_indices=idx, sorted_indices=sort)
+        x = mx.gather_mm(x, w2.swapaxes(-1, -2), rhs_indices=idx, sorted_indices=sort)
+        if sort:
+            x = scatter_unsort(x, inv_order, indices.shape)
+        return x
+
+    time_fn(gather_mm, x, w1, w2, indices, False)
+    time_fn(gather_mm, x, w1, w2, sorted_indices, False)
+    time_fn(gather_mm, x, w1, w2, indices, True)
+
+    x = mx.random.normal((N * I, D)) / 1024**0.5
+    w1 = mx.random.normal((M, D)) / 1024**0.5
+    w2 = mx.random.normal((D, M)) / 1024**0.5
+    mx.eval(x, w1, w2)
+
+    def equivalent_matmul(x, w1, w2):
+        x = x @ w1.T
+        x = x @ w2.T
+        return x
+
+    time_fn(equivalent_matmul, x, w1, w2)
+
+
+if __name__ == "__main__":
+    time_gather_mm()

docs/Doxyfile

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

docs/src/dev/extensions.rst

@@ -93,9 +93,9 @@ Primitives
 ^^^^^^^^^^^
 
 A :class:`Primitive` is part of the computation graph of an :class:`array`. It
-defines how to create outputs arrays given a input arrays. Further, a
+defines how to create output arrays given input arrays. Further, a
 :class:`Primitive` has methods to run on the CPU or GPU and for function
-transformations such as ``vjp`` and ``jvp``.  Lets go back to our example to be
+transformations such as ``vjp`` and ``jvp``.  Let's go back to our example to be
 more concrete:
 
 .. code-block:: C++
@@ -128,7 +128,7 @@ more concrete:
         /** The vector-Jacobian product. */
         std::vector<array> vjp(
             const std::vector<array>& primals,
-            const array& cotan,
+            const std::vector<array>& cotangents,
             const std::vector<int>& argnums,
             const std::vector<array>& outputs) override;
 
@@ -247,9 +247,7 @@ point-wise. This is captured in the templated function :meth:`axpby_impl`.
       float alpha_,
       float beta_,
       mx::Stream stream) {
-    // Allocate the output with `malloc_or_wait` which synchronously allocates
-    // memory, potentially waiting if the system is under memory pressure
-    out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(mx::allocator::malloc(out.nbytes()));
 
     // Get the CPU command encoder and register input and output arrays
     auto& encoder = mx::cpu::get_command_encoder(stream);
@@ -393,7 +391,7 @@ below.
         auto& d = metal::device(s.device);
 
         // Allocate output memory
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
+        out.set_data(allocator::malloc(out.nbytes()));
 
         // Resolve name of kernel
         std::ostringstream kname;
@@ -471,7 +469,7 @@ one we just defined:
             const std::vector<array>& tangents,
             const std::vector<int>& argnums) {
         // Forward mode diff that pushes along the tangents
-        // The jvp transform on the primitive can built with ops
+        // The jvp transform on the primitive can be built with ops
         // that are scheduled on the same stream as the primitive
 
         // If argnums = {0}, we only push along x in which case the
@@ -483,7 +481,7 @@ one we just defined:
             auto scale_arr = array(scale, tangents[0].dtype());
             return {multiply(scale_arr, tangents[0], stream())};
         }
-        // If, argnums = {0, 1}, we take contributions from both
+        // If argnums = {0, 1}, we take contributions from both
         // which gives us jvp = tangent_x * alpha + tangent_y * beta
         else {
             return {axpby(tangents[0], tangents[1], alpha_, beta_, stream())};
@@ -737,7 +735,7 @@ Let's look at a simple script and its results:
 
     print(f"c shape: {c.shape}")
     print(f"c dtype: {c.dtype}")
-    print(f"c correct: {mx.all(c == 6.0).item()}")
+    print(f"c is correct: {mx.all(c == 6.0).item()}")
 
 Output:
 
@@ -745,7 +743,7 @@ Output:
 
     c shape: [3, 4]
     c dtype: float32
-    c correctness: True
+    c is correct: True
 
 Results
 ^^^^^^^

docs/src/index.rst

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

docs/src/python/array.rst

@@ -38,6 +38,7 @@ Array
     array.log10
     array.log1p
     array.log2
+    array.logcumsumexp
     array.logsumexp
     array.max
     array.mean

docs/src/python/linalg.rst

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

docs/src/python/memory_management.rst

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

docs/src/python/metal.rst

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

docs/src/python/ops.rst

@@ -36,10 +36,12 @@ Operations
    bitwise_or
    bitwise_xor
    block_masked_mm
+   broadcast_arrays
    broadcast_to
    ceil
    clip
    concatenate
+   contiguous
    conj
    conjugate
    convolve
@@ -101,6 +103,7 @@ Operations
    log10
    log1p
    logaddexp
+   logcumsumexp
    logical_not
    logical_and
    logical_or

docs/src/python/optimizers/common_optimizers.rst

@@ -18,3 +18,4 @@ Common Optimizers
    AdamW
    Adamax
    Lion
+   MultiOptimizer

docs/src/python/transforms.rst

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

examples/extensions/axpby/axpby.cpp

@@ -72,9 +72,7 @@ void axpby_impl(
     float alpha_,
     float beta_,
     mx::Stream stream) {
-  // Allocate the output with `malloc_or_wait` which synchronously allocates
-  // memory, potentially waiting if the system is under memory pressure
-  out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(mx::allocator::malloc(out.nbytes()));
 
   // Get the CPU command encoder and register input and output arrays
   auto& encoder = mx::cpu::get_command_encoder(stream);
@@ -160,12 +158,12 @@ void Axpby::eval_gpu(
   // Allocate output memory with strides based on specialization
   if (contiguous_kernel) {
     out.set_data(
-        mx::allocator::malloc_or_wait(x.data_size() * out.itemsize()),
+        mx::allocator::malloc(x.data_size() * out.itemsize()),
         x.data_size(),
         x.strides(),
         x.flags());
   } else {
-    out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(mx::allocator::malloc(out.nbytes()));
   }
 
   // Resolve name of kernel (corresponds to axpby.metal)

mlx/allocator.cpp

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

mlx/allocator.h

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

mlx/backend/common/CMakeLists.txt

@@ -1,6 +1,7 @@
 target_sources(
   mlx
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/broadcasting.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/common.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/load.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp

mlx/backend/common/binary.h

@@ -44,14 +44,14 @@ inline void set_binary_op_output_data(
   switch (bopt) {
     case BinaryOpType::ScalarScalar:
       out.set_data(
-          allocator::malloc_or_wait(out.itemsize()), 1, a.strides(), a.flags());
+          allocator::malloc(out.itemsize()), 1, a.strides(), a.flags());
       break;
     case BinaryOpType::ScalarVector:
       if (b_donatable) {
         out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            allocator::malloc_or_wait(b.data_size() * out.itemsize()),
+            allocator::malloc(b.data_size() * out.itemsize()),
             b.data_size(),
             b.strides(),
             b.flags());
@@ -62,7 +62,7 @@ inline void set_binary_op_output_data(
         out.copy_shared_buffer(a);
       } else {
         out.set_data(
-            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
+            allocator::malloc(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -75,7 +75,7 @@ inline void set_binary_op_output_data(
         out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
+            allocator::malloc(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -88,7 +88,7 @@ inline void set_binary_op_output_data(
           b_donatable && b.flags().row_contiguous && b.size() == out.size()) {
         out.copy_shared_buffer(b);
       } else {
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
+        out.set_data(allocator::malloc(out.nbytes()));
       }
       break;
   }

mlx/backend/common/broadcasting.cpp

@@ -0,0 +1,24 @@
+// Copyright © 2024 Apple Inc.
+
+#include "mlx/backend/common/utils.h"
+
+namespace mlx::core {
+
+void broadcast(const array& in, array& out) {
+  if (out.size() == 0) {
+    out.set_data(nullptr);
+    return;
+  }
+  Strides strides(out.ndim(), 0);
+  int diff = out.ndim() - in.ndim();
+  for (int i = in.ndim() - 1; i >= 0; --i) {
+    strides[i + diff] = (in.shape()[i] == 1) ? 0 : in.strides()[i];
+  }
+  auto flags = in.flags();
+  if (out.size() > in.size()) {
+    flags.row_contiguous = flags.col_contiguous = false;
+  }
+  out.copy_shared_buffer(in, strides, flags, in.data_size());
+}
+
+} // namespace mlx::core

mlx/backend/common/broadcasting.h

@@ -0,0 +1,11 @@
+// Copyright © 2024 Apple Inc.
+
+#pragma once
+
+#include "mlx/array.h"
+
+namespace mlx::core {
+
+void broadcast(const array& in, array& out);
+
+} // namespace mlx::core

mlx/backend/common/common.cpp

@@ -1,6 +1,7 @@
 // Copyright © 2024 Apple Inc.
 #include <cassert>
 
+#include "mlx/backend/common/broadcasting.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/primitives.h"
 
@@ -42,23 +43,6 @@ void AsStrided::eval(const std::vector<array>& inputs, array& out) {
   return out.copy_shared_buffer(in, strides_, flags, data_size, offset_);
 }
 
-void broadcast(const array& in, array& out) {
-  if (out.size() == 0) {
-    out.set_data(nullptr);
-    return;
-  }
-  Strides strides(out.ndim(), 0);
-  int diff = out.ndim() - in.ndim();
-  for (int i = in.ndim() - 1; i >= 0; --i) {
-    strides[i + diff] = (in.shape()[i] == 1) ? 0 : in.strides()[i];
-  }
-  auto flags = in.flags();
-  if (out.size() > in.size()) {
-    flags.row_contiguous = flags.col_contiguous = false;
-  }
-  out.copy_shared_buffer(in, strides, flags, in.data_size());
-}
-
 void Broadcast::eval(const std::vector<array>& inputs, array& out) {
   broadcast(inputs[0], out);
 }
@@ -103,7 +87,7 @@ void ExpandDims::eval(const std::vector<array>& inputs, array& out) {
 
 void NumberOfElements::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   double numel = 1;
   for (auto ax : axes_) {

mlx/backend/common/compiled.cpp

@@ -188,7 +188,7 @@ void compiled_allocate_outputs(
     }
     for (; o < outputs.size(); ++o) {
       outputs[o].set_data(
-          allocator::malloc_or_wait(data_size * outputs[o].itemsize()),
+          allocator::malloc(data_size * outputs[o].itemsize()),
           data_size,
           strides,
           flags);
@@ -211,7 +211,7 @@ void compiled_allocate_outputs(
       }
     }
     for (; o < outputs.size(); ++o) {
-      outputs[o].set_data(allocator::malloc_or_wait(outputs[o].nbytes()));
+      outputs[o].set_data(allocator::malloc(outputs[o].nbytes()));
     }
   }
 }

mlx/backend/common/copy.h

@@ -31,14 +31,14 @@ inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
       return true;
     } else {
       out.set_data(
-          allocator::malloc_or_wait(in.data_size() * out.itemsize()),
+          allocator::malloc(in.data_size() * out.itemsize()),
           in.data_size(),
           in.strides(),
           in.flags());
       return false;
     }
   } else {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(allocator::malloc(out.nbytes()));
     return false;
   }
 }

mlx/backend/common/load.cpp

@@ -28,7 +28,7 @@ void swap_endianness(uint8_t* data_bytes, size_t N) {
 namespace mlx::core {
 
 void Load::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   auto read_task = [out_ptr = out.data<char>(),
                     size = out.size(),
                     itemsize = out.itemsize(),

mlx/backend/common/ternary.h

@@ -48,12 +48,12 @@ inline void set_ternary_op_output_data(
   switch (topt) {
     case TernaryOpType::ScalarScalarScalar:
       out.set_data(
-          allocator::malloc_or_wait(out.itemsize()), 1, b.strides(), b.flags());
+          allocator::malloc(out.itemsize()), 1, b.strides(), b.flags());
       break;
     case TernaryOpType::VectorVectorVector:
       if (!(maybe_donate(a) || maybe_donate(b) || maybe_donate(c))) {
         out.set_data(
-            allocator::malloc_or_wait(out.itemsize() * b.data_size()),
+            allocator::malloc(out.itemsize() * b.data_size()),
             b.data_size(),
             b.strides(),
             b.flags());
@@ -64,7 +64,7 @@ inline void set_ternary_op_output_data(
       if (!((a.flags().row_contiguous && maybe_donate(a)) ||
             (b.flags().row_contiguous && maybe_donate(b)) ||
             (c.flags().row_contiguous && maybe_donate(c)))) {
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
+        out.set_data(allocator::malloc(out.nbytes()));
       }
       break;
   }

mlx/backend/cpu/CMakeLists.txt

@@ -58,6 +58,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/select.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/threefry.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
@@ -73,8 +74,8 @@ target_sources(
 if(MLX_BUILD_ACCELERATE)
   target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/bnns.cpp)
 else()
-  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_fp16.cpp
-                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_bf16.cpp)
+  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_fp16.cpp
+                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_bf16.cpp)
 endif()
 
 if(IOS)

mlx/backend/cpu/arg_reduce.cpp

@@ -68,7 +68,7 @@ void arg_reduce_dispatch(
 void ArgReduce::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_input_array(in);
   encoder.set_output_array(out);

mlx/backend/cpu/conv.cpp

@@ -921,7 +921,7 @@ void explicit_gemm_conv_1D_cpu(
 
   if (out.dtype() != float32) {
     gemm_out = array(out.shape(), float32, nullptr, {});
-    gemm_out.set_data(allocator::malloc_or_wait(gemm_out.nbytes()));
+    gemm_out.set_data(allocator::malloc(gemm_out.nbytes()));
     temps.push_back(gemm_out);
   }
 
@@ -1048,7 +1048,7 @@ void explicit_gemm_conv_2D_cpu(
 
   if (out.dtype() != float32) {
     gemm_out = array(out.shape(), float32, nullptr, {});
-    gemm_out.set_data(allocator::malloc_or_wait(gemm_out.nbytes()));
+    gemm_out.set_data(allocator::malloc(gemm_out.nbytes()));
     temps.push_back(gemm_out);
   }
 
@@ -1214,7 +1214,7 @@ void explicit_gemm_conv_ND_cpu(
 
   if (out.dtype() != float32) {
     gemm_out = array(out.shape(), float32, nullptr, {});
-    gemm_out.set_data(allocator::malloc_or_wait(gemm_out.nbytes()));
+    gemm_out.set_data(allocator::malloc(gemm_out.nbytes()));
     temps.push_back(gemm_out);
   }
 
@@ -1327,7 +1327,7 @@ void conv_3D_cpu(
 } // namespace
 
 void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& in = inputs[0];
   auto& wt = inputs[1];

mlx/backend/cpu/distributed.cpp

@@ -30,7 +30,7 @@ void AllReduce::eval_cpu(
       if (in.is_donatable()) {
         out.copy_shared_buffer(in);
       } else {
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
+        out.set_data(allocator::malloc(out.nbytes()));
       }
       return in;
     } else {
@@ -46,8 +46,15 @@ void AllReduce::eval_cpu(
     case Sum:
       distributed::detail::all_sum(group(), in, outputs[0], stream());
       break;
+    case Max:
+      distributed::detail::all_max(group(), in, outputs[0], stream());
+      break;
+    case Min:
+      distributed::detail::all_min(group(), in, outputs[0], stream());
+      break;
     default:
-      throw std::runtime_error("Only all reduce sum is supported for now");
+      throw std::runtime_error(
+          "Only all reduce sum, min and max are supported for now");
   }
 }
 
@@ -58,7 +65,7 @@ void AllGather::eval_cpu(
   assert(outputs.size() == 1);
 
   auto [in, copied] = ensure_row_contiguous(inputs[0], stream());
-  outputs[0].set_data(allocator::malloc_or_wait(outputs[0].nbytes()));
+  outputs[0].set_data(allocator::malloc(outputs[0].nbytes()));
   distributed::detail::all_gather(group(), in, outputs[0], stream());
   if (copied) {
     auto& enc = cpu::get_command_encoder(stream());
@@ -87,7 +94,7 @@ void Recv::eval_cpu(
   assert(inputs.size() == 0);
   assert(outputs.size() == 1);
 
-  outputs[0].set_data(allocator::malloc_or_wait(outputs[0].nbytes()));
+  outputs[0].set_data(allocator::malloc(outputs[0].nbytes()));
   distributed::detail::recv(group(), outputs[0], src_, stream());
 }
 

mlx/backend/cpu/eigh.cpp

@@ -55,9 +55,8 @@ void eigh_impl(
       liwork = iwork;
     }
 
-    auto work_buf = array::Data{allocator::malloc_or_wait(sizeof(T) * lwork)};
-    auto iwork_buf =
-        array::Data{allocator::malloc_or_wait(sizeof(int) * liwork)};
+    auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
+    auto iwork_buf = array::Data{allocator::malloc(sizeof(int) * liwork)};
     for (size_t i = 0; i < size / (N * N); ++i) {
       syevd<T>(
           &jobz,
@@ -98,7 +97,7 @@ void Eigh::eval_cpu(
       ? outputs[1]
       : array(a.shape(), a.dtype(), nullptr, {});
 
-  values.set_data(allocator::malloc_or_wait(values.nbytes()));
+  values.set_data(allocator::malloc(values.nbytes()));
 
   copy(
       a,

mlx/backend/cpu/fft.cpp

@@ -22,7 +22,7 @@ void FFT::eval_cpu(const std::vector<array>& inputs, array& out) {
     s *= out.itemsize();
   }
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   std::vector<size_t> shape;
   if (out.dtype() == float32) {

mlx/backend/cpu/gemms/no_bf16.cpp

@@ -1,27 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cpu/gemm.h"
-
-namespace mlx::core {
-
-template <>
-void matmul<bfloat16_t>(
-    const bfloat16_t*,
-    const bfloat16_t*,
-    bfloat16_t*,
-    bool,
-    bool,
-    size_t,
-    size_t,
-    size_t,
-    float,
-    float,
-    size_t,
-    const Shape&,
-    const Strides&,
-    const Shape&,
-    const Strides&) {
-  throw std::runtime_error("[Matmul::eval_cpu] bfloat16 not supported.");
-}
-
-} // namespace mlx::core

mlx/backend/cpu/gemms/no_fp16.cpp

@@ -1,27 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cpu/gemm.h"
-
-namespace mlx::core {
-
-template <>
-void matmul<float16_t>(
-    const float16_t*,
-    const float16_t*,
-    float16_t*,
-    bool,
-    bool,
-    size_t,
-    size_t,
-    size_t,
-    float,
-    float,
-    size_t,
-    const Shape&,
-    const Strides&,
-    const Shape&,
-    const Strides&) {
-  throw std::runtime_error("[Matmul::eval_cpu] float16 not supported.");
-}
-
-} // namespace mlx::core

mlx/backend/cpu/gemms/simd_bf16.cpp

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

mlx/backend/cpu/gemms/simd_fp16.cpp

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

mlx/backend/cpu/gemms/simd_gemm.h

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

mlx/backend/cpu/indexing.cpp

@@ -197,7 +197,7 @@ void dispatch_gather(
 }
 
 void Gather::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& src = inputs[0];
   std::vector<array> inds;
@@ -354,7 +354,7 @@ void dispatch_gather_axis(
 }
 
 void GatherAxis::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& src = inputs[0];
   auto& inds = inputs[1];

mlx/backend/cpu/inverse.cpp

@@ -11,7 +11,7 @@ namespace mlx::core {
 template <typename T>
 void general_inv(T* inv, int N) {
   int info;
-  auto ipiv = array::Data{allocator::malloc_or_wait(sizeof(int) * N)};
+  auto ipiv = array::Data{allocator::malloc(sizeof(int) * N)};
   // Compute LU factorization.
   getrf<T>(
       /* m = */ &N,
@@ -49,7 +49,7 @@ void general_inv(T* inv, int N) {
   }
 
   const int lwork = workspace_size;
-  auto scratch = array::Data{allocator::malloc_or_wait(sizeof(T) * lwork)};
+  auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
 
   // Compute inverse.
   getri<T>(

mlx/backend/cpu/logsumexp.cpp

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

mlx/backend/cpu/luf.cpp

@@ -30,8 +30,7 @@ void luf_impl(
   auto strides = lu.strides();
   strides[ndim - 1] = M;
   strides[ndim - 2] = 1;
-  lu.set_data(
-      allocator::malloc_or_wait(lu.nbytes()), lu.nbytes(), strides, flags);
+  lu.set_data(allocator::malloc(lu.nbytes()), lu.nbytes(), strides, flags);
   copy_inplace(
       a,
       lu,
@@ -44,8 +43,8 @@ void luf_impl(
       stream);
 
   auto a_ptr = lu.data<T>();
-  pivots.set_data(allocator::malloc_or_wait(pivots.nbytes()));
-  row_indices.set_data(allocator::malloc_or_wait(row_indices.nbytes()));
+  pivots.set_data(allocator::malloc(pivots.nbytes()));
+  row_indices.set_data(allocator::malloc(row_indices.nbytes()));
   auto pivots_ptr = pivots.data<uint32_t>();
   auto row_indices_ptr = row_indices.data<uint32_t>();
   size_t num_matrices = a.size() / (M * N);

mlx/backend/cpu/masked_mm.cpp

@@ -59,7 +59,7 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[BlockMaskedMM::eval] Currently only supports float32.");
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& a_pre = inputs[0];
   auto& b_pre = inputs[1];
@@ -318,7 +318,7 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[GatherMM::eval] Currently only supports float32.");
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& a_pre = inputs[0];
   auto& b_pre = inputs[1];

mlx/backend/cpu/matmul.cpp

@@ -115,7 +115,7 @@ void matmul_general(
 }
 
 void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   if (inputs[0].shape(-1) == 0) {
     auto& encoder = cpu::get_command_encoder(stream());
     encoder.set_output_array(out);

mlx/backend/cpu/primitives.cpp

@@ -21,7 +21,7 @@ namespace mlx::core {
 void reshape(const array& in, array& out) {
   auto [copy_necessary, out_strides] = prepare_reshape(in, out);
   if (copy_necessary) {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(allocator::malloc(out.nbytes()));
     copy_inplace(in, out, CopyType::General, out.primitive().stream());
   } else {
     shared_buffer_reshape(in, out_strides, out);
@@ -39,7 +39,7 @@ static std::pair<array, bool> compute_dynamic_offset(
   if (donate) {
     offset.copy_shared_buffer(indices);
   } else {
-    offset.set_data(allocator::malloc_or_wait(offset.itemsize()));
+    offset.set_data(allocator::malloc(offset.itemsize()));
   }
 
   auto& encoder = cpu::get_command_encoder(stream);
@@ -124,7 +124,7 @@ void Transpose::eval_cpu(const std::vector<array>& inputs, array& out) {
 
 void Arange::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 0);
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   switch (out.dtype()) {
     case bool_:
       throw std::runtime_error("Bool type unsupported for arange.");
@@ -186,7 +186,7 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
   }
   std::partial_sum(sizes.cbegin(), sizes.cend(), sizes.begin());
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto strides = out.strides();
   auto flags = out.flags();
@@ -205,8 +205,10 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
 void Contiguous::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  if (in.flags().row_contiguous ||
-      (allow_col_major_ && in.flags().col_contiguous)) {
+  constexpr size_t extra_bytes = 16384;
+  if (in.buffer_size() <= out.nbytes() + extra_bytes &&
+      (in.flags().row_contiguous ||
+       (allow_col_major_ && in.flags().col_contiguous))) {
     out.copy_shared_buffer(in);
   } else {
     copy(in, out, CopyType::General, stream());
@@ -276,7 +278,7 @@ void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   size_t elems_per_key = out.size() / num_keys;
   size_t bytes_per_key = out.itemsize() * elems_per_key;
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto kptr = inputs[0].data<uint32_t>();
   auto cptr = out.data<char>();
@@ -335,7 +337,7 @@ void DynamicSlice::eval_cpu(const std::vector<array>& inputs, array& out) {
     return;
   }
   auto& in = inputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   auto [in_offset, donated] =
       compute_dynamic_offset(inputs[1], in.strides(), axes_, stream());
   copy_inplace(
@@ -450,7 +452,7 @@ void View::eval_cpu(const std::vector<array>& inputs, array& out) {
   } else {
     auto tmp = array(
         in.shape(), in.dtype() == bool_ ? uint8 : in.dtype(), nullptr, {});
-    tmp.set_data(allocator::malloc_or_wait(tmp.nbytes()));
+    tmp.set_data(allocator::malloc(tmp.nbytes()));
     if (in.dtype() == bool_) {
       auto in_tmp = array(in.shape(), uint8, nullptr, {});
       in_tmp.copy_shared_buffer(in);

mlx/backend/cpu/qrf.cpp

@@ -25,12 +25,11 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   auto strides = in.strides();
   strides[in.ndim() - 2] = 1;
   strides[in.ndim() - 1] = M;
-  in.set_data(
-      allocator::malloc_or_wait(in.nbytes()), in.nbytes(), strides, flags);
+  in.set_data(allocator::malloc(in.nbytes()), in.nbytes(), strides, flags);
   copy_inplace(a, in, CopyType::GeneralGeneral, stream);
   auto& encoder = cpu::get_command_encoder(stream);
-  q.set_data(allocator::malloc_or_wait(q.nbytes()));
-  r.set_data(allocator::malloc_or_wait(r.nbytes()));
+  q.set_data(allocator::malloc(q.nbytes()));
+  r.set_data(allocator::malloc(r.nbytes()));
 
   auto in_ptr = in.data<T>();
   auto r_ptr = r.data<T>();
@@ -41,8 +40,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   encoder.set_output_array(r);
   encoder.dispatch([in_ptr, q_ptr, r_ptr, M, N, lda, num_matrices]() {
     int num_reflectors = std::min(M, N);
-    auto tau =
-        allocator::malloc_or_wait(sizeof(T) * num_matrices * num_reflectors);
+    auto tau = allocator::malloc(sizeof(T) * num_matrices * num_reflectors);
 
     T optimal_work;
     int lwork = -1;
@@ -53,7 +51,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
 
     // Update workspace size
     lwork = optimal_work;
-    auto work = allocator::malloc_or_wait(sizeof(T) * lwork);
+    auto work = allocator::malloc(sizeof(T) * lwork);
 
     // Loop over matrices
     for (int i = 0; i < num_matrices; ++i) {
@@ -96,7 +94,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
         &lwork,
         &info);
     lwork = optimal_work;
-    work = allocator::malloc_or_wait(sizeof(T) * lwork);
+    work = allocator::malloc(sizeof(T) * lwork);
 
     // Loop over matrices
     for (int i = 0; i < num_matrices; ++i) {

mlx/backend/cpu/quantized.cpp

@@ -515,7 +515,7 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto scales = ensure_row_contiguous(scales_pre);
   auto biases = ensure_row_contiguous(biases_pre);
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.add_temporaries(std::move(temps));
@@ -565,7 +565,7 @@ void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto scales = ensure_row_contiguous_last_dims(scales_pre);
   auto biases = ensure_row_contiguous_last_dims(biases_pre);
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.add_temporaries(std::move(temps));
@@ -691,12 +691,12 @@ void fast::AffineQuantize::eval_cpu(
 
   auto [w, copied] = ensure_row_contiguous(inputs[0]);
   auto& out = outputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& scales = outputs[1];
   auto& biases = outputs[2];
-  scales.set_data(allocator::malloc_or_wait(scales.nbytes()));
-  biases.set_data(allocator::malloc_or_wait(biases.nbytes()));
+  scales.set_data(allocator::malloc(scales.nbytes()));
+  biases.set_data(allocator::malloc(biases.nbytes()));
   auto& encoder = cpu::get_command_encoder(stream());
   if (copied) {
     encoder.add_temporary(w);

mlx/backend/cpu/reduce.cpp

@@ -433,7 +433,7 @@ void reduce_dispatch_min_max(
 void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_input_array(in);
   encoder.set_output_array(out);

mlx/backend/cpu/scan.cpp

@@ -3,6 +3,7 @@
 #include <cassert>
 
 #include "mlx/backend/common/utils.h"
+#include "mlx/backend/cpu/binary_ops.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
@@ -226,6 +227,16 @@ void scan_dispatch(
       scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
       break;
     }
+    case Scan::LogAddExp: {
+      auto op = [](U a, T b) {
+        return detail::LogAddExp{}(a, static_cast<U>(b));
+      };
+      auto init = (issubdtype(in.dtype(), floating))
+          ? static_cast<U>(-std::numeric_limits<float>::infinity())
+          : std::numeric_limits<U>::min();
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
+      break;
+    }
   }
 }
 
@@ -244,7 +255,7 @@ void Scan::eval_cpu(const std::vector<array>& inputs, array& out) {
     in = arr_copy;
     encoder.add_temporary(arr_copy);
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   encoder.set_input_array(in);
   encoder.set_output_array(out);

mlx/backend/cpu/simd/accelerate_fp16_simd.h

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

mlx/backend/cpu/simd/accelerate_simd.h

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

mlx/backend/cpu/simd/base_simd.h

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

mlx/backend/cpu/softmax.cpp

@@ -119,17 +119,12 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   // Make sure that the last dimension is contiguous
   auto set_output = [s = stream(), &out](const array& x) {
-    bool no_copy = x.strides()[x.ndim() - 1] == 1;
-    if (x.ndim() > 1) {
-      auto s = x.strides()[x.ndim() - 2];
-      no_copy &= (s == 0 || s == x.shape().back());
-    }
-    if (no_copy) {
+    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
       if (x.is_donatable()) {
         out.copy_shared_buffer(x);
       } else {
         out.set_data(
-            allocator::malloc_or_wait(x.data_size() * x.itemsize()),
+            allocator::malloc(x.data_size() * x.itemsize()),
             x.data_size(),
             x.strides(),
             x.flags());
@@ -146,18 +141,6 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto in = set_output(inputs[0]);
 
   switch (in.dtype()) {
-    case bool_:
-    case uint8:
-    case uint16:
-    case uint32:
-    case uint64:
-    case int8:
-    case int16:
-    case int32:
-    case int64:
-      throw std::runtime_error(
-          "Softmax is defined only for floating point types");
-      break;
     case float32:
       softmax<float, float>(in, out, stream());
       break;
@@ -178,9 +161,9 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
     case float64:
       softmax<double, double>(in, out, stream());
       break;
-    case complex64:
-      throw std::invalid_argument(
-          "[Softmax] Not yet implemented for complex64");
+    default:
+      throw std::runtime_error(
+          "[softmax] Only defined for floating point types.");
       break;
   }
 }

mlx/backend/cpu/sort.cpp

@@ -288,7 +288,7 @@ void ArgSort::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& in = inputs[0];
 
   // Allocate output
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_input_array(in);
@@ -379,7 +379,7 @@ void ArgPartition::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& in = inputs[0];
 
   // Allocate output
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_input_array(in);

mlx/backend/cpu/svd.cpp

@@ -50,9 +50,9 @@ void svd_impl(
     array& s = outputs[1];
     array& vt = outputs[2];
 
-    u.set_data(allocator::malloc_or_wait(u.nbytes()));
-    s.set_data(allocator::malloc_or_wait(s.nbytes()));
-    vt.set_data(allocator::malloc_or_wait(vt.nbytes()));
+    u.set_data(allocator::malloc(u.nbytes()));
+    s.set_data(allocator::malloc(s.nbytes()));
+    vt.set_data(allocator::malloc(vt.nbytes()));
 
     encoder.set_output_array(u);
     encoder.set_output_array(s);
@@ -64,7 +64,7 @@ void svd_impl(
   } else {
     array& s = outputs[0];
 
-    s.set_data(allocator::malloc_or_wait(s.nbytes()));
+    s.set_data(allocator::malloc(s.nbytes()));
 
     encoder.set_output_array(s);
 
@@ -91,7 +91,7 @@ void svd_impl(
 
     // Will contain the indices of eigenvectors that failed to converge (not
     // used here but required by lapack).
-    auto iwork = array::Data{allocator::malloc_or_wait(sizeof(int) * 12 * K)};
+    auto iwork = array::Data{allocator::malloc(sizeof(int) * 12 * K)};
 
     static const int lwork_query = -1;
 
@@ -132,7 +132,7 @@ void svd_impl(
     }
 
     const int lwork = workspace_dimension;
-    auto scratch = array::Data{allocator::malloc_or_wait(sizeof(T) * lwork)};
+    auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
 
     // Loop over matrices.
     for (int i = 0; i < num_matrices; i++) {

mlx/backend/cpu/unary.cpp

@@ -1,5 +1,8 @@
 // Copyright © 2024 Apple Inc.
 
+// Required for using M_LN2 in MSVC.
+#define _USE_MATH_DEFINES
+
 #include <cassert>
 
 #include "mlx/backend/cpu/unary.h"

mlx/backend/cpu/unary.h

@@ -18,13 +18,13 @@ void set_unary_output_data(const array& in, array& out) {
     } else {
       auto size = in.data_size();
       out.set_data(
-          allocator::malloc_or_wait(size * out.itemsize()),
+          allocator::malloc(size * out.itemsize()),
           size,
           in.strides(),
           in.flags());
     }
   } else {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(allocator::malloc(out.nbytes()));
   }
 }
 

mlx/backend/cpu/unary_ops.h

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

mlx/backend/metal/CMakeLists.txt

@@ -47,6 +47,7 @@ if(MLX_METAL_JIT)
   make_jit_source(binary)
   make_jit_source(binary_two)
   make_jit_source(fft kernels/fft/radix.h kernels/fft/readwrite.h)
+  make_jit_source(logsumexp)
   make_jit_source(ternary)
   make_jit_source(softmax)
   make_jit_source(scan)
@@ -60,6 +61,7 @@ if(MLX_METAL_JIT)
     kernels/steel/gemm/transforms.h)
   make_jit_source(steel/gemm/kernels/steel_gemm_fused)
   make_jit_source(steel/gemm/kernels/steel_gemm_masked kernels/steel/defines.h)
+  make_jit_source(steel/gemm/kernels/steel_gemm_gather)
   make_jit_source(steel/gemm/kernels/steel_gemm_splitk)
   make_jit_source(
     steel/conv/conv
@@ -95,6 +97,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/hadamard.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/metal.cpp

mlx/backend/metal/allocator.cpp

@@ -3,6 +3,7 @@
 #include "mlx/backend/metal/metal.h"
 #include "mlx/backend/metal/metal_impl.h"
 #include "mlx/backend/metal/resident.h"
+#include "mlx/memory.h"
 
 #include <mach/vm_page_size.h>
 #include <unistd.h>
@@ -32,8 +33,11 @@ namespace metal {
 
 namespace {
 
-BufferCache::BufferCache(MTL::Device* device)
-    : device_(device), head_(nullptr), tail_(nullptr), pool_size_(0) {}
+BufferCache::BufferCache(ResidencySet& residency_set)
+    : head_(nullptr),
+      tail_(nullptr),
+      pool_size_(0),
+      residency_set_(residency_set) {}
 
 BufferCache::~BufferCache() {
   auto pool = metal::new_scoped_memory_pool();
@@ -44,6 +48,9 @@ int BufferCache::clear() {
   int n_release = 0;
   for (auto& [size, holder] : buffer_pool_) {
     if (holder->buf) {
+      if (!holder->buf->heap()) {
+        residency_set_.erase(holder->buf);
+      }
       holder->buf->release();
       n_release++;
     }
@@ -101,6 +108,9 @@ int BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
     while (tail_ && (total_bytes_freed < min_bytes_to_free)) {
       if (tail_->buf) {
         total_bytes_freed += tail_->buf->length();
+        if (!tail_->buf->heap()) {
+          residency_set_.erase(tail_->buf);
+        }
         tail_->buf->release();
         tail_->buf = nullptr;
         n_release++;
@@ -155,7 +165,7 @@ void BufferCache::remove_from_list(BufferCache::BufferHolder* to_remove) {
 MetalAllocator::MetalAllocator()
     : device_(device(mlx::core::Device::gpu).mtl_device()),
       residency_set_(device_),
-      buffer_cache_(device_) {
+      buffer_cache_(residency_set_) {
   auto pool = metal::new_scoped_memory_pool();
   auto memsize = std::get<size_t>(device_info().at("memory_size"));
   auto max_rec_size =
@@ -192,16 +202,19 @@ size_t MetalAllocator::set_cache_limit(size_t limit) {
   return limit;
 };
 
-size_t MetalAllocator::set_memory_limit(size_t limit, bool relaxed) {
+size_t MetalAllocator::set_memory_limit(size_t limit) {
   std::unique_lock lk(mutex_);
   std::swap(limit, block_limit_);
-  relaxed_ = relaxed;
   gc_limit_ = std::min(
       block_limit_,
       static_cast<size_t>(0.95 * device_->recommendedMaxWorkingSetSize()));
   return limit;
 };
 
+size_t MetalAllocator::get_memory_limit() {
+  return block_limit_;
+}
+
 size_t MetalAllocator::set_wired_limit(size_t limit) {
   std::unique_lock lk(mutex_);
   std::swap(limit, wired_limit_);
@@ -209,7 +222,7 @@ size_t MetalAllocator::set_wired_limit(size_t limit) {
   return limit;
 };
 
-Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
+Buffer MetalAllocator::malloc(size_t size) {
   // Metal doesn't like empty buffers
   if (size == 0) {
     return Buffer{nullptr};
@@ -236,11 +249,6 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
   if (!buf) {
     size_t mem_required = get_active_memory() + get_cache_memory() + size;
 
-    // If there is too much memory pressure, fail (likely causes a wait).
-    if (!(allow_swap && relaxed_) && mem_required >= block_limit_) {
-      return Buffer{nullptr};
-    }
-
     auto pool = metal::new_scoped_memory_pool();
 
     // If we have a lot of memory pressure or are over the maximum cache size,
@@ -264,9 +272,13 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
     if (!buf) {
       buf = device_->newBuffer(size, resource_options);
     }
+    if (!buf) {
+      return Buffer{nullptr};
+    }
     lk.lock();
-    if (buf) {
-      num_resources_++;
+    num_resources_++;
+    if (!buf->heap()) {
+      residency_set_.insert(buf);
     }
   }
 
@@ -280,10 +292,6 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
         get_cache_memory() - max_pool_size_);
   }
 
-  if (!buf->heap()) {
-    residency_set_.insert(buf);
-  }
-
   return Buffer{static_cast<void*>(buf)};
 }
 
@@ -299,14 +307,14 @@ void MetalAllocator::free(Buffer buffer) {
     return;
   }
   std::unique_lock lk(mutex_);
-  if (!buf->heap()) {
-    residency_set_.erase(buf);
-  }
   active_memory_ -= buf->length();
   if (get_cache_memory() < max_pool_size_) {
     buffer_cache_.recycle_to_cache(buf);
   } else {
     num_resources_--;
+    if (!buf->heap()) {
+      residency_set_.erase(buf);
+    }
     lk.unlock();
     auto pool = metal::new_scoped_memory_pool();
     buf->release();
@@ -325,37 +333,40 @@ MetalAllocator& allocator() {
   return *allocator_;
 }
 
+} // namespace metal
+
 size_t set_cache_limit(size_t limit) {
-  return allocator().set_cache_limit(limit);
+  return metal::allocator().set_cache_limit(limit);
 }
-size_t set_memory_limit(size_t limit, bool relaxed /* = true */) {
-  return allocator().set_memory_limit(limit, relaxed);
+size_t set_memory_limit(size_t limit) {
+  return metal::allocator().set_memory_limit(limit);
+}
+size_t get_memory_limit() {
+  return metal::allocator().get_memory_limit();
 }
 size_t set_wired_limit(size_t limit) {
-  if (limit >
-      std::get<size_t>(device_info().at("max_recommended_working_set_size"))) {
+  if (limit > std::get<size_t>(metal::device_info().at(
+                  "max_recommended_working_set_size"))) {
     throw std::invalid_argument(
         "[metal::set_wired_limit] Setting a wired limit larger than "
         "the maximum working set size is not allowed.");
   }
-  return allocator().set_wired_limit(limit);
+  return metal::allocator().set_wired_limit(limit);
 }
 size_t get_active_memory() {
-  return allocator().get_active_memory();
+  return metal::allocator().get_active_memory();
 }
 size_t get_peak_memory() {
-  return allocator().get_peak_memory();
+  return metal::allocator().get_peak_memory();
 }
 void reset_peak_memory() {
-  allocator().reset_peak_memory();
+  metal::allocator().reset_peak_memory();
 }
 size_t get_cache_memory() {
-  return allocator().get_cache_memory();
+  return metal::allocator().get_cache_memory();
 }
 void clear_cache() {
-  return allocator().clear_cache();
+  return metal::allocator().clear_cache();
 }
 
-} // namespace metal
-
 } // namespace mlx::core

mlx/backend/metal/allocator.h

@@ -18,7 +18,7 @@ namespace {
 
 class BufferCache {
  public:
-  BufferCache(MTL::Device* device);
+  BufferCache(ResidencySet& residency_set);
   ~BufferCache();
 
   MTL::Buffer* reuse_from_cache(size_t size);
@@ -42,13 +42,11 @@ class BufferCache {
   void add_at_head(BufferHolder* to_add);
   void remove_from_list(BufferHolder* to_remove);
 
-  MTL::Device* device_;
-  MTL::Heap* heap_{nullptr};
-
   std::multimap<size_t, BufferHolder*> buffer_pool_;
   BufferHolder* head_;
   BufferHolder* tail_;
   size_t pool_size_;
+  ResidencySet& residency_set_;
 };
 
 } // namespace
@@ -56,7 +54,7 @@ class BufferCache {
 class MetalAllocator : public allocator::Allocator {
   /** Allocator for Metal GPUs. */
  public:
-  virtual Buffer malloc(size_t size, bool allow_swap = false) override;
+  virtual Buffer malloc(size_t size) override;
   virtual void free(Buffer buffer) override;
   virtual size_t size(Buffer buffer) const override;
   size_t get_active_memory() {
@@ -73,7 +71,8 @@ class MetalAllocator : public allocator::Allocator {
     return buffer_cache_.cache_size();
   };
   size_t set_cache_limit(size_t limit);
-  size_t set_memory_limit(size_t limit, bool relaxed);
+  size_t set_memory_limit(size_t limit);
+  size_t get_memory_limit();
   size_t set_wired_limit(size_t limit);
   void clear_cache();
 

mlx/backend/metal/conv.cpp

@@ -37,7 +37,7 @@ void explicit_gemm_conv_ND_gpu(
   Shape unfolded_shape{implicit_M, implicit_K};
   array in_unfolded(unfolded_shape, in.dtype(), nullptr, {});
 
-  in_unfolded.set_data(allocator::malloc_or_wait(in_unfolded.nbytes()));
+  in_unfolded.set_data(allocator::malloc(in_unfolded.nbytes()));
 
   // Prepare unfolding kernel
   std::ostringstream kname;
@@ -115,7 +115,7 @@ void explicit_gemm_conv_group_ND_gpu(
   // Prepare unfolding array
   Shape unfolded_shape{implicit_M, implicit_K * groups};
   array in_unfolded(unfolded_shape, in.dtype(), nullptr, {});
-  in_unfolded.set_data(allocator::malloc_or_wait(in_unfolded.nbytes()));
+  in_unfolded.set_data(allocator::malloc(in_unfolded.nbytes()));
 
   // Prepare unfolding kernel
   std::ostringstream kname;
@@ -613,7 +613,7 @@ void winograd_conv_2D_gpu(
   // Do filter transform
   Shape filt_wg_shape = {8 * 8, conv_params.C, conv_params.O};
   array filt_wg(std::move(filt_wg_shape), wt.dtype(), nullptr, {});
-  filt_wg.set_data(allocator::malloc_or_wait(filt_wg.nbytes()));
+  filt_wg.set_data(allocator::malloc(filt_wg.nbytes()));
   copies_w.push_back(filt_wg);
   {
     int bc = 32;
@@ -640,7 +640,7 @@ void winograd_conv_2D_gpu(
   // Do input transform
   Shape inp_wg_shape = {8 * 8, N_tiles, conv_params.C};
   array inp_wg(std::move(inp_wg_shape), in.dtype(), nullptr, {});
-  inp_wg.set_data(allocator::malloc_or_wait(inp_wg.nbytes()));
+  inp_wg.set_data(allocator::malloc(inp_wg.nbytes()));
   copies_w.push_back(inp_wg);
   {
     int bc = 32;
@@ -667,7 +667,7 @@ void winograd_conv_2D_gpu(
   // Do batched gemm
   Shape out_wg_shape = {8 * 8, N_tiles, conv_params.O};
   array out_wg(std::move(out_wg_shape), in.dtype(), nullptr, {});
-  out_wg.set_data(allocator::malloc_or_wait(out_wg.nbytes()));
+  out_wg.set_data(allocator::malloc(out_wg.nbytes()));
   copies_w.push_back(out_wg);
   {
     std::vector<array> empty_copies;
@@ -712,6 +712,65 @@ void winograd_conv_2D_gpu(
   }
 }
 
+void depthwise_conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<2>& conv_params) {
+  std::ostringstream kname;
+  kname << "depthwise_conv_2d_" << type_to_name(out);
+  std::string base_name = kname.str();
+
+  const int N = conv_params.N;
+  const int ker_h = conv_params.wS[0];
+  const int ker_w = conv_params.wS[1];
+  const int str_h = conv_params.str[0];
+  const int str_w = conv_params.str[1];
+  const int tc = 8;
+  const int tw = 8;
+  const int th = 4;
+  const bool do_flip = conv_params.flip;
+
+  metal::MTLFCList func_consts = {
+      {&ker_h, MTL::DataType::DataTypeInt, 00},
+      {&ker_w, MTL::DataType::DataTypeInt, 01},
+      {&str_h, MTL::DataType::DataTypeInt, 10},
+      {&str_w, MTL::DataType::DataTypeInt, 11},
+      {&th, MTL::DataType::DataTypeInt, 100},
+      {&tw, MTL::DataType::DataTypeInt, 101},
+      {&do_flip, MTL::DataType::DataTypeBool, 200},
+  };
+
+  // clang-format off
+  kname << "_ker_h_" << ker_h
+        << "_ker_w_" << ker_w
+        << "_str_h_" << str_h
+        << "_str_w_" << str_w
+        << "_tgp_h_" << th
+        << "_tgp_w_" << tw
+        << "_do_flip_" << (do_flip ? 't' : 'n'); // clang-format on
+
+  std::string hash_name = kname.str();
+
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(base_name, "mlx", hash_name, func_consts);
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_input_array(wt, 1);
+  compute_encoder.set_output_array(out, 2);
+
+  compute_encoder.set_bytes(conv_params, 3);
+
+  MTL::Size group_dims = MTL::Size(tc, tw, th);
+  MTL::Size grid_dims = MTL::Size(
+      conv_params.C / tc, conv_params.oS[1] / tw, (conv_params.oS[0] / th) * N);
+
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+}
+
 void conv_2D_gpu(
     const Stream& s,
     metal::Device& d,
@@ -754,11 +813,20 @@ void conv_2D_gpu(
   bool is_kdil_one = conv_params.kdil[0] == 1 && conv_params.kdil[1] == 1;
   bool is_idil_one = conv_params.idil[0] == 1 && conv_params.idil[1] == 1;
 
-  if (groups > 1) {
+  if (is_idil_one && groups > 1) {
     const int C_per_group = conv_params.C / groups;
     const int O_per_group = conv_params.O / groups;
 
-    if (is_idil_one && (C_per_group <= 4 || C_per_group % 16 == 0) &&
+    if (C_per_group == 1 && O_per_group == 1 && is_kdil_one &&
+        conv_params.wS[0] <= 7 && conv_params.wS[1] <= 7 &&
+        conv_params.str[0] <= 2 && conv_params.str[1] <= 2 &&
+        conv_params.oS[0] % 8 == 0 && conv_params.oS[1] % 8 == 0 &&
+        conv_params.wt_strides[1] == conv_params.wS[1] &&
+        conv_params.C % 16 == 0 && conv_params.C == conv_params.O) {
+      return depthwise_conv_2D_gpu(s, d, in, wt, out, conv_params);
+    }
+
+    if ((C_per_group <= 4 || C_per_group % 16 == 0) &&
         (O_per_group <= 16 || O_per_group % 16 == 0)) {
       return implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
     } else {
@@ -855,7 +923,7 @@ void conv_3D_gpu(
 } // namespace
 
 void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   auto& s = stream();
   auto& d = metal::device(s.device);
 

mlx/backend/metal/copy.cpp

@@ -202,7 +202,7 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
   if (out.size() == 0) {
     return;
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   bool large = out.data_size() > UINT32_MAX;
   auto& d = metal::device(s.device);
   std::string kernel_name = std::string(large ? "s2" : "s") + "_copy" +

mlx/backend/metal/custom_kernel.cpp

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

mlx/backend/metal/device.cpp

@@ -55,7 +55,10 @@ std::pair<MTL::Library*, NS::Error*> load_library_from_path(
 }
 
 #ifdef SWIFTPM_BUNDLE
-MTL::Library* try_load_bundle(MTL::Device* device, NS::URL* url) {
+MTL::Library* try_load_bundle(
+    MTL::Device* device,
+    NS::URL* url,
+    const std::string& lib_name) {
   std::string bundle_path = std::string(url->fileSystemRepresentation()) + "/" +
       SWIFTPM_BUNDLE + ".bundle";
   auto bundle = NS::Bundle::alloc()->init(
@@ -63,8 +66,8 @@ MTL::Library* try_load_bundle(MTL::Device* device, NS::URL* url) {
   if (bundle != nullptr) {
     std::string resource_path =
         std::string(bundle->resourceURL()->fileSystemRepresentation()) + "/" +
-        "default.metallib";
-    auto [lib, error] = load_library_from_path(device, resource_path.c_str());
+        lib_name + ".metallib" auto [lib, error] =
+            load_library_from_path(device, resource_path.c_str());
     if (lib) {
       return lib;
     }
@@ -73,51 +76,124 @@ MTL::Library* try_load_bundle(MTL::Device* device, NS::URL* url) {
 }
 #endif
 
+// Firstly, search for the metallib in the same path as this binary
+std::pair<MTL::Library*, NS::Error*> load_colocated_library(
+    MTL::Device* device,
+    const std::string& lib_name) {
+  std::string lib_path = get_colocated_mtllib_path(lib_name);
+  if (lib_path.size() != 0) {
+    return load_library_from_path(device, lib_path.c_str());
+  }
+  return {nullptr, nullptr};
+}
+
+std::pair<MTL::Library*, NS::Error*> load_swiftpm_library(
+    MTL::Device* device,
+    const std::string& lib_name) {
+#ifdef SWIFTPM_BUNDLE
+  MTL::Library* library =
+      try_load_bundle(device, NS::Bundle::mainBundle()->bundleURL(), lib_name);
+  if (library != nullptr) {
+    return {library, nullptr};
+  }
+  auto bundles = NS::Bundle::allBundles();
+  for (int i = 0, c = (int)bundles->count(); i < c; i++) {
+    auto bundle = reinterpret_cast<NS::Bundle*>(bundles->object(i));
+    library = try_load_bundle(device, bundle->resourceURL());
+    if (library != nullptr) {
+      return {library, nullptr};
+    }
+  }
+#endif
+  return {nullptr, nullptr};
+}
+
+MTL::Library* load_default_library(MTL::Device* device) {
+  NS::Error *error1, *error2, *error3;
+  MTL::Library* lib;
+  // First try the colocated mlx.metallib
+  std::tie(lib, error1) = load_colocated_library(device, "mlx");
+  if (lib) {
+    return lib;
+  }
+
+  // Then try default.metallib in a SwiftPM bundle if we have one
+  std::tie(lib, error2) = load_swiftpm_library(device, "default");
+  if (lib) {
+    return lib;
+  }
+
+  // Finally try default_mtllib_path
+  std::tie(lib, error3) = load_library_from_path(device, default_mtllib_path);
+  if (!lib) {
+    std::ostringstream msg;
+    msg << "Failed to load the default metallib. ";
+    if (error1 != nullptr) {
+      msg << error1->localizedDescription()->utf8String() << " ";
+    }
+    if (error2 != nullptr) {
+      msg << error2->localizedDescription()->utf8String() << " ";
+    }
+    if (error3 != nullptr) {
+      msg << error3->localizedDescription()->utf8String() << " ";
+    }
+    throw std::runtime_error(msg.str());
+  }
+  return lib;
+}
+
 MTL::Library* load_library(
     MTL::Device* device,
-    const std::string& lib_name = "mlx",
-    const char* lib_path = default_mtllib_path) {
-  // Firstly, search for the metallib in the same path as this binary
-  std::string first_path = get_colocated_mtllib_path(lib_name);
-  if (first_path.size() != 0) {
-    auto [lib, error] = load_library_from_path(device, first_path.c_str());
+    const std::string& lib_name,
+    const std::string& lib_path) {
+  // We have been given a path that ends in metallib so try to load it
+  if (lib_path.size() > 9 &&
+      std::equal(lib_path.end() - 9, lib_path.end(), ".metallib")) {
+    auto [lib, error] = load_library_from_path(device, lib_path.c_str());
+    if (!lib) {
+      std::ostringstream msg;
+      msg << "Failed to load the metallib from <" << lib_path << "> with error "
+          << error->localizedDescription()->utf8String();
+      throw std::runtime_error(msg.str());
+    }
+  }
+
+  // We have been given a path so try to load from lib_path / lib_name.metallib
+  if (lib_path.size() > 0) {
+    std::string full_path = lib_path + "/" + lib_name + ".metallib";
+    auto [lib, error] = load_library_from_path(device, full_path.c_str());
+    if (!lib) {
+      std::ostringstream msg;
+      msg << "Failed to load the metallib from <" << full_path
+          << "> with error " << error->localizedDescription()->utf8String();
+      throw std::runtime_error(msg.str());
+    }
+  }
+
+  // Try to load the colocated library
+  {
+    auto [lib, error] = load_colocated_library(device, lib_name);
     if (lib) {
       return lib;
     }
   }
 
-#ifdef SWIFTPM_BUNDLE
-  // try to load from a swiftpm resource bundle -- scan the available bundles to
-  // find one that contains the named bundle
+  // Try to load the library from swiftpm
   {
-    MTL::Library* library =
-        try_load_bundle(device, NS::Bundle::mainBundle()->bundleURL());
-    if (library != nullptr) {
-      return library;
-    }
-    auto bundles = NS::Bundle::allBundles();
-    for (int i = 0, c = (int)bundles->count(); i < c; i++) {
-      auto bundle = reinterpret_cast<NS::Bundle*>(bundles->object(i));
-      library = try_load_bundle(device, bundle->resourceURL());
-      if (library != nullptr) {
-        return library;
-      }
+    auto [lib, error] = load_swiftpm_library(device, lib_name);
+    if (lib) {
+      return lib;
     }
   }
-#endif
 
-  // Couldn't find it so let's load it from default_mtllib_path
-  {
-    auto [lib, error] = load_library_from_path(device, lib_path);
-    if (!lib) {
-      std::ostringstream msg;
-      msg << error->localizedDescription()->utf8String() << "\n"
-          << "Failed to load device library from <" << lib_path << ">"
-          << " or <" << first_path << ">.";
-      throw std::runtime_error(msg.str());
-    }
-    return lib;
-  }
+  std::ostringstream msg;
+  msg << "Failed to load the metallib " << lib_name << ".metallib. "
+      << "We attempted to load it from <" << get_colocated_mtllib_path(lib_name)
+      << ">";
+#ifdef SWIFTPM_BUNDLE
+  msg << " and from the Swift PM bundle.";
+#endif
+  throw std::runtime_error(msg.str());
 }
 
 } // namespace
@@ -210,7 +286,7 @@ void CommandEncoder::barrier() {
 Device::Device() {
   auto pool = new_scoped_memory_pool();
   device_ = load_device();
-  library_map_ = {{"mlx", load_library(device_)}};
+  library_map_ = {{"mlx", load_default_library(device_)}};
   arch_ = std::string(device_->architecture()->name()->utf8String());
   auto arch = arch_.back();
   switch (arch) {

mlx/backend/metal/device.h

@@ -189,15 +189,7 @@ class Device {
 
   void register_library(
       const std::string& lib_name,
-      const std::string& lib_path);
-
-  // Note, this should remain in the header so that it is not dynamically
-  // linked
-  void register_library(const std::string& lib_name) {
-    if (auto it = library_map_.find(lib_name); it == library_map_.end()) {
-      register_library(lib_name, get_colocated_mtllib_path(lib_name));
-    }
-  }
+      const std::string& lib_path = "");
 
   MTL::Library* get_library(
       const std::string& name,

mlx/backend/metal/event.cpp

@@ -24,10 +24,6 @@ void Event::wait() {
   }
 }
 
-void Event::signal() {
-  static_cast<MTL::SharedEvent*>(event_.get())->setSignaledValue(value());
-}
-
 void Event::wait(Stream stream) {
   if (stream.device == Device::cpu) {
     scheduler::enqueue(stream, [*this]() mutable { wait(); });
@@ -42,7 +38,9 @@ void Event::wait(Stream stream) {
 
 void Event::signal(Stream stream) {
   if (stream.device == Device::cpu) {
-    scheduler::enqueue(stream, [*this]() mutable { signal(); });
+    scheduler::enqueue(stream, [*this]() mutable {
+      static_cast<MTL::SharedEvent*>(event_.get())->setSignaledValue(value());
+    });
   } else {
     auto& d = metal::device(stream.device);
     d.end_encoding(stream.index);

mlx/backend/metal/fence.cpp

@@ -20,7 +20,7 @@ struct FenceImpl {
       auto p = metal::new_scoped_memory_pool();
       fence = static_cast<void*>(d->newSharedEvent());
     } else {
-      auto buf = allocator::malloc_or_wait(sizeof(uint32_t)).ptr();
+      auto buf = allocator::malloc(sizeof(uint32_t)).ptr();
       fence = static_cast<void*>(buf);
       cpu_value()[0] = 0;
     }

mlx/backend/metal/fft.cpp

@@ -281,7 +281,7 @@ std::tuple<array, array, array> compute_raders_constants(
   }
 
   array b_q_fft({rader_n - 1}, complex64, nullptr, {});
-  b_q_fft.set_data(allocator::malloc_or_wait(b_q_fft.nbytes()));
+  b_q_fft.set_data(allocator::malloc(b_q_fft.nbytes()));
   auto b_q_fft_ptr =
       reinterpret_cast<std::complex<float>*>(b_q_fft.data<complex64_t>());
   std::ptrdiff_t item_size = b_q_fft.itemsize();
@@ -327,11 +327,11 @@ std::pair<array, array> compute_bluestein_constants(int n, int bluestein_n) {
   }
 
   array w_k({n}, complex64, nullptr, {});
-  w_k.set_data(allocator::malloc_or_wait(w_k.nbytes()));
+  w_k.set_data(allocator::malloc(w_k.nbytes()));
   std::copy(w_k_vec.begin(), w_k_vec.end(), w_k.data<complex64_t>());
 
   array w_q({bluestein_n}, complex64, nullptr, {});
-  w_q.set_data(allocator::malloc_or_wait(w_q.nbytes()));
+  w_q.set_data(allocator::malloc(w_q.nbytes()));
   auto w_q_ptr =
       reinterpret_cast<std::complex<float>*>(w_q.data<complex64_t>());
 
@@ -356,20 +356,14 @@ void multi_upload_bluestein_fft(
     bool inverse,
     bool real,
     FFTPlan& plan,
-    std::vector<array> copies,
+    std::vector<array>& copies,
     const Stream& s) {
   // TODO(alexbarron) Implement fused kernels for mutli upload bluestein's
   // algorithm
   int n = inverse ? out.shape(axis) : in.shape(axis);
   auto [w_k, w_q] = compute_bluestein_constants(n, plan.bluestein_n);
-
-  // Broadcast w_q and w_k to the batch size
-  Strides b_strides(in.ndim(), 0);
-  b_strides[axis] = 1;
-  array w_k_broadcast({}, complex64, nullptr, {});
-  array w_q_broadcast({}, complex64, nullptr, {});
-  w_k_broadcast.copy_shared_buffer(w_k, b_strides, {}, w_k.data_size());
-  w_q_broadcast.copy_shared_buffer(w_q, b_strides, {}, w_q.data_size());
+  copies.push_back(w_k);
+  copies.push_back(w_q);
 
   auto temp_shape = inverse ? out.shape() : in.shape();
   array temp(temp_shape, complex64, nullptr, {});
@@ -378,13 +372,13 @@ void multi_upload_bluestein_fft(
   if (real && !inverse) {
     // Convert float32->complex64
     copy_gpu(in, temp, CopyType::General, s);
+    copies.push_back(temp);
   } else if (real && inverse) {
     int back_offset = n % 2 == 0 ? 2 : 1;
     auto slice_shape = in.shape();
     slice_shape[axis] -= back_offset;
     array slice_temp(slice_shape, complex64, nullptr, {});
     array conj_temp(in.shape(), complex64, nullptr, {});
-    copies.push_back(slice_temp);
     copies.push_back(conj_temp);
 
     Shape rstarts(in.ndim(), 0);
@@ -394,19 +388,28 @@ void multi_upload_bluestein_fft(
     unary_op_gpu({in}, conj_temp, "Conjugate", s);
     slice_gpu(in, slice_temp, rstarts, rstrides, s);
     concatenate_gpu({conj_temp, slice_temp}, temp, (int)axis, s);
+    copies.push_back(temp);
   } else if (inverse) {
     unary_op_gpu({in}, temp, "Conjugate", s);
+    copies.push_back(temp);
   } else {
     temp.copy_shared_buffer(in);
   }
 
+  Strides b_strides(in.ndim(), 0);
+  b_strides[axis] = 1;
+  array w_k_broadcast(temp.shape(), complex64, nullptr, {});
+  w_k_broadcast.copy_shared_buffer(w_k, b_strides, {}, w_k.data_size());
   binary_op_gpu({temp, w_k_broadcast}, temp1, "Multiply", s);
 
   std::vector<std::pair<int, int>> pads;
   auto padded_shape = out.shape();
   padded_shape[axis] = plan.bluestein_n;
   array pad_temp(padded_shape, complex64, nullptr, {});
-  pad_gpu(temp1, array(complex64_t{0.0f, 0.0f}), pad_temp, {(int)axis}, {0}, s);
+  auto zero = array(complex64_t{0.0f, 0.0f});
+  copies.push_back(zero);
+  pad_gpu(temp1, zero, pad_temp, {(int)axis}, {0}, s);
+  copies.push_back(pad_temp);
 
   array pad_temp1(padded_shape, complex64, nullptr, {});
   fft_op(
@@ -418,7 +421,10 @@ void multi_upload_bluestein_fft(
       FourStepParams(),
       /*inplace=*/false,
       s);
+  copies.push_back(pad_temp1);
 
+  array w_q_broadcast(pad_temp1.shape(), complex64, nullptr, {});
+  w_q_broadcast.copy_shared_buffer(w_q, b_strides, {}, w_q.data_size());
   binary_op_gpu_inplace({pad_temp1, w_q_broadcast}, pad_temp, "Multiply", s);
 
   fft_op(
@@ -435,9 +441,11 @@ void multi_upload_bluestein_fft(
   Shape starts(in.ndim(), 0);
   Shape strides(in.ndim(), 1);
   starts[axis] = plan.bluestein_n - offset - n;
-  slice_gpu(pad_temp1, temp, starts, strides, s);
 
-  binary_op_gpu_inplace({temp, w_k_broadcast}, temp1, "Multiply", s);
+  array temp2(temp_shape, complex64, nullptr, {});
+  slice_gpu(pad_temp1, temp2, starts, strides, s);
+
+  binary_op_gpu_inplace({temp2, w_k_broadcast}, temp1, "Multiply", s);
 
   if (real && !inverse) {
     Shape rstarts(in.ndim(), 0);
@@ -449,26 +457,21 @@ void multi_upload_bluestein_fft(
     array temp_float(out.shape(), out.dtype(), nullptr, {});
     copies.push_back(temp_float);
     copies.push_back(inv_n);
+    copies.push_back(temp1);
 
     copy_gpu(temp1, temp_float, CopyType::General, s);
     binary_op_gpu({temp_float, inv_n}, out, "Multiply", s);
   } else if (inverse) {
     auto inv_n = array({1.0f / n}, {1}, complex64);
-    unary_op_gpu({temp1}, temp, "Conjugate", s);
-    binary_op_gpu({temp, inv_n}, out, "Multiply", s);
+    array temp3(temp_shape, complex64, nullptr, {});
+    unary_op_gpu({temp1}, temp3, "Conjugate", s);
+    binary_op_gpu({temp3, inv_n}, out, "Multiply", s);
     copies.push_back(inv_n);
+    copies.push_back(temp1);
+    copies.push_back(temp3);
   } else {
     out.copy_shared_buffer(temp1);
   }
-
-  copies.push_back(w_k);
-  copies.push_back(w_q);
-  copies.push_back(w_k_broadcast);
-  copies.push_back(w_q_broadcast);
-  copies.push_back(temp);
-  copies.push_back(temp1);
-  copies.push_back(pad_temp);
-  copies.push_back(pad_temp1);
 }
 
 void four_step_fft(
@@ -478,8 +481,9 @@ void four_step_fft(
     bool inverse,
     bool real,
     FFTPlan& plan,
-    std::vector<array> copies,
-    const Stream& s) {
+    std::vector<array>& copies,
+    const Stream& s,
+    bool in_place) {
   auto& d = metal::device(s.device);
 
   if (plan.bluestein_n == -1) {
@@ -492,7 +496,14 @@ void four_step_fft(
         in, temp, axis, inverse, real, four_step_params, /*inplace=*/false, s);
     four_step_params.first_step = false;
     fft_op(
-        temp, out, axis, inverse, real, four_step_params, /*inplace=*/false, s);
+        temp,
+        out,
+        axis,
+        inverse,
+        real,
+        four_step_params,
+        /*inplace=*/in_place,
+        s);
     copies.push_back(temp);
   } else {
     multi_upload_bluestein_fft(in, out, axis, inverse, real, plan, copies, s);
@@ -551,8 +562,7 @@ void fft_op(
       flags.row_contiguous = is_row_contiguous;
       flags.contiguous = data_size == x_copy.size();
 
-      x_copy.set_data(
-          allocator::malloc_or_wait(x.nbytes()), data_size, strides, flags);
+      x_copy.set_data(allocator::malloc(x.nbytes()), data_size, strides, flags);
       copy_gpu_inplace(x, x_copy, CopyType::GeneralGeneral, s);
       copies.push_back(x_copy);
       return x_copy;
@@ -575,7 +585,7 @@ void fft_op(
 
   auto plan = plan_fft(n);
   if (plan.four_step) {
-    four_step_fft(in, out, axis, inverse, real, plan, copies, s);
+    four_step_fft(in, out, axis, inverse, real, plan, copies, s, inplace);
     d.add_temporaries(std::move(copies), s.index);
     return;
   }
@@ -583,7 +593,7 @@ void fft_op(
   // TODO: allow donation here
   if (!inplace) {
     out.set_data(
-        allocator::malloc_or_wait(out.nbytes()),
+        allocator::malloc(out.nbytes()),
         out_data_size,
         out_strides,
         in_contiguous.flags());

mlx/backend/metal/hadamard.cpp

@@ -84,7 +84,7 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (in_contiguous.is_donatable()) {
     out.copy_shared_buffer(in_contiguous);
   } else {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(allocator::malloc(out.nbytes()));
   }
 
   int n, m;
@@ -161,7 +161,7 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
     // Upload 2:
     // y = h12 @ tmp
     array temp(in.shape(), in.dtype(), nullptr, {});
-    temp.set_data(allocator::malloc_or_wait(temp.nbytes()));
+    temp.set_data(allocator::malloc(temp.nbytes()));
     copies.push_back(temp);
 
     launch_hadamard(in_contiguous, temp, "n" + kernel_name, 1.0);

mlx/backend/metal/indexing.cpp

@@ -43,7 +43,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(msg.str());
   }
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   if (out.size() == 0) {
     return;
   }
@@ -393,7 +393,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto& src = inputs[0];
   auto& idx = inputs[1];
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   if (out.size() == 0) {
     return;
   }

mlx/backend/metal/jit/arange.h

@@ -1,9 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-constexpr std::string_view arange_kernels = R"(
-template [[host_name("{0}")]] [[kernel]] void arange<{1}>(
-    constant const {1}& start,
-    constant const {1}& step,
-    device {1}* out,
-    uint index [[thread_position_in_grid]]);
-)";

mlx/backend/metal/jit/includes.h

@@ -20,6 +20,7 @@ const char* copy();
 const char* fft();
 const char* gather_axis();
 const char* hadamard();
+const char* logsumexp();
 const char* quantized();
 const char* ternary();
 const char* scan();
@@ -32,6 +33,7 @@ const char* gemm();
 const char* steel_gemm_fused();
 const char* steel_gemm_masked();
 const char* steel_gemm_splitk();
+const char* steel_gemm_gather();
 const char* conv();
 const char* steel_conv();
 const char* steel_conv_general();

mlx/backend/metal/jit/softmax.h

@@ -1,23 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-constexpr std::string_view softmax_kernels = R"(
-template [[host_name("block_{0}")]] [[kernel]] void
-softmax_single_row<{1}, {2}>(
-    const device {1}* in,
-    device {1}* out,
-    constant int& axis_size,
-    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("looped_{0}")]] [[kernel]] void
-softmax_looped<{1}, {2}>(
-    const device {1}* in,
-    device {1}* out,
-    constant int& axis_size,
-    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]]);
-)";

mlx/backend/metal/jit_kernels.cpp

@@ -1,8 +1,6 @@
 // Copyright © 2024 Apple Inc.
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/metal/jit/arange.h"
 #include "mlx/backend/metal/jit/includes.h"
-#include "mlx/backend/metal/jit/softmax.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/utils.h"
 
@@ -21,13 +19,11 @@ MTL::ComputePipelineState* get_arange_kernel(
     const std::string& kernel_name,
     const array& out) {
   auto lib = d.get_library(kernel_name, [&]() {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::arange()
-                  << fmt::format(
-                         arange_kernels,
-                         kernel_name,
-                         get_type_string(out.dtype()));
-    return kernel_source.str();
+    std::string kernel_source = metal::utils();
+    kernel_source += metal::arange();
+    kernel_source += get_template_definition(
+        kernel_name, "arange", get_type_string(out.dtype()));
+    return kernel_source;
   });
   return d.get_kernel(kernel_name, lib);
 }
@@ -259,14 +255,34 @@ MTL::ComputePipelineState* get_softmax_kernel(
     const array& out) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
   auto lib = d.get_library(lib_name, [&] {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::softmax()
-                  << fmt::format(
-                         softmax_kernels,
-                         lib_name,
-                         get_type_string(out.dtype()),
-                         get_type_string(precise ? float32 : out.dtype()));
-    return kernel_source.str();
+    std::string kernel_source = metal::utils();
+    auto in_type = get_type_string(out.dtype());
+    auto acc_type = get_type_string(precise ? float32 : out.dtype());
+    kernel_source += metal::softmax();
+    kernel_source += get_template_definition(
+        "block_" + lib_name, "softmax_single_row", in_type, acc_type);
+    kernel_source += get_template_definition(
+        "looped_" + lib_name, "softmax_looped", in_type, acc_type);
+    return kernel_source;
+  });
+  return d.get_kernel(kernel_name, lib);
+}
+
+MTL::ComputePipelineState* get_logsumexp_kernel(
+    metal::Device& d,
+    const std::string& kernel_name,
+    const array& out) {
+  std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
+  auto lib = d.get_library(lib_name, [&] {
+    auto t_str = get_type_string(out.dtype());
+    std::string kernel_source;
+    kernel_source = metal::utils();
+    kernel_source += metal::logsumexp();
+    kernel_source +=
+        get_template_definition("block_" + lib_name, "logsumexp", t_str);
+    kernel_source += get_template_definition(
+        "looped_" + lib_name, "logsumexp_looped", t_str);
+    return kernel_source;
   });
   return d.get_kernel(kernel_name, lib);
 }
@@ -568,6 +584,44 @@ MTL::ComputePipelineState* get_steel_gemm_masked_kernel(
   return d.get_kernel(kernel_name, lib);
 }
 
+MTL::ComputePipelineState* get_steel_gemm_gather_kernel(
+    metal::Device& d,
+    const std::string& kernel_name,
+    const std::string& hash_name,
+    const metal::MTLFCList& func_consts,
+    const array& out,
+    bool transpose_a,
+    bool transpose_b,
+    int bm,
+    int bn,
+    int bk,
+    int wm,
+    int wn,
+    bool rhs) {
+  const auto& lib_name = kernel_name;
+  auto lib = d.get_library(lib_name, [&]() {
+    std::string kernel_source;
+    concatenate(
+        kernel_source,
+        metal::utils(),
+        metal::gemm(),
+        metal::steel_gemm_gather(),
+        get_template_definition(
+            lib_name,
+            rhs ? "gather_mm_rhs" : "gather_mm",
+            get_type_string(out.dtype()),
+            bm,
+            bn,
+            bk,
+            wm,
+            wn,
+            transpose_a,
+            transpose_b));
+    return kernel_source;
+  });
+  return d.get_kernel(kernel_name, lib, hash_name, func_consts);
+}
+
 MTL::ComputePipelineState* get_gemv_masked_kernel(
     metal::Device& d,
     const std::string& kernel_name,

mlx/backend/metal/kernels.h

@@ -59,6 +59,11 @@ MTL::ComputePipelineState* get_softmax_kernel(
     bool precise,
     const array& out);
 
+MTL::ComputePipelineState* get_logsumexp_kernel(
+    metal::Device& d,
+    const std::string& kernel_name,
+    const array& out);
+
 MTL::ComputePipelineState* get_scan_kernel(
     metal::Device& d,
     const std::string& kernel_name,
@@ -155,6 +160,21 @@ MTL::ComputePipelineState* get_steel_gemm_masked_kernel(
     bool mn_aligned,
     bool k_aligned);
 
+MTL::ComputePipelineState* get_steel_gemm_gather_kernel(
+    metal::Device& d,
+    const std::string& kernel_name,
+    const std::string& hash_name,
+    const metal::MTLFCList& func_consts,
+    const array& out,
+    bool transpose_a,
+    bool transpose_b,
+    int bm,
+    int bn,
+    int bk,
+    int wm,
+    int wn,
+    bool rhs);
+
 MTL::ComputePipelineState* get_steel_conv_kernel(
     metal::Device& d,
     const std::string& kernel_name,

mlx/backend/metal/kernels/CMakeLists.txt

@@ -13,6 +13,10 @@ function(build_kernel_base TARGET SRCFILE DEPS)
   if(MLX_METAL_DEBUG)
     set(METAL_FLAGS ${METAL_FLAGS} -gline-tables-only -frecord-sources)
   endif()
+  if(NOT CMAKE_OSX_DEPLOYMENT_TARGET STREQUAL "")
+    set(METAL_FLAGS ${METAL_FLAGS}
+                    "-mmacosx-version-min=${CMAKE_OSX_DEPLOYMENT_TARGET}")
+  endif()
   if(MLX_METAL_VERSION GREATER_EQUAL 310)
     set(VERSION_INCLUDES
         ${PROJECT_SOURCE_DIR}/mlx/backend/metal/kernels/metal_3_1)
@@ -65,6 +69,7 @@ set(STEEL_HEADERS
     steel/gemm/loader.h
     steel/gemm/transforms.h
     steel/gemm/kernels/steel_gemm_fused.h
+    steel/gemm/kernels/steel_gemm_gather.h
     steel/gemm/kernels/steel_gemm_masked.h
     steel/gemm/kernels/steel_gemm_splitk.h
     steel/utils/type_traits.h
@@ -105,12 +110,14 @@ if(NOT MLX_METAL_JIT)
   build_kernel(quantized quantized.h ${STEEL_HEADERS})
   build_kernel(scan scan.h)
   build_kernel(softmax softmax.h)
+  build_kernel(logsumexp logsumexp.h)
   build_kernel(sort sort.h)
   build_kernel(ternary ternary.h ternary_ops.h)
   build_kernel(unary unary.h unary_ops.h)
   build_kernel(steel/conv/kernels/steel_conv ${STEEL_HEADERS})
   build_kernel(steel/conv/kernels/steel_conv_general ${STEEL_HEADERS})
   build_kernel(steel/gemm/kernels/steel_gemm_fused ${STEEL_HEADERS})
+  build_kernel(steel/gemm/kernels/steel_gemm_gather ${STEEL_HEADERS})
   build_kernel(steel/gemm/kernels/steel_gemm_masked ${STEEL_HEADERS})
   build_kernel(steel/gemm/kernels/steel_gemm_splitk ${STEEL_HEADERS})
   build_kernel(gemv_masked steel/utils.h)

mlx/backend/metal/kernels/arange.metal

@@ -5,11 +5,7 @@
 #include "mlx/backend/metal/kernels/arange.h"
 
 #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_kernel("arange" #tname, arange, type)
 
 instantiate_arange(uint8, uint8_t)
 instantiate_arange(uint16, uint16_t)

mlx/backend/metal/kernels/conv.metal

@@ -275,6 +275,128 @@ instantiate_naive_conv_2d_blocks(float32, float);
 instantiate_naive_conv_2d_blocks(float16, half);
 instantiate_naive_conv_2d_blocks(bfloat16, bfloat16_t);
 
+///////////////////////////////////////////////////////////////////////////////
+/// Depthwise convolution kernels
+///////////////////////////////////////////////////////////////////////////////
+
+constant int ker_h [[function_constant(00)]];
+constant int ker_w [[function_constant(01)]];
+constant int str_h [[function_constant(10)]];
+constant int str_w [[function_constant(11)]];
+constant int tgp_h [[function_constant(100)]];
+constant int tgp_w [[function_constant(101)]];
+constant bool do_flip [[function_constant(200)]];
+
+constant int span_h = tgp_h * str_h + ker_h - 1;
+constant int span_w = tgp_w * str_w + ker_w - 1;
+constant int span_hw = span_h * span_w;
+
+template <typename T>
+[[kernel]] void depthwise_conv_2d(
+    const device T* in [[buffer(0)]],
+    const device T* wt [[buffer(1)]],
+    device T* out [[buffer(2)]],
+    const constant MLXConvParams<2>& params [[buffer(3)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint3 gid [[thread_position_in_grid]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  constexpr int tc = 8;
+  constexpr int tw = 8;
+  constexpr int th = 4;
+
+  constexpr int c_per_thr = 8;
+
+  constexpr int TGH = th * 2 + 6;
+  constexpr int TGW = tw * 2 + 6;
+  constexpr int TGC = tc;
+
+  threadgroup T ins[TGH * TGW * TGC];
+
+  const int n_tgblocks_h = params.oS[0] / th;
+  const int n = tid.z / n_tgblocks_h;
+  const int tghid = tid.z % n_tgblocks_h;
+  const int oh = tghid * th + lid.z;
+  const int ow = gid.y;
+  const int c = gid.x;
+
+  in += n * params.in_strides[0];
+
+  // Load in
+  {
+    constexpr int n_threads = th * tw * tc;
+    const int tg_oh = (tghid * th) * str_h - params.pad[0];
+    const int tg_ow = (tid.y * tw) * str_w - params.pad[1];
+    const int tg_c = tid.x * tc;
+
+    const int thread_idx = simd_gid * 32 + simd_lid;
+    constexpr int thr_per_hw = tc / c_per_thr;
+    constexpr int hw_per_group = n_threads / thr_per_hw;
+
+    const int thr_c = thread_idx % thr_per_hw;
+    const int thr_hw = thread_idx / thr_per_hw;
+
+    for (int hw = thr_hw; hw < span_hw; hw += hw_per_group) {
+      const int h = hw / span_w;
+      const int w = hw % span_w;
+
+      const int ih = tg_oh + h;
+      const int iw = tg_ow + w;
+
+      const int in_s_offset = h * span_w * TGC + w * TGC;
+
+      if (ih >= 0 && ih < params.iS[0] && iw >= 0 && iw < params.iS[1]) {
+        const auto in_load =
+            in + ih * params.in_strides[1] + iw * params.in_strides[2] + tg_c;
+
+        MLX_MTL_PRAGMA_UNROLL
+        for (int cc = 0; cc < c_per_thr; ++cc) {
+          ins[in_s_offset + c_per_thr * thr_c + cc] =
+              in_load[c_per_thr * thr_c + cc];
+        }
+      } else {
+        MLX_MTL_PRAGMA_UNROLL
+        for (int cc = 0; cc < c_per_thr; ++cc) {
+          ins[in_s_offset + c_per_thr * thr_c + cc] = T(0);
+        }
+      }
+    }
+  }
+
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  wt += c * params.wt_strides[0];
+
+  const auto ins_ptr =
+      &ins[lid.z * str_h * span_w * TGC + lid.y * str_w * TGC + lid.x];
+  float o = 0.;
+  for (int h = 0; h < ker_h; ++h) {
+    for (int w = 0; w < ker_w; ++w) {
+      int wt_h = h;
+      int wt_w = w;
+      if (do_flip) {
+        wt_h = ker_h - h - 1;
+        wt_w = ker_w - w - 1;
+      }
+      auto inv = ins_ptr[h * span_w * TGC + w * TGC];
+      auto wtv = wt[wt_h * ker_w + wt_w];
+      o += inv * wtv;
+    }
+  }
+  threadgroup_barrier(mem_flags::mem_none);
+
+  out += n * params.out_strides[0] + oh * params.out_strides[1] +
+      ow * params.out_strides[2];
+  out[c] = static_cast<T>(o);
+}
+
+#define instantiate_depthconv2d(iname, itype) \
+  instantiate_kernel("depthwise_conv_2d_" #iname, depthwise_conv_2d, itype)
+
+instantiate_depthconv2d(float32, float);
+instantiate_depthconv2d(float16, half);
+instantiate_depthconv2d(bfloat16, bfloat16_t);
+
 ///////////////////////////////////////////////////////////////////////////////
 /// Winograd kernels
 ///////////////////////////////////////////////////////////////////////////////

mlx/backend/metal/kernels/fft.h

@@ -483,4 +483,4 @@ template <
   perform_fft(fft_idx, &p, m, n, buf);
 
   read_writer.write_strided(stride, overall_n);
-}
+}

mlx/backend/metal/kernels/gemv.metal

@@ -341,7 +341,7 @@ struct GEMVTKernel {
 
         MLX_MTL_PRAGMA_UNROLL
         for (int tm = 0; tm < TM; tm++) {
-          auto vc = float(v_coeff[tm]);
+          auto vc = static_cast<AccT>(v_coeff[tm]);
           for (int tn = 0; tn < TN; tn++) {
             inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
           }

mlx/backend/metal/kernels/layer_norm.metal

@@ -493,71 +493,11 @@ template <typename T, int N_READS = RMS_N_READS>
 }
 
 // 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]],            \
-      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]],                   \
-      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)      \
-  instantiate_layer_norm_single_row(name, itype) \
-  instantiate_layer_norm_looped(name, itype)
+#define instantiate_layer_norm(name, itype)                                       \
+  instantiate_kernel("layer_norm" #name, layer_norm_single_row, itype)            \
+  instantiate_kernel("vjp_layer_norm" #name, vjp_layer_norm_single_row, itype)    \
+  instantiate_kernel("layer_norm_looped" #name, layer_norm_looped, itype)         \
+  instantiate_kernel("vjp_layer_norm_looped" #name, vjp_layer_norm_looped, itype)
 
 instantiate_layer_norm(float32, float)
 instantiate_layer_norm(float16, half)

mlx/backend/metal/kernels/logsumexp.h

@@ -0,0 +1,143 @@
+// Copyright © 2025 Apple Inc.
+
+template <typename T, typename AccT = float, int N_READS = 4>
+[[kernel]] void logsumexp(
+    const device T* in,
+    device T* out,
+    constant int& axis_size,
+    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]]) {
+  int lid = _lid;
+
+  constexpr int SIMD_SIZE = 32;
+
+  threadgroup AccT local_max[SIMD_SIZE];
+  threadgroup AccT local_normalizer[SIMD_SIZE];
+
+  AccT ld[N_READS];
+
+  in += gid * size_t(axis_size) + lid * N_READS;
+  if (lid * N_READS + N_READS <= axis_size) {
+    for (int i = 0; i < N_READS; i++) {
+      ld[i] = AccT(in[i]);
+    }
+  } else {
+    for (int i = 0; i < N_READS; i++) {
+      ld[i] =
+          ((lid * N_READS + i) < axis_size) ? AccT(in[i]) : Limits<AccT>::min;
+    }
+  }
+  if (simd_group_id == 0) {
+    local_max[simd_lane_id] = Limits<AccT>::min;
+    local_normalizer[simd_lane_id] = 0;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  // Get the max
+  AccT maxval = Limits<AccT>::finite_min;
+  for (int i = 0; i < N_READS; i++) {
+    maxval = (maxval < ld[i]) ? ld[i] : maxval;
+  }
+  maxval = simd_max(maxval);
+  if (simd_lane_id == 0) {
+    local_max[simd_group_id] = maxval;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  if (simd_group_id == 0) {
+    maxval = simd_max(local_max[simd_lane_id]);
+    if (simd_lane_id == 0) {
+      local_max[0] = maxval;
+    }
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  maxval = local_max[0];
+
+  // Compute exp(x_i - maxval) and store the partial sums in local_normalizer
+  AccT normalizer = 0;
+  for (int i = 0; i < N_READS; i++) {
+    normalizer += fast::exp(ld[i] - maxval);
+  }
+  normalizer = simd_sum(normalizer);
+  if (simd_lane_id == 0) {
+    local_normalizer[simd_group_id] = normalizer;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  if (simd_group_id == 0) {
+    normalizer = simd_sum(local_normalizer[simd_lane_id]);
+    if (simd_lane_id == 0) {
+      out[gid] = isinf(maxval) ? T(maxval) : T(log(normalizer) + maxval);
+    }
+  }
+}
+
+template <typename T, typename AccT = float, int N_READS = 4>
+[[kernel]] void logsumexp_looped(
+    const device T* in,
+    device T* out,
+    constant int& axis_size,
+    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]]) {
+  in += gid * size_t(axis_size);
+
+  constexpr int SIMD_SIZE = 32;
+
+  threadgroup AccT local_max[SIMD_SIZE];
+  threadgroup AccT local_normalizer[SIMD_SIZE];
+
+  // Get the max and the normalizer in one go
+  AccT prevmax;
+  AccT maxval = Limits<AccT>::finite_min;
+  AccT normalizer = 0;
+  for (int r = 0; r < static_cast<int>(ceildiv(axis_size, N_READS * lsize));
+       r++) {
+    int offset = r * lsize * N_READS + lid * N_READS;
+    AccT vals[N_READS];
+    if (offset + N_READS <= axis_size) {
+      for (int i = 0; i < N_READS; i++) {
+        vals[i] = AccT(in[offset + i]);
+      }
+    } else {
+      for (int i = 0; i < N_READS; i++) {
+        vals[i] = (offset + i < axis_size) ? AccT(in[offset + i])
+                                           : Limits<AccT>::finite_min;
+      }
+    }
+    prevmax = maxval;
+    for (int i = 0; i < N_READS; i++) {
+      maxval = (maxval < vals[i]) ? vals[i] : maxval;
+    }
+    normalizer *= fast::exp(prevmax - maxval);
+    for (int i = 0; i < N_READS; i++) {
+      normalizer += fast::exp(vals[i] - maxval);
+    }
+  }
+  prevmax = maxval;
+  maxval = simd_max(maxval);
+  normalizer *= fast::exp(prevmax - maxval);
+  normalizer = simd_sum(normalizer);
+
+  prevmax = maxval;
+  if (simd_lane_id == 0) {
+    local_max[simd_group_id] = maxval;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  maxval = simd_max(local_max[simd_lane_id]);
+  normalizer *= fast::exp(prevmax - maxval);
+  if (simd_lane_id == 0) {
+    local_normalizer[simd_group_id] = normalizer;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  normalizer = simd_sum(local_normalizer[simd_lane_id]);
+
+  if (simd_group_id == 0) {
+    normalizer = simd_sum(local_normalizer[simd_lane_id]);
+    if (simd_lane_id == 0) {
+      out[gid] = isinf(maxval) ? T(maxval) : T(log(normalizer) + maxval);
+    }
+  }
+}

mlx/backend/metal/kernels/logsumexp.metal

@@ -0,0 +1,18 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include <metal_common>
+#include <metal_simdgroup>
+
+using namespace metal;
+
+// clang-format off
+#include "mlx/backend/metal/kernels/utils.h"
+#include "mlx/backend/metal/kernels/logsumexp.h"
+
+#define instantiate_logsumexp(name, itype)                               \
+  instantiate_kernel("block_logsumexp_" #name, logsumexp, itype)         \
+  instantiate_kernel("looped_logsumexp_" #name, logsumexp_looped, itype) \
+
+instantiate_logsumexp(float32, float)
+instantiate_logsumexp(float16, half)
+instantiate_logsumexp(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/quantized.h

@@ -586,13 +586,13 @@ METAL_FUNC void qmv_quad_impl(
   // Adjust positions
   const int in_vec_size_w = in_vec_size / pack_factor;
   const int in_vec_size_g = in_vec_size / group_size;
-  const int out_row = tid.x * quads_per_simd * results_per_quadgroup + quad_gid;
+  const int out_row = tid.y * quads_per_simd * results_per_quadgroup + quad_gid;
 
   w += out_row * in_vec_size_w + quad_lid * packs_per_thread;
   scales += out_row * in_vec_size_g + quad_lid / scale_step_per_thread;
   biases += out_row * in_vec_size_g + quad_lid / scale_step_per_thread;
-  x += tid.y * in_vec_size + quad_lid * values_per_thread;
-  y += tid.y * out_vec_size + out_row;
+  x += tid.x * in_vec_size + quad_lid * values_per_thread;
+  y += tid.x * out_vec_size + out_row;
 
   U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
 

mlx/backend/metal/kernels/rms_norm.metal

@@ -380,69 +380,11 @@ template <typename T, int N_READS = RMS_N_READS>
 }
 
 // clang-format off
-#define instantiate_rms_single_row(name, itype)               \
-  template [[host_name("rms" #name)]] [[kernel]] void         \
-  rms_single_row<itype>(                                      \
-      const device itype* x,                                  \
-      const device itype* w,                                  \
-      device itype* out,                                      \
-      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]]); \
-                                                              \
-  template [[host_name("vjp_rms" #name)]] [[kernel]] void     \
-  vjp_rms_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_rms_looped(name, itype)                      \
-  template [[host_name("rms_looped" #name)]] [[kernel]] void     \
-  rms_looped<itype>(                                             \
-      const device itype* x,                                     \
-      const device itype* w,                                     \
-      device itype* out,                                         \
-      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]]);    \
-                                                                 \
-  template [[host_name("vjp_rms_looped" #name)]] [[kernel]] void \
-  vjp_rms_looped<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 lsize [[threads_per_threadgroup]],                    \
-      uint simd_lane_id [[thread_index_in_simdgroup]],           \
-      uint simd_group_id [[simdgroup_index_in_threadgroup]]);
-
-#define instantiate_rms(name, itype)      \
-  instantiate_rms_single_row(name, itype) \
-  instantiate_rms_looped(name, itype)
+#define instantiate_rms(name, itype)                                \
+  instantiate_kernel("rms" #name, rms_single_row, itype)            \
+  instantiate_kernel("vjp_rms" #name, vjp_rms_single_row, itype)    \
+  instantiate_kernel("rms_looped" #name, rms_looped, itype)         \
+  instantiate_kernel("vjp_rms_looped" #name, vjp_rms_looped, itype)
 
 instantiate_rms(float32, float)
 instantiate_rms(float16, half)

mlx/backend/metal/kernels/scaled_dot_product_attention.metal

@@ -1,11 +1,11 @@
 #include <metal_stdlib>
 
-#include "mlx/backend/metal/kernels/sdpa_vector.h"
+// clang-format off
 #include "mlx/backend/metal/kernels/utils.h"
+#include "mlx/backend/metal/kernels/sdpa_vector.h"
 
 using namespace metal;
 
-// clang-format off
 // SDPA vector instantiations
 #define instantiate_sdpa_vector_aggregation(type, value_dim) \
   instantiate_kernel(                                        \
@@ -32,9 +32,11 @@ using namespace metal;
   instantiate_sdpa_vector(type, 64, 64)          \
   instantiate_sdpa_vector(type, 96, 96)          \
   instantiate_sdpa_vector(type, 128, 128)        \
+  instantiate_sdpa_vector(type, 256, 256)        \
   instantiate_sdpa_vector_aggregation(type, 64)  \
   instantiate_sdpa_vector_aggregation(type, 96)  \
-  instantiate_sdpa_vector_aggregation(type, 128)
+  instantiate_sdpa_vector_aggregation(type, 128) \
+  instantiate_sdpa_vector_aggregation(type, 256)
 
 instantiate_sdpa_vector_heads(float)
 instantiate_sdpa_vector_heads(bfloat16_t)

mlx/backend/metal/kernels/scan.h

@@ -2,6 +2,8 @@
 
 #pragma once
 
+#include "mlx/backend/metal/kernels/binary_ops.h"
+
 #define DEFINE_SIMD_SCAN()                                               \
   template <typename T, metal::enable_if_t<sizeof(T) < 8, bool> = true>  \
   T simd_scan(T val) {                                                   \
@@ -139,6 +141,29 @@ struct CumMin {
   }
 };
 
+template <typename U>
+struct CumLogaddexp {
+  static constexpr constant U init = Limits<U>::min;
+
+  template <typename T>
+  U operator()(U a, T b) {
+    return LogAddExp{}(a, static_cast<U>(b));
+  }
+
+  U simd_scan(U x) {
+    for (int i = 1; i <= 16; i *= 2) {
+      U other = simd_shuffle_and_fill_up(x, init, i);
+      x = LogAddExp{}(x, other);
+    }
+    return x;
+  }
+
+  U simd_exclusive_scan(U x) {
+    x = simd_scan(x);
+    return simd_shuffle_and_fill_up(x, init, 1);
+  }
+};
+
 template <typename T, typename U, int N_READS, bool reverse>
 inline void load_unsafe(U values[N_READS], const device T* input) {
   if (reverse) {

mlx/backend/metal/kernels/scan.metal

@@ -101,4 +101,7 @@ instantiate_scan_helper(min_int64_int64,         int64_t,     int64_t,     CumMi
 instantiate_scan_helper(min_float16_float16,     half,        half,        CumMin, 4)
 instantiate_scan_helper(min_float32_float32,     float,       float,       CumMin, 4)
 instantiate_scan_helper(min_bfloat16_bfloat16,   bfloat16_t,  bfloat16_t,  CumMin, 4)
-instantiate_scan_helper(min_complex64_complex64, complex64_t, complex64_t, CumMin, 2) // clang-format on
+instantiate_scan_helper(min_complex64_complex64, complex64_t, complex64_t, CumMin, 2)
+instantiate_scan_helper(logaddexp_float16_float16,     half,        half,        CumLogaddexp, 4)
+instantiate_scan_helper(logaddexp_float32_float32,     float,       float,       CumLogaddexp, 4)
+instantiate_scan_helper(logaddexp_bfloat16_bfloat16,   bfloat16_t,  bfloat16_t,  CumLogaddexp, 4) // clang-format on

mlx/backend/metal/kernels/sdpa_vector.h

@@ -6,6 +6,9 @@ using namespace metal;
 
 constant bool has_mask [[function_constant(20)]];
 constant bool query_transposed [[function_constant(21)]];
+constant bool do_causal [[function_constant(22)]];
+constant bool bool_mask [[function_constant(23)]];
+constant bool float_mask [[function_constant(24)]];
 
 template <typename T, int D, int V = D>
 [[kernel]] void sdpa_vector(
@@ -13,17 +16,21 @@ template <typename T, int D, int V = D>
     const device T* keys [[buffer(1)]],
     const device T* values [[buffer(2)]],
     device T* out [[buffer(3)]],
-    const constant int& gqa_factor,
-    const constant int& N,
-    const constant size_t& k_head_stride,
-    const constant size_t& k_seq_stride,
-    const constant size_t& v_head_stride,
-    const constant size_t& v_seq_stride,
-    const constant float& scale,
-    const device bool* mask [[function_constant(has_mask)]],
-    const constant int& mask_kv_seq_stride [[function_constant(has_mask)]],
-    const constant int& mask_q_seq_stride [[function_constant(has_mask)]],
-    const constant int& mask_head_stride [[function_constant(has_mask)]],
+    const constant int& gqa_factor [[buffer(4)]],
+    const constant int& N [[buffer(5)]],
+    const constant size_t& k_head_stride [[buffer(6)]],
+    const constant size_t& k_seq_stride [[buffer(7)]],
+    const constant size_t& v_head_stride [[buffer(8)]],
+    const constant size_t& v_seq_stride [[buffer(9)]],
+    const constant float& scale [[buffer(10)]],
+    const device bool* bmask [[buffer(11), function_constant(bool_mask)]],
+    const device T* fmask [[buffer(12), function_constant(float_mask)]],
+    const constant int& mask_kv_seq_stride
+    [[buffer(13), function_constant(has_mask)]],
+    const constant int& mask_q_seq_stride
+    [[buffer(14), function_constant(has_mask)]],
+    const constant int& mask_head_stride
+    [[buffer(15), function_constant(has_mask)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 tpg [[threadgroups_per_grid]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
@@ -57,8 +64,12 @@ template <typename T, int D, int V = D>
       simd_lid * qk_per_thread;
   values += kv_head_idx * v_head_stride + simd_gid * v_seq_stride +
       simd_lid * v_per_thread;
-  if (has_mask) {
-    mask += head_idx * mask_head_stride + simd_gid * mask_kv_seq_stride +
+  if (bool_mask) {
+    bmask += head_idx * mask_head_stride + simd_gid * mask_kv_seq_stride +
+        q_seq_idx * mask_q_seq_stride;
+  }
+  if (float_mask) {
+    fmask += head_idx * mask_head_stride + simd_gid * mask_kv_seq_stride +
         q_seq_idx * mask_q_seq_stride;
   }
 
@@ -77,7 +88,13 @@ template <typename T, int D, int V = D>
 
   // For each key
   for (int i = simd_gid; i < N; i += BN) {
-    if (!has_mask || mask[0]) {
+    bool use_key = true;
+    if (do_causal) {
+      use_key = i <= (N - int(tpg.y) + int(q_seq_idx));
+    } else if (bool_mask) {
+      use_key = bmask[0];
+    }
+    if (use_key) {
       // Read the key
       for (int j = 0; j < qk_per_thread; j++) {
         k[j] = keys[j];
@@ -89,6 +106,9 @@ template <typename T, int D, int V = D>
         score += q[j] * k[j];
       }
       score = simd_sum(score);
+      if (float_mask) {
+        score += max(Limits<U>::finite_min, static_cast<U>(fmask[0]));
+      }
 
       // Update the accumulators
       U new_max = max(max_score, score);
@@ -107,8 +127,11 @@ template <typename T, int D, int V = D>
     // Move the pointers to the next kv
     keys += inner_k_stride;
     values += inner_v_stride;
-    if (has_mask) {
-      mask += BN * mask_kv_seq_stride;
+    if (bool_mask) {
+      bmask += BN * mask_kv_seq_stride;
+    }
+    if (float_mask) {
+      fmask += BN * mask_kv_seq_stride;
     }
   }
 
@@ -149,17 +172,21 @@ template <typename T, int D, int V = D>
     device float* out [[buffer(3)]],
     device float* sums [[buffer(4)]],
     device float* maxs [[buffer(5)]],
-    const constant int& gqa_factor,
-    const constant int& N,
-    const constant size_t& k_head_stride,
-    const constant size_t& k_seq_stride,
-    const constant size_t& v_head_stride,
-    const constant size_t& v_seq_stride,
-    const constant float& scale,
-    const device bool* mask [[function_constant(has_mask)]],
-    const constant int& mask_kv_seq_stride [[function_constant(has_mask)]],
-    const constant int& mask_q_seq_stride [[function_constant(has_mask)]],
-    const constant int& mask_head_stride [[function_constant(has_mask)]],
+    const constant int& gqa_factor [[buffer(6)]],
+    const constant int& N [[buffer(7)]],
+    const constant size_t& k_head_stride [[buffer(8)]],
+    const constant size_t& k_seq_stride [[buffer(9)]],
+    const constant size_t& v_head_stride [[buffer(10)]],
+    const constant size_t& v_seq_stride [[buffer(11)]],
+    const constant float& scale [[buffer(12)]],
+    const device bool* bmask [[buffer(13), function_constant(bool_mask)]],
+    const device T* fmask [[buffer(14), function_constant(float_mask)]],
+    const constant int& mask_kv_seq_stride
+    [[buffer(15), function_constant(has_mask)]],
+    const constant int& mask_q_seq_stride
+    [[buffer(16), function_constant(has_mask)]],
+    const constant int& mask_head_stride
+    [[buffer(17), function_constant(has_mask)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 tpg [[threadgroups_per_grid]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
@@ -197,8 +224,13 @@ template <typename T, int D, int V = D>
   values += kv_head_idx * v_head_stride +
       (block_idx * BN + simd_gid) * v_seq_stride + simd_lid * v_per_thread;
   out += o_offset * blocks * V + block_idx * V + simd_lid * v_per_thread;
-  if (has_mask) {
-    mask += head_idx * mask_head_stride +
+  if (bool_mask) {
+    bmask += head_idx * mask_head_stride +
+        (block_idx * BN + simd_gid) * mask_kv_seq_stride +
+        q_seq_idx * mask_q_seq_stride;
+  }
+  if (float_mask) {
+    fmask += head_idx * mask_head_stride +
         (block_idx * BN + simd_gid) * mask_kv_seq_stride +
         q_seq_idx * mask_q_seq_stride;
   }
@@ -218,7 +250,13 @@ template <typename T, int D, int V = D>
 
   // For each key
   for (int i = block_idx * BN + simd_gid; i < N; i += blocks * BN) {
-    if (!has_mask || mask[0]) {
+    bool use_key = true;
+    if (do_causal) {
+      use_key = i <= (N - int(tpg.y) + int(q_seq_idx));
+    } else if (bool_mask) {
+      use_key = bmask[0];
+    }
+    if (use_key) {
       // Read the key
       for (int i = 0; i < qk_per_thread; i++) {
         k[i] = keys[i];
@@ -230,6 +268,9 @@ template <typename T, int D, int V = D>
         score += q[i] * k[i];
       }
       score = simd_sum(score);
+      if (float_mask) {
+        score += fmask[0];
+      }
 
       // Update the accumulators
       U new_max = max(max_score, score);
@@ -248,8 +289,11 @@ template <typename T, int D, int V = D>
     // Move the pointers to the next kv
     keys += blocks * inner_k_stride;
     values += blocks * inner_v_stride;
-    if (has_mask) {
-      mask += BN * blocks * mask_kv_seq_stride;
+    if (bool_mask) {
+      bmask += BN * blocks * mask_kv_seq_stride;
+    }
+    if (float_mask) {
+      fmask += BN * blocks * mask_kv_seq_stride;
     }
   }
 

mlx/backend/metal/kernels/softmax.metal

@@ -9,47 +9,13 @@ using namespace metal;
 #include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/softmax.h"
 
-#define instantiate_softmax(name, itype)                          \
-  template [[host_name("block_softmax_" #name)]] [[kernel]] void        \
-  softmax_single_row<itype>(                                      \
-      const device itype* in,                                     \
-      device itype* out,                                          \
-      constant int& axis_size,                                    \
-      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("looped_softmax_" #name)]] [[kernel]] void \
-  softmax_looped<itype>(                                          \
-      const device itype* in,                                     \
-      device itype* out,                                          \
-      constant int& axis_size,                                    \
-      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]]);
+#define instantiate_softmax(name, itype)                                \
+  instantiate_kernel("block_softmax_" #name, softmax_single_row, itype) \
+  instantiate_kernel("looped_softmax_" #name, softmax_looped, itype)
 
-#define instantiate_softmax_precise(name, itype)                          \
-  template [[host_name("block_softmax_precise_" #name)]] [[kernel]] void        \
-  softmax_single_row<itype, float>(                                       \
-      const device itype* in,                                             \
-      device itype* out,                                                  \
-      constant int& axis_size,                                            \
-      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("looped_softmax_precise_" #name)]] [[kernel]] void \
-  softmax_looped<itype, float>(                                           \
-      const device itype* in,                                             \
-      device itype* out,                                                  \
-      constant int& axis_size,                                            \
-      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]]);
+#define instantiate_softmax_precise(name, itype)                                       \
+  instantiate_kernel("block_softmax_precise_" #name, softmax_single_row, itype, float) \
+  instantiate_kernel("looped_softmax_precise_" #name, softmax_looped, itype, float)
 
 instantiate_softmax(float32, float)
 instantiate_softmax(float16, half)

mlx/backend/metal/kernels/steel/attn/kernels/steel_attention.h

@@ -229,7 +229,7 @@ template <
   // Init to -Inf
   STEEL_PRAGMA_UNROLL
   for (short i = 0; i < kRowsPT; ++i) {
-    max_score[i] = Limits<AccumType>::min;
+    max_score[i] = Limits<AccumType>::finite_min;
   }
 
   int kb_lim = params->NK;
@@ -237,6 +237,7 @@ template <
   if (do_causal) {
     int q_max = (tid.x + 1) * BQ + params->qL_off;
     kb_lim = (q_max + BK - 1) / BK;
+    kb_lim = min(params->NK, kb_lim);
   }
 
   // Loop over KV seq length
@@ -272,7 +273,7 @@ template <
     if (!align_K && kb == (params->NK_aligned)) {
       using stile_t = decltype(Stile);
       using selem_t = typename stile_t::elem_type;
-      constexpr auto neg_inf = -metal::numeric_limits<selem_t>::infinity();
+      constexpr auto neg_inf = Limits<selem_t>::finite_min;
 
       STEEL_PRAGMA_UNROLL
       for (short i = 0; i < stile_t::kTileRows; i++) {
@@ -290,10 +291,10 @@ template <
     }
 
     // Mask out if causal
-    if (do_causal && kb >= (kb_lim - (BQ + BK - 1) / BK - int(!align_K))) {
+    if (do_causal && kb >= (kb_lim - ((BQ + BK - 1) / BK) - int(!align_K))) {
       using stile_t = decltype(Stile);
       using selem_t = typename stile_t::elem_type;
-      constexpr auto neg_inf = -metal::numeric_limits<selem_t>::infinity();
+      constexpr auto neg_inf = Limits<selem_t>::finite_min;
 
       STEEL_PRAGMA_UNROLL
       for (short i = 0; i < stile_t::kTileRows; i++) {
@@ -316,7 +317,7 @@ template <
     if (has_mask) {
       using stile_t = decltype(Stile);
       using selem_t = typename stile_t::elem_type;
-      constexpr auto neg_inf = -metal::numeric_limits<selem_t>::infinity();
+      constexpr auto neg_inf = Limits<selem_t>::finite_min;
 
       constexpr bool is_bool = is_same_v<MaskType, bool>;
       using melem_t = typename metal::conditional_t<is_bool, bool, selem_t>;

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_fused.h

@@ -15,10 +15,6 @@ constant bool align_M [[function_constant(200)]];
 constant bool align_N [[function_constant(201)]];
 constant bool align_K [[function_constant(202)]];
 
-constant bool do_gather [[function_constant(300)]];
-
-constant bool gather_bias = do_gather && use_out_source;
-
 // clang-format off
 template <
     typename T,
@@ -39,12 +35,6 @@ template <
     const constant GEMMAddMMParams* addmm_params [[buffer(5), function_constant(use_out_source)]],
     const constant int* batch_shape [[buffer(6)]],
     const constant int64_t* batch_strides [[buffer(7)]],
-    const constant uint32_t* lhs_indices [[buffer(10), function_constant(do_gather)]],
-    const constant uint32_t* rhs_indices [[buffer(11), function_constant(do_gather)]],
-    const constant uint32_t* C_indices [[buffer(12), function_constant(gather_bias)]],
-    const constant int* operand_shape [[buffer(13), function_constant(do_gather)]],
-    const constant int64_t* operand_strides [[buffer(14), function_constant(do_gather)]],
-    const constant packed_int3& operand_batch_ndim [[buffer(15), function_constant(do_gather)]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint3 tid [[threadgroup_position_in_grid]],
@@ -81,84 +71,26 @@ template <
   }
 
   // Adjust for batch
+  if (has_batch) {
+    const constant auto* A_bstrides = batch_strides;
+    const constant auto* B_bstrides = batch_strides + params->batch_ndim;
 
-  // Handle gather
-  if (do_gather) {
-    // Read indices
-    uint32_t indx_A, indx_B, indx_C;
+    ulong2 batch_offsets = elem_to_loc_broadcast(
+        tid.z, batch_shape, A_bstrides, B_bstrides, params->batch_ndim);
 
-    if (has_batch) {
-      const constant auto* indx_A_bstrides = batch_strides;
-      const constant auto* indx_B_bstrides = batch_strides + params->batch_ndim;
-
-      ulong2 indx_offsets = elem_to_loc_broadcast(
-          tid.z,
-          batch_shape,
-          indx_A_bstrides,
-          indx_B_bstrides,
-          params->batch_ndim);
-      indx_A = lhs_indices[indx_offsets.x];
-      indx_B = rhs_indices[indx_offsets.y];
-
-      if (use_out_source) {
-        const constant auto* indx_C_bstrides =
-            indx_B_bstrides + params->batch_ndim;
-        auto indx_offset_C = elem_to_loc(
-            tid.z, batch_shape, indx_C_bstrides, params->batch_ndim);
-        indx_C = C_indices[indx_offset_C];
-      }
-    } else {
-      indx_A = lhs_indices[params->batch_stride_a * tid.z];
-      indx_B = rhs_indices[params->batch_stride_b * tid.z];
-
-      if (use_out_source) {
-        indx_C = C_indices[addmm_params->batch_stride_c * tid.z];
-      }
-    }
-
-    // Translate indices to offsets
-    int batch_ndim_A = operand_batch_ndim.x;
-    const constant int* batch_shape_A = operand_shape;
-    const constant auto* batch_strides_A = operand_strides;
-    A += elem_to_loc(indx_A, batch_shape_A, batch_strides_A, batch_ndim_A);
-
-    int batch_ndim_B = operand_batch_ndim.y;
-    const constant int* batch_shape_B = batch_shape_A + batch_ndim_A;
-    const constant auto* batch_strides_B = batch_strides_A + batch_ndim_A;
-    B += elem_to_loc(indx_B, batch_shape_B, batch_strides_B, batch_ndim_B);
+    A += batch_offsets.x;
+    B += batch_offsets.y;
 
     if (use_out_source) {
-      int batch_ndim_C = operand_batch_ndim.z;
-      const constant int* batch_shape_C = batch_shape_B + batch_ndim_B;
-      const constant auto* batch_strides_C = batch_strides_B + batch_ndim_B;
-      C += elem_to_loc(indx_C, batch_shape_C, batch_strides_C, batch_ndim_C);
+      const constant auto* C_bstrides = B_bstrides + params->batch_ndim;
+      C += elem_to_loc(tid.z, batch_shape, C_bstrides, params->batch_ndim);
     }
+  } else {
+    A += params->batch_stride_a * tid.z;
+    B += params->batch_stride_b * tid.z;
 
-  }
-
-  // Handle regular batch
-  else {
-    if (has_batch) {
-      const constant auto* A_bstrides = batch_strides;
-      const constant auto* B_bstrides = batch_strides + params->batch_ndim;
-
-      ulong2 batch_offsets = elem_to_loc_broadcast(
-          tid.z, batch_shape, A_bstrides, B_bstrides, params->batch_ndim);
-
-      A += batch_offsets.x;
-      B += batch_offsets.y;
-
-      if (use_out_source) {
-        const constant auto* C_bstrides = B_bstrides + params->batch_ndim;
-        C += elem_to_loc(tid.z, batch_shape, C_bstrides, params->batch_ndim);
-      }
-    } else {
-      A += params->batch_stride_a * tid.z;
-      B += params->batch_stride_b * tid.z;
-
-      if (use_out_source) {
-        C += addmm_params->batch_stride_c * tid.z;
-      }
+    if (use_out_source) {
+      C += addmm_params->batch_stride_c * tid.z;
     }
   }
 

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_gather.h

@@ -0,0 +1,459 @@
+// Copyright © 2024 Apple Inc.
+
+using namespace mlx::steel;
+
+constant bool has_batch [[function_constant(10)]];
+constant bool align_M [[function_constant(200)]];
+constant bool align_N [[function_constant(201)]];
+constant bool align_K [[function_constant(202)]];
+
+template <
+    typename T,
+    int BM,
+    int BN,
+    int BK,
+    int WM,
+    int WN,
+    bool transpose_a,
+    bool transpose_b,
+    typename AccumType = float>
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void gather_mm_rhs(
+    const device T* A [[buffer(0)]],
+    const device T* B [[buffer(1)]],
+    const device uint32_t* rhs_indices [[buffer(2)]],
+    device T* C [[buffer(3)]],
+    const constant GEMMParams* params [[buffer(4)]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]],
+    uint3 tid [[threadgroup_position_in_grid]]) {
+  using gemm_kernel = GEMMKernel<
+      T,
+      T,
+      BM,
+      BN,
+      BK,
+      WM,
+      WN,
+      transpose_a,
+      transpose_b,
+      true,
+      true,
+      AccumType>;
+
+  using loader_a_t = typename gemm_kernel::loader_a_t;
+  using loader_b_t = typename gemm_kernel::loader_b_t;
+  using mma_t = typename gemm_kernel::mma_t;
+
+  if (params->tiles_n <= static_cast<int>(tid.x) ||
+      params->tiles_m <= static_cast<int>(tid.y)) {
+    return;
+  }
+
+  // Prepare threadgroup memory
+  threadgroup T As[gemm_kernel::tgp_mem_size_a];
+  threadgroup T Bs[gemm_kernel::tgp_mem_size_b];
+
+  // Find the block in A, B, C
+  const int c_row = tid.y * BM;
+  const int c_col = tid.x * BN;
+  const size_t c_row_long = size_t(c_row);
+  const size_t c_col_long = size_t(c_col);
+
+  // Prepare threadgroup bounds
+  const short tgp_bm = align_M ? BM : short(min(BM, params->M - c_row));
+  const short tgp_bn = align_N ? BN : short(min(BN, params->N - c_col));
+
+  A += transpose_a ? c_row_long : c_row_long * params->lda;
+  B += transpose_b ? c_col_long * params->ldb : c_col_long;
+  C += c_row_long * params->ldd + c_col_long;
+
+  // Do as many matmuls as necessary
+  uint32_t index;
+  short offset;
+  uint32_t index_next = rhs_indices[c_row];
+  short offset_next = 0;
+  int n = 0;
+  while (n < tgp_bm) {
+    n++;
+    offset = offset_next;
+    index = index_next;
+    offset_next = tgp_bm;
+    for (; n < tgp_bm; n++) {
+      if (rhs_indices[c_row + n] != index) {
+        offset_next = n;
+        index_next = rhs_indices[c_row + n];
+        break;
+      }
+    }
+    threadgroup_barrier(mem_flags::mem_none);
+
+    // Prepare threadgroup mma operation
+    thread mma_t mma_op(simd_group_id, simd_lane_id);
+
+    // Prepare threadgroup loading operations
+    thread loader_a_t loader_a(A, params->lda, As, simd_group_id, simd_lane_id);
+    thread loader_b_t loader_b(
+        B + index * params->batch_stride_b,
+        params->ldb,
+        Bs,
+        simd_group_id,
+        simd_lane_id);
+
+    // Prepare iterations
+    const int gemm_k_iterations = params->gemm_k_iterations_aligned;
+
+    // Do unaligned K iterations first
+    if (!align_K) {
+      const int k_last = params->gemm_k_iterations_aligned * BK;
+      const int k_remain = params->K - k_last;
+      const size_t k_jump_a =
+          transpose_a ? params->lda * size_t(k_last) : size_t(k_last);
+      const size_t k_jump_b =
+          transpose_b ? size_t(k_last) : params->ldb * size_t(k_last);
+
+      // Move loader source ahead to end
+      loader_a.src += k_jump_a;
+      loader_b.src += k_jump_b;
+
+      // Load tile
+      const short2 tile_dims_A =
+          transpose_a ? short2(tgp_bm, k_remain) : short2(k_remain, tgp_bm);
+      const short2 tile_dims_B =
+          transpose_b ? short2(k_remain, tgp_bn) : short2(tgp_bn, k_remain);
+
+      loader_a.load_safe(tile_dims_A);
+      loader_b.load_safe(tile_dims_B);
+
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+
+      // Do matmul
+      mma_op.mma(As, Bs);
+
+      // Reset source back to start
+      loader_a.src -= k_jump_a;
+      loader_b.src -= k_jump_b;
+    }
+
+    // Matrix level aligned never check
+    if (align_M && align_N) {
+      for (int k = 0; k < gemm_k_iterations; k++) {
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // Load elements into threadgroup
+        loader_a.load_unsafe();
+        loader_b.load_unsafe();
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // Multiply and accumulate threadgroup elements
+        mma_op.mma(As, Bs);
+
+        // Prepare for next iteration
+        loader_a.next();
+        loader_b.next();
+      }
+
+      // Store results to device memory
+      if (offset_next - offset == BM) {
+        mma_op.store_result(C, params->ldd);
+      } else {
+        mma_op.store_result_slice(
+            C, params->ldd, short2(0, offset), short2(BN, offset_next));
+      }
+    } else {
+      const short lbk = 0;
+
+      // Tile aligned don't check
+      if ((align_M || tgp_bm == BM) && (align_N || tgp_bn == BN)) {
+        gemm_kernel::gemm_loop(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            lbk,
+            LoopAlignment<true, true, true>{});
+        if (offset_next - offset == BM) {
+          mma_op.store_result(C, params->ldd);
+        } else {
+          mma_op.store_result_slice(
+              C, params->ldd, short2(0, offset), short2(BN, offset_next));
+        }
+      }
+
+      // Tile partially aligned check rows
+      else if (align_N || tgp_bn == BN) {
+        gemm_kernel::gemm_loop(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            lbk,
+            LoopAlignment<false, true, true>{});
+        mma_op.store_result_slice(
+            C, params->ldd, short2(0, offset), short2(BN, offset_next));
+      }
+
+      // Tile partially aligned check cols
+      else if (align_M || tgp_bm == BM) {
+        gemm_kernel::gemm_loop(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            lbk,
+            LoopAlignment<true, false, true>{});
+        mma_op.store_result_slice(
+            C, params->ldd, short2(0, offset), short2(tgp_bn, offset_next));
+      }
+
+      // Nothing aligned so check both rows and cols
+      else {
+        gemm_kernel::gemm_loop(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            lbk,
+            LoopAlignment<false, false, true>{});
+        mma_op.store_result_slice(
+            C, params->ldd, short2(0, offset), short2(tgp_bn, offset_next));
+      }
+    }
+  }
+}
+
+template <
+    typename T,
+    int BM,
+    int BN,
+    int BK,
+    int WM,
+    int WN,
+    bool transpose_a,
+    bool transpose_b,
+    typename AccumType = float>
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void gather_mm(
+    const device T* A [[buffer(0)]],
+    const device T* B [[buffer(1)]],
+    const device uint32_t* lhs_indices [[buffer(2)]],
+    const device uint32_t* rhs_indices [[buffer(3)]],
+    device T* C [[buffer(4)]],
+    const constant GEMMParams* params [[buffer(5)]],
+    const constant int* indices_shape [[buffer(6)]],
+    const constant int64_t* lhs_strides [[buffer(7)]],
+    const constant int64_t* rhs_strides [[buffer(8)]],
+    const constant int& batch_ndim_a [[buffer(9)]],
+    const constant int* batch_shape_a [[buffer(10)]],
+    const constant int64_t* batch_strides_a [[buffer(11)]],
+    const constant int& batch_ndim_b [[buffer(12)]],
+    const constant int* batch_shape_b [[buffer(13)]],
+    const constant int64_t* batch_strides_b [[buffer(14)]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]],
+    uint3 tid [[threadgroup_position_in_grid]]) {
+  using gemm_kernel = GEMMKernel<
+      T,
+      T,
+      BM,
+      BN,
+      BK,
+      WM,
+      WN,
+      transpose_a,
+      transpose_b,
+      true,
+      true,
+      AccumType>;
+
+  using loader_a_t = typename gemm_kernel::loader_a_t;
+  using loader_b_t = typename gemm_kernel::loader_b_t;
+  using mma_t = typename gemm_kernel::mma_t;
+
+  if (params->tiles_n <= static_cast<int>(tid.x) ||
+      params->tiles_m <= static_cast<int>(tid.y)) {
+    return;
+  }
+
+  // Move A and B to the locations pointed by lhs_indices and rhs_indices.
+  uint32_t indx_A, indx_B;
+  if (has_batch) {
+    ulong2 indices_offsets = elem_to_loc_broadcast(
+        tid.z, indices_shape, lhs_strides, rhs_strides, params->batch_ndim);
+    indx_A = lhs_indices[indices_offsets.x];
+    indx_B = rhs_indices[indices_offsets.y];
+  } else {
+    indx_A = lhs_indices[params->batch_stride_a * tid.z];
+    indx_B = rhs_indices[params->batch_stride_b * tid.z];
+  }
+  A += elem_to_loc(indx_A, batch_shape_a, batch_strides_a, batch_ndim_a);
+  B += elem_to_loc(indx_B, batch_shape_b, batch_strides_b, batch_ndim_b);
+  C += params->batch_stride_d * tid.z;
+
+  // Prepare threadgroup memory
+  threadgroup T As[gemm_kernel::tgp_mem_size_a];
+  threadgroup T Bs[gemm_kernel::tgp_mem_size_b];
+
+  // Just make sure everybody's finished with the indexing math above.
+  threadgroup_barrier(mem_flags::mem_none);
+
+  // Find block in A, B, C
+  const int c_row = tid.y * BM;
+  const int c_col = tid.x * BN;
+  const size_t c_row_long = size_t(c_row);
+  const size_t c_col_long = size_t(c_col);
+
+  A += transpose_a ? c_row_long : c_row_long * params->lda;
+  B += transpose_b ? c_col_long * params->ldb : c_col_long;
+  C += c_row_long * params->ldd + c_col_long;
+
+  // Prepare threadgroup mma operation
+  thread mma_t mma_op(simd_group_id, simd_lane_id);
+
+  // Prepare threadgroup loading operations
+  thread loader_a_t loader_a(A, params->lda, As, simd_group_id, simd_lane_id);
+  thread loader_b_t loader_b(B, params->ldb, Bs, simd_group_id, simd_lane_id);
+
+  // Prepare threadgroup bounds
+  const short tgp_bm = align_M ? BM : short(min(BM, params->M - c_row));
+  const short tgp_bn = align_N ? BN : short(min(BN, params->N - c_col));
+
+  // Prepare iterations
+  int gemm_k_iterations = params->gemm_k_iterations_aligned;
+
+  // Do unaligned K iterations first
+  if (!align_K) {
+    const int k_last = params->gemm_k_iterations_aligned * BK;
+    const int k_remain = params->K - k_last;
+    const size_t k_jump_a =
+        transpose_a ? params->lda * size_t(k_last) : size_t(k_last);
+    const size_t k_jump_b =
+        transpose_b ? size_t(k_last) : params->ldb * size_t(k_last);
+
+    // Move loader source ahead to end
+    loader_a.src += k_jump_a;
+    loader_b.src += k_jump_b;
+
+    // Load tile
+    const short2 tile_dims_A =
+        transpose_a ? short2(tgp_bm, k_remain) : short2(k_remain, tgp_bm);
+    const short2 tile_dims_B =
+        transpose_b ? short2(k_remain, tgp_bn) : short2(tgp_bn, k_remain);
+
+    loader_a.load_safe(tile_dims_A);
+    loader_b.load_safe(tile_dims_B);
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Do matmul
+    mma_op.mma(As, Bs);
+
+    // Reset source back to start
+    loader_a.src -= k_jump_a;
+    loader_b.src -= k_jump_b;
+  }
+
+  // Matrix level aligned never check
+  if (align_M && align_N) {
+    for (int k = 0; k < gemm_k_iterations; k++) {
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+
+      // Load elements into threadgroup
+      loader_a.load_unsafe();
+      loader_b.load_unsafe();
+
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+
+      // Multiply and accumulate threadgroup elements
+      mma_op.mma(As, Bs);
+
+      // Prepare for next iteration
+      loader_a.next();
+      loader_b.next();
+    }
+
+    // Store results to device memory
+    mma_op.store_result(C, params->ldd);
+  } else {
+    const short lbk = 0;
+
+    // Tile aligned don't check
+    if ((align_M || tgp_bm == BM) && (align_N || tgp_bn == BN)) {
+      gemm_kernel::gemm_loop(
+          As,
+          Bs,
+          gemm_k_iterations,
+          loader_a,
+          loader_b,
+          mma_op,
+          tgp_bm,
+          tgp_bn,
+          lbk,
+          LoopAlignment<true, true, true>{});
+      mma_op.store_result(C, params->ldd);
+    }
+
+    // Tile partially aligned check rows
+    else if (align_N || tgp_bn == BN) {
+      gemm_kernel::gemm_loop(
+          As,
+          Bs,
+          gemm_k_iterations,
+          loader_a,
+          loader_b,
+          mma_op,
+          tgp_bm,
+          tgp_bn,
+          lbk,
+          LoopAlignment<false, true, true>{});
+      mma_op.store_result_safe(C, params->ldd, short2(tgp_bn, tgp_bm));
+    }
+
+    // Tile partially aligned check cols
+    else if (align_M || tgp_bm == BM) {
+      gemm_kernel::gemm_loop(
+          As,
+          Bs,
+          gemm_k_iterations,
+          loader_a,
+          loader_b,
+          mma_op,
+          tgp_bm,
+          tgp_bn,
+          lbk,
+          LoopAlignment<true, false, true>{});
+      mma_op.store_result_safe(C, params->ldd, short2(tgp_bn, tgp_bm));
+    }
+
+    // Nothing aligned so check both rows and cols
+    else {
+      gemm_kernel::gemm_loop(
+          As,
+          Bs,
+          gemm_k_iterations,
+          loader_a,
+          loader_b,
+          mma_op,
+          tgp_bm,
+          tgp_bn,
+          lbk,
+          LoopAlignment<false, false, true>{});
+      mma_op.store_result_safe(C, params->ldd, short2(tgp_bn, tgp_bm));
+    }
+  }
+}

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_gather.metal

@@ -0,0 +1,59 @@
+// Copyright © 2024 Apple Inc.
+
+// clang-format off
+#include "mlx/backend/metal/kernels/utils.h"
+#include "mlx/backend/metal/kernels/steel/gemm/gemm.h"
+#include "mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_gather.h"
+
+#define instantiate_gather_mm_rhs(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+  instantiate_kernel(                                                         \
+      "steel_gather_mm_rhs_" #tname "_" #iname "_" #oname "_bm" #bm "_bn" #bn \
+      "_bk" #bk "_wm" #wm "_wn" #wn,                                          \
+      gather_mm_rhs,                                                          \
+      itype,                                                                  \
+      bm,                                                                     \
+      bn,                                                                     \
+      bk,                                                                     \
+      wm,                                                                     \
+      wn,                                                                     \
+      trans_a,                                                                \
+      trans_b,                                                                \
+      float)
+
+#define instantiate_gather_mm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+  instantiate_kernel(                                                     \
+      "steel_gather_mm_" #tname "_" #iname "_" #oname "_bm" #bm "_bn" #bn \
+      "_bk" #bk "_wm" #wm "_wn" #wn,                                      \
+      gather_mm,                                                          \
+      itype,                                                              \
+      bm,                                                                 \
+      bn,                                                                 \
+      bk,                                                                 \
+      wm,                                                                 \
+      wn,                                                                 \
+      trans_a,                                                            \
+      trans_b,                                                            \
+      float)
+
+#define instantiate_gather_mm_rhs_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+  instantiate_gather_mm_rhs(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn)  \
+  instantiate_gather_mm_rhs(nt, false,  true, iname, itype, oname, otype, bm, bn, bk, wm, wn)
+
+#define instantiate_gather_mm_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+  instantiate_gather_mm(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn)      \
+  instantiate_gather_mm(nt, false, true , iname, itype, oname, otype, bm, bn, bk, wm, wn)      \
+  instantiate_gather_mm(tn, true , false, iname, itype, oname, otype, bm, bn, bk, wm, wn)      \
+  instantiate_gather_mm(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn)
+
+#define instantiate_gather_mm_shapes_helper(iname, itype, oname, otype)                     \
+  instantiate_gather_mm_rhs_transpose_helper(iname, itype, oname, otype, 16, 64, 16, 1, 2)  \
+  instantiate_gather_mm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 2, 2)      \
+  instantiate_gather_mm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 1, 2)      \
+  instantiate_gather_mm_transpose_helper(iname, itype, oname, otype, 64, 32, 32, 2, 2)      \
+  instantiate_gather_mm_transpose_helper(iname, itype, oname, otype, 32, 64, 16, 1, 2)      \
+  instantiate_gather_mm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2)
+// clang-format on
+
+instantiate_gather_mm_shapes_helper(float16, half, float16, half);
+instantiate_gather_mm_shapes_helper(bfloat16, bfloat16_t, bfloat16, bfloat16_t);
+instantiate_gather_mm_shapes_helper(float32, float, float32, float);

mlx/backend/metal/kernels/steel/gemm/mma.h

@@ -142,6 +142,42 @@ struct BaseMMAFrag<T, 8, 8> {
     }
   }
 
+  template <
+      typename DstPtrType,
+      typename StrX,
+      typename StrY,
+      typename StartX,
+      typename StopX,
+      typename StartY,
+      typename StopY,
+      typename OffX,
+      typename OffY>
+  METAL_FUNC static constexpr void store_slice(
+      const thread frag_type& src,
+      DstPtrType dst,
+      StrX str_x,
+      StrY str_y,
+      StartX start_x,
+      StopX stop_x,
+      StartY start_y,
+      StopY stop_y,
+      OffX off_x = Int<0>{},
+      OffY off_y = Int<0>{}) {
+    using U = pointer_element_t<DstPtrType>;
+
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kElemRows; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kElemCols; j++) {
+        if ((off_x + i) < stop_x && (off_x + i) >= start_x &&
+            (off_y + j) < stop_y && (off_y + j) >= start_y) {
+          dst[(off_x + i) * str_x + (off_y + j) * str_y] =
+              static_cast<U>(src[i * kElemCols + j]);
+        }
+      }
+    }
+  }
+
   METAL_FUNC static constexpr void mma(
       thread frag_type& D,
       thread frag_type& A,
@@ -335,6 +371,31 @@ struct MMATile {
       }
     }
   }
+
+  template <typename U, int w_x, int w_y>
+  METAL_FUNC void store_slice(
+      device U* dst,
+      const int ld,
+      const short2 start,
+      const short2 stop) const {
+    STEEL_PRAGMA_UNROLL
+    for (int i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (int j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::store_slice(
+            frag_at(i, j),
+            dst,
+            ld,
+            Int<1>{},
+            start.y,
+            stop.y,
+            start.x,
+            stop.x,
+            (i * kFragRows) * w_x,
+            (j * kFragCols) * w_y);
+      }
+    }
+  }
 };
 
 template <typename T, typename U, int M, int N, int K>
@@ -474,6 +535,26 @@ struct BlockMMA {
     Ctile.template store<U, WM, WN>(D, ldd);
   }
 
+  METAL_FUNC void
+  store_result_slice(device U* D, const int ldd, short2 start, short2 stop) {
+    // Apply epilogue
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < decltype(Ctile)::kElemsPerTile; i++) {
+      Ctile.elems()[i] = Epilogue::apply(Ctile.elems()[i]);
+    }
+
+    D += sm * ldd + sn;
+    start -= short2(sn, sm);
+    stop -= short2(sn, sm);
+
+    // TODO: Check the start as well
+    if (stop.y <= 0 || stop.x <= 0) {
+      return;
+    }
+
+    Ctile.template store_slice<U, WM, WN>(D, ldd, start, stop);
+  }
+
   METAL_FUNC void
   store_result_safe(device U* D, const int ldd, short2 dst_tile_dims) {
     // Apply epilogue

mlx/backend/metal/kernels/unary.metal

@@ -73,6 +73,9 @@ instantiate_unary_all_same(Conjugate, complex64, complex64_t)
 instantiate_unary_all_same(Cos, complex64, complex64_t)
 instantiate_unary_all_same(Cosh, complex64, complex64_t)
 instantiate_unary_all_same(Exp, complex64, complex64_t)
+instantiate_unary_all_same(Log, complex64, complex64_t)
+instantiate_unary_all_same(Log2, complex64, complex64_t)
+instantiate_unary_all_same(Log10, complex64, complex64_t)
 instantiate_unary_all_same(Negative, complex64, complex64_t)
 instantiate_unary_all_same(Sign, complex64, complex64_t)
 instantiate_unary_all_same(Sin, complex64, complex64_t)

mlx/backend/metal/kernels/unary_ops.h

@@ -257,6 +257,13 @@ struct Log {
   T operator()(T x) {
     return metal::precise::log(x);
   };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    auto r = metal::precise::log(Abs{}(x).real);
+    auto i = metal::precise::atan2(x.imag, x.real);
+    return {r, i};
+  };
 };
 
 struct Log2 {
@@ -264,6 +271,12 @@ struct Log2 {
   T operator()(T x) {
     return metal::precise::log2(x);
   };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    auto y = Log{}(x);
+    return {y.real / M_LN2_F, y.imag / M_LN2_F};
+  };
 };
 
 struct Log10 {
@@ -271,6 +284,12 @@ struct Log10 {
   T operator()(T x) {
     return metal::precise::log10(x);
   };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    auto y = Log{}(x);
+    return {y.real / M_LN10_F, y.imag / M_LN10_F};
+  };
 };
 
 struct Log1p {

mlx/backend/metal/logsumexp.cpp

@@ -0,0 +1,96 @@
+// Copyright © 2023-2024 Apple Inc.
+#include <algorithm>
+
+#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/metal/device.h"
+#include "mlx/backend/metal/kernels.h"
+#include "mlx/backend/metal/utils.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+constexpr int LOGSUMEXP_LOOPED_LIMIT = 4096;
+
+void LogSumExp::eval_gpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  if (!issubdtype(out.dtype(), floating)) {
+    throw std::runtime_error(
+        "[logsumexp] Does not support non-floating point types.");
+  }
+  auto& s = stream();
+  auto& d = metal::device(s.device);
+
+  // Make sure that the last dimension is contiguous
+  auto ensure_contiguous = [&s, &d](const array& x) {
+    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
+      return x;
+    } else {
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy_gpu(x, x_copy, CopyType::General, s);
+      d.add_temporary(x_copy, s.index);
+      return x_copy;
+    }
+  };
+
+  auto in = ensure_contiguous(inputs[0]);
+  if (in.flags().row_contiguous) {
+    out.set_data(allocator::malloc(out.nbytes()));
+  } else {
+    auto n = in.shape(-1);
+    auto flags = in.flags();
+    auto strides = in.strides();
+    for (auto& s : strides) {
+      s /= n;
+    }
+    bool col_contig = strides[0] == 1;
+    for (int i = 1; col_contig && i < strides.size(); ++i) {
+      col_contig &=
+          (out.shape(i) == 1 || strides[i - 1] == out.shape(i) * strides[i]);
+    }
+    flags.col_contiguous = col_contig;
+    out.set_data(
+        allocator::malloc(in.nbytes() / n),
+        in.data_size() / n,
+        std::move(strides),
+        flags);
+  }
+
+  int axis_size = in.shape().back();
+  int n_rows = in.data_size() / axis_size;
+
+  const int simd_size = 32;
+  const int n_reads = 4;
+  const int looped_limit = LOGSUMEXP_LOOPED_LIMIT;
+
+  std::string kernel_name = (axis_size > looped_limit) ? "looped_" : "block_";
+  kernel_name += "logsumexp_";
+  kernel_name += type_to_name(out);
+
+  auto kernel = get_logsumexp_kernel(d, kernel_name, out);
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  {
+    MTL::Size grid_dims, group_dims;
+    if (axis_size <= looped_limit) {
+      size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
+      size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
+      size_t threadgroup_size = simd_size * simds_needed;
+      assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
+      size_t n_threads = n_rows * threadgroup_size;
+      grid_dims = MTL::Size(n_threads, 1, 1);
+      group_dims = MTL::Size(threadgroup_size, 1, 1);
+    } else {
+      size_t threadgroup_size = kernel->maxTotalThreadsPerThreadgroup();
+      size_t n_threads = n_rows * threadgroup_size;
+      grid_dims = MTL::Size(n_threads, 1, 1);
+      group_dims = MTL::Size(threadgroup_size, 1, 1);
+    }
+
+    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder.set_input_array(in, 0);
+    compute_encoder.set_output_array(out, 1);
+    compute_encoder.set_bytes(axis_size, 2);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
+  }
+}
+
+} // namespace mlx::core

mlx/backend/metal/matmul.cpp

@@ -5,6 +5,7 @@
 #include <numeric>
 #include <sstream>
 
+#include "mlx/backend/common/broadcasting.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/metal/copy.h"
 #include "mlx/backend/metal/device.h"
@@ -102,6 +103,47 @@ std::tuple<bool, int64_t, array> check_transpose(
   }
 };
 
+inline array
+ensure_row_contiguous(const array& x, metal::Device& d, const Stream& s) {
+  if (!x.flags().row_contiguous) {
+    array x_copy(x.shape(), x.dtype(), nullptr, {});
+    copy_gpu(x, x_copy, CopyType::General, s);
+    d.add_temporary(x_copy, s.index);
+    return x_copy;
+  } else {
+    return x;
+  }
+}
+
+inline std::tuple<bool, int64_t, array>
+ensure_batch_contiguous(const array& x, metal::Device& d, const Stream& s) {
+  if (x.flags().row_contiguous) {
+    return std::make_tuple(false, x.strides()[x.ndim() - 2], x);
+  }
+
+  bool rc = true;
+  for (int i = 0; i < x.ndim() - 3; i++) {
+    rc &= x.strides()[i + 1] * x.shape(i) == x.strides()[i];
+  }
+  if (rc) {
+    auto stx = x.strides()[x.ndim() - 2];
+    auto sty = x.strides()[x.ndim() - 1];
+    auto K = x.shape(-2);
+    auto N = x.shape(-1);
+    if (sty == 1 && (N != 1 || stx == N)) {
+      return std::make_tuple(false, stx, x);
+    }
+    if (stx == 1 && (N != 1 || sty == K)) {
+      return std::make_tuple(true, sty, x);
+    }
+  }
+
+  array x_copy(x.shape(), x.dtype(), nullptr, {});
+  copy_gpu(x, x_copy, CopyType::General, s);
+  d.add_temporary(x_copy, s.index);
+  return std::make_tuple(false, x_copy.strides()[x_copy.ndim() - 2], x_copy);
+}
+
 } // namespace
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -230,7 +272,6 @@ void steel_matmul_regular(
   const bool align_M = (M % bm) == 0;
   const bool align_N = (N % bn) == 0;
   const bool align_K = (K % bk) == 0;
-  const bool do_gather = false;
 
   metal::MTLFCList func_consts = {
       {&has_batch, MTL::DataType::DataTypeBool, 10},
@@ -239,7 +280,6 @@ void steel_matmul_regular(
       {&align_M, MTL::DataType::DataTypeBool, 200},
       {&align_N, MTL::DataType::DataTypeBool, 201},
       {&align_K, MTL::DataType::DataTypeBool, 202},
-      {&do_gather, MTL::DataType::DataTypeBool, 300},
   };
 
   // clang-format off
@@ -248,8 +288,7 @@ void steel_matmul_regular(
         << "_do_axpby_" << (do_axpby ? 't' : 'n')
         << "_align_M_" << (align_M ? 't' : 'n')
         << "_align_N_" << (align_N ? 't' : 'n')
-        << "_align_K_" << (align_K ? 't' : 'n')
-        << "_do_gather_" << (do_gather ? 't' : 'n'); // clang-format on
+        << "_align_K_" << (align_K ? 't' : 'n'); // clang-format on
 
   std::string hash_name = kname.str();
 
@@ -382,7 +421,7 @@ void steel_matmul(
     int split_k_partition_size = gemm_k_iterations * bk;
 
     array C_split({split_k_partitions, M, N}, float32, nullptr, {});
-    C_split.set_data(allocator::malloc_or_wait(C_split.nbytes()));
+    C_split.set_data(allocator::malloc(C_split.nbytes()));
     copies.push_back(C_split);
 
     bool mn_aligned = M % bm == 0 && N % bn == 0;
@@ -513,7 +552,7 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   /////////////////////////////////////////////////////////////////////////////
   // Init checks and prep
@@ -677,7 +716,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[matmul] Does not yet support non-floating point types.");
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   auto& s = stream();
   auto& d = metal::device(s.device);
 
@@ -860,7 +899,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     int split_k_partition_size = gemm_k_iterations * bk;
 
     array C_split({split_k_partitions, M, N}, float32, nullptr, {});
-    C_split.set_data(allocator::malloc_or_wait(C_split.nbytes()));
+    C_split.set_data(allocator::malloc(C_split.nbytes()));
     copies.push_back(C_split);
 
     bool mn_aligned = M % bm == 0 && N % bn == 0;
@@ -975,7 +1014,6 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   const bool align_M = (M % bm) == 0;
   const bool align_N = (N % bn) == 0;
   const bool align_K = (K % bk) == 0;
-  const bool do_gather = false;
 
   metal::MTLFCList func_consts = {
       {&has_batch, MTL::DataType::DataTypeBool, 10},
@@ -984,7 +1022,6 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       {&align_M, MTL::DataType::DataTypeBool, 200},
       {&align_N, MTL::DataType::DataTypeBool, 201},
       {&align_K, MTL::DataType::DataTypeBool, 202},
-      {&do_gather, MTL::DataType::DataTypeBool, 300},
   };
 
   // clang-format off
@@ -993,8 +1030,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
         << "_do_axpby_" << (do_axpby ? 't' : 'n')
         << "_align_M_" << (align_M ? 't' : 'n')
         << "_align_N_" << (align_N ? 't' : 'n')
-        << "_align_K_" << (align_K ? 't' : 'n')
-        << "_do_gather_" << (do_gather ? 't' : 'n'); // clang-format on
+        << "_align_K_" << (align_K ? 't' : 'n'); // clang-format on
 
   std::string hash_name = kname.str();
 
@@ -1096,7 +1132,7 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   /////////////////////////////////////////////////////////////////////////////
   // Init checks and prep
@@ -1464,267 +1500,337 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   d.add_temporaries(std::move(copies), s.index);
 }
 
-void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
-  using namespace mlx::steel;
-  // assert(inputs.size() == 2);
-  if (!issubdtype(out.dtype(), floating)) {
-    throw std::runtime_error(
-        "[GatherMM] Does not yet support non-floating point types.");
-  }
-  auto& s = stream();
-  auto& d = metal::device(s.device);
+void gather_mm_rhs(
+    const array& a_,
+    const array& b_,
+    const array& indices_,
+    array& out,
+    metal::Device& d,
+    const Stream& s) {
+  array indices = ensure_row_contiguous(indices_, d, s);
+  auto [transpose_b, ldb, b] = ensure_batch_contiguous(b_, d, s);
 
-  auto& a_pre = inputs[0];
-  auto& b_pre = inputs[1];
-  // Return 0s if either input is empty
-  if (a_pre.size() == 0 || b_pre.size() == 0) {
-    array zero = array(0, a_pre.dtype());
-    fill_gpu(zero, out, s);
-    d.add_temporary(std::move(zero), s.index);
-    return;
-  }
+  // Broadcast a with indices. If we are here that means lhs_indices were not
+  // provided so the lhs_indices are implied to be the shape of a broadcasted
+  // with rhs_indices. We need only broadcast a and copy it as if applying the
+  // lhs_indices.
+  auto broadcast_with_indices = [&d, &s, &indices](const array& x) {
+    if (x.size() / x.shape(-2) / x.shape(-1) == indices.size()) {
+      return ensure_row_contiguous(x, d, s);
+    }
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    auto x_shape = indices.shape();
+    x_shape.push_back(x.shape(-2));
+    x_shape.push_back(x.shape(-1));
+    array new_x(std::move(x_shape), x.dtype(), nullptr, {});
+    broadcast(x, new_x);
+    return ensure_row_contiguous(new_x, d, s);
+  };
+  array a = broadcast_with_indices(a_);
 
-  /////////////////////////////////////////////////////////////////////////////
-  // Init checks and prep
+  // Extract the matmul shapes
+  int K = a.shape(-1);
+  int M = a.size() / K;
+  int N = b.shape(-1);
+  int lda = a.strides()[a.ndim() - 2]; // should be K
 
-  int M = a_pre.shape(-2);
-  int N = b_pre.shape(-1);
-  int K = a_pre.shape(-1);
+  // Define the dispatch blocks
+  int bm = 16, bn = 64, bk = 16;
+  int wm = 1, wn = 2;
 
-  // Keep a vector with copies to be cleared in the completed buffer to release
-  // the arrays
-  std::vector<array> copies;
-  auto [transpose_a, a_cols, a] = check_transpose(copies, s, a_pre, M == 1);
-  auto [transpose_b, b_cols, b] = check_transpose(copies, s, b_pre, N == 1);
+  const bool align_M = (M % bm) == 0;
+  const bool align_N = (N % bn) == 0;
+  const bool align_K = (K % bk) == 0;
 
-  int lda = a_cols;
-  int ldb = b_cols;
+  // Define the kernel name
+  std::string base_name;
+  base_name.reserve(64);
+  concatenate(
+      base_name,
+      "steel_gather_mm_rhs_n",
+      transpose_b ? 't' : 'n',
+      '_',
+      type_to_name(a),
+      '_',
+      type_to_name(out),
+      "_bm",
+      bm,
+      "_bn",
+      bn,
+      "_bk",
+      bk,
+      "_wm",
+      wm,
+      "_wn",
+      wn);
 
-  /////////////////////////////////////////////////////////////////////////////
-  // Check and collapse batch dimensions
-
-  auto get_batch_dims = [](const auto& v) {
-    return decltype(v){v.begin(), v.end() - 2};
+  metal::MTLFCList func_consts = {
+      {&align_M, MTL::DataType::DataTypeBool, 200},
+      {&align_N, MTL::DataType::DataTypeBool, 201},
+      {&align_K, MTL::DataType::DataTypeBool, 202},
   };
 
-  auto& lhs_indices = inputs[2];
-  auto& rhs_indices = inputs[3];
+  // And the kernel hash that includes the function constants
+  std::string hash_name;
+  hash_name.reserve(128);
+  concatenate(
+      hash_name,
+      base_name,
+      "_align_M_",
+      align_M ? 't' : 'n',
+      "_align_N_",
+      align_N ? 't' : 'n',
+      "_align_K_",
+      align_K ? 't' : 'n');
 
-  Shape batch_shape = get_batch_dims(out.shape());
-  Strides batch_strides;
+  // Get and set the kernel
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = get_steel_gemm_gather_kernel(
+      d,
+      base_name,
+      hash_name,
+      func_consts,
+      out,
+      false,
+      transpose_b,
+      bm,
+      bn,
+      bk,
+      wm,
+      wn,
+      true);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
-  batch_strides.insert(
-      batch_strides.end(),
-      lhs_indices.strides().begin(),
-      lhs_indices.strides().end());
-  auto lhs_indices_str = batch_strides.empty() ? 0 : batch_strides.back();
+  // Prepare the matmul params
+  auto batch_stride_b = b.ndim() > 2 ? b.strides()[b.ndim() - 3] : b.size();
+  steel::GEMMParams params{
+      /* const int M = */ M,
+      /* const int N = */ N,
+      /* const int K = */ K,
+      /* const int lda = */ lda,
+      /* const int ldb = */ static_cast<int>(ldb),
+      /* const int ldd = */ N,
+      /* const int tiles_n = */ (N + bn - 1) / bn,
+      /* const int tiles_m = */ (M + bm - 1) / bm,
+      /* const int64_t batch_stride_a = */ 0,
+      /* const int64_t batch_stride_b = */ static_cast<int64_t>(batch_stride_b),
+      /* const int64_t batch_stride_d = */ 0,
+      /* const int swizzle_log = */ 0,
+      /* const int gemm_k_iterations_aligned = */ (K / bk),
+      /* const int batch_ndim = */ 0};
 
-  batch_strides.insert(
-      batch_strides.end(),
-      rhs_indices.strides().begin(),
-      rhs_indices.strides().end());
-  auto rhs_indices_str = batch_strides.empty() ? 0 : batch_strides.back();
+  // Prepare the grid
+  MTL::Size group_dims = MTL::Size(32, wn, wm);
+  MTL::Size grid_dims = MTL::Size(params.tiles_n, params.tiles_m, 1);
 
-  int batch_ndim = batch_shape.size();
+  // Launch kernel
+  compute_encoder.set_input_array(a, 0);
+  compute_encoder.set_input_array(b, 1);
+  compute_encoder.set_input_array(indices, 2);
+  compute_encoder.set_output_array(out, 3);
+  compute_encoder.set_bytes(params, 4);
 
-  if (batch_ndim == 0) {
-    batch_shape = {1};
-    batch_strides = {0};
-  }
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+}
 
-  int batch_ndim_A = a.ndim() - 2;
-  int batch_ndim_B = b.ndim() - 2;
-  std::vector<int> operand_batch_ndim = {batch_ndim_A, batch_ndim_B};
+void gather_mv(
+    const array& mat_,
+    const array& vec_,
+    const array& mat_indices_,
+    const array& vec_indices_,
+    array& out,
+    int N,
+    int K,
+    bool is_mv,
+    metal::Device& d,
+    const Stream& s) {
+  // Copy if needed
+  std::vector<array> copies;
+  auto [transpose_mat, mat_cols, mat] =
+      check_transpose(copies, s, mat_, N == 1);
+  auto [transpose_vec, vec_cols, vec] = check_transpose(copies, s, vec_, true);
+  d.add_temporaries(std::move(copies), s.index);
 
-  Shape batch_shape_A = get_batch_dims(a.shape());
-  Strides batch_strides_A = get_batch_dims(a.strides());
-  Shape batch_shape_B = get_batch_dims(b.shape());
-  Strides batch_strides_B = get_batch_dims(b.strides());
+  // If we are doing vector matrix instead of matrix vector we need to flip the
+  // matrix transposition. Basically m @ v = v @ m.T assuming that v is treated
+  // as a one dimensional array.
+  transpose_mat = (!is_mv) ^ transpose_mat;
 
-  if (batch_ndim_A == 0) {
-    batch_shape_A = {1};
-    batch_strides_A = {0};
-  }
+  // Define some shapes
+  int in_vector_len = K;
+  int out_vector_len = N;
+  int mat_ld = mat_cols;
 
-  if (batch_ndim_B == 0) {
-    batch_shape_B = {1};
-    batch_strides_B = {0};
-  }
+  int batch_size_out = out.size() / N;
+  int batch_ndim = out.ndim() - 2;
+  int batch_ndim_mat = mat.ndim() - 2;
+  int batch_ndim_vec = vec.ndim() - 2;
+  Strides index_strides = vec_indices_.strides();
+  index_strides.insert(
+      index_strides.end(),
+      mat_indices_.strides().begin(),
+      mat_indices_.strides().end());
 
-  auto matrix_stride_out = static_cast<int64_t>(M) * N;
-  auto batch_size_out = out.size() / matrix_stride_out;
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Gemv specialization
-
-  // Route to gemv if needed
-  if (std::min(M, N) == 1) {
-    // Collect problem info
-    bool is_b_matrix = N != 1;
-
-    auto& mat = is_b_matrix ? b : a;
-    auto& vec = is_b_matrix ? a : b;
-    bool transpose_mat = is_b_matrix ? !transpose_b : transpose_a;
-    int in_vector_len = K;
-    int out_vector_len = is_b_matrix ? N : M;
-
-    int mat_cols = transpose_mat ? out_vector_len : in_vector_len;
-    int mat_rows = transpose_mat ? in_vector_len : out_vector_len;
-    int mat_ld = is_b_matrix ? b_cols : a_cols;
-
-    auto batch_strides_mat = is_b_matrix ? batch_strides_B : batch_strides_A;
-    auto batch_strides_vec = is_b_matrix ? batch_strides_A : batch_strides_B;
-
-    auto batch_shape_mat = is_b_matrix ? batch_shape_B : batch_shape_A;
-    auto batch_shape_vec = is_b_matrix ? batch_shape_A : batch_shape_B;
-
-    if (!is_b_matrix) {
-      batch_strides = rhs_indices.strides();
-      batch_strides.insert(
-          batch_strides.end(),
-          lhs_indices.strides().begin(),
-          lhs_indices.strides().end());
-    }
-
-    int batch_ndim = batch_shape.size();
-
-    // Determine dispatch kernel
-    int tm = 4, tn = 4;
-    int sm = 1, sn = 32;
-    int bm = 1, bn = 1;
-    int n_out_per_tgp;
-    std::ostringstream kname;
-
-    if (transpose_mat) {
-      if (in_vector_len >= 8192 && out_vector_len >= 2048) {
-        sm = 4;
-        sn = 8;
-      } else {
-        sm = 8;
-        sn = 4;
-      }
-
-      if (out_vector_len >= 2048) {
-        bn = 16;
-      } else if (out_vector_len >= 512) {
-        bn = 4;
-      } else {
-        bn = 2;
-      }
-
-      // Specialized kernel for very small outputs
-      tn = out_vector_len < tn ? 1 : tn;
-
-      n_out_per_tgp = bn * sn * tn;
-      kname << "gemv_t_gather_" << type_to_name(out);
+  // Determine dispatch kernel
+  int tm = 4, tn = 4;
+  int sm = 1, sn = 32;
+  int bm = 1, bn = 1;
+  int n_out_per_tgp;
+  std::ostringstream kname;
 
+  if (transpose_mat) {
+    if (in_vector_len >= 8192 && out_vector_len >= 2048) {
+      sm = 4;
+      sn = 8;
     } else {
-      bm = out_vector_len >= 4096 ? 8 : 4;
-      sn = 32;
-
-      // Specialized kernel for very small outputs
-      tm = out_vector_len < tm ? 1 : tm;
-
-      n_out_per_tgp = bm * sm * tm;
-      kname << "gemv_gather_" << type_to_name(out);
+      sm = 8;
+      sn = 4;
     }
 
-    kname << "_bm" << bm << "_bn" << bn << "_sm" << sm << "_sn" << sn << "_tm"
-          << tm << "_tn" << tn;
+    if (out_vector_len >= 2048) {
+      bn = 16;
+    } else if (out_vector_len >= 512) {
+      bn = 4;
+    } else {
+      bn = 2;
+    }
 
-    // Encode and dispatch kernel
-    auto& compute_encoder = d.get_command_encoder(s.index);
-    auto kernel = d.get_kernel(kname.str());
-    compute_encoder.set_compute_pipeline_state(kernel);
+    // Specialized kernel for very small outputs
+    tn = out_vector_len < tn ? 1 : tn;
 
-    int n_tgp = (out_vector_len + n_out_per_tgp - 1) / n_out_per_tgp;
-    MTL::Size group_dims = MTL::Size(32, bn, bm);
-    MTL::Size grid_dims = MTL::Size(n_tgp, 1, batch_size_out);
+    n_out_per_tgp = bn * sn * tn;
+    kname << "gemv_t_gather_" << type_to_name(out);
 
-    compute_encoder.set_input_array(mat, 0);
-    compute_encoder.set_input_array(vec, 1);
-    compute_encoder.set_output_array(out, 3);
+  } else {
+    bm = out_vector_len >= 4096 ? 8 : 4;
+    sn = 32;
 
-    compute_encoder.set_bytes(in_vector_len, 4);
-    compute_encoder.set_bytes(out_vector_len, 5);
-    compute_encoder.set_bytes(mat_ld, 6);
+    // Specialized kernel for very small outputs
+    tm = out_vector_len < tm ? 1 : tm;
 
-    compute_encoder.set_bytes(batch_ndim, 9);
-    compute_encoder.set_vector_bytes(batch_shape, 10);
-    compute_encoder.set_vector_bytes(batch_strides, 11);
-
-    int batch_ndim_vec = batch_shape_vec.size();
-    compute_encoder.set_bytes(batch_ndim_vec, 12);
-    compute_encoder.set_vector_bytes(batch_shape_vec, 13);
-    compute_encoder.set_vector_bytes(batch_strides_vec, 14);
-
-    int batch_ndim_mat = batch_shape_mat.size();
-    compute_encoder.set_bytes(batch_ndim_mat, 15);
-    compute_encoder.set_vector_bytes(batch_shape_mat, 16);
-    compute_encoder.set_vector_bytes(batch_strides_mat, 17);
-
-    compute_encoder.set_input_array(lhs_indices, 18 + int(!is_b_matrix));
-    compute_encoder.set_input_array(rhs_indices, 18 + int(is_b_matrix));
-
-    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
-
-    d.add_temporaries(std::move(copies), s.index);
-    return;
+    n_out_per_tgp = bm * sm * tm;
+    kname << "gemv_gather_" << type_to_name(out);
   }
 
-  /////////////////////////////////////////////////////////////////////////////
-  // Regular kernel dispatch
+  kname << "_bm" << bm << "_bn" << bn << "_sm" << sm << "_sn" << sn << "_tm"
+        << tm << "_tn" << tn;
+
+  // Encode and dispatch kernel
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(kname.str());
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  int n_tgp = (out_vector_len + n_out_per_tgp - 1) / n_out_per_tgp;
+  MTL::Size group_dims = MTL::Size(32, bn, bm);
+  MTL::Size grid_dims = MTL::Size(n_tgp, 1, batch_size_out);
+
+  compute_encoder.set_input_array(mat, 0);
+  compute_encoder.set_input_array(vec, 1);
+  compute_encoder.set_output_array(out, 3);
+
+  compute_encoder.set_bytes(in_vector_len, 4);
+  compute_encoder.set_bytes(out_vector_len, 5);
+  compute_encoder.set_bytes(mat_ld, 6);
+
+  compute_encoder.set_bytes(batch_ndim, 9);
+  compute_encoder.set_vector_bytes(out.shape(), 10);
+  compute_encoder.set_vector_bytes(index_strides, 11);
+
+  compute_encoder.set_bytes(batch_ndim_vec, 12);
+  compute_encoder.set_vector_bytes(vec.shape(), 13);
+  compute_encoder.set_vector_bytes(vec.strides(), 14);
+
+  compute_encoder.set_bytes(batch_ndim_mat, 15);
+  compute_encoder.set_vector_bytes(mat.shape(), 16);
+  compute_encoder.set_vector_bytes(mat.strides(), 17);
+
+  compute_encoder.set_input_array(vec_indices_, 18);
+  compute_encoder.set_input_array(mat_indices_, 19);
+
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+}
+
+void gather_mm(
+    const array& a_,
+    const array& b_,
+    const array& lhs_indices,
+    const array& rhs_indices,
+    array& out,
+    int M,
+    int N,
+    int K,
+    metal::Device& d,
+    const Stream& s) {
+  // Copy if needed
+  std::vector<array> copies;
+  auto [transpose_a, lda, a] = check_transpose(copies, s, a_, false);
+  auto [transpose_b, ldb, b] = check_transpose(copies, s, b_, false);
+  d.add_temporaries(std::move(copies), s.index);
 
   // Determine dispatch kernel
   int bm = 64, bn = 64, bk = 16;
   int wm = 2, wn = 2;
+  size_t batch_size_out = out.size() / M / N;
+  int batch_ndim = out.ndim() - 2;
+  int batch_ndim_a = a.ndim() - 2;
+  int batch_ndim_b = b.ndim() - 2;
 
   char devc = d.get_architecture().back();
   GEMM_TPARAM_MACRO(devc)
 
-  // Prepare kernel name
-  std::ostringstream kname;
-  kname << "steel_gemm_fused_" << (transpose_a ? 't' : 'n')
-        << (transpose_b ? 't' : 'n') << "_" << type_to_name(a) << "_"
-        << type_to_name(out) << "_bm" << bm << "_bn" << bn << "_bk" << bk
-        << "_wm" << wm << "_wn" << wn;
-
-  std::string base_name = kname.str();
-
   const bool has_batch = batch_ndim > 1;
-  const bool use_out_source = false;
-  const bool do_axpby = false;
   const bool align_M = (M % bm) == 0;
   const bool align_N = (N % bn) == 0;
   const bool align_K = (K % bk) == 0;
-  const bool do_gather = true;
+
+  // Define the kernel name
+  std::string base_name;
+  base_name.reserve(128);
+  concatenate(
+      base_name,
+      "steel_gather_mm_",
+      transpose_a ? 't' : 'n',
+      transpose_b ? 't' : 'n',
+      "_",
+      type_to_name(a),
+      "_",
+      type_to_name(out),
+      "_bm",
+      bm,
+      "_bn",
+      bn,
+      "_bk",
+      bk,
+      "_wm",
+      wm,
+      "_wn",
+      wn);
 
   metal::MTLFCList func_consts = {
       {&has_batch, MTL::DataType::DataTypeBool, 10},
-      {&use_out_source, MTL::DataType::DataTypeBool, 100},
-      {&do_axpby, MTL::DataType::DataTypeBool, 110},
       {&align_M, MTL::DataType::DataTypeBool, 200},
       {&align_N, MTL::DataType::DataTypeBool, 201},
       {&align_K, MTL::DataType::DataTypeBool, 202},
-      {&do_gather, MTL::DataType::DataTypeBool, 300},
   };
 
-  // clang-format off
-  kname << "_has_batch_" << (has_batch ? 't' : 'n')
-        << "_use_out_source_" << (use_out_source ? 't' : 'n')
-        << "_do_axpby_" << (do_axpby ? 't' : 'n')
-        << "_align_M_" << (align_M ? 't' : 'n')
-        << "_align_N_" << (align_N ? 't' : 'n')
-        << "_align_K_" << (align_K ? 't' : 'n')
-        << "_do_gather_" << (do_gather ? 't' : 'n'); // clang-format on
+  // And the kernel hash that includes the function constants
+  std::string hash_name;
+  hash_name.reserve(128);
+  concatenate(
+      hash_name,
+      base_name,
+      "_has_batch_",
+      has_batch ? 't' : 'n',
+      "_align_M_",
+      align_M ? 't' : 'n',
+      "_align_N_",
+      align_N ? 't' : 'n',
+      "_align_K_",
+      align_K ? 't' : 'n');
 
-  std::string hash_name = kname.str();
-
-  // Encode and dispatch kernel
+  // Get and set the kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = get_steel_gemm_fused_kernel(
+  auto kernel = get_steel_gemm_gather_kernel(
       d,
       base_name,
       hash_name,
@@ -1736,72 +1842,97 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       bn,
       bk,
       wm,
-      wn);
-
+      wn,
+      false);
   compute_encoder.set_compute_pipeline_state(kernel);
 
-  // Use problem size to determine threadblock swizzle
-  int tn = (N + bn - 1) / bn;
-  int tm = (M + bm - 1) / bm;
-
-  // TODO: Explore device-based tuning for swizzle
-  int swizzle_log = 0; // tm >= 6 ? 3 : (tm <= 3 ? 0 : 2);
-
-  // Prepare steel matmul params
-  GEMMParams params{
+  // Prepare the matmul params
+  steel::GEMMParams params{
       /* const int M = */ M,
       /* const int N = */ N,
       /* const int K = */ K,
-      /* const int lda = */ lda,
-      /* const int ldb = */ ldb,
+      /* const int lda = */ static_cast<int>(lda),
+      /* const int ldb = */ static_cast<int>(ldb),
       /* const int ldd = */ N,
-      /* const int tiles_n = */ tn,
-      /* const int tiles_m = */ tm,
-      /* const int64_t batch_stride_a = */ lhs_indices_str,
-      /* const int64_t batch_stride_b = */ rhs_indices_str,
-      /* const int64_t batch_stride_d = */ matrix_stride_out,
-      /* const int swizzle_log = */ swizzle_log,
+      /* const int tiles_n = */ (N + bn - 1) / bn,
+      /* const int tiles_m = */ (M + bm - 1) / bm,
+      /* const int64_t batch_stride_a = */
+      (batch_ndim > 0) ? lhs_indices.strides()[0] : 0,
+      /* const int64_t batch_stride_b = */
+      (batch_ndim > 0) ? rhs_indices.strides()[0] : 0,
+      /* const int64_t batch_stride_d = */ M * N,
+      /* const int swizzle_log = */ 0,
       /* const int gemm_k_iterations_aligned = */ (K / bk),
       /* const int batch_ndim = */ batch_ndim};
 
-  // Prepare launch grid params
-  int tile = 1 << swizzle_log;
-  tm = (tm + tile - 1) / tile;
-  tn = tn * tile;
-
+  // Prepare the grid
   MTL::Size group_dims = MTL::Size(32, wn, wm);
-  MTL::Size grid_dims = MTL::Size(tn, tm, batch_size_out);
+  MTL::Size grid_dims =
+      MTL::Size(params.tiles_n, params.tiles_m, batch_size_out);
 
   // Launch kernel
   compute_encoder.set_input_array(a, 0);
   compute_encoder.set_input_array(b, 1);
-  compute_encoder.set_output_array(out, 3);
-
-  compute_encoder.set_bytes(params, 4);
-
-  compute_encoder.set_vector_bytes(batch_shape, 6);
-  compute_encoder.set_vector_bytes(batch_strides, 7);
-
-  compute_encoder.set_input_array(lhs_indices, 10);
-  compute_encoder.set_input_array(rhs_indices, 11);
-
-  std::vector operand_shape = batch_shape_A;
-  operand_shape.insert(
-      operand_shape.end(), batch_shape_B.begin(), batch_shape_B.end());
-
-  std::vector operand_strides = batch_strides_A;
-  operand_strides.insert(
-      operand_strides.end(), batch_strides_B.begin(), batch_strides_B.end());
-
-  operand_batch_ndim.push_back(0);
-
-  compute_encoder.set_vector_bytes(operand_shape, 13);
-  compute_encoder.set_vector_bytes(operand_strides, 14);
-  compute_encoder.set_vector_bytes(operand_batch_ndim, 15);
-
+  compute_encoder.set_input_array(lhs_indices, 2);
+  compute_encoder.set_input_array(rhs_indices, 3);
+  compute_encoder.set_output_array(out, 4);
+  compute_encoder.set_bytes(params, 5);
+  compute_encoder.set_vector_bytes(lhs_indices.shape(), 6);
+  compute_encoder.set_vector_bytes(lhs_indices.strides(), 7);
+  compute_encoder.set_vector_bytes(rhs_indices.strides(), 8);
+  compute_encoder.set_bytes(batch_ndim_a, 9);
+  compute_encoder.set_vector_bytes(a.shape(), 10);
+  compute_encoder.set_vector_bytes(a.strides(), 11);
+  compute_encoder.set_bytes(batch_ndim_b, 12);
+  compute_encoder.set_vector_bytes(b.shape(), 13);
+  compute_encoder.set_vector_bytes(b.strides(), 14);
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+}
 
-  d.add_temporaries(std::move(copies), s.index);
+void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
+  auto& s = stream();
+  auto& d = metal::device(s.device);
+
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  auto& lhs_indices = inputs[2];
+  auto& rhs_indices = inputs[3];
+
+  // Return 0s if either input is empty
+  if (a.size() == 0 || b.size() == 0) {
+    array zero = array(0, a.dtype());
+    fill_gpu(zero, out, s);
+    d.add_temporary(std::move(zero), s.index);
+    return;
+  }
+
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  // Extract shapes strides from inputs and copy in case of non-contiguous
+  // vectors.
+  int M = a.shape(-2);
+  int N = b.shape(-1);
+  int K = a.shape(-1);
+
+  // We are walking a in order and b is also in order so we can batch up the
+  // matmuls and reuse reading a and b.
+  if (M == 1 && right_sorted_ == true) {
+    gather_mm_rhs(a, b, rhs_indices, out, d, s);
+    return;
+  }
+
+  // Route to gather gemv if any of a or b are vectors
+  if (M == 1) {
+    gather_mv(b, a, rhs_indices, lhs_indices, out, N, K, false, d, s);
+    return;
+  }
+  if (N == 1) {
+    gather_mv(a, b, lhs_indices, rhs_indices, out, M, K, true, d, s);
+    return;
+  }
+
+  // Route to non specialized gather mm
+  gather_mm(a, b, lhs_indices, rhs_indices, out, M, N, K, d, s);
 }
 
 } // namespace mlx::core