version (#1617)

* Rough INIT

* [WIP]: Loading and Matmuls added

* [WIP]: Reductions and min working aligned kernel at headdim = 64

* [WIP] Added headdim 80 for testing

* [WIP] Update dispatch params for testing

* [WIP] Add support for unaligned seq lengths - still looks messy

* Update sdpa_benchmarks

* Update sdpa_benchmarks

* Update sdpa_benchmarks

* Enable gqa support

* Update benchmark and switch off 128 headdim

* Update headdim 128 tuning

* Remove older fast attention code. Write out O strided

* Disable hd=128 until further optimizations

* Enable bf16

* Fix data size bug

* Enable attn build outside of jit

.circleci/config.yml

@@ -13,8 +13,62 @@ parameters:
   test_release:
     type: boolean
     default: false
+  linux_release:
+    type: boolean
+    default: false
 
 jobs:
+  build_documentation:
+    parameters:
+      upload-docs:
+        type: boolean
+        default: false
+    macos:
+      xcode: "15.2.0"
+    resource_class: macos.m1.medium.gen1
+    steps:
+      - checkout
+      - run:
+          name: Install
+          command: |
+            brew install python@3.9
+            brew install doxygen
+            python3.9 -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
+            pip install --upgrade cmake
+            pip install -r docs/requirements.txt
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` pip install . -v
+      - when:
+          condition:
+            not: << parameters.upload-docs >>
+          steps:
+            - run:
+               name: Build documentation
+               command: |
+                 source env/bin/activate
+                 cd docs && doxygen && make html O=-W
+      - when:
+          condition: << parameters.upload-docs >>
+          steps:
+            - add_ssh_keys:
+                fingerprints:
+                  - "SHA256:OhcVVMovbT0pkgMeiVRyxMnjV9R2t+hKBsNcuxq9h+0"
+            - run:
+               name: Upload documentation
+               command: |
+                 source env/bin/activate
+                 git config user.email "mlx@group.apple.com"
+                 git config user.name "CircleCI Docs"
+                 git checkout gh-pages
+                 git rebase main
+                 cd docs
+                 git rm -rf build/html
+                 doxygen && make html O=-W
+                 git add -f build/html
+                 git commit -m "rebase"
+                 git push -f origin gh-pages
+
   linux_build_and_test:
     docker:
       - image: cimg/python:3.9
@@ -31,7 +85,7 @@ jobs:
           name: Install dependencies
           command: |
             pip install --upgrade cmake
-            pip install nanobind==2.1.0
+            pip install nanobind==2.2.0
             pip install numpy
             sudo apt-get update
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
@@ -77,13 +131,13 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            brew install python@3.8
+            brew install python@3.9
             brew install openmpi
-            python3.8 -m venv env
+            python3.9 -m venv env
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install nanobind==2.1.0
+            pip install nanobind==2.2.0
             pip install numpy
             pip install torch
             pip install tensorflow
@@ -105,7 +159,7 @@ jobs:
             source env/bin/activate
             LOW_MEMORY=1 DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
-            mpirun -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
+            mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
       - run:
           name: Build example extension
           command: |
@@ -172,7 +226,7 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install nanobind==2.1.0
+            pip install nanobind==2.2.0
             pip install --upgrade setuptools
             pip install numpy
             pip install twine
@@ -208,7 +262,7 @@ jobs:
       - store_artifacts:
           path: dist/
 
-  build_linux_test_release:
+  build_linux_release:
     parameters:
       python_version:
         type: string
@@ -237,12 +291,13 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install nanobind==2.1.0
+            pip install nanobind==2.2.0
             pip install --upgrade setuptools
             pip install numpy
             pip install auditwheel
             pip install patchelf
             pip install build
+            pip install twine
             << parameters.extra_env >> \
               CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
               pip install . -v
@@ -253,6 +308,11 @@ jobs:
               python -m build --wheel
             auditwheel show dist/*
             auditwheel repair dist/* --plat manylinux_2_31_x86_64
+      - run:
+          name: Upload package
+          command: |
+            source env/bin/activate
+            twine upload wheelhouse/*
       - store_artifacts:
           path: wheelhouse/
 
@@ -272,6 +332,7 @@ workflows:
             parameters:
               xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
       - linux_build_and_test
+      - build_documentation 
 
   build_pypi_release:
     when:
@@ -288,9 +349,17 @@ workflows:
               ignore: /.*/
           matrix:
             parameters:
-              python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               xcode_version: ["15.0.0", "15.2.0"]
               build_env: ["PYPI_RELEASE=1"]
+      - build_documentation:
+          filters:
+            tags:
+              only: /^v.*/
+            branches:
+              ignore: /.*/
+          upload-docs: true
+
   prb:
     when:
       matches:
@@ -317,7 +386,7 @@ workflows:
       - build_release:
           matrix:
             parameters:
-              python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               xcode_version: ["15.0.0", "15.2.0"]
   weekly_build:
     when:
@@ -328,17 +397,17 @@ workflows:
       - build_release:
           matrix:
             parameters:
-              python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
               build_env: ["DEV_RELEASE=1"]
   linux_test_release:
     when:
       and:
         - equal: [ main, << pipeline.git.branch >> ]
-        - << pipeline.parameters.test_release >>
+        - << pipeline.parameters.linux_release >>
     jobs:
-      - build_linux_test_release:
+      - build_linux_release:
           matrix:
             parameters:
-              python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               extra_env: ["PYPI_RELEASE=1"]

CMakeLists.txt

@@ -24,7 +24,7 @@ option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
 
 if(NOT MLX_VERSION)
-  set(MLX_VERSION 0.18.0)
+  set(MLX_VERSION 0.21.0)
 endif()
 
 # --------------------- Processor tests -------------------------
@@ -47,17 +47,20 @@ if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
           "https://ml-explore.github.io/mlx/build/html/install.html#build-from-source"
       )
     else()
+      set(MLX_BUILD_METAL OFF)
       message(WARNING "Building for x86_64 arch is not officially supported.")
     endif()
-    set(MLX_BUILD_METAL OFF)
-  elseif(${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm64")
-    set(MLX_BUILD_ARM ON)
   endif()
 
 else()
+  set(MLX_BUILD_METAL OFF)
   message(WARNING "MLX is prioritised for Apple silicon systems using macOS.")
 endif()
 
+if(${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm64")
+  set(MLX_BUILD_ARM ON)
+endif()
+
 # ----------------------------- Lib -----------------------------
 
 include(FetchContent)
@@ -86,25 +89,27 @@ elseif(MLX_BUILD_METAL)
   # Throw an error if xcrun not found
   execute_process(
     COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
-    OUTPUT_VARIABLE MACOS_VERSION COMMAND_ERROR_IS_FATAL ANY)
+    OUTPUT_VARIABLE MACOS_SDK_VERSION COMMAND_ERROR_IS_FATAL ANY)
 
-  if(${MACOS_VERSION} LESS 14.0)
+  if(${MACOS_SDK_VERSION} LESS 14.0)
     message(
       FATAL_ERROR
         "MLX requires macOS SDK >= 14.0 to be built with MLX_BUILD_METAL=ON")
   endif()
-  message(STATUS "Building with SDK for macOS version ${MACOS_VERSION}")
+  message(STATUS "Building with macOS SDK version ${MACOS_SDK_VERSION}")
 
   set(METAL_CPP_URL
       https://developer.apple.com/metal/cpp/files/metal-cpp_macOS15_iOS18-beta.zip
   )
-  # Get the metal version
+
+  if(NOT CMAKE_OSX_DEPLOYMENT_TARGET STREQUAL "")
+    set(XCRUN_FLAGS "-mmacosx-version-min=${CMAKE_OSX_DEPLOYMENT_TARGET}")
+  endif()
   execute_process(
     COMMAND
       zsh "-c"
-      "echo \"__METAL_VERSION__\" | xcrun -sdk macosx metal -E -x metal -P - | tail -1 | tr -d '\n'"
+      "echo \"__METAL_VERSION__\" | xcrun -sdk macosx metal ${XCRUN_FLAGS} -E -x metal -P - | tail -1 | tr -d '\n'"
     OUTPUT_VARIABLE MLX_METAL_VERSION COMMAND_ERROR_IS_FATAL ANY)
-
   FetchContent_Declare(metal_cpp URL ${METAL_CPP_URL})
 
   FetchContent_MakeAvailable(metal_cpp)
@@ -112,8 +117,6 @@ elseif(MLX_BUILD_METAL)
     mlx PUBLIC $<BUILD_INTERFACE:${metal_cpp_SOURCE_DIR}>
                $<INSTALL_INTERFACE:include/metal_cpp>)
   target_link_libraries(mlx PUBLIC ${METAL_LIB} ${FOUNDATION_LIB} ${QUARTZ_LIB})
-
-  add_compile_definitions("MLX_METAL_VERSION=${MLX_METAL_VERSION}")
 endif()
 
 if(MLX_BUILD_CPU)

README.md

@@ -6,7 +6,7 @@
 
 [![CircleCI](https://circleci.com/gh/ml-explore/mlx.svg?style=svg)](https://circleci.com/gh/ml-explore/mlx)
 
-MLX is an array framework for machine learning research on Apple silicon,
+MLX is an array framework for machine learning on Apple silicon,
 brought to you by Apple machine learning research.
 
 Some key features of MLX include:

benchmarks/python/comparative/bench_mlx.py

@@ -144,6 +144,13 @@ def reduction(op, axis, x):
     mx.eval(ys)
 
 
+def sum_and_add(axis, x, y):
+    z = x.sum(axis=axis, keepdims=True)
+    for i in range(50):
+        z = (z + y).sum(axis=axis, keepdims=True)
+    mx.eval(z)
+
+
 def softmax(axis, x):
     ys = []
     for i in range(100):
@@ -505,5 +512,8 @@ if __name__ == "__main__":
     elif args.benchmark == "selu":
         print(bench(selu, x))
 
+    elif args.benchmark == "sum_and_add":
+        print(bench(sum_and_add, axis, *xs))
+
     else:
         raise ValueError("Unknown benchmark")

benchmarks/python/scatter_bench.py

@@ -9,7 +9,7 @@ from time_utils import measure_runtime
 
 def benchmark_scatter_mlx(dst_shape, x_shape, idx_shapes):
     def scatter(dst, x, idx):
-        dst[*idx] = x
+        dst[tuple(idx)] = x
         mx.eval(dst)
 
     idx = []
@@ -23,8 +23,8 @@ def benchmark_scatter_mlx(dst_shape, x_shape, idx_shapes):
 
 
 def benchmark_scatter_torch(dst_shape, x_shape, idx_shapes, device):
-    def gather(dst, x, idx, device):
-        dst[*idx] = x
+    def scatter(dst, x, idx, device):
+        dst[tuple(idx)] = x
         if device == torch.device("mps"):
             torch.mps.synchronize()
 
@@ -34,7 +34,7 @@ def benchmark_scatter_torch(dst_shape, x_shape, idx_shapes, device):
     x = torch.randn(x_shape, dtype=torch.float32).to(device)
     dst = torch.randn(dst_shape, dtype=torch.float32).to(device)
 
-    runtime = measure_runtime(gather, dst=dst, x=x, idx=idx, device=device)
+    runtime = measure_runtime(scatter, dst=dst, x=x, idx=idx, device=device)
     print(f"PyTorch: {runtime:.3f}ms")
 
 
@@ -54,7 +54,7 @@ if __name__ == "__main__":
         (100_000, 64),
         (1_000_000, 64),
         (100_000,),
-        (2_000_00,),
+        (200_000,),
         (20_000_000,),
         (10000, 64),
         (100, 64),
@@ -91,6 +91,6 @@ if __name__ == "__main__":
 
     for dst_shape, x_shape, idx_shape in zip(dst_shapes, x_shapes, idx_shapes):
         print("=" * 20)
-        print(f"X {x_shape}, Indices {idx_shape}")
+        print(f"Dst: {dst_shape}, X {x_shape}, Indices {idx_shape}")
         benchmark_scatter_mlx(dst_shape, x_shape, idx_shape)
         benchmark_scatter_torch(dst_shape, x_shape, idx_shape, device=device)

benchmarks/python/sdpa_bench.py

@@ -1,62 +1,189 @@
+# Copyright © 2024 Apple Inc.
+
 import argparse
 import math
+import os
+import subprocess
+import time
 
 import mlx.core as mx
-from time_utils import time_fn
+import numpy as np
 
-MAX_SEQ = 300
-START_SEQ = 100
-SEQ_INCREMENT = 50
+device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
+device_name = device_name.decode("utf-8").strip("\n")
+
+N_warmup = 5
+N_iter_bench = 40
+N_iter_func = 8
 
 
-def time_self_attention_primitives():
-    mx.random.seed(3)
-    B = 2
-    H = 38
-    D = 64
-    for R in range(START_SEQ, MAX_SEQ, SEQ_INCREMENT):
-        q = mx.random.uniform(shape=(B, H, R, D))
-        k = mx.random.uniform(shape=(B, H, R, D))
-        v = mx.random.uniform(shape=(B, H, R, D))
-        scale = 1.0 / math.sqrt(float(D))
-        mx.eval(q, k, v)
+def bench(f, *args):
+    for i in range(N_warmup):
+        f(*args)
 
-        def sdpa_primitives(qs, ks, vs, alpha):
-            s = (alpha * qs) @ ks.transpose(0, 1, 3, 2)
-            p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
-            o = p @ vs
-            return o
-
-        time_fn(sdpa_primitives, q, k, v, scale)
+    s = time.perf_counter_ns()
+    for i in range(N_iter_bench):
+        f(*args)
+    e = time.perf_counter_ns()
+    return (e - s) * 1e-9
 
 
-def time_self_attention_sdpa():
-    mx.random.seed(3)
-    B = 2
-    H = 38
-    D = 64
-    for R in range(START_SEQ, MAX_SEQ, SEQ_INCREMENT):
-        q = mx.random.uniform(shape=(B, H, R, D))
-        k = mx.random.uniform(shape=(B, H, R, D))
-        v = mx.random.uniform(shape=(B, H, R, D))
-        scale = 1.0 / math.sqrt(float(D))
-        mx.eval(q, k, v)
+def mlx_sdpa_fused_inner(q, k, v, scale):
+    return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=None)
 
-        def sdpa_fused(qs, ks, vs, alpha):
-            o = mx.fast.scaled_dot_product_attention(qs, ks, vs, scale=alpha)
-            return o
 
-        time_fn(sdpa_fused, q, k, v, scale)
+def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
+    q_dtype = q.dtype
+    q = q * mx.array(scale, q_dtype)
+    n_q_heads = q.shape[-3]
+    n_kv_heads = k.shape[-3]
+    n_repeats = n_q_heads // n_kv_heads
+
+    B = q.shape[0]
+    L = q.shape[2]
+
+    if n_repeats > 1:
+        q = mx.reshape(q, [B, n_kv_heads, n_repeats, L, -1])
+        k = mx.expand_dims(k, 2)
+        v = mx.expand_dims(v, 2)
+
+    scores = q @ mx.swapaxes(k, -1, -2)
+    if f32softmax:
+        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(q_dtype)
+    else:
+        scores = mx.softmax(scores, axis=-1)
+
+    out = scores @ v
+    if n_repeats > 1:
+        out = mx.reshape(out, [B, n_q_heads, L, -1])
+
+    return out
+
+
+def mlx_spda_unfused(q, k, v, scale, transpose):
+    q_out = q
+    if transpose:
+        k = mx.transpose(k, (0, 2, 1, 3))
+        v = mx.transpose(v, (0, 2, 1, 3))
+
+    for i in range(N_iter_func):
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+        q_out = mlx_sdpa_unfused_inner(q_out, k, v, scale)
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+
+    mx.eval(q_out)
+    return q_out
+
+
+def mlx_spda_fused(q, k, v, scale, transpose):
+    q_out = q
+    if transpose:
+        k = mx.transpose(k, (0, 2, 1, 3))
+        v = mx.transpose(v, (0, 2, 1, 3))
+
+    for i in range(N_iter_func):
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+        q_out = mlx_sdpa_fused_inner(q_out, k, v, scale)
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+
+    mx.eval(q_out)
+    return q_out
+
+
+def bench_shape(B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose=True):
+    shape_q = (
+        (B, qsl, n_q_heads, head_dim) if transpose else (B, n_q_heads, qsl, head_dim)
+    )
+    shape_kv = (
+        (B, ksl, n_kv_heads, head_dim) if transpose else (B, n_kv_heads, ksl, head_dim)
+    )
+
+    q_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_q).astype(np_dtype)
+    k_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
+    v_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
+
+    scale = math.sqrt(1.0 / head_dim)
+
+    q_mx = mx.array(q_np)
+    k_mx = mx.array(k_np)
+    v_mx = mx.array(v_np)
+
+    time_mlx_unfused = bench(mlx_spda_unfused, q_mx, k_mx, v_mx, scale, transpose)
+    time_mlx_fused = bench(mlx_spda_fused, q_mx, k_mx, v_mx, scale, transpose)
+
+    if transpose:
+        q_mx = mx.transpose(q_mx, (0, 2, 1, 3))
+        k_mx = mx.transpose(k_mx, (0, 2, 1, 3))
+        v_mx = mx.transpose(v_mx, (0, 2, 1, 3))
+
+    o_mlx_fused = mlx_sdpa_fused_inner(q_mx, k_mx, v_mx, scale)
+    o_mlx_unfused = mlx_sdpa_unfused_inner(q_mx, k_mx, v_mx, scale, f32softmax=True)
+
+    atol = 1e-5 if np_dtype == np.float32 else 1e-4
+
+    if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol):
+        print(
+            f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}"
+        )
+
+    return time_mlx_fused, time_mlx_unfused
+
+
+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)
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser("MLX benchmarks.")
-    parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
-    args = parser.parse_args()
-    if args.gpu:
-        mx.set_default_device(mx.gpu)
-    else:
-        mx.set_default_device(mx.cpu)
+    parser = argparse.ArgumentParser(description="Run gemm benchmarks")
 
-    time_self_attention_sdpa()
-    time_self_attention_primitives()
+    dtypes = ("float16", "float32")[:1]
+    transposes = (False,)
+
+    # fmt: off
+    shapes_64 = (
+        # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
+          (  1,    32,    32,       64,   32,    32),
+          (  1,    64,    64,       64,   32,    32),
+          (  1,   128,   128,       64,   32,    32),
+          (  1,   256,   256,       64,   32,    32),
+          (  1,   512,   512,       64,   32,    32),
+          (  1,  1024,  1024,       64,   32,    32),
+          (  1,  2048,  2048,       64,   32,    32),
+          (  1,  4096,  4096,       64,   32,    32),
+    )
+
+    shapes_80 = (
+        # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
+          (  1,  1024,  1024,       80,   32,    32),
+          (  1,  2048,  2048,       80,   32,    32),
+          (  1,  4096,  4096,       80,   32,    32),
+    )
+
+    shapes_128 = (
+        # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
+          (  1,  1024,  1024,      128,   32,    32),
+          (  1,  2048,  2048,      128,   32,    32),
+          (  1,  4096,  4096,      128,   32,    32),
+    )
+    # fmt: on
+
+    shapes = shapes_64 + shapes_80 + shapes_128
+
+    print("  B,   qsl,   ksl, hdim, n_qh, n_kvh, tpose,   dtype, t_unfs, t_fuse, diff%")
+
+    for dtype in dtypes:
+        for transpose in transposes:
+            for B, qsl, ksl, head_dim, n_q_heads, n_kv_heads in shapes:
+                np_dtype = getattr(np, dtype)
+                time_mlx_fused, time_mlx_unfused = bench_shape(
+                    B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose
+                )
+                diff = time_mlx_unfused / time_mlx_fused - 1.0
+                t_str = 1 if transpose else 0
+                print(
+                    f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:5d}, {dtype}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%"
+                )

benchmarks/python/sdpa_vector_bench.py

@@ -0,0 +1,58 @@
+import argparse
+import math
+
+import mlx.core as mx
+from time_utils import time_fn
+
+L = 16384
+H = 32
+H_k = H // 4
+D = 128
+dtype = mx.float16
+loops = 10
+
+
+def attention(q, k, v):
+    def _sdpa(q, k, v):
+        B, Hq, L, D = q.shape
+        _, Hk, S, _ = k.shape
+        q = q.reshape(B, Hk, Hq // Hk, L, D)
+        k = k[:, :, None, :, :]
+        v = v[:, :, None, :, :]
+        s = q @ k.transpose(0, 1, 2, 4, 3)
+        p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
+        o = p @ v
+        return o.reshape(B, Hq, L, D)
+
+    for i in range(loops):
+        q = _sdpa(q, k, v)
+    return q
+
+
+def sdpa(q, k, v):
+    for i in range(loops):
+        q = mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0)
+    return q
+
+
+def time_self_attention_primitives():
+    mx.random.seed(3)
+    q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
+    k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
+    v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
+    mx.eval(q, k, v)
+    time_fn(attention, q, k, v)
+
+
+def time_self_attention_sdpa():
+    mx.random.seed(3)
+    q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
+    k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
+    v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
+    mx.eval(q, k, v)
+    time_fn(sdpa, q, k, v)
+
+
+if __name__ == "__main__":
+    time_self_attention_sdpa()
+    time_self_attention_primitives()

docs/src/conf.py

@@ -60,6 +60,7 @@ html_theme_options = {
     },
 }
 
+html_favicon = html_theme_options["logo"]["image_light"]
 
 # -- Options for HTMLHelp output ---------------------------------------------
 

docs/src/dev/custom_metal_kernels.rst

@@ -1,3 +1,5 @@
+.. _custom_metal_kernels:
+
 Custom Metal Kernels
 ====================
 
@@ -76,6 +78,10 @@ Putting this all together, the generated function signature for ``myexp`` is as
 
   template [[host_name("custom_kernel_myexp_float")]] [[kernel]] decltype(custom_kernel_myexp_float<float>) custom_kernel_myexp_float<float>;
 
+Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads <https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_ function.
+This means we will launch ``mx.prod(grid)`` threads, subdivided into ``threadgroup`` size threadgroups.
+For optimal performance, each thread group dimension should be less than or equal to the corresponding grid dimension.
+
 Passing ``verbose=True`` to ``mx.fast.metal_kernel.__call__`` will print the generated code for debugging purposes.
 
 Using Shape/Strides

docs/src/dev/extensions.rst

@@ -494,7 +494,7 @@ below.
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);
-        compute_encoder->setComputePipelineState(kernel);
+        compute_encoder.set_compute_pipeline_state(kernel);
 
         // Kernel parameters are registered with buffer indices corresponding to
         // those in the kernel declaration at axpby.metal
@@ -509,14 +509,14 @@ below.
         compute_encoder.set_output_array(out, 2);
 
         // Encode alpha and beta
-        compute_encoder->setBytes(&alpha_, sizeof(float), 3);
-        compute_encoder->setBytes(&beta_, sizeof(float), 4);
+        compute_encoder.set_bytes(alpha_, 3);
+        compute_encoder.set_bytes(beta_, 4);
 
         // Encode shape, strides and ndim
-        compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5);
-        compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6);
-        compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7);
-        compute_encoder->setBytes(&ndim, sizeof(int), 8);
+        compute_encoder.set_vector_bytes(x.shape(), 5);
+        compute_encoder.set_vector_bytes(x.strides(), 6);
+        compute_encoder.set_bytes(y.strides(), 7);
+        compute_encoder.set_bytes(ndim, 8);
 
         // We launch 1 thread for each input and make sure that the number of
         // threads in any given threadgroup is not higher than the max allowed
@@ -530,7 +530,7 @@ below.
 
         // Launch the grid with the given number of threads divided among
         // the given threadgroups
-        compute_encoder.dispatchThreads(grid_dims, group_dims);
+        compute_encoder.dispatch_threads(grid_dims, group_dims);
     }
 
 We can now call the :meth:`axpby` operation on both the CPU and the GPU!

docs/src/install.rst

@@ -14,7 +14,7 @@ silicon computer is
 To install from PyPI you must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
-- Using a native Python >= 3.8
+- Using a native Python >= 3.9
 - macOS >= 13.5
 
 .. note::
@@ -209,7 +209,7 @@ Metal library by run-time compiling kernels the first time they are used in MLX
 on a given machine. Note run-time compilation incurs a cold-start cost which can
 be anwywhere from a few hundred millisecond to a few seconds depending on the
 application. Once a kernel is compiled, it will be cached by the system. The
-Metal kernel cache persists accross reboots.
+Metal kernel cache persists across reboots.
 
 Troubleshooting
 ^^^^^^^^^^^^^^^
@@ -240,7 +240,7 @@ x86 Shell
 
 .. _build shell:
 
-If the ouptut of ``uname -p``  is ``x86`` then your shell is running as x86 via
+If the output of ``uname -p``  is ``x86`` then your shell is running as x86 via
 Rosetta instead of natively.
 
 To fix this, find the application in Finder (``/Applications`` for iTerm,
@@ -264,4 +264,4 @@ Also check that cmake is using the correct architecture:
 
 If you see ``"x86_64"``, try re-installing ``cmake``. If you see ``"arm64"``
 but the build errors out with "Building for x86_64 on macOS is not supported."
-wipe your build cahce with ``rm -rf build/`` and try again.
+wipe your build cache with ``rm -rf build/`` and try again.

docs/src/python/fast.rst

@@ -12,5 +12,4 @@ Fast
   layer_norm
   rope
   scaled_dot_product_attention
-  affine_quantize
   metal_kernel

docs/src/python/linalg.rst

@@ -16,3 +16,5 @@ Linear Algebra
     cross
     qr
     svd
+    eigvalsh
+    eigh

docs/src/python/metal.rst

@@ -14,6 +14,7 @@ Metal
   get_cache_memory
   set_memory_limit
   set_cache_limit
+  set_wired_limit
   clear_cache
   start_capture
   stop_capture

docs/src/python/nn/layers.rst

@@ -12,6 +12,7 @@ Layers
    ALiBi
    AvgPool1d
    AvgPool2d
+   AvgPool3d
    BatchNorm
    CELU
    Conv1d
@@ -41,6 +42,7 @@ Layers
    LSTM
    MaxPool1d
    MaxPool2d
+   MaxPool3d
    Mish
    MultiHeadAttention
    PReLU

docs/src/python/ops.rst

@@ -80,6 +80,7 @@ Operations
    greater_equal
    hadamard_transform
    identity
+   imag
    inner
    isfinite
    isclose
@@ -125,11 +126,13 @@ Operations
    quantize
    quantized_matmul
    radians
+   real
    reciprocal
    remainder
    repeat
    reshape
    right_shift
+   roll
    round
    rsqrt
    save

docs/src/python/random.rst

@@ -45,3 +45,4 @@ we use a splittable version of Threefry, which is a counter-based PRNG.
    truncated_normal
    uniform
    laplace
+   permutation

docs/src/usage/compile.rst

@@ -33,12 +33,12 @@ Let's start with a simple example:
   # Compile the function
   compiled_fun = mx.compile(fun)
 
-  # Prints: array(2.36788, dtype=float32) 
+  # Prints: array(2.36788, dtype=float32)
   print(compiled_fun(x, y))
 
 The output of both the regular function and the compiled function is the same
 up to numerical precision.
-   
+
 The first time you call a compiled function, MLX will build the compute
 graph, optimize it, and generate and compile code. This can be relatively
 slow. However, MLX will cache compiled functions, so calling a compiled
@@ -96,7 +96,7 @@ element-wise operations:
 
 .. code-block:: python
 
-  def gelu(x):  
+  def gelu(x):
       return x * (1 + mx.erf(x / math.sqrt(2))) / 2
 
 If you use this function with small arrays, it will be overhead bound. If you
@@ -136,13 +136,6 @@ Now make an array, and benchmark both functions:
 On an M1 Max the times are 15.5 and 3.1 milliseconds. The compiled ``gelu`` is
 five times faster.
 
-.. note::
-
-  As of the latest MLX, CPU functions are not fully compiled. Compiling CPU
-  functions can still be helpful, but won't typically result in as large a
-  speedup as compiling operations that run on the GPU.
-
-
 Debugging
 ---------
 
@@ -287,7 +280,7 @@ to the function. In some cases this can be pretty inconvenient. Hence,
   print(fun(mx.array(1.0)))
 
 
-Compiling Training Graphs 
+Compiling Training Graphs
 -------------------------
 
 This section will step through how to use :func:`compile` with a simple example
@@ -297,7 +290,7 @@ full forward, backward, and update with :func:`compile`.
 
 To start, here is the simple example without any compilation:
 
-.. code-block:: python 
+.. code-block:: python
 
   import mlx.core as mx
   import mlx.nn as nn
@@ -330,7 +323,7 @@ To start, here is the simple example without any compilation:
 To compile the update we can put it all in a function and compile it with the
 appropriate input and output captures. Here's the same example but compiled:
 
-.. code-block:: python 
+.. code-block:: python
 
   import mlx.core as mx
   import mlx.nn as nn
@@ -355,7 +348,7 @@ appropriate input and output captures. Here's the same example but compiled:
 
   # The state that will be captured as input and output
   state = [model.state, optimizer.state]
-      
+
   @partial(mx.compile, inputs=state, outputs=state)
   def step(x, y):
       loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
@@ -410,7 +403,7 @@ Compiling transformed functions works just as expected:
 
    In order to compile as much as possible, a transformation of a compiled
    function will not by default be compiled. To compile the transformed
-   function simply pass it through :func:`compile`. 
+   function simply pass it through :func:`compile`.
 
 You can also compile functions which themselves call compiled functions. A
 good practice is to compile the outer most function to give :func:`compile`

docs/src/usage/function_transforms.rst

@@ -25,7 +25,7 @@ Here is a simple example:
 
 The output of :func:`grad` on :func:`sin` is simply another function. In this
 case it is the gradient of the sine function which is exactly the cosine
-function. To get the second derivative you can do: 
+function. To get the second derivative you can do:
 
 .. code-block:: shell
 
@@ -50,7 +50,7 @@ Automatic Differentiation
 .. _auto diff:
 
 Automatic differentiation in MLX works on functions rather than on implicit
-graphs. 
+graphs.
 
 .. note::
 
@@ -114,7 +114,7 @@ way to do that is the following:
 
    def loss_fn(params, x, y):
       w, b = params["weight"], params["bias"]
-      h = w * x + b 
+      h = w * x + b
       return mx.mean(mx.square(h - y))
 
    params = {"weight": mx.array(1.0), "bias": mx.array(0.0)}
@@ -132,7 +132,7 @@ way to do that is the following:
 
 Notice the tree structure of the parameters is preserved in the gradients.
 
-In some cases you may want to stop gradients from propagating through a 
+In some cases you may want to stop gradients from propagating through a
 part of the function. You can use the :func:`stop_gradient` for that.
 
 
@@ -161,19 +161,19 @@ A naive way to add the elements from two sets of vectors is with a loop:
   ys = mx.random.uniform(shape=(100, 4096))
 
   def naive_add(xs, ys):
-      return [xs[i] + ys[:, i] for i in range(xs.shape[1])]
+      return [xs[i] + ys[:, i] for i in range(xs.shape[0])]
 
 Instead you can use :func:`vmap` to automatically vectorize the addition:
 
 .. code-block:: python
-   
+
    # Vectorize over the second dimension of x and the
    # first dimension of y
-   vmap_add = mx.vmap(lambda x, y: x + y, in_axes=(1, 0))
+   vmap_add = mx.vmap(lambda x, y: x + y, in_axes=(0, 1))
 
 The ``in_axes`` parameter can be used to specify which dimensions of the
 corresponding input to vectorize over. Similarly, use ``out_axes`` to specify
-where the vectorized axes should be in the outputs. 
+where the vectorized axes should be in the outputs.
 
 Let's time these two different versions:
 
@@ -184,8 +184,8 @@ Let's time these two different versions:
   print(timeit.timeit(lambda: mx.eval(naive_add(xs, ys)), number=100))
   print(timeit.timeit(lambda: mx.eval(vmap_add(xs, ys)), number=100))
 
-On an M1 Max the naive version takes in total ``0.390`` seconds whereas the
-vectorized version takes only ``0.025`` seconds, more than ten times faster.
+On an M1 Max the naive version takes in total ``5.639`` seconds whereas the
+vectorized version takes only ``0.024`` seconds, more than 200 times faster.
 
 Of course, this operation is quite contrived. A better approach is to simply do
 ``xs + ys.T``, but for more complex functions :func:`vmap` can be quite handy.

docs/src/usage/indexing.rst

@@ -51,7 +51,7 @@ You can also use an :obj:`array` to index another :obj:`array`:
 .. code-block:: shell
 
   >>> arr = mx.arange(10)
-  >>> idx = mx.array([5, 7]) 
+  >>> idx = mx.array([5, 7])
   >>> arr[idx]
   array([5, 7], dtype=int32)
 
@@ -77,12 +77,12 @@ from the GPU. Performing bounds checking for array indices before launching the
 kernel would be extremely inefficient.
 
 Indexing with boolean masks is something that MLX may support in the future. In
-general, MLX has limited support for operations for which outputs
+general, MLX has limited support for operations for which output
 *shapes* are dependent on input *data*. Other examples of these types of
 operations which MLX does not yet support include :func:`numpy.nonzero` and the
 single input version of :func:`numpy.where`.
 
-In Place Updates 
+In Place Updates
 ----------------
 
 In place updates to indexed arrays are possible in MLX. For example:

docs/src/usage/lazy_evaluation.rst

@@ -13,7 +13,7 @@ compute graph is recorded. The actual computation only happens if an
 :func:`eval` is performed.
 
 MLX uses lazy evaluation because it has some nice features, some of which we
-describe below. 
+describe below.
 
 Transforming Compute Graphs
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -109,14 +109,14 @@ Here is a concrete example:
 
 An important behavior to be aware of is when the graph will be implicitly
 evaluated. Anytime you ``print`` an array, convert it to an
-:obj:`numpy.ndarray`, or otherwise access it's memory via :obj:`memoryview`,
+:obj:`numpy.ndarray`, or otherwise access its memory via :obj:`memoryview`,
 the graph will be evaluated. Saving arrays via :func:`save` (or any other MLX
 saving functions) will also evaluate the array.
 
 
 Calling :func:`array.item` on a scalar array will also evaluate it. In the
 example above, printing the loss (``print(loss)``) or adding the loss scalar to
-a list (``losses.append(loss.item())``) would cause a graph evaluation. If 
+a list (``losses.append(loss.item())``) would cause a graph evaluation. If
 these lines are before ``mx.eval(loss, model.parameters())`` then this
 will be a partial evaluation, computing only the forward pass.
 

docs/src/usage/numpy.rst

@@ -3,10 +3,10 @@
 Conversion to NumPy and Other Frameworks
 ========================================
 
-MLX array supports conversion between other frameworks with either:  
+MLX array supports conversion between other frameworks with either:
 
-* The `Python Buffer Protocol <https://docs.python.org/3/c-api/buffer.html>`_. 
-* `DLPack <https://dmlc.github.io/dlpack/latest/>`_.  
+* The `Python Buffer Protocol <https://docs.python.org/3/c-api/buffer.html>`_.
+* `DLPack <https://dmlc.github.io/dlpack/latest/>`_.
 
 Let's convert an array to NumPy and back.
 
@@ -66,7 +66,7 @@ even though no in-place operations on MLX memory are executed.
 PyTorch
 -------
 
-.. warning:: 
+.. warning::
 
    PyTorch Support for :obj:`memoryview` is experimental and can break for
    multi-dimensional arrays. Casting to NumPy first is advised for now.

docs/src/usage/quick_start.rst

@@ -64,4 +64,4 @@ Other gradient transformations include :func:`vjp` for vector-Jacobian products
 and :func:`jvp` for Jacobian-vector products.
 
 Use :func:`value_and_grad` to efficiently compute both a function's output and
-gradient with respect to the function's input. 
+gradient with respect to the function's input.

docs/src/usage/saving_and_loading.rst

@@ -8,33 +8,33 @@ Saving and Loading Arrays
 MLX supports multiple array serialization formats.
 
 .. list-table:: Serialization Formats
-   :widths: 20 8 25 25 
+   :widths: 20 8 25 25
    :header-rows: 1
 
-   * - Format 
-     - Extension 
+   * - Format
+     - Extension
      - Function
-     - Notes 
-   * - NumPy 
-     - ``.npy`` 
+     - Notes
+   * - NumPy
+     - ``.npy``
      - :func:`save`
      - Single arrays only
-   * - NumPy archive 
-     - ``.npz`` 
+   * - NumPy archive
+     - ``.npz``
      - :func:`savez` and :func:`savez_compressed`
-     - Multiple arrays 
+     - Multiple arrays
    * - Safetensors
-     - ``.safetensors`` 
+     - ``.safetensors``
      - :func:`save_safetensors`
-     - Multiple arrays 
-   * - GGUF 
-     - ``.gguf`` 
+     - Multiple arrays
+   * - GGUF
+     - ``.gguf``
      - :func:`save_gguf`
      - Multiple arrays
 
 The :func:`load` function will load any of the supported serialization
 formats. It determines the format from the extensions. The output of
-:func:`load` depends on the format. 
+:func:`load` depends on the format.
 
 Here's an example of saving a single array to a file:
 

docs/src/usage/unified_memory.rst

@@ -20,7 +20,7 @@ Both ``a`` and ``b`` live in unified memory.
 
 In MLX, rather than moving arrays to devices, you specify the device when you
 run the operation. Any device can perform any operation on ``a`` and ``b``
-without needing to move them from one memory location to another. For example: 
+without needing to move them from one memory location to another. For example:
 
 .. code-block:: python
 

examples/extensions/axpby/axpby.cpp

@@ -257,7 +257,7 @@ void Axpby::eval_gpu(
 
   // Prepare to encode kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Kernel parameters are registered with buffer indices corresponding to
   // those in the kernel declaration at axpby.metal
@@ -272,15 +272,15 @@ void Axpby::eval_gpu(
   compute_encoder.set_output_array(out, 2);
 
   // Encode alpha and beta
-  compute_encoder->setBytes(&alpha_, sizeof(float), 3);
-  compute_encoder->setBytes(&beta_, sizeof(float), 4);
+  compute_encoder.set_bytes(alpha_, 3);
+  compute_encoder.set_bytes(beta_, 4);
 
   // Encode shape, strides and ndim if needed
   if (!contiguous_kernel) {
-    compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5);
-    compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6);
-    compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7);
-    compute_encoder->setBytes(&ndim, sizeof(int), 8);
+    compute_encoder.set_vector_bytes(x.shape(), 5);
+    compute_encoder.set_vector_bytes(x.strides(), 6);
+    compute_encoder.set_bytes(y.strides(), 7);
+    compute_encoder.set_bytes(ndim, 8);
   }
 
   // We launch 1 thread for each input and make sure that the number of
@@ -295,7 +295,7 @@ void Axpby::eval_gpu(
 
   // Launch the grid with the given number of threads divided among
   // the given threadgroups
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 #else // Metal is not available

examples/extensions/axpby/axpby.metal

@@ -2,7 +2,6 @@
 
 #include <metal_stdlib>
 
-#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/utils.h"
 
 template <typename T>
@@ -60,4 +59,4 @@ template <typename T>
 instantiate_axpby(float32, float);
 instantiate_axpby(float16, half);
 instantiate_axpby(bfloat16, bfloat16_t);
-instantiate_axpby(complex64, complex64_t);
+instantiate_axpby(complex64, complex64_t);

examples/extensions/pyproject.toml

@@ -2,7 +2,7 @@
 requires = [
   "setuptools>=42",
   "cmake>=3.24",
-  "mlx>=0.17.0",
-  "nanobind==2.1.0",
+  "mlx>=0.18.0",
+  "nanobind==2.2.0",
 ]
 build-backend = "setuptools.build_meta"

examples/extensions/requirements.txt

@@ -1,4 +1,4 @@
 setuptools>=42
 cmake>=3.24
-mlx>=0.17.0
-nanobind==2.1.0
+mlx>=0.21.0
+nanobind==2.2.0

mlx.pc.in

@@ -28,10 +28,19 @@ endif()
 if (@MLX_BUILD_METAL@)
     set(MLX_BUILD_METAL @MLX_BUILD_METAL@)
     set(MLX_CXX_FLAGS ${MLX_CXX_FLAGS} -D_METAL_)
-    set_and_check(MLX_INCLUDE_DIRS 
-        ${MLX_INCLUDE_DIRS} 
+    set(MLX_INCLUDE_DIRS 
+        "${MLX_INCLUDE_DIRS};"
         @PACKAGE_CMAKE_INSTALL_INCLUDEDIR@/metal_cpp
     )
+    if(@MLX_METAL_VERSION@ GREATER_EQUAL 310)
+      set(MLX_INCLUDE_DIRS
+        "${MLX_INCLUDE_DIRS};"
+        @PACKAGE_CMAKE_INSTALL_INCLUDEDIR@/mlx/backend/metal/kernels/metal_3_1)
+    else()
+      set(MLX_INCLUDE_DIRS
+        "${MLX_INCLUDE_DIRS};"
+        @PACKAGE_CMAKE_INSTALL_INCLUDEDIR@/mlx/backend/metal/kernels/metal_3_0)
+    endif()
 endif()
 
 set_target_properties(mlx PROPERTIES
@@ -40,4 +49,4 @@ set_target_properties(mlx PROPERTIES
 )
 
 include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(MLX DEFAULT_MSG MLX_LIBRARY MLX_INCLUDE_DIRS)
+find_package_handle_standard_args(MLX DEFAULT_MSG MLX_LIBRARY MLX_INCLUDE_DIRS)

mlx/allocator.cpp

@@ -19,7 +19,7 @@ Buffer malloc(size_t size) {
 }
 
 void free(Buffer buffer) {
-  return allocator().free(buffer);
+  allocator().free(buffer);
 }
 
 Buffer CommonAllocator::malloc(size_t size, bool) {

mlx/array.cpp

@@ -95,13 +95,29 @@ void array::detach() {
   array_desc_->primitive = nullptr;
 }
 
-void array::eval() {
-  // Ensure the array is ready to be read
-  if (status() == Status::scheduled) {
+bool array::is_available() const {
+  if (status() == Status::available) {
+    return true;
+  } else if (status() == Status::evaluated && event().is_signaled()) {
+    set_status(Status::available);
+    return true;
+  }
+  return false;
+}
+
+void array::wait() {
+  if (!is_available()) {
     event().wait();
     set_status(Status::available);
-  } else if (status() == Status::unscheduled) {
+  }
+}
+
+void array::eval() {
+  // Ensure the array is ready to be read
+  if (status() == Status::unscheduled) {
     mlx::core::eval({*this});
+  } else {
+    wait();
   }
 }
 
@@ -162,8 +178,10 @@ void array::move_shared_buffer(
   array_desc_->flags = flags;
   array_desc_->data_size = data_size;
   auto char_offset = sizeof(char) * itemsize() * offset;
-  array_desc_->data_ptr = static_cast<void*>(
-      static_cast<char*>(other.array_desc_->data_ptr) + char_offset);
+  auto data_ptr = other.array_desc_->data_ptr;
+  other.array_desc_->data_ptr = nullptr;
+  array_desc_->data_ptr =
+      static_cast<void*>(static_cast<char*>(data_ptr) + char_offset);
 }
 
 void array::move_shared_buffer(array other) {
@@ -196,6 +214,8 @@ array::~array() {
     if (do_detach) {
       for (auto& s : siblings()) {
         for (auto& ss : s.siblings()) {
+          // Set to null here to avoid descending into array destructor
+          // for siblings
           ss.array_desc_ = nullptr;
         }
         s.array_desc_->siblings.clear();
@@ -242,25 +262,46 @@ array::ArrayDesc::~ArrayDesc() {
   // This calls recursively the destructor and can result in stack overflow, we
   // instead put them in a vector and destroy them one at a time resulting in a
   // max stack depth of 2.
+  if (inputs.empty()) {
+    return;
+  }
+
   std::vector<std::shared_ptr<ArrayDesc>> for_deletion;
 
-  for (array& a : inputs) {
-    if (a.array_desc_.use_count() == 1) {
-      for_deletion.push_back(std::move(a.array_desc_));
+  auto append_deletable_inputs = [&for_deletion](ArrayDesc& ad) {
+    std::unordered_map<std::uintptr_t, array> input_map;
+    for (array& a : ad.inputs) {
+      if (a.array_desc_) {
+        input_map.insert({a.id(), a});
+        for (auto& s : a.siblings()) {
+          input_map.insert({s.id(), s});
+        }
+      }
     }
-  }
+    ad.inputs.clear();
+    for (auto& [_, a] : input_map) {
+      if (a.array_desc_.use_count() <= a.siblings().size() + 1) {
+        for_deletion.push_back(std::move(a.array_desc_));
+      }
+    }
+  };
+
+  append_deletable_inputs(*this);
 
   while (!for_deletion.empty()) {
     // top is going to be deleted at the end of the block *after* the arrays
     // with inputs have been moved into the vector
     auto top = std::move(for_deletion.back());
     for_deletion.pop_back();
+    append_deletable_inputs(*top);
 
-    for (array& a : top->inputs) {
-      if (a.array_desc_.use_count() == 1) {
-        for_deletion.push_back(std::move(a.array_desc_));
-      }
+    // Clear out possible siblings to break circular references
+    for (auto& s : top->siblings) {
+      // Set to null here to avoid descending into top-level
+      // array destructor for siblings
+      s.array_desc_ = nullptr;
     }
+    top->siblings.clear();
   }
 }
 

mlx/array.h

@@ -344,11 +344,33 @@ class array {
     return static_cast<T*>(array_desc_->data_ptr);
   }
 
-  enum Status { unscheduled, scheduled, available };
+  enum Status {
+    // The ouptut of a computation which has not been scheduled.
+    // For example, the status of `x` in `auto x = a + b`.
+    unscheduled,
 
-  bool is_available() const {
-    return status() == Status::available;
-  }
+    // The ouptut of a computation which has been scheduled but `eval_*` has
+    // not yet been called on the array's primitive. A possible
+    // status of `x` in `auto x = a + b; eval(x);`
+    scheduled,
+
+    // The array's `eval_*` function has been run, but the computation is not
+    // necessarily complete. The array will have memory allocated and if it is
+    // not a tracer then it will be detached from the graph.
+    evaluated,
+
+    // If the array is the output of a computation then the computation
+    // is complete. Constant arrays are always available (e.g. `array({1, 2,
+    // 3})`)
+    available
+  };
+
+  // Check if the array is safe to read.
+  bool is_available() const;
+
+  // Wait on the array to be available. After this `is_available` returns
+  // `true`.
+  void wait();
 
   Status status() const {
     return array_desc_->status;

mlx/backend/accelerate/primitives.cpp

@@ -81,6 +81,7 @@ DEFAULT_MULTI(SVD)
 DEFAULT(Transpose)
 DEFAULT(Inverse)
 DEFAULT(Cholesky)
+DEFAULT_MULTI(Eigh)
 
 void Abs::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);

mlx/backend/accelerate/quantized.cpp

@@ -18,49 +18,61 @@ void _qmm_t_4_64(
     const float* biases,
     int M,
     int N,
-    int K) {
+    int K,
+    int B,
+    bool batched_w) {
   constexpr int bits = 4;
   constexpr int group_size = 64;
   constexpr int bitmask = (1 << bits) - 1;
   constexpr int pack_factor = 32 / bits;
   constexpr int packs_in_group = group_size / pack_factor;
 
-  for (int m = 0; m < M; m++) {
-    const uint32_t* w_local = w;
-    const float* scales_local = scales;
-    const float* biases_local = biases;
+  int w_els = N * K / pack_factor;
+  int g_els = w_els * pack_factor / group_size;
 
-    for (int n = 0; n < N; n++) {
-      const simd_float16* x_local = (simd_float16*)x;
-      simd_float16 sum = 0;
-      for (int k = 0; k < K; k += group_size) {
-        float scale = *scales_local++;
-        float bias = *biases_local++;
+  for (int i = 0; i < B; i++) {
+    for (int m = 0; m < M; m++) {
+      const uint32_t* w_local = w;
+      const float* scales_local = scales;
+      const float* biases_local = biases;
 
-        for (int kw = 0; kw < packs_in_group; kw += 2) {
-          // TODO: vectorize this properly
-          simd_uint16 wi;
-          for (int e = 0; e < 2; e++) {
-            uint32_t wii = *w_local++;
-            for (int p = 0; p < 8; p++) {
-              wi[e * 8 + p] = wii & bitmask;
-              wii >>= bits;
+      for (int n = 0; n < N; n++) {
+        const simd_float16* x_local = (simd_float16*)x;
+        simd_float16 sum = 0;
+        for (int k = 0; k < K; k += group_size) {
+          float scale = *scales_local++;
+          float bias = *biases_local++;
+
+          for (int kw = 0; kw < packs_in_group; kw += 2) {
+            // TODO: vectorize this properly
+            simd_uint16 wi;
+            for (int e = 0; e < 2; e++) {
+              uint32_t wii = *w_local++;
+              for (int p = 0; p < 8; p++) {
+                wi[e * 8 + p] = wii & bitmask;
+                wii >>= bits;
+              }
             }
-          }
-          simd_float16 wf = simd_float(wi);
-          wf *= scale;
-          wf += bias;
+            simd_float16 wf = simd_float(wi);
+            wf *= scale;
+            wf += bias;
 
-          sum += (*x_local) * wf;
-          x_local++;
+            sum += (*x_local) * wf;
+            x_local++;
+          }
         }
+
+        *result = simd_reduce_add(sum);
+        result++;
       }
 
-      *result = simd_reduce_add(sum);
-      result++;
+      x += K;
+    }
+    if (batched_w) {
+      w += w_els;
+      scales += g_els;
+      biases += g_els;
     }
-
-    x += K;
   }
 }
 
@@ -82,8 +94,10 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
   if (condition) {
     out.set_data(allocator::malloc_or_wait(out.nbytes()));
     int K = x.shape(-1);
-    int M = x.size() / K;
+    int M = x.shape(-2);
     int N = out.shape(-1);
+    int B = x.size() / K / M;
+    bool batched_w = w.ndim() > 2;
     _qmm_t_4_64(
         out.data<float>(),
         x.data<float>(),
@@ -92,7 +106,9 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
         biases.data<float>(),
         M,
         N,
-        K);
+        K,
+        B,
+        batched_w);
   } else {
     eval(inputs, out);
   }

mlx/backend/accelerate/softmax.cpp

@@ -33,8 +33,8 @@ namespace {
  * Note: The implementation below is a general fast exp. There could be faster
  *       implementations for numbers strictly < 0.
  */
-inline simd_float16 simd_fast_exp(simd_float16 x) {
-  x *= 1.442695; // multiply with log_2(e)
+inline simd_float16 simd_fast_exp(simd_float16 x_init) {
+  auto x = x_init * 1.442695; // multiply with log_2(e)
   simd_float16 ipart, fpart;
   simd_int16 epart;
   x = simd_clamp(x, -80, 80);
@@ -53,7 +53,9 @@ inline simd_float16 simd_fast_exp(simd_float16 x) {
   // bitshifting
   epart = (simd_int(ipart) + 127) << 23;
 
-  return (*(simd_float16*)&epart) * x;
+  // Avoid supressing NaNs
+  simd_int16 eq = (x_init == x_init);
+  return simd_bitselect(x_init, (*(simd_float16*)&epart) * x, eq);
 }
 
 #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC

mlx/backend/common/CMakeLists.txt

@@ -31,6 +31,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/common.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/eigh.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/erf.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/hadamard.cpp

mlx/backend/common/cholesky.cpp

@@ -2,46 +2,12 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/lapack.h"
 #include "mlx/linalg.h"
 #include "mlx/primitives.h"
 
-#ifdef ACCELERATE_NEW_LAPACK
-#include <Accelerate/Accelerate.h>
-#else
-#include <lapack.h>
-#endif
-
 namespace mlx::core {
 
-namespace {
-
-// Delegate to the Cholesky factorization taking into account differences in
-// LAPACK implementations (basically how to pass the 'uplo' string to fortran).
-int spotrf_wrapper(char uplo, float* matrix, int N) {
-  int info;
-
-#ifdef LAPACK_FORTRAN_STRLEN_END
-  spotrf_(
-      /* uplo = */ &uplo,
-      /* n = */ &N,
-      /* a = */ matrix,
-      /* lda = */ &N,
-      /* info = */ &info,
-      /* uplo_len = */ static_cast<size_t>(1));
-#else
-  spotrf_(
-      /* uplo = */ &uplo,
-      /* n = */ &N,
-      /* a = */ matrix,
-      /* lda = */ &N,
-      /* info = */ &info);
-#endif
-
-  return info;
-}
-
-} // namespace
-
 void cholesky_impl(const array& a, array& factor, bool upper) {
   // Lapack uses the column-major convention. We take advantage of the fact that
   // the matrix should be symmetric:
@@ -66,7 +32,14 @@ void cholesky_impl(const array& a, array& factor, bool upper) {
 
   for (int i = 0; i < num_matrices; i++) {
     // Compute Cholesky factorization.
-    int info = spotrf_wrapper(uplo, matrix, N);
+    int info;
+    MLX_LAPACK_FUNC(spotrf)
+    (
+        /* uplo = */ &uplo,
+        /* n = */ &N,
+        /* a = */ matrix,
+        /* lda = */ &N,
+        /* info = */ &info);
 
     // TODO: We do nothing when the matrix is not positive semi-definite
     // because throwing an error would result in a crash. If we figure out how

mlx/backend/common/common.cpp

@@ -39,7 +39,7 @@ void AsStrided::eval(const std::vector<array>& inputs, array& out) {
   // rely on data_size anyway.
   size_t data_size = out.size();
 
-  return out.copy_shared_buffer(in, strides_, flags, data_size, offset_);
+  return move_or_copy(in, out, strides_, flags, data_size, offset_);
 }
 
 void Broadcast::eval(const std::vector<array>& inputs, array& out) {
@@ -58,12 +58,12 @@ void Broadcast::eval(const std::vector<array>& inputs, array& out) {
   if (out.size() > in.size()) {
     flags.row_contiguous = flags.col_contiguous = false;
   }
-  out.copy_shared_buffer(in, strides, flags, in.data_size());
+  move_or_copy(in, out, strides, flags, in.data_size());
 }
 
 void Copy::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  out.copy_shared_buffer(inputs[0]);
+  move_or_copy(inputs[0], out);
 }
 
 void CustomTransforms::eval(
@@ -72,7 +72,7 @@ void CustomTransforms::eval(
   assert(inputs.size() > outputs.size());
   for (int i = 0, j = inputs.size() - outputs.size(); i < outputs.size();
        i++, j++) {
-    outputs[i].copy_shared_buffer(inputs[j]);
+    move_or_copy(inputs[j], outputs[i]);
   }
 }
 
@@ -81,7 +81,7 @@ void Depends::eval(
     std::vector<array>& outputs) {
   assert(inputs.size() > outputs.size());
   for (int i = 0; i < outputs.size(); i++) {
-    outputs[i].copy_shared_buffer(inputs[i]);
+    move_or_copy(inputs[i], outputs[i]);
   }
 }
 
@@ -194,7 +194,7 @@ void Reshape::shared_buffer_reshape(
     auto max_dim = std::max_element(out.shape().begin(), out.shape().end());
     flags.col_contiguous = out.size() <= 1 || out.size() == *max_dim;
   }
-  out.copy_shared_buffer(in, out_strides, flags, in.data_size());
+  move_or_copy(in, out, out_strides, flags, in.data_size());
 }
 
 void Split::eval(
@@ -263,7 +263,7 @@ std::tuple<int64_t, std::vector<int64_t>> SliceUpdate::prepare_slice(
 
 void StopGradient::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  out.copy_shared_buffer(inputs[0]);
+  move_or_copy(inputs[0], out);
 }
 
 void Transpose::eval(const std::vector<array>& inputs, array& out) {
@@ -297,7 +297,7 @@ void Transpose::eval(const std::vector<array>& inputs, array& out) {
       b_stride *= out.shape(ri);
     }
   }
-  out.copy_shared_buffer(in, out_strides, flags, in.data_size());
+  move_or_copy(in, out, out_strides, flags, in.data_size());
 }
 
 } // namespace mlx::core

mlx/backend/common/compiled_cpu.cpp

@@ -4,6 +4,8 @@
 #include <filesystem>
 #include <fstream>
 #include <list>
+#include <mutex>
+#include <shared_mutex>
 
 #include "mlx/backend/common/compiled.h"
 #include "mlx/backend/common/compiled_preamble.h"
@@ -12,22 +14,7 @@
 
 namespace mlx::core {
 
-// GPU compile is always available if the GPU is available and since we are in
-// this file CPU compile is also available.
-namespace detail {
-bool compile_available_for_device(const Device& device) {
-  return true;
-}
-} // namespace detail
-
-std::string get_temp_file(const std::string& name) {
-  return std::filesystem::temp_directory_path().append(name);
-}
-
-// Return a pointer to a compiled function
-void* compile(
-    const std::string& kernel_name,
-    const std::string& source_code = "") {
+struct CompilerCache {
   struct DLib {
     DLib(const std::string& libname) {
       lib = dlopen(libname.c_str(), RTLD_NOW);
@@ -44,15 +31,41 @@ void* compile(
     void* lib;
   };
   // Statics to cache compiled libraries and functions
-  static std::list<DLib> libs;
-  static std::unordered_map<std::string, void*> kernels;
-  if (auto it = kernels.find(kernel_name); it != kernels.end()) {
-    return it->second;
-  }
-  if (source_code.empty()) {
-    return nullptr;
+  std::list<DLib> libs;
+  std::unordered_map<std::string, void*> kernels;
+  std::shared_mutex mtx;
+};
+
+static CompilerCache cache{};
+
+// GPU compile is always available if the GPU is available and since we are in
+// this file CPU compile is also available.
+namespace detail {
+bool compile_available_for_device(const Device& device) {
+  return true;
+}
+} // namespace detail
+
+std::string get_temp_file(const std::string& name) {
+  return std::filesystem::temp_directory_path().append(name);
+}
+
+// Return a pointer to a compiled function
+void* compile(
+    const std::string& kernel_name,
+    const std::function<std::string(void)>& source_builder) {
+  {
+    std::shared_lock lock(cache.mtx);
+    if (auto it = cache.kernels.find(kernel_name); it != cache.kernels.end()) {
+      return it->second;
+    }
   }
 
+  std::unique_lock lock(cache.mtx);
+  if (auto it = cache.kernels.find(kernel_name); it != cache.kernels.end()) {
+    return it->second;
+  }
+  std::string source_code = source_builder();
   std::string kernel_file_name;
 
   // Deal with long kernel names. Maximum length for files on macOS is 255
@@ -90,8 +103,8 @@ void* compile(
     source_file.close();
 
     std::ostringstream build_command;
-    build_command << "g++ -std=c++17 -O2 -Wall -fPIC -shared "
-                  << source_file_path << " -o " << shared_lib_path;
+    build_command << "g++ -std=c++17 -O3 -Wall -fPIC -shared '"
+                  << source_file_path << "' -o '" << shared_lib_path << "'";
     std::string build_command_str = build_command.str();
     auto return_code = system(build_command_str.c_str());
     if (return_code) {
@@ -103,10 +116,10 @@ void* compile(
   }
 
   // load library
-  libs.emplace_back(shared_lib_path);
+  cache.libs.emplace_back(shared_lib_path);
 
   // Load function
-  void* fun = dlsym(libs.back().lib, kernel_name.c_str());
+  void* fun = dlsym(cache.libs.back().lib, kernel_name.c_str());
   if (!fun) {
     std::ostringstream msg;
     msg << "[Compile::eval_cpu] Failed to load compiled function "
@@ -114,7 +127,7 @@ void* compile(
         << dlerror();
     throw std::runtime_error(msg.str());
   }
-  kernels.insert({kernel_name, fun});
+  cache.kernels.insert({kernel_name, fun});
   return fun;
 }
 
@@ -266,7 +279,7 @@ void Compiled::eval_cpu(
 
   // Figure out which kernel we are using
   auto& shape = outputs[0].shape();
-  bool contiguous = compiled_check_contiguity(inputs, shape);
+  auto contiguous = compiled_check_contiguity(inputs, shape);
 
   // Handle all broadcasting and collect function input arguments
   std::vector<void*> args;
@@ -316,10 +329,7 @@ void Compiled::eval_cpu(
   }
 
   // Get the function
-  auto fn_ptr = compile(kernel_name);
-
-  // If it doesn't exist, compile it
-  if (fn_ptr == nullptr) {
+  auto fn_ptr = compile(kernel_name, [&]() {
     std::ostringstream kernel;
     kernel << get_kernel_preamble() << std::endl;
     kernel << "extern \"C\"  {" << std::endl;
@@ -334,10 +344,8 @@ void Compiled::eval_cpu(
         ndim);
     // Close extern "C"
     kernel << "}" << std::endl;
-
-    // Compile and get function pointer
-    fn_ptr = compile(kernel_name, kernel.str());
-  }
+    return kernel.str();
+  });
 
   compiled_allocate_outputs(
       inputs, outputs, inputs_, constant_ids_, contiguous, false);

mlx/backend/common/conv.cpp

@@ -3,13 +3,8 @@
 #include <cassert>
 #include <numeric>
 
-#ifdef ACCELERATE_NEW_LAPACK
-#include <Accelerate/Accelerate.h>
-#else
-#include <cblas.h>
-#endif
-
 #include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/lapack.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 

mlx/backend/common/default_primitives.cpp

@@ -1,14 +1,10 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#ifdef ACCELERATE_NEW_LAPACK
-#include <Accelerate/Accelerate.h>
-#else
-#include <cblas.h>
-#endif
 #include <cstring>
 
 #include "mlx/array.h"
 #include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/lapack.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/primitives.h"
 
@@ -114,6 +110,7 @@ DEFAULT(Tanh)
 DEFAULT(Transpose)
 DEFAULT(Inverse)
 DEFAULT(Cholesky)
+DEFAULT_MULTI(Eigh)
 
 namespace {
 

mlx/backend/common/eigh.cpp

@@ -0,0 +1,117 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include "mlx/allocator.h"
+#include "mlx/array.h"
+#include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/lapack.h"
+#include "mlx/linalg.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+namespace {
+
+void ssyevd(
+    char jobz,
+    char uplo,
+    float* a,
+    int N,
+    float* w,
+    float* work,
+    int lwork,
+    int* iwork,
+    int liwork) {
+  int info;
+  MLX_LAPACK_FUNC(ssyevd)
+  (
+      /* jobz = */ &jobz,
+      /* uplo = */ &uplo,
+      /* n = */ &N,
+      /* a = */ a,
+      /* lda = */ &N,
+      /* w = */ w,
+      /* work = */ work,
+      /* lwork = */ &lwork,
+      /* iwork = */ iwork,
+      /* liwork = */ &liwork,
+      /* info = */ &info);
+  if (info != 0) {
+    std::stringstream msg;
+    msg << "[Eigh::eval_cpu] Eigenvalue decomposition failed with error code "
+        << info;
+    throw std::runtime_error(msg.str());
+  }
+}
+
+} // namespace
+
+void Eigh::eval(const std::vector<array>& inputs, std::vector<array>& outputs) {
+  const auto& a = inputs[0];
+  auto& values = outputs[0];
+
+  auto vectors = compute_eigenvectors_
+      ? outputs[1]
+      : array(a.shape(), a.dtype(), nullptr, {});
+
+  values.set_data(allocator::malloc_or_wait(values.nbytes()));
+
+  copy(
+      a,
+      vectors,
+      a.flags().row_contiguous ? CopyType::Vector : CopyType::General);
+
+  if (compute_eigenvectors_) {
+    // Set the strides and flags so the eigenvectors
+    // are in the columns of the output
+    auto flags = vectors.flags();
+    auto strides = vectors.strides();
+    auto ndim = a.ndim();
+    std::swap(strides[ndim - 1], strides[ndim - 2]);
+
+    if (a.size() > 1) {
+      flags.row_contiguous = false;
+      if (ndim > 2) {
+        flags.col_contiguous = false;
+      } else {
+        flags.col_contiguous = true;
+      }
+    }
+    vectors.move_shared_buffer(vectors, strides, flags, vectors.data_size());
+  }
+
+  auto vec_ptr = vectors.data<float>();
+  auto eig_ptr = values.data<float>();
+
+  char jobz = compute_eigenvectors_ ? 'V' : 'N';
+  auto N = a.shape(-1);
+
+  // Work query
+  int lwork;
+  int liwork;
+  {
+    float work;
+    int iwork;
+    ssyevd(jobz, uplo_[0], nullptr, N, nullptr, &work, -1, &iwork, -1);
+    lwork = static_cast<int>(work);
+    liwork = iwork;
+  }
+
+  auto work_buf = array::Data{allocator::malloc_or_wait(sizeof(float) * lwork)};
+  auto iwork_buf = array::Data{allocator::malloc_or_wait(sizeof(int) * liwork)};
+  for (size_t i = 0; i < a.size() / (N * N); ++i) {
+    ssyevd(
+        jobz,
+        uplo_[0],
+        vec_ptr,
+        N,
+        eig_ptr,
+        static_cast<float*>(work_buf.buffer.raw_ptr()),
+        lwork,
+        static_cast<int*>(iwork_buf.buffer.raw_ptr()),
+        liwork);
+    vec_ptr += N * N;
+    eig_ptr += N;
+  }
+}
+
+} // namespace mlx::core

mlx/backend/common/indexing.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2023 Apple Inc.
-
 #include <algorithm>
 #include <cassert>
 #include <cmath>
@@ -81,11 +80,18 @@ void gather(
   T* dst_ptr = out.data<T>();
   size_t out_idx = 0;
 
+  std::vector<ContiguousIterator<size_t>> its(inds.begin(), inds.end());
+  ContiguousIterator<size_t> src_it;
+  if (!can_copy && src.ndim() > 0) {
+    src_it = std::move(
+        ContiguousIterator<size_t>(slice_sizes, src.strides(), src.ndim()));
+  }
   for (int idx = 0; idx < ind_size; idx++) {
     size_t src_idx = 0;
     for (int ii = 0; ii < inds.size(); ++ii) {
       auto ax = axes[ii];
-      auto idx_loc = elem_to_loc(idx, inds[ii]);
+      auto idx_loc = its[ii].loc;
+      its[ii].step();
       auto idx_val =
           offset_neg_idx(inds[ii].data<IdxT>()[idx_loc], src.shape(ax));
       src_idx += (idx_val * src.strides()[ax]);
@@ -99,9 +105,10 @@ void gather(
       out_idx += slice_size;
     } else {
       for (int jj = 0; jj < slice_size; jj++) {
-        auto src_offset = elem_to_loc(jj, slice_sizes, src.strides());
-        dst_ptr[out_idx++] = src_ptr[src_idx + src_offset];
+        dst_ptr[out_idx++] = src_ptr[src_idx + src_it.loc];
+        src_it.step();
       }
+      src_it.reset();
     }
   }
 }
@@ -223,21 +230,29 @@ void scatter(
     update_size *= us;
   }
 
+  std::vector<ContiguousIterator<size_t>> its(inds.begin(), inds.end());
+  ContiguousIterator<size_t> update_it(updates);
+  ContiguousIterator<size_t> out_it(update_shape, out.strides(), out.ndim());
+
   for (int i = 0; i < n_updates; ++i) {
     size_t out_offset = 0;
     for (int j = 0; j < nind; ++j) {
       auto ax = axes[j];
-      auto idx_loc = elem_to_loc(i, inds[j]);
+      auto idx_loc = its[j].loc;
+      its[j].step();
       auto idx_val =
           offset_neg_idx(inds[j].data<IdxT>()[idx_loc], out.shape(ax));
       out_offset += (idx_val * out.strides()[ax]);
     }
+    update_it.seek(i * update_size);
     for (int j = 0; j < update_size; ++j) {
-      auto update_loc = elem_to_loc(i * update_size + j, updates);
-      auto out_loc = elem_to_loc(j, update_shape, out.strides());
-      op(updates.data<InT>()[update_loc],
-         out.data<InT>() + out_offset + out_loc);
+      op(updates.data<InT>()[update_it.loc],
+         out.data<InT>() + out_offset + out_it.loc);
+      update_it.step();
+      out_it.step();
     }
+    out_it.reset();
+    update_it.reset();
   }
 }
 

mlx/backend/common/inverse.cpp

@@ -2,39 +2,19 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/lapack.h"
 #include "mlx/primitives.h"
 
-#ifdef ACCELERATE_NEW_LAPACK
-#include <Accelerate/Accelerate.h>
-#else
-#include <lapack.h>
-#endif
-
-// Wrapper to account for differences in
-// LAPACK implementations (basically how to pass the 'uplo' string to fortran).
 int strtri_wrapper(char uplo, char diag, float* matrix, int N) {
   int info;
-
-#ifdef LAPACK_FORTRAN_STRLEN_END
-  strtri_(
-      /* uplo = */ &uplo,
-      /* diag = */ &diag,
-      /* N = */ &N,
-      /* a = */ matrix,
-      /* lda = */ &N,
-      /* info = */ &info,
-      /* uplo_len = */ static_cast<size_t>(1),
-      /* diag_len = */ static_cast<size_t>(1));
-#else
-  strtri_(
+  MLX_LAPACK_FUNC(strtri)
+  (
       /* uplo = */ &uplo,
       /* diag = */ &diag,
       /* N = */ &N,
       /* a = */ matrix,
       /* lda = */ &N,
       /* info = */ &info);
-#endif
-
   return info;
 }
 

mlx/backend/common/lapack.h

@@ -1,10 +1,11 @@
-// Copyright © 2024 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.
 
 #pragma once
 
 #ifdef ACCELERATE_NEW_LAPACK
 #include <Accelerate/Accelerate.h>
 #else
+#include <cblas.h>
 #include <lapack.h>
 #endif
 

mlx/backend/common/make_compiled_preamble.sh

@@ -18,10 +18,12 @@ if [ "$CLANG" = "TRUE" ]; then
   #include <cstdint>
   #include <vector>
 EOM
-
+CC_FLAGS=""
+else
+CC_FLAGS="-std=c++17"
 fi
 
-CONTENT=$($GCC -I "$SRCDIR" -E "$SRCDIR/mlx/backend/common/compiled_preamble.h" 2>/dev/null)
+CONTENT=$($GCC $CC_FLAGS -I "$SRCDIR" -E "$SRCDIR/mlx/backend/common/compiled_preamble.h" 2>/dev/null)
 
 cat << EOF > "$OUTPUT_FILE"
 const char* get_kernel_preamble() {

mlx/backend/common/masked_mm.cpp

@@ -1,15 +1,10 @@
 // Copyright © 2024 Apple Inc.
 
-#ifdef ACCELERATE_NEW_LAPACK
-#include <Accelerate/Accelerate.h>
-#else
-#include <cblas.h>
-#endif
-
 #include <cstring>
 
 #include "mlx/array.h"
 #include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/lapack.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/primitives.h"
 

mlx/backend/common/ops.h

@@ -295,6 +295,13 @@ struct Floor {
   }
 };
 
+struct Imag {
+  template <typename T>
+  T operator()(T x) {
+    return std::imag(x);
+  }
+};
+
 struct Log {
   template <typename T>
   T operator()(T x) {
@@ -337,6 +344,13 @@ struct Negative {
   }
 };
 
+struct Real {
+  template <typename T>
+  T operator()(T x) {
+    return std::real(x);
+  }
+};
+
 struct Round {
   template <typename T>
   T operator()(T x) {

mlx/backend/common/primitives.cpp

@@ -159,6 +159,17 @@ void Conjugate::eval(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)) {
+    out.copy_shared_buffer(in);
+  } else {
+    copy(in, out, CopyType::General);
+  }
+}
+
 void Cos::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
@@ -273,6 +284,10 @@ void Full::eval(const std::vector<array>& inputs, array& out) {
   copy(in, out, ctype);
 }
 
+void Imag::eval_cpu(const std::vector<array>& inputs, array& out) {
+  unary_op<complex64_t, float>(inputs[0], out, detail::Imag());
+}
+
 void Log::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
@@ -398,6 +413,10 @@ void RandomBits::eval(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void Real::eval_cpu(const std::vector<array>& inputs, array& out) {
+  unary_op<complex64_t, float>(inputs[0], out, detail::Real());
+}
+
 void Reshape::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
@@ -598,7 +617,7 @@ void View::eval_cpu(const std::vector<array>& inputs, array& out) {
   if (ibytes == obytes || obytes < ibytes && in.strides().back() == 1 ||
       in.flags().row_contiguous) {
     auto strides = in.strides();
-    for (int i = 0; i < strides.size() - 1; ++i) {
+    for (int i = 0; i < static_cast<int>(strides.size()) - 1; ++i) {
       strides[i] *= ibytes;
       strides[i] /= obytes;
     }

mlx/backend/common/qrf.cpp

@@ -2,14 +2,9 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/lapack.h"
 #include "mlx/primitives.h"
 
-#ifdef ACCELERATE_NEW_LAPACK
-#include <Accelerate/Accelerate.h>
-#else
-#include <lapack.h>
-#endif
-
 namespace mlx::core {
 
 template <typename T>

mlx/backend/common/quantized.cpp

@@ -2,13 +2,38 @@
 
 #include <cassert>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/ops.h"
+#include "mlx/fast_primitives.h"
 #include "mlx/primitives.h"
+#include "mlx/utils.h"
 
 namespace mlx::core {
 
 namespace {
 
+template <typename T, int bits>
+void extract_bits(const uint8_t* w_in, T* w_out) {
+  assert(bits == 3 || bits == 6);
+  if (bits == 3) {
+    w_out[0] = static_cast<T>(w_in[0] & 0x7);
+    w_out[1] = static_cast<T>((w_in[0] & 0x38) >> 3);
+    w_out[2] = static_cast<T>(((w_in[0] & 0xc0) >> 6) + ((w_in[1] & 0x1) << 2));
+    w_out[3] = static_cast<T>((w_in[1] & 0xe) >> 1);
+    w_out[4] = static_cast<T>((w_in[1] & 0x70) >> 4);
+    w_out[5] = static_cast<T>(((w_in[1] & 0x80) >> 7) + ((w_in[2] & 0x3) << 1));
+    w_out[6] = static_cast<T>((w_in[2] & 0x1c) >> 2);
+    w_out[7] = static_cast<T>((w_in[2] & 0xe0) >> 5);
+  } else if (bits == 6) {
+    w_out[0] = static_cast<T>(w_in[0] & 0x3f);
+    w_out[1] =
+        static_cast<T>(((w_in[0] >> 6) & 0x03) + ((w_in[1] & 0x0f) << 2));
+    w_out[2] =
+        static_cast<T>(((w_in[1] >> 4) & 0x0f) + ((w_in[2] & 0x03) << 4));
+    w_out[3] = static_cast<T>((w_in[2] >> 2) & 0x3f);
+  }
+}
+
 template <typename T, int bits, int group_size>
 void _qmm(
     T* result,
@@ -20,13 +45,12 @@ void _qmm(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-  constexpr int pack_factor = 32 / bits;
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
+  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
   constexpr int packs_in_group = group_size / pack_factor;
-  const int Ng = N / group_size;
-  const int Nw = N / pack_factor;
 
   for (int m = 0; m < M; m++) {
-    const uint32_t* w_local = w;
+    const uint8_t* w_local = (const uint8_t*)w;
     const T* scales_local = scales;
     const T* biases_local = biases;
 
@@ -40,13 +64,25 @@ void _qmm(
         T scale = *scales_local++;
         T bias = *biases_local++;
         for (int ng = 0; ng < packs_in_group; ng++) {
-          uint32_t wi = *w_local++;
-
+          if (bits == 3 || bits == 6) {
+            T wl[pack_factor];
+            extract_bits<T, bits>(w_local, wl);
 #pragma clang loop unroll(full)
-          for (int p = 0; p < pack_factor; p++) {
-            (*result_local++) +=
-                xi * (scale * static_cast<T>(wi & bitmask) + bias);
-            wi >>= bits;
+            for (int p = 0; p < pack_factor; p++) {
+              (*result_local++) += xi * (scale * wl[p] + bias);
+            }
+            w_local += bytes_per_pack;
+
+          } else {
+            uint8_t wi = *w_local++;
+#pragma clang loop unroll(full)
+            for (int p = 0; p < pack_factor; p++) {
+              (*result_local++) +=
+                  xi * (scale * static_cast<T>(wi & bitmask) + bias);
+              if (bits != 8) {
+                wi >>= bits;
+              }
+            }
           }
         }
       }
@@ -67,13 +103,12 @@ void _qmm_t(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-  constexpr int pack_factor = 32 / bits;
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
+  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
   constexpr int packs_in_group = group_size / pack_factor;
-  const int Kg = K / group_size;
-  const int Kw = K / pack_factor;
 
   for (int m = 0; m < M; m++) {
-    const uint32_t* w_local = w;
+    const uint8_t* w_local = (const uint8_t*)w;
     const T* scales_local = scales;
     const T* biases_local = biases;
 
@@ -85,12 +120,26 @@ void _qmm_t(
         T bias = *biases_local++;
 
         for (int kw = 0; kw < packs_in_group; kw++) {
-          uint32_t wi = *w_local++;
-
+          if (bits == 3 || bits == 6) {
+            T wl[pack_factor];
+            extract_bits<T, bits>(w_local, wl);
 #pragma clang loop unroll(full)
-          for (int p = 0; p < pack_factor; p++) {
-            sum += (*x_local++) * (scale * static_cast<T>(wi & bitmask) + bias);
-            wi >>= bits;
+            for (int p = 0; p < pack_factor; p++) {
+              sum += x_local[p] * (scale * wl[p] + bias);
+            }
+            w_local += bytes_per_pack;
+            x_local += pack_factor;
+
+          } else {
+            uint8_t wi = *w_local++;
+#pragma clang loop unroll(full)
+            for (int p = 0; p < pack_factor; p++) {
+              sum +=
+                  (*x_local++) * (scale * static_cast<T>(wi & bitmask) + bias);
+              if (bits != 8) {
+                wi >>= bits;
+              }
+            }
           }
         }
       }
@@ -102,6 +151,55 @@ void _qmm_t(
   }
 }
 
+template <typename T, int bits, int group_size>
+void _qmm_dispatch_transpose(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const T* scales,
+    const T* biases,
+    int M,
+    int N,
+    int K,
+    bool transposed_w) {
+  if (transposed_w) {
+    return _qmm_t<T, bits, group_size>(result, x, w, scales, biases, M, N, K);
+  } else {
+    return _qmm<T, bits, group_size>(result, x, w, scales, biases, M, N, K);
+  }
+}
+
+template <typename T, int bits>
+void _qmm_dispatch_group(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const T* scales,
+    const T* biases,
+    int M,
+    int N,
+    int K,
+    int group_size,
+    bool transposed_w) {
+  switch (group_size) {
+    case 32:
+      _qmm_dispatch_transpose<T, bits, 32>(
+          result, x, w, scales, biases, M, N, K, transposed_w);
+      break;
+    case 64:
+      _qmm_dispatch_transpose<T, bits, 64>(
+          result, x, w, scales, biases, M, N, K, transposed_w);
+      break;
+    case 128:
+      _qmm_dispatch_transpose<T, bits, 128>(
+          result, x, w, scales, biases, M, N, K, transposed_w);
+      break;
+    default:
+      throw std::invalid_argument(
+          "Quantization group size must be 32, 64 or 128.");
+  }
+}
+
 template <typename T>
 void _qmm_dispatch_typed(
     T* result,
@@ -116,79 +214,29 @@ void _qmm_dispatch_typed(
     int bits,
     bool transposed_w) {
   switch (bits) {
-    case 2: {
-      switch (group_size) {
-        case 32:
-          if (transposed_w) {
-            return _qmm_t<T, 2, 32>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 2, 32>(result, x, w, scales, biases, M, N, K);
-          }
-        case 64:
-          if (transposed_w) {
-            return _qmm_t<T, 2, 64>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 2, 64>(result, x, w, scales, biases, M, N, K);
-          }
-        case 128:
-          if (transposed_w) {
-            return _qmm_t<T, 2, 128>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 2, 128>(result, x, w, scales, biases, M, N, K);
-          }
-      }
-    }
-    case 4: {
-      switch (group_size) {
-        case 32:
-          if (transposed_w) {
-            return _qmm_t<T, 4, 32>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 4, 32>(result, x, w, scales, biases, M, N, K);
-          }
-        case 64:
-          if (transposed_w) {
-            return _qmm_t<T, 4, 64>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 4, 64>(result, x, w, scales, biases, M, N, K);
-          }
-        case 128:
-          if (transposed_w) {
-            return _qmm_t<T, 4, 128>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 4, 128>(result, x, w, scales, biases, M, N, K);
-          }
-      }
-    }
-    case 8: {
-      switch (group_size) {
-        case 32:
-          if (transposed_w) {
-            return _qmm_t<T, 8, 32>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 8, 32>(result, x, w, scales, biases, M, N, K);
-          }
-        case 64:
-          if (transposed_w) {
-            return _qmm_t<T, 8, 64>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 8, 64>(result, x, w, scales, biases, M, N, K);
-          }
-        case 128:
-          if (transposed_w) {
-            return _qmm_t<T, 8, 128>(result, x, w, scales, biases, M, N, K);
-          } else {
-            return _qmm<T, 8, 128>(result, x, w, scales, biases, M, N, K);
-          }
-      }
-    }
+    case 2:
+      _qmm_dispatch_group<T, 2>(
+          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
+      break;
+    case 3:
+      _qmm_dispatch_group<T, 3>(
+          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
+      break;
+    case 4:
+      _qmm_dispatch_group<T, 4>(
+          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
+      break;
+    case 6:
+      _qmm_dispatch_group<T, 6>(
+          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
+      break;
+    case 8:
+      _qmm_dispatch_group<T, 8>(
+          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
+      break;
+    default:
+      throw std::invalid_argument("Quantization bits must be 2, 3, 4, 6 or 8.");
   }
-  std::ostringstream msg;
-  msg << "Quantization type not supported. Provided bits=" << bits
-      << " and group_size=" << group_size
-      << ". The supported options are bits in "
-      << "{2, 4, 8} and group_size in {64, 128}.";
-  throw std::invalid_argument(msg.str());
 }
 
 void _qmm_dispatch(
@@ -201,55 +249,61 @@ void _qmm_dispatch(
     int group_size,
     bool transposed_w) {
   int K = x.shape(-1);
-  int M = x.size() / K;
+  int M = x.shape(-2);
   int N = out.shape(-1);
 
-  switch (x.dtype()) {
-    case float32:
-      _qmm_dispatch_typed<float>(
-          out.data<float>(),
-          x.data<float>(),
-          w.data<uint32_t>(),
-          scales.data<float>(),
-          biases.data<float>(),
-          M,
-          N,
-          K,
-          bits,
-          group_size,
-          transposed_w);
-      break;
-    case float16:
-      _qmm_dispatch_typed<float16_t>(
-          out.data<float16_t>(),
-          x.data<float16_t>(),
-          w.data<uint32_t>(),
-          scales.data<float16_t>(),
-          biases.data<float16_t>(),
-          M,
-          N,
-          K,
-          bits,
-          group_size,
-          transposed_w);
-      break;
-    case bfloat16:
-      _qmm_dispatch_typed<bfloat16_t>(
-          out.data<bfloat16_t>(),
-          x.data<bfloat16_t>(),
-          w.data<uint32_t>(),
-          scales.data<bfloat16_t>(),
-          biases.data<bfloat16_t>(),
-          M,
-          N,
-          K,
-          bits,
-          group_size,
-          transposed_w);
-      break;
-    default:
-      throw std::invalid_argument(
-          "[quantized_matmul] only floating types are supported");
+  int w_els = w.ndim() > 2 ? w.shape(-1) * w.shape(-2) : 0;
+  int g_els = w.ndim() > 2 ? scales.shape(-1) * scales.shape(-2) : 0;
+
+  int batch_size = x.size() / x.shape(-1) / x.shape(-2);
+  for (int i = 0; i < batch_size; i++) {
+    switch (x.dtype()) {
+      case float32:
+        _qmm_dispatch_typed<float>(
+            out.data<float>() + i * M * N,
+            x.data<float>() + elem_to_loc(i * M * K, x),
+            w.data<uint32_t>() + elem_to_loc(i * w_els, w),
+            scales.data<float>() + elem_to_loc(i * g_els, scales),
+            biases.data<float>() + elem_to_loc(i * g_els, biases),
+            M,
+            N,
+            K,
+            bits,
+            group_size,
+            transposed_w);
+        break;
+      case float16:
+        _qmm_dispatch_typed<float16_t>(
+            out.data<float16_t>() + i * M * N,
+            x.data<float16_t>() + elem_to_loc(i * M * K, x),
+            w.data<uint32_t>() + elem_to_loc(i * w_els, w),
+            scales.data<float16_t>() + elem_to_loc(i * g_els, scales),
+            biases.data<float16_t>() + elem_to_loc(i * g_els, biases),
+            M,
+            N,
+            K,
+            bits,
+            group_size,
+            transposed_w);
+        break;
+      case bfloat16:
+        _qmm_dispatch_typed<bfloat16_t>(
+            out.data<bfloat16_t>() + i * M * N,
+            x.data<bfloat16_t>() + elem_to_loc(i * M * K, x),
+            w.data<uint32_t>() + elem_to_loc(i * w_els, w),
+            scales.data<bfloat16_t>() + elem_to_loc(i * g_els, scales),
+            biases.data<bfloat16_t>() + elem_to_loc(i * g_els, biases),
+            M,
+            N,
+            K,
+            bits,
+            group_size,
+            transposed_w);
+        break;
+      default:
+        throw std::invalid_argument(
+            "[quantized_matmul] only floating types are supported");
+    }
   }
 }
 
@@ -398,4 +452,114 @@ void GatherQMM::eval(const std::vector<array>& inputs, array& out) {
       transpose_);
 }
 
+template <typename T, typename U>
+void quantize(
+    const array& w_,
+    array& out_,
+    array& scales_,
+    array& biases_,
+    int bits,
+    int group_size) {
+  const T* w = w_.data<T>();
+
+  auto out = out_.data<U>();
+  T* scales = scales_.data<T>();
+  T* biases = biases_.data<T>();
+
+  T n_bins = (1 << bits) - 1;
+  T eps = 1e-7;
+  bool power_of_2_bits = is_power_of_2(bits);
+  int el_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
+  // For 3/6 bits we read 3 uint8s at a time instead of 1 uint32
+  int bytes_per_pack = power_of_2_bits ? 1 : 3;
+  int int_per_group = group_size * bytes_per_pack / el_per_int;
+  size_t n_groups = w_.size() / group_size;
+
+  for (size_t i = 0; i < n_groups; ++i) {
+    size_t w_idx = i * group_size;
+    T w_min = std::numeric_limits<float>::infinity();
+    T w_max = -w_min;
+    for (int j = 0; j < group_size; ++j) {
+      w_max = std::max(w_max, w[w_idx + j]);
+      w_min = std::min(w_min, w[w_idx + j]);
+    }
+    bool mask = std::abs(w_min) > std::abs(w_max);
+    T scale = std::max(T((w_max - w_min) / n_bins), eps);
+    scale = mask ? scale : -scale;
+
+    auto edge = mask ? w_min : w_max;
+    auto q0 = std::rint(edge / scale);
+    if (q0 == 0) {
+      scales[i] = scale;
+      biases[i] = 0;
+    } else {
+      scales[i] = edge / q0;
+      biases[i] = edge;
+    }
+    size_t out_idx = i * int_per_group;
+    for (int j = 0; j < int_per_group / bytes_per_pack; ++j) {
+      uint32_t out_el = 0;
+      for (int k = 0; k < el_per_int; ++k) {
+        T w_el = w[w_idx + j * el_per_int + k];
+        w_el = std::rint((w_el - biases[i]) / scales[i]);
+        w_el = std::min(std::max(w_el, T(0)), n_bins);
+        out_el |= static_cast<uint32_t>(w_el) << (k * bits);
+      }
+      if (power_of_2_bits) {
+        out[out_idx + j] = out_el;
+      } else {
+        out[out_idx + bytes_per_pack * j] = out_el & 0xff;
+        out[out_idx + bytes_per_pack * j + 1] = (out_el & 0xff00) >> 8;
+        out[out_idx + bytes_per_pack * j + 2] = (out_el & 0xff0000) >> 16;
+      }
+    }
+  }
+}
+
+void fast::AffineQuantize::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  auto ensure_row_contiguous = [](const array& arr) {
+    if (arr.flags().row_contiguous) {
+      return arr;
+    } else {
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy(arr, arr_copy, CopyType::General);
+      return arr_copy;
+    }
+  };
+  auto w = ensure_row_contiguous(inputs[0]);
+
+  auto& out = outputs[0];
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
+  auto& scales = outputs[1];
+  auto& biases = outputs[2];
+  scales.set_data(allocator::malloc_or_wait(scales.nbytes()));
+  biases.set_data(allocator::malloc_or_wait(biases.nbytes()));
+  if (w.dtype() == float16) {
+    if (is_power_of_2(bits_)) {
+      quantize<float16_t, uint32_t>(w, out, scales, biases, bits_, group_size_);
+    } else {
+      quantize<float16_t, uint8_t>(w, out, scales, biases, bits_, group_size_);
+    }
+  } else if (w.dtype() == bfloat16) {
+    if (is_power_of_2(bits_)) {
+      quantize<bfloat16_t, uint32_t>(
+          w, out, scales, biases, bits_, group_size_);
+    } else {
+      quantize<bfloat16_t, uint8_t>(w, out, scales, biases, bits_, group_size_);
+    }
+  } else if (w.dtype() == float32) {
+    if (is_power_of_2(bits_)) {
+      quantize<float, uint32_t>(w, out, scales, biases, bits_, group_size_);
+    } else {
+      quantize<float, uint8_t>(w, out, scales, biases, bits_, group_size_);
+    }
+  } else {
+    throw std::runtime_error(
+        "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
+  }
+}
+
 } // namespace mlx::core

mlx/backend/common/reduce.cpp

@@ -120,48 +120,56 @@ struct MinReduce {
 };
 
 template <typename InT>
-void reduce_dispatch_out(
+void reduce_dispatch_and_or(
     const array& in,
     array& out,
     Reduce::ReduceType rtype,
     const std::vector<int>& axes) {
-  switch (rtype) {
-    case Reduce::And: {
-      reduction_op<InT, bool>(in, out, axes, true, AndReduce());
-      break;
+  if (rtype == Reduce::And) {
+    reduction_op<InT, bool>(in, out, axes, true, AndReduce());
+  } else {
+    reduction_op<InT, bool>(in, out, axes, false, OrReduce());
+  }
+}
+
+template <typename InT>
+void reduce_dispatch_sum_prod(
+    const array& in,
+    array& out,
+    Reduce::ReduceType rtype,
+    const std::vector<int>& axes) {
+  if (rtype == Reduce::Sum) {
+    auto op = [](auto y, auto x) { (*y) = (*y) + x; };
+    if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
+      reduction_op<InT, int32_t>(in, out, axes, 0, op);
+    } else {
+      reduction_op<InT, InT>(in, out, axes, 0, op);
     }
-    case Reduce::Or: {
-      reduction_op<InT, bool>(in, out, axes, false, OrReduce());
-      break;
-    }
-    case Reduce::Sum: {
-      auto op = [](auto y, auto x) { (*y) = (*y) + x; };
-      if (out.dtype() == int32) {
-        // special case since the input type can be bool
-        reduction_op<InT, int32_t>(in, out, axes, 0, op);
-      } else {
-        reduction_op<InT, InT>(in, out, axes, 0, op);
-      }
-      break;
-    }
-    case Reduce::Prod: {
-      auto op = [](auto y, auto x) { (*y) *= x; };
+  } else {
+    auto op = [](auto y, auto x) { (*y) *= x; };
+    if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
+      reduction_op<InT, int32_t>(in, out, axes, 1, op);
+    } else {
       reduction_op<InT, InT>(in, out, axes, 1, op);
-      break;
-    }
-    case Reduce::Max: {
-      auto init = Limits<InT>::min;
-      reduction_op<InT, InT>(in, out, axes, init, MaxReduce());
-      break;
-    }
-    case Reduce::Min: {
-      auto init = Limits<InT>::max;
-      reduction_op<InT, InT>(in, out, axes, init, MinReduce());
-      break;
     }
   }
 }
 
+template <typename InT>
+void reduce_dispatch_min_max(
+    const array& in,
+    array& out,
+    Reduce::ReduceType rtype,
+    const std::vector<int>& axes) {
+  if (rtype == Reduce::Max) {
+    auto init = Limits<InT>::min;
+    reduction_op<InT, InT>(in, out, axes, init, MaxReduce());
+  } else {
+    auto init = Limits<InT>::max;
+    reduction_op<InT, InT>(in, out, axes, init, MinReduce());
+  }
+}
+
 } // namespace
 
 void nd_loop(
@@ -190,46 +198,114 @@ void nd_loop(
 void Reduce::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  switch (in.dtype()) {
-    case bool_:
-      reduce_dispatch_out<bool>(in, out, reduce_type_, axes_);
+  switch (reduce_type_) {
+    case Reduce::And:
+    case Reduce::Or: {
+      switch (in.dtype()) {
+        case bool_:
+        case uint8:
+        case int8:
+          reduce_dispatch_and_or<int8_t>(in, out, reduce_type_, axes_);
+          break;
+        case int16:
+        case uint16:
+        case float16:
+        case bfloat16:
+          reduce_dispatch_and_or<int16_t>(in, out, reduce_type_, axes_);
+          break;
+        case uint32:
+        case int32:
+        case float32:
+          reduce_dispatch_and_or<int32_t>(in, out, reduce_type_, axes_);
+          break;
+        case uint64:
+        case int64:
+        case complex64:
+          reduce_dispatch_and_or<int64_t>(in, out, reduce_type_, axes_);
+          break;
+      }
       break;
-    case uint8:
-      reduce_dispatch_out<uint8_t>(in, out, reduce_type_, axes_);
+    }
+    case Reduce::Sum:
+    case Reduce::Prod: {
+      switch (in.dtype()) {
+        case bool_:
+        case uint8:
+        case int8:
+          reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
+          break;
+        case int16:
+        case uint16:
+          reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
+          break;
+        case int32:
+        case uint32:
+          reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
+          break;
+        case int64:
+        case uint64:
+          reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
+          break;
+        case float16:
+          reduce_dispatch_sum_prod<float16_t>(in, out, reduce_type_, axes_);
+          break;
+        case bfloat16:
+          reduce_dispatch_sum_prod<bfloat16_t>(in, out, reduce_type_, axes_);
+          break;
+        case float32:
+          reduce_dispatch_sum_prod<float>(in, out, reduce_type_, axes_);
+          break;
+        case complex64:
+          reduce_dispatch_sum_prod<complex64_t>(in, out, reduce_type_, axes_);
+          break;
+      }
       break;
-    case uint16:
-      reduce_dispatch_out<uint16_t>(in, out, reduce_type_, axes_);
-      break;
-    case uint32:
-      reduce_dispatch_out<uint32_t>(in, out, reduce_type_, axes_);
-      break;
-    case uint64:
-      reduce_dispatch_out<uint64_t>(in, out, reduce_type_, axes_);
-      break;
-    case int8:
-      reduce_dispatch_out<uint8_t>(in, out, reduce_type_, axes_);
-      break;
-    case int16:
-      reduce_dispatch_out<uint16_t>(in, out, reduce_type_, axes_);
-      break;
-    case int32:
-      reduce_dispatch_out<int32_t>(in, out, reduce_type_, axes_);
-      break;
-    case int64:
-      reduce_dispatch_out<int64_t>(in, out, reduce_type_, axes_);
-      break;
-    case float16:
-      reduce_dispatch_out<float16_t>(in, out, reduce_type_, axes_);
-      break;
-    case float32:
-      reduce_dispatch_out<float>(in, out, reduce_type_, axes_);
-      break;
-    case bfloat16:
-      reduce_dispatch_out<bfloat16_t>(in, out, reduce_type_, axes_);
-      break;
-    case complex64:
-      reduce_dispatch_out<complex64_t>(in, out, reduce_type_, axes_);
+    }
+    case Reduce::Max:
+    case Reduce::Min: {
+      switch (in.dtype()) {
+        case bool_:
+          reduce_dispatch_min_max<bool>(in, out, reduce_type_, axes_);
+          break;
+        case uint8:
+          reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
+          break;
+        case uint16:
+          reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
+          break;
+        case uint32:
+          reduce_dispatch_min_max<uint32_t>(in, out, reduce_type_, axes_);
+          break;
+        case uint64:
+          reduce_dispatch_min_max<uint64_t>(in, out, reduce_type_, axes_);
+          break;
+        case int8:
+          reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
+          break;
+        case int16:
+          reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
+          break;
+        case int32:
+          reduce_dispatch_min_max<int32_t>(in, out, reduce_type_, axes_);
+          break;
+        case int64:
+          reduce_dispatch_min_max<int64_t>(in, out, reduce_type_, axes_);
+          break;
+        case float16:
+          reduce_dispatch_min_max<float16_t>(in, out, reduce_type_, axes_);
+          break;
+        case float32:
+          reduce_dispatch_min_max<float>(in, out, reduce_type_, axes_);
+          break;
+        case bfloat16:
+          reduce_dispatch_min_max<bfloat16_t>(in, out, reduce_type_, axes_);
+          break;
+        case complex64:
+          reduce_dispatch_min_max<complex64_t>(in, out, reduce_type_, axes_);
+          break;
+      }
       break;
+    }
   }
 }
 

mlx/backend/common/slicing.cpp

@@ -34,7 +34,7 @@ void shared_buffer_slice(
   flags.col_contiguous = is_col_contiguous;
   flags.contiguous = (no_bsx_size == data_size);
 
-  out.copy_shared_buffer(in, out_strides, flags, data_size, data_offset);
+  move_or_copy(in, out, out_strides, flags, data_size, data_offset);
 }
 
 } // namespace mlx::core

mlx/backend/common/sort.cpp

@@ -111,7 +111,8 @@ void sort(const array& in, array& out, int axis) {
 
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
-  size_t n_rows = in.size() / in.shape(axis);
+  size_t in_size = in.flags().contiguous ? in.data_size() : in.size();
+  size_t n_rows = in_size / in.shape(axis);
 
   auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -123,14 +124,16 @@ void sort(const array& in, array& out, int axis) {
   int axis_size = out.shape(axis);
 
   // Perform sorting in place
+  ContiguousIterator<size_t> src_it(
+      remaining_shape, remaining_strides, remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
-    size_t loc = elem_to_loc(i, remaining_shape, remaining_strides);
-    T* data_ptr = out.data<T>() + loc;
+    T* data_ptr = out.data<T>() + src_it.loc;
 
     StridedIterator st(data_ptr, axis_stride, 0);
     StridedIterator ed(data_ptr, axis_stride, axis_size);
 
     std::stable_sort(st, ed);
+    src_it.step();
   }
 }
 
@@ -160,11 +163,15 @@ void argsort(const array& in, array& out, int axis) {
   int axis_size = in.shape(axis);
 
   // Perform sorting
+  ContiguousIterator<size_t> in_it(
+      in_remaining_shape, in_remaining_strides, in_remaining_shape.size());
+  ContiguousIterator<size_t> out_it(
+      out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
-    size_t in_loc = elem_to_loc(i, in_remaining_shape, in_remaining_strides);
-    size_t out_loc = elem_to_loc(i, out_remaining_shape, out_remaining_strides);
-    const T* data_ptr = in.data<T>() + in_loc;
-    IdxT* idx_ptr = out.data<IdxT>() + out_loc;
+    const T* data_ptr = in.data<T>() + in_it.loc;
+    IdxT* idx_ptr = out.data<IdxT>() + out_it.loc;
+    in_it.step();
+    out_it.step();
 
     StridedIterator st_(idx_ptr, out_stride, 0);
     StridedIterator ed_(idx_ptr, out_stride, axis_size);
@@ -192,7 +199,8 @@ void partition(const array& in, array& out, int axis, int kth) {
 
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
-  size_t n_rows = in.size() / in.shape(axis);
+  size_t in_size = in.flags().contiguous ? in.data_size() : in.size();
+  size_t n_rows = in_size / in.shape(axis);
 
   auto remaining_shape = in.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -206,9 +214,11 @@ void partition(const array& in, array& out, int axis, int kth) {
   kth = kth < 0 ? kth + axis_size : kth;
 
   // Perform partition in place
+  ContiguousIterator<size_t> src_it(
+      remaining_shape, remaining_strides, remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
-    size_t loc = elem_to_loc(i, remaining_shape, remaining_strides);
-    T* data_ptr = out.data<T>() + loc;
+    T* data_ptr = out.data<T>() + src_it.loc;
+    src_it.step();
 
     StridedIterator st(data_ptr, axis_stride, 0);
     StridedIterator md(data_ptr, axis_stride, kth);
@@ -227,37 +237,49 @@ void argpartition(const array& in, array& out, int axis, int kth) {
   axis = axis < 0 ? axis + in.ndim() : axis;
   size_t n_rows = in.size() / in.shape(axis);
 
-  auto remaining_shape = in.shape();
-  remaining_shape.erase(remaining_shape.begin() + axis);
+  auto in_remaining_shape = in.shape();
+  in_remaining_shape.erase(in_remaining_shape.begin() + axis);
 
-  auto remaining_strides = in.strides();
-  remaining_strides.erase(remaining_strides.begin() + axis);
+  auto in_remaining_strides = in.strides();
+  in_remaining_strides.erase(in_remaining_strides.begin() + axis);
 
-  size_t axis_stride = in.strides()[axis];
+  auto out_remaining_shape = out.shape();
+  out_remaining_shape.erase(out_remaining_shape.begin() + axis);
+
+  auto out_remaining_strides = out.strides();
+  out_remaining_strides.erase(out_remaining_strides.begin() + axis);
+
+  size_t in_stride = in.strides()[axis];
+  size_t out_stride = out.strides()[axis];
   int axis_size = in.shape(axis);
 
   kth = kth < 0 ? kth + axis_size : kth;
 
   // Perform partition
+  ContiguousIterator<size_t> in_it(
+      in_remaining_shape, in_remaining_strides, in_remaining_shape.size());
+  ContiguousIterator<size_t> out_it(
+      out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
-    size_t loc = elem_to_loc(i, remaining_shape, remaining_strides);
-    const T* data_ptr = in.data<T>() + loc;
-    IdxT* idx_ptr = out.data<IdxT>() + loc;
+    const T* data_ptr = in.data<T>() + in_it.loc;
+    IdxT* idx_ptr = out.data<IdxT>() + out_it.loc;
+    in_it.step();
+    out_it.step();
 
-    StridedIterator st_(idx_ptr, axis_stride, 0);
-    StridedIterator ed_(idx_ptr, axis_stride, axis_size);
+    StridedIterator st_(idx_ptr, out_stride, 0);
+    StridedIterator ed_(idx_ptr, out_stride, axis_size);
 
     // Initialize with iota
     std::iota(st_, ed_, IdxT(0));
 
     // Sort according to vals
-    StridedIterator st(idx_ptr, axis_stride, 0);
-    StridedIterator md(idx_ptr, axis_stride, kth);
-    StridedIterator ed(idx_ptr, axis_stride, axis_size);
+    StridedIterator st(idx_ptr, out_stride, 0);
+    StridedIterator md(idx_ptr, out_stride, kth);
+    StridedIterator ed(idx_ptr, out_stride, axis_size);
 
-    std::nth_element(st, md, ed, [data_ptr, axis_stride](IdxT a, IdxT b) {
-      auto v1 = data_ptr[a * axis_stride];
-      auto v2 = data_ptr[b * axis_stride];
+    std::nth_element(st, md, ed, [data_ptr, in_stride](IdxT a, IdxT b) {
+      auto v1 = data_ptr[a * in_stride];
+      auto v2 = data_ptr[b * in_stride];
       return v1 < v2 || (v1 == v2 && a < b);
     });
   }

mlx/backend/common/svd.cpp

@@ -2,7 +2,7 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/copy.h"
-#include "mlx/backend/common/lapack_helper.h"
+#include "mlx/backend/common/lapack.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {

mlx/backend/common/unary.h

@@ -24,26 +24,26 @@ void set_unary_output_data(const array& in, array& out) {
   }
 }
 
-template <typename T, typename Op>
-void unary_op(const T* a, T* out, Op op, size_t shape, size_t stride) {
+template <typename T, typename U = T, typename Op>
+void unary_op(const T* a, U* out, Op op, size_t shape, size_t stride) {
   for (size_t i = 0; i < shape; i += 1) {
     out[i] = op(*a);
     a += stride;
   }
 }
 
-template <typename T, typename Op>
+template <typename T, typename U = T, typename Op>
 void unary_op(const array& a, array& out, Op op) {
   const T* a_ptr = a.data<T>();
   if (a.flags().contiguous) {
     set_unary_output_data(a, out);
-    T* dst = out.data<T>();
+    U* dst = out.data<U>();
     for (size_t i = 0; i < a.data_size(); ++i) {
       dst[i] = op(a_ptr[i]);
     }
   } else {
     out.set_data(allocator::malloc_or_wait(out.nbytes()));
-    T* dst = out.data<T>();
+    U* dst = out.data<U>();
     size_t shape = a.ndim() > 0 ? a.shape(-1) : 1;
     size_t stride = a.ndim() > 0 ? a.strides(-1) : 1;
     if (a.ndim() <= 1) {

mlx/backend/common/utils.cpp

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

mlx/backend/common/utils.h

@@ -88,7 +88,11 @@ std::pair<std::vector<int>, std::vector<size_t>> collapse_contiguous_dims(
 template <typename StrideT>
 struct ContiguousIterator {
   inline void step() {
-    int i = dims_;
+    int dims = shape_.size();
+    if (dims == 0) {
+      return;
+    }
+    int i = dims - 1;
     while (pos_[i] == (shape_[i] - 1) && i > 0) {
       pos_[i] = 0;
       loc -= (shape_[i] - 1) * strides_[i];
@@ -98,15 +102,41 @@ struct ContiguousIterator {
     loc += strides_[i];
   }
 
+  void seek(StrideT n) {
+    loc = 0;
+    for (int i = shape_.size() - 1; i >= 0; --i) {
+      auto q_and_r = ldiv(n, shape_[i]);
+      loc += q_and_r.rem * strides_[i];
+      pos_[i] = q_and_r.rem;
+      n = q_and_r.quot;
+    }
+  }
+
+  void reset() {
+    loc = 0;
+    std::fill(pos_.begin(), pos_.end(), 0);
+  }
+
+  ContiguousIterator() {};
+
+  explicit ContiguousIterator(const array& a)
+      : shape_(a.shape()), strides_(a.strides()) {
+    if (!shape_.empty()) {
+      std::tie(shape_, strides_) = collapse_contiguous_dims(shape_, strides_);
+      pos_ = std::vector<int>(shape_.size(), 0);
+    }
+  }
+
   explicit ContiguousIterator(
       const std::vector<int>& shape,
       const std::vector<StrideT>& strides,
       int dims)
       : shape_(shape.begin(), shape.begin() + dims),
         strides_(strides.begin(), strides.begin() + dims) {
-    std::tie(shape_, strides_) = collapse_contiguous_dims(shape_, strides_);
-    dims_ = shape_.size() - 1;
-    pos_ = std::vector<int>(dims_ + 1, 0);
+    if (!shape_.empty()) {
+      std::tie(shape_, strides_) = collapse_contiguous_dims(shape_, strides_);
+      pos_ = std::vector<int>(shape_.size(), 0);
+    }
   }
 
   StrideT loc{0};
@@ -115,7 +145,6 @@ struct ContiguousIterator {
   std::vector<int> shape_;
   std::vector<StrideT> strides_;
   std::vector<int> pos_;
-  int dims_;
 };
 
 template <typename StrideT>
@@ -149,4 +178,13 @@ inline bool is_donatable(const array& in, const array& out) {
       in.buffer_size() <= out.nbytes() + donation_extra;
 }
 
+void move_or_copy(const array& in, array& out);
+void move_or_copy(
+    const array& in,
+    array& out,
+    const std::vector<size_t>& strides,
+    array::Flags flags,
+    size_t data_size,
+    size_t offset = 0);
+
 } // namespace mlx::core

mlx/backend/metal/CMakeLists.txt

@@ -14,20 +14,27 @@ function(make_jit_source SRC_FILE)
     COMMAND
       /bin/bash ${CMAKE_CURRENT_SOURCE_DIR}/make_compiled_preamble.sh
       ${CMAKE_CURRENT_BINARY_DIR}/jit ${CMAKE_C_COMPILER} ${PROJECT_SOURCE_DIR}
-      ${SRC_FILE} "-DMLX_METAL_VERSION=${MLX_METAL_VERSION}"
+      ${SRC_FILE}
     DEPENDS make_compiled_preamble.sh kernels/${SRC_FILE}.h ${ARGN})
   add_custom_target(${SRC_NAME} DEPENDS jit/${SRC_NAME}.cpp)
   add_dependencies(mlx ${SRC_NAME})
   target_sources(mlx PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/jit/${SRC_NAME}.cpp)
 endfunction(make_jit_source)
 
-make_jit_source(utils kernels/bf16.h kernels/complex.h kernels/defines.h)
+make_jit_source(
+  utils
+  kernels/jit/bf16.h
+  kernels/metal_3_0/bf16.h
+  kernels/metal_3_1/bf16.h
+  kernels/bf16_math.h
+  kernels/complex.h
+  kernels/defines.h)
 make_jit_source(unary_ops kernels/erf.h kernels/expm1f.h)
 make_jit_source(binary_ops)
 make_jit_source(ternary_ops)
 make_jit_source(reduce_utils kernels/atomic.h kernels/reduction/ops.h)
-make_jit_source(scatter)
-make_jit_source(gather)
+make_jit_source(scatter kernels/indexing.h)
+make_jit_source(gather kernels/indexing.h)
 make_jit_source(hadamard)
 
 if(MLX_METAL_JIT)
@@ -99,6 +106,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/ternary.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/unary.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/resident.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp)
 
 if(NOT MLX_METAL_PATH)

mlx/backend/metal/allocator.cpp

@@ -2,6 +2,7 @@
 #include "mlx/backend/metal/allocator.h"
 #include "mlx/backend/metal/metal.h"
 #include "mlx/backend/metal/metal_impl.h"
+#include "mlx/backend/metal/resident.h"
 
 #include <mach/vm_page_size.h>
 #include <unistd.h>
@@ -140,6 +141,7 @@ 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_) {
   auto memsize = std::get<size_t>(device_info()["memory_size"]);
   block_limit_ =
@@ -148,6 +150,8 @@ MetalAllocator::MetalAllocator()
       static_cast<size_t>(0.95 * device_->recommendedMaxWorkingSetSize()),
       block_limit_);
   max_pool_size_ = block_limit_;
+  device(mlx::core::Device::gpu)
+      .set_residency_set(residency_set_.mtl_residency_set());
 }
 
 size_t MetalAllocator::set_cache_limit(size_t limit) {
@@ -164,6 +168,12 @@ size_t MetalAllocator::set_memory_limit(size_t limit, bool relaxed) {
   return limit;
 };
 
+size_t MetalAllocator::set_wired_limit(size_t limit) {
+  std::swap(limit, wired_limit_);
+  residency_set_.resize(wired_limit_);
+  return limit;
+};
+
 Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
   // Metal doesn't like empty buffers
   if (size == 0) {
@@ -205,7 +215,7 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
 
     // Allocate new buffer if needed
     size_t res_opt = MTL::ResourceStorageModeShared;
-    res_opt |= MTL::ResourceHazardTrackingModeTracked;
+    res_opt |= MTL::ResourceHazardTrackingModeUntracked;
     lk.unlock();
     buf = device_->newBuffer(size, res_opt);
     lk.lock();
@@ -220,6 +230,8 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
     buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
   }
 
+  residency_set_.insert(buf);
+
   return Buffer{static_cast<void*>(buf)};
 }
 
@@ -230,7 +242,11 @@ void MetalAllocator::clear_cache() {
 
 void MetalAllocator::free(Buffer buffer) {
   auto buf = static_cast<MTL::Buffer*>(buffer.ptr());
+  if (buf == nullptr) {
+    return;
+  }
   std::unique_lock lk(mutex_);
+  residency_set_.erase(buf);
   active_memory_ -= buf->length();
   if (get_cache_memory() < max_pool_size_) {
     buffer_cache_.recycle_to_cache(buf);
@@ -246,15 +262,9 @@ size_t MetalAllocator::size(Buffer buffer) const {
 }
 
 MetalAllocator& allocator() {
-  // By creating the |allocator_| on heap, the destructor of MetalAllocator will
-  // not be called on exit and all the buffers will be leaked. This is necessary
-  // because releasing buffers can take more than 30sec when the program holds a
-  // lot of RAM (for example inferencing a LLM), and it would feel frozen to
-  // users when exiting.
-  // TODO(zcbenz): Consider using the `base::NoDestructor` class from Chromium
-  // when applying this pattern to more places, or when introducing sanitizers
-  // to MLX.
-  // https://source.chromium.org/chromium/chromium/src/+/main:base/no_destructor.h
+  // By creating the |allocator_| on heap, the destructor of MetalAllocator
+  // will not be called on exit and buffers in the cache will be leaked. This
+  // can save some time at program exit.
   static MetalAllocator* allocator_ = new MetalAllocator;
   return *allocator_;
 }
@@ -265,6 +275,15 @@ size_t set_cache_limit(size_t limit) {
 size_t set_memory_limit(size_t limit, bool relaxed /* = true */) {
   return allocator().set_memory_limit(limit, relaxed);
 }
+size_t set_wired_limit(size_t limit) {
+  if (limit >
+      std::get<size_t>(device_info()["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);
+}
 size_t get_active_memory() {
   return allocator().get_active_memory();
 }

mlx/backend/metal/allocator.h

@@ -8,6 +8,7 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/metal/device.h"
+#include "mlx/backend/metal/resident.h"
 
 namespace mlx::core::metal {
 
@@ -72,6 +73,7 @@ class MetalAllocator : public allocator::Allocator {
   };
   size_t set_cache_limit(size_t limit);
   size_t set_memory_limit(size_t limit, bool relaxed);
+  size_t set_wired_limit(size_t limit);
   void clear_cache();
 
  private:
@@ -82,12 +84,15 @@ class MetalAllocator : public allocator::Allocator {
   // Caching allocator
   BufferCache buffer_cache_;
 
+  ResidencySet residency_set_;
+
   // Allocation stats
   size_t block_limit_;
   size_t gc_limit_;
   size_t active_memory_{0};
   size_t peak_memory_{0};
   size_t max_pool_size_;
+  size_t wired_limit_{0};
   bool relaxed_{true};
 
   std::mutex mutex_;

mlx/backend/metal/binary.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2024 Apple Inc.
-
 #include "mlx/backend/common/binary.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
@@ -23,37 +22,37 @@ std::string get_kernel_name(
     BinaryOpType bopt,
     const std::string& op,
     const array& a,
-    bool use_2d,
+    bool large,
     int ndim,
     int work_per_thread) {
-  std::ostringstream kname;
+  std::string kname;
   switch (bopt) {
     case BinaryOpType::ScalarScalar:
-      kname << "ss";
+      kname = "ss";
       break;
     case BinaryOpType::ScalarVector:
-      kname << (use_2d ? "sv2" : "sv");
+      kname = (large ? "sv2" : "sv");
       break;
     case BinaryOpType::VectorScalar:
-      kname << (use_2d ? "vs2" : "vs");
+      kname = (large ? "vs2" : "vs");
       break;
     case BinaryOpType::VectorVector:
-      kname << (use_2d ? "vv2" : "vv");
+      kname = (large ? "vv2" : "vv");
       break;
     case BinaryOpType::General:
-      kname << "g";
+      kname = "g";
       if (ndim <= 3) {
-        kname << ndim;
+        kname += std::to_string(ndim);
       } else {
-        kname << "n";
-        if (work_per_thread > 1) {
-          kname << work_per_thread;
-        }
+        concatenate(kname, "n", std::to_string(work_per_thread));
+      }
+      if (large) {
+        kname += "large";
       }
       break;
   }
-  kname << "_" << op << type_to_name(a);
-  return kname.str();
+  concatenate(kname, "_", op, type_to_name(a));
+  return kname;
 }
 
 void binary_op_gpu_inplace(
@@ -82,19 +81,23 @@ void binary_op_gpu_inplace(
   };
   auto [shape, strides_a, strides_b, strides_out] = maybe_collapse();
 
-  bool use_2d = out.data_size() > UINT32_MAX;
+  bool large = out.data_size() > UINT32_MAX;
   auto ndim = shape.size();
-  int work_per_thread =
-      (bopt == BinaryOpType::General && shape[ndim - 1] > 4) ? 4 : 1;
+  int work_per_thread;
+  if (bopt == BinaryOpType::General) {
+    work_per_thread = large ? 4 : 2;
+  } else {
+    work_per_thread = 1;
+  }
   std::string kernel_name =
-      get_kernel_name(bopt, op, a, use_2d, shape.size(), work_per_thread);
+      get_kernel_name(bopt, op, a, large, shape.size(), work_per_thread);
   auto& d = metal::device(s.device);
 
   auto kernel = outputs.size() == 2
       ? get_binary_two_kernel(d, kernel_name, a.dtype(), out.dtype(), op)
       : get_binary_kernel(d, kernel_name, a.dtype(), out.dtype(), op);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // - If a is donated it goes to the first output
   // - If b is donated it goes to the first output if a was not donated
@@ -111,6 +114,7 @@ void binary_op_gpu_inplace(
     compute_encoder.set_output_array(outputs[1], arg_idx++);
   }
 
+  auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
   if (bopt == BinaryOpType::General) {
     // Launch up to 3D grid of threads
     size_t dim0 = ndim > 0 ? shape[ndim - 1] : 1;
@@ -118,39 +122,33 @@ void binary_op_gpu_inplace(
     size_t rest = out.size() / (dim0 * dim1);
 
     if (ndim > 3) {
-      compute_encoder->setBytes(shape.data(), ndim * sizeof(int), arg_idx++);
-      compute_encoder->setBytes(
-          strides_a.data(), ndim * sizeof(size_t), arg_idx++);
-      compute_encoder->setBytes(
-          strides_b.data(), ndim * sizeof(size_t), arg_idx++);
-      compute_encoder->setBytes(&ndim, sizeof(int), arg_idx++);
+      compute_encoder.set_vector_bytes(shape, arg_idx++);
+      compute_encoder.set_vector_bytes(strides_a, arg_idx++);
+      compute_encoder.set_vector_bytes(strides_b, arg_idx++);
+      compute_encoder.set_bytes<int>(ndim, arg_idx++);
       dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     } else {
       // The shape is implicit in the grid for <= 3D
-      compute_encoder->setBytes(
-          strides_a.data(), ndim * sizeof(size_t), arg_idx++);
-      compute_encoder->setBytes(
-          strides_b.data(), ndim * sizeof(size_t), arg_idx++);
+      compute_encoder.set_vector_bytes(strides_a, arg_idx++);
+      compute_encoder.set_vector_bytes(strides_b, arg_idx++);
     }
 
-    NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
     if (thread_group_size != 1024) {
       throw std::runtime_error("[Metal::binary] Must use 1024 sized block");
     }
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     // Launch a 1D or 2D grid of threads
     size_t nthreads = out.data_size();
-    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                                 : MTL::Size(nthreads, 1, 1);
-    NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
+                                : MTL::Size(nthreads, 1, 1);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }
 

mlx/backend/metal/compiled.cpp

@@ -1,5 +1,6 @@
 // Copyright © 2023-2024 Apple Inc.
-
+#include <fmt/format.h>
+#include <iostream> //TODO
 #include <sstream>
 
 #include "mlx/backend/common/compiled.h"
@@ -11,10 +12,12 @@
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
+using namespace fmt::literals;
+
 namespace mlx::core {
 
 inline void build_kernel(
-    std::ostream& os,
+    std::string& os,
     const std::string& kernel_name,
     const std::vector<array>& inputs,
     const std::vector<array>& outputs,
@@ -23,7 +26,8 @@ inline void build_kernel(
     bool contiguous,
     int ndim,
     bool dynamic_dims,
-    bool use_big_index = false) {
+    bool use_big_index = false,
+    int work_per_thread = 1) {
   // All outputs should have the exact same shape and will be row contiguous
   auto output_shape = outputs[0].shape();
   auto output_strides = outputs[0].strides();
@@ -38,8 +42,8 @@ inline void build_kernel(
   int cnt = 0;
 
   // Start the kernel
-  os << "[[host_name(\"" << kernel_name << "\")]]" << std::endl
-     << "[[kernel]] void " << kernel_name << "(" << std::endl;
+  os += fmt::format(
+      "[[host_name(\"{0}\")]]\n[[kernel]] void {0}(\n", kernel_name);
 
   // Add the input arguments
   for (auto& x : inputs) {
@@ -51,135 +55,203 @@ inline void build_kernel(
     }
 
     // Scalars and contiguous need no strides
-    if (is_scalar(x) || contiguous) {
-      os << "    device const " << get_type_string(x.dtype()) << "* " << xname
-         << " [[buffer(" << cnt++ << ")]]," << std::endl;
-    } else {
+    if (!is_scalar(x) && !contiguous) {
       add_indices = true;
-      os << "    device const " << get_type_string(x.dtype()) << "* " << xname
-         << " [[buffer(" << cnt++ << ")]]," << std::endl;
     }
+    os += fmt::format(
+        "    device const {0}* {1} [[buffer({2})]],\n",
+        get_type_string(x.dtype()),
+        xname,
+        cnt++);
   }
 
   if (add_indices) {
-    os << "    constant const size_t* in_strides [[buffer(" << cnt++
-       << ")]],\n";
+    os += fmt::format(
+        "    constant const size_t* in_strides [[buffer({0})]],\n", cnt++);
   }
 
   // Add the output arguments
   for (auto& x : outputs) {
-    os << "    device " << get_type_string(x.dtype()) << "* "
-       << namer.get_name(x) << " [[buffer(" << cnt++ << ")]]," << std::endl;
+    os += fmt::format(
+        "    device {0}* {1} [[buffer({2})]],\n",
+        get_type_string(x.dtype()),
+        namer.get_name(x),
+        cnt++);
   }
   // Add output strides and shape to extract the indices.
   if (!contiguous) {
-    os << "    constant const size_t* output_strides [[buffer(" << cnt++
-       << ")]]," << std::endl
-       << "    constant const int* output_shape [[buffer(" << cnt++ << ")]],"
-       << std::endl;
+    os += fmt::format(
+        "    constant const size_t* output_strides [[buffer({0})]],\n", cnt++);
+    os += fmt::format(
+        "    constant const int* output_shape [[buffer({0})]],\n", cnt++);
   }
   if (dynamic_dims) {
-    os << "    constant const int& ndim [[buffer(" << cnt++ << ")]],"
-       << std::endl;
+    os += fmt::format("    constant const int& ndim [[buffer({0})]],\n", cnt++);
   }
 
   // The thread index in the whole grid
-  os << "    uint3 pos [[thread_position_in_grid]]," << std::endl
-     << "    uint3 grid [[threads_per_grid]]) {" << std::endl;
-  if (use_big_index) {
+  os += "    uint3 pos [[thread_position_in_grid]],\n";
+  os += "    uint3 grid [[threads_per_grid]]) {\n";
+
+  std::string idx_type = use_big_index ? "size_t" : "uint";
+  if (contiguous && use_big_index) {
     // This is only used for contiguous kernels which don't have
     // a third grid dimension
-    os << "  size_t index = pos.x + grid.x * size_t(pos.y);";
+    os += "  size_t index = pos.x + grid.x * size_t(pos.y);\n";
+  } else if (work_per_thread > 1) {
+    os += fmt::format("  constexpr int N_ = {0};\n", work_per_thread);
+    os += fmt::format(
+        "  int xshape = output_shape[{0}];\n",
+        dynamic_dims ? "ndim - 1" : std::to_string(ndim - 1));
+    os += fmt::format(
+        "  {0} index = N_ * pos.x + xshape * (pos.y + {0}(grid.y) * pos.z);\n",
+        idx_type);
   } else {
-    os << "  uint index = pos.x + grid.x * (pos.y + grid.y * pos.z);";
-  }
-  os << std::endl;
-
-  // Extract the indices per axis to individual uints if we have arrays that
-  // are broadcasted or transposed
-  if (add_indices) {
-    if (!dynamic_dims) {
-      if (ndim == 1) {
-        os << "  uint index_0 = pos.x;" << std::endl;
-      } else if (ndim == 2) {
-        os << "  uint index_0 = pos.y;" << std::endl
-           << "  uint index_1 = pos.x;" << std::endl;
-      } else if (ndim == 3) {
-        os << "  uint index_0 = pos.z;" << std::endl
-           << "  uint index_1 = pos.y;" << std::endl
-           << "  uint index_2 = pos.x;" << std::endl;
-      } else {
-        for (int i = 0; i < ndim - 2; i++) {
-          os << "  uint index_" << i << " = (index / uint(output_strides[" << i
-             << "])) % output_shape[" << i << "];" << std::endl;
-        }
-        os << "  uint index_" << ndim - 2 << " = pos.y;" << std::endl
-           << "  uint index_" << ndim - 1 << " = pos.x;" << std::endl;
-      }
-    }
+    os += fmt::format(
+        "  {0} index = pos.x + grid.x * (pos.y + {0}(grid.y) * pos.z);\n",
+        idx_type);
   }
 
-  // Read the inputs in tmps
-  int nc_in_count = 0;
+  // Read constant / contiguous inputs in tmps
+  std::vector<array> nc_inputs;
   for (int i = 0; i < inputs.size(); ++i) {
     auto& x = inputs[i];
     auto& xname = namer.get_name(x);
 
     if (is_constant(x)) {
       auto type_str = get_type_string(x.dtype());
-      os << "  auto tmp_" << xname << " = static_cast<"
-         << get_type_string(x.dtype()) << ">(";
-      print_constant(os, x);
-      os << ");" << std::endl;
+      std::ostringstream ss;
+      print_constant(ss, x);
+      os += fmt::format(
+          "  auto tmp_{0} = static_cast<{1}>({2});\n",
+          xname,
+          get_type_string(x.dtype()),
+          ss.str());
     } else if (is_scalar(x)) {
-      os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
-         << xname << "[0];" << std::endl;
+      os += fmt::format(
+          "  {0} tmp_{1} = {1}[0];\n", get_type_string(x.dtype()), xname);
     } else if (contiguous) {
-      os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
-         << xname << "[index];" << std::endl;
-    } else if (!dynamic_dims) {
-      int offset = nc_in_count * ndim;
-      os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
-         << xname << "[";
-      os << "index_0 * " << "in_strides[" << offset << "]";
-      for (int i = 1; i < ndim; i++) {
-        os << " + index_" << i << " * " << "in_strides[" << offset + i << "]";
-      }
-      os << "];" << std::endl;
-      nc_in_count++;
+      os += fmt::format(
+          "  {0} tmp_{1} = {1}[index];\n", get_type_string(x.dtype()), xname);
     } else {
-      os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
-         << xname << "[elem_to_loc(index, output_shape, in_strides + "
-         << nc_in_count * ndim << ", ndim)];" << std::endl;
-      nc_in_count++;
+      nc_inputs.push_back(x);
     }
   }
 
+  // Initialize the indices for non-contiguous inputs
+  for (int i = 0; i < nc_inputs.size(); ++i) {
+    auto& xname = namer.get_name(nc_inputs[i]);
+    os += fmt::format("  {0} index_{1} = ", idx_type, xname);
+    if (ndim == 1) {
+      int offset = i * ndim;
+      os += fmt::format(
+          "elem_to_loc_1<size_t, uint>(pos.x, in_strides[{0}]);\n", offset);
+    } else if (ndim == 2) {
+      int offset = i * ndim;
+      os += fmt::format(
+          "elem_to_loc_2<size_t, {0}>({{pos.x, pos.y}}, in_strides + {1});\n",
+          idx_type,
+          offset);
+    } else if (ndim == 3) {
+      int offset = i * ndim;
+      os += fmt::format(
+          "elem_to_loc_3<size_t, {0}>(pos, in_strides + {1});\n",
+          idx_type,
+          offset);
+    } else if (!dynamic_dims) {
+      int offset = (i + 1) * ndim;
+      os += fmt::format(
+          "N_ * pos.x * {0}(in_strides[{1}]) + pos.y * {0}(in_strides[{2}]);\n",
+          idx_type,
+          offset - 1,
+          offset - 2);
+    } else {
+      os += fmt::format(
+          "N_ * pos.x * {0}(in_strides[ndim * {1} + ndim - 1]) + pos.y * {0}(in_strides[ndim * {1} + ndim - 2]);\n",
+          idx_type,
+          i);
+    }
+  }
+
+  if (!nc_inputs.empty() && (ndim > 3 || dynamic_dims)) {
+    os += "  uint zpos = pos.z;\n";
+    if (dynamic_dims) {
+      os += "  for (int d = ndim - 3; d >= 0; --d) {\n";
+    } else {
+      os += fmt::format("  for (int d = {0}; d >= 0; --d) {{\n", ndim - 3);
+    }
+    os += "    uint l = zpos % output_shape[d];\n";
+    for (int i = 0; i < nc_inputs.size(); ++i) {
+      auto& xname = namer.get_name(nc_inputs[i]);
+      os += fmt::format("    index_{0} += ", xname);
+      if (dynamic_dims) {
+        os +=
+            fmt::format("l * {0}(in_strides[{1} * ndim + d]);\n", idx_type, i);
+      } else {
+        os +=
+            fmt::format("l * {0}(in_strides[{1} + d]);\n", idx_type, i * ndim);
+      }
+    }
+    os += "    zpos /= output_shape[d];\n  }\n";
+  }
+
+  // Open per-thread loop
+  if (work_per_thread > 1) {
+    os +=
+        "  for (int i = 0; i < N_ && (int(N_ * pos.x) + i) < xshape; ++i) {\n";
+  }
+
+  // Read non-contiguous inputs into tmps
+  for (int i = 0; i < nc_inputs.size(); ++i) {
+    auto& x = nc_inputs[i];
+    auto& xname = namer.get_name(x);
+    os += fmt::format(
+        "  {0} tmp_{1} = {1}[index_{1}];\n", get_type_string(x.dtype()), xname);
+  }
+
   // Actually write the computation
   for (auto& x : tape) {
-    os << "  " << get_type_string(x.dtype()) << " tmp_" << namer.get_name(x)
-       << " = ";
+    os += fmt::format(
+        "  {0} tmp_{1} = ", get_type_string(x.dtype()), namer.get_name(x));
     if (is_static_cast(x.primitive())) {
-      os << "static_cast<" << get_type_string(x.dtype()) << ">(tmp_"
-         << namer.get_name(x.inputs()[0]) << ");" << std::endl;
+      os += fmt::format(
+          "static_cast<{0}>(tmp_{1});\n",
+          get_type_string(x.dtype()),
+          namer.get_name(x.inputs()[0]));
     } else {
-      x.primitive().print(os);
-      os << "()(";
+      std::ostringstream ss;
+      x.primitive().print(ss);
+      os += ss.str();
+      os += "()(";
       for (int i = 0; i < x.inputs().size() - 1; i++) {
-        os << "tmp_" << namer.get_name(x.inputs()[i]) << ", ";
+        os += fmt::format("tmp_{0}, ", namer.get_name(x.inputs()[i]));
       }
-      os << "tmp_" << namer.get_name(x.inputs().back()) << ");" << std::endl;
+      os += fmt::format("tmp_{0});\n", namer.get_name(x.inputs().back()));
     }
   }
 
   // Write the outputs from tmps
   for (auto& x : outputs) {
-    os << "  " << namer.get_name(x) << "[index] = tmp_" << namer.get_name(x)
-       << ";" << std::endl;
+    os += fmt::format("  {0}[index] = tmp_{0};\n", namer.get_name(x));
+  }
+  // Increment indices and close per thread loop
+  if (work_per_thread > 1) {
+    for (int i = 0; i < nc_inputs.size(); ++i) {
+      auto& x = nc_inputs[i];
+      auto& xname = namer.get_name(x);
+      if (!dynamic_dims) {
+        os += fmt::format(
+            "  index_{0} += in_strides[{1}];\n", xname, i * ndim + ndim - 1);
+      } else {
+        os += fmt::format(
+            "  index_{0} += in_strides[{1} * ndim + ndim - 1];\n", xname, i);
+      }
+    }
+    os += "  index++;\n  }\n";
   }
 
   // Finish the kernel
-  os << "}" << std::endl;
+  os += "}\n";
 
   if (cnt > 31) {
     std::ostringstream msg;
@@ -202,13 +274,10 @@ void Compiled::eval_gpu(
   // Get the kernel if someone else built it already
   auto& s = stream();
   auto& d = metal::device(s.device);
-  auto lib = d.get_library(kernel_lib_);
-
-  // If not we have to build it ourselves
-  if (lib == nullptr) {
-    std::ostringstream kernel;
-    kernel << metal::utils() << metal::unary_ops() << metal::binary_ops()
-           << metal::ternary_ops();
+  auto lib = d.get_library(kernel_lib_, [&]() {
+    std::string kernel = metal::utils();
+    concatenate(
+        kernel, metal::unary_ops(), metal::binary_ops(), metal::ternary_ops());
     build_kernel(
         kernel,
         kernel_lib_ + "_contiguous",
@@ -221,7 +290,7 @@ void Compiled::eval_gpu(
         /* dynamic_dims = */ false);
     build_kernel(
         kernel,
-        kernel_lib_ + "_contiguous_big",
+        kernel_lib_ + "_contiguous_large",
         inputs_,
         outputs_,
         tape_,
@@ -240,7 +309,23 @@ void Compiled::eval_gpu(
           constant_ids_,
           /* contiguous = */ false,
           /* ndim = */ i,
-          /* dynamic_dims = */ false);
+          /* dynamic_dims = */ false,
+          /* use_big_index = */ false,
+          /* work_per_thread = */ i > 3 ? 2 : 1);
+      if (i > 1) {
+        build_kernel(
+            kernel,
+            kernel_lib_ + "_strided_" + std::to_string(i) + "_large",
+            inputs_,
+            outputs_,
+            tape_,
+            constant_ids_,
+            /* contiguous = */ false,
+            /* ndim = */ i,
+            /* dynamic_dims = */ false,
+            /* use_big_index = */ true,
+            /* work_per_thread = */ i > 3 ? 4 : 1);
+      }
     }
     build_kernel(
         kernel,
@@ -251,14 +336,27 @@ void Compiled::eval_gpu(
         constant_ids_,
         /* contiguous = */ false,
         /* ndim = */ 0,
-        /* dynamic_dims = */ true);
-
-    lib = d.get_library(kernel_lib_, kernel.str());
-  }
+        /* dynamic_dims = */ true,
+        /* use_big_index = */ false,
+        /* work_per_thread = */ 2);
+    build_kernel(
+        kernel,
+        kernel_lib_ + "_strided_dynamic_large",
+        inputs_,
+        outputs_,
+        tape_,
+        constant_ids_,
+        /* contiguous = */ false,
+        /* ndim = */ 0,
+        /* dynamic_dims = */ true,
+        /* use_big_index = */ true,
+        /* work_per_thread = */ 4);
+    return kernel;
+  });
 
   // Figure out which kernel we are using
   auto& output_shape = outputs[0].shape();
-  bool contiguous = compiled_check_contiguity(inputs, output_shape);
+  auto contiguous = compiled_check_contiguity(inputs, output_shape);
 
   // Collapse contiguous dims to route to a faster kernel if possible. Also
   // handle all broadcasting.
@@ -306,13 +404,19 @@ void Compiled::eval_gpu(
         collapse_contiguous_dims(output_shape, initial_strides, INT32_MAX);
   }
 
-  bool use_2d = false;
+  bool large;
   if (contiguous) {
     size_t max_size = 0;
     for (auto& in : inputs) {
       max_size = std::max(max_size, in.data_size());
     }
-    use_2d = (max_size > UINT32_MAX);
+    large = (max_size > UINT32_MAX);
+  } else {
+    size_t max_size = 0;
+    for (auto& o : outputs) {
+      max_size = std::max(max_size, o.size());
+    }
+    large = (max_size > UINT32_MAX);
   }
 
   // Get the kernel from the lib
@@ -325,12 +429,13 @@ void Compiled::eval_gpu(
     } else {
       kernel_name += std::to_string(shape.size());
     }
-  } else if (use_2d) {
-    kernel_name += "_big";
+  }
+  if (large) {
+    kernel_name += "_large";
   }
   auto kernel = d.get_kernel(kernel_name, lib);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Put the inputs in
   int cnt = 0;
@@ -351,8 +456,7 @@ void Compiled::eval_gpu(
     }
   }
   if (!in_strides.empty()) {
-    compute_encoder->setBytes(
-        in_strides.data(), in_strides.size() * sizeof(size_t), cnt++);
+    compute_encoder.set_vector_bytes(in_strides, cnt++);
   }
 
   compiled_allocate_outputs(
@@ -365,36 +469,43 @@ void Compiled::eval_gpu(
 
   // Put the output shape and strides in
   if (!contiguous) {
-    compute_encoder->setBytes(
-        strides[0].data(), strides[0].size() * sizeof(size_t), cnt++);
-    compute_encoder->setBytes(shape.data(), shape.size() * sizeof(int), cnt++);
+    compute_encoder.set_vector_bytes(strides[0], cnt++);
+    compute_encoder.set_vector_bytes(shape, cnt++);
   }
 
   // Put the number of dims in if it is dynamic
   if (dynamic) {
-    compute_encoder->setBytes(&ndim, sizeof(int), cnt++);
+    compute_encoder.set_bytes(ndim, cnt++);
   }
 
   // Launch the kernel
   if (contiguous) {
     size_t nthreads = outputs[0].data_size();
-    MTL::Size grid_dims = use_2d
-        ? get_2d_grid_dims(outputs[0].shape(), outputs[0].strides())
-        : MTL::Size(nthreads, 1, 1);
     MTL::Size group_dims(
         std::min(nthreads, kernel->maxTotalThreadsPerThreadgroup()), 1, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+
+    MTL::Size grid_dims = large
+        ? get_2d_grid_dims(outputs[0].shape(), outputs[0].strides())
+        : MTL::Size(nthreads, 1, 1);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     size_t dim0 = ndim > 0 ? shape[ndim - 1] : 1;
     size_t dim1 = ndim > 1 ? shape[ndim - 2] : 1;
     size_t rest = outputs[0].size() / (dim0 * dim1);
+    int work_per_thread = ndim > 3 ? (large ? 4 : 2) : 1;
+    dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
-    if (thread_group_size != 1024) {
-      throw std::runtime_error("[Metal::binary] Must use 1024 sized block");
+    int pow2;
+    if (thread_group_size == 1024) {
+      pow2 = 10;
+    } else if (thread_group_size > 512) {
+      pow2 = 9;
+    } else {
+      throw std::runtime_error("[Metal::compiled] Must use > 512 sized block");
     }
-    auto group_dims = get_block_dims(dim0, dim1, rest);
+    auto group_dims = get_block_dims(dim0, dim1, rest, pow2);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }
 

mlx/backend/metal/conv.cpp

@@ -44,23 +44,24 @@ void explicit_gemm_conv_ND_gpu(
   kname << "naive_unfold_nd_" << type_to_name(in_unfolded) << "_" << N;
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(in_unfolded, 1);
 
-  compute_encoder->setBytes(&conv_params, sizeof(conv_params), 2);
+  compute_encoder.set_bytes(conv_params, 2);
 
   // Launch unfolding kernel
-  int tgp_x = std::min(conv_params.C, 64);
+  size_t tgp_x = std::min(conv_params.C, 64);
   tgp_x = 32 * ((tgp_x + 32 - 1) / 32);
-  int tgp_y = 256 / tgp_x;
+  size_t tgp_y = 256 / tgp_x;
 
-  MTL::Size group_dims = MTL::Size(tgp_x, tgp_y, 1);
   MTL::Size grid_dims = MTL::Size(
       conv_params.C, unfolded_shape[1] / conv_params.C, unfolded_shape[0]);
+  MTL::Size group_dims = MTL::Size(
+      std::min(tgp_x, grid_dims.width), std::min(tgp_y, grid_dims.height), 1);
 
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 
   // Reshape weight
   std::vector<int> wt_reshape{implicit_K, implicit_N};
@@ -72,7 +73,7 @@ void explicit_gemm_conv_ND_gpu(
   wt_reshaped.copy_shared_buffer(wt, wt_restride, wt_flags, wt.data_size());
 
   // Perform gemm
-  std::vector<array> copies = {in_unfolded, wt_reshaped};
+  std::vector<array> copies = {in_unfolded};
   return steel_matmul(
       s,
       d,
@@ -122,23 +123,24 @@ void explicit_gemm_conv_group_ND_gpu(
         << N;
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(in_unfolded, 1);
 
-  compute_encoder->setBytes(&conv_params, sizeof(conv_params), 2);
+  compute_encoder.set_bytes(conv_params, 2);
 
   // Launch unfolding kernel
-  int tgp_x = std::min(conv_params.C, 64);
+  size_t tgp_x = std::min(conv_params.C, 64);
   tgp_x = 32 * ((tgp_x + 32 - 1) / 32);
-  int tgp_y = 256 / tgp_x;
+  size_t tgp_y = 256 / tgp_x;
 
-  MTL::Size group_dims = MTL::Size(tgp_x, tgp_y, 1);
   MTL::Size grid_dims = MTL::Size(
       conv_params.C, unfolded_shape[1] / conv_params.C, unfolded_shape[0]);
+  MTL::Size group_dims = MTL::Size(
+      std::min(tgp_x, grid_dims.width), std::min(tgp_y, grid_dims.height), 1);
 
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 
   // Transpose kernel weights so that we can slice them by contiguous chunks
   // of channel groups.
@@ -155,22 +157,27 @@ void explicit_gemm_conv_group_ND_gpu(
   copy_gpu(wt_view, wt_transpose, CopyType::General, s);
 
   // Perform gemm
-  std::vector<array> copies = {in_unfolded, wt_view, wt_transpose};
-  return steel_matmul_conv_groups(
+  std::vector<array> copies = {in_unfolded, wt_transpose};
+  return steel_matmul_regular(
       s,
       d,
-      /*a = */ in_unfolded,
-      /*b = */ wt_transpose,
-      /*c = */ out,
-      /*M = */ implicit_M,
-      /*N = */ implicit_N,
-      /*K = */ implicit_K,
-      /*a_cols = */ implicit_K * groups,
-      /*b_cols = */ implicit_K,
-      /*out_cols = */ implicit_N * groups,
-      /*a_transposed = */ false,
-      /*b_transposed = */ true,
-      /* groups = */ groups,
+      /* a = */ in_unfolded,
+      /* b = */ wt_transpose,
+      /* c = */ out,
+      /* M = */ implicit_M,
+      /* N = */ implicit_N,
+      /* K = */ implicit_K,
+      /* batch_size_out = */ groups,
+      /* a_cols = */ implicit_K * groups,
+      /* b_cols = */ implicit_K,
+      /* out_cols = */ implicit_N * groups,
+      /* a_transposed = */ false,
+      /* b_transposed = */ true,
+      /* batch_shape = */ {1},
+      /* batch_strides = */ {0},
+      /* A_batch_strides = */ size_t(implicit_K),
+      /* B_batch_strides = */ size_t(implicit_N) * implicit_K,
+      /* matrix_stride_out = */ size_t(implicit_N),
       /*copies = */ copies);
 }
 
@@ -232,7 +239,7 @@ void slow_conv_2D_gpu(
   // Encode and dispatch kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   size_t n_pixels = conv_params.oS[0] * conv_params.oS[1];
 
@@ -247,8 +254,8 @@ void slow_conv_2D_gpu(
   compute_encoder.set_input_array(wt, 1);
   compute_encoder.set_output_array(out, 2);
 
-  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.set_bytes(conv_params, 3);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
 void implicit_gemm_conv_2D_gpu(
@@ -347,7 +354,7 @@ void implicit_gemm_conv_2D_gpu(
       wn,
       n_channel_specialization,
       small_filter);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Deduce grid launch dimensions
   int tile = 1 << swizzle_log;
@@ -363,11 +370,11 @@ void implicit_gemm_conv_2D_gpu(
   compute_encoder.set_output_array(out, 2);
 
   // Encode params
-  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
-  compute_encoder->setBytes(&gemm_params, sizeof(ImplicitGemmConv2DParams), 4);
+  compute_encoder.set_bytes(conv_params, 3);
+  compute_encoder.set_bytes(gemm_params, 4);
 
   // Launch kernel
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
 void implicit_gemm_conv_2D_general_gpu(
@@ -501,7 +508,7 @@ void implicit_gemm_conv_2D_general_gpu(
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel =
       get_steel_conv_general_kernel(d, kname.str(), out, bm, bn, bk, wm, wn);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Deduce grid launch dimensions
   int tile = 1 << swizzle_log;
@@ -518,17 +525,15 @@ void implicit_gemm_conv_2D_general_gpu(
   compute_encoder.set_output_array(out, 2);
 
   // Encode params
-  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
-  compute_encoder->setBytes(&gemm_params, sizeof(ImplicitGemmConv2DParams), 4);
-  compute_encoder->setBytes(&jump_params, sizeof(Conv2DGeneralJumpParams), 5);
+  compute_encoder.set_bytes(conv_params, 3);
+  compute_encoder.set_bytes(gemm_params, 4);
+  compute_encoder.set_bytes(jump_params, 5);
 
-  compute_encoder->setBytes(
-      base_h.data(), sizeof(Conv2DGeneralBaseInfo) * base_h.size(), 6);
-  compute_encoder->setBytes(
-      base_w.data(), sizeof(Conv2DGeneralBaseInfo) * base_w.size(), 7);
+  compute_encoder.set_vector_bytes(base_h, 6);
+  compute_encoder.set_vector_bytes(base_w, 7);
 
   // Launch kernel
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
 void winograd_conv_2D_gpu(
@@ -617,18 +622,18 @@ void winograd_conv_2D_gpu(
           << bc;
     auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
 
     compute_encoder.set_input_array(wt, 0);
     compute_encoder.set_output_array(filt_wg, 1);
 
-    compute_encoder->setBytes(&C_c, sizeof(int), 2);
-    compute_encoder->setBytes(&O_c, sizeof(int), 3);
+    compute_encoder.set_bytes(C_c, 2);
+    compute_encoder.set_bytes(O_c, 3);
 
     MTL::Size group_dims = MTL::Size(32, bo, 1);
     MTL::Size grid_dims = MTL::Size(O_c / bo, 1, 1);
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
   }
 
   // Do input transform
@@ -645,18 +650,17 @@ void winograd_conv_2D_gpu(
           << bc;
     auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
 
     compute_encoder.set_input_array(in_padded, 0);
     compute_encoder.set_output_array(inp_wg, 1);
 
-    compute_encoder->setBytes(
-        &conv_params_updated, sizeof(MLXConvParams<2>), 2);
+    compute_encoder.set_bytes(conv_params_updated, 2);
 
     MTL::Size group_dims = MTL::Size(32, wn, wm);
     MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
   }
 
   // Do batched gemm
@@ -693,18 +697,17 @@ void winograd_conv_2D_gpu(
           << bc;
     auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
 
     compute_encoder.set_input_array(out_wg, 0);
     compute_encoder.set_output_array(out, 1);
 
-    compute_encoder->setBytes(
-        &conv_params_updated, sizeof(MLXConvParams<2>), 2);
+    compute_encoder.set_bytes(conv_params_updated, 2);
 
     MTL::Size group_dims = MTL::Size(32, wn, wm);
     MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
   }
 }
 
@@ -747,10 +750,6 @@ 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;
 
-  bool inp_large = (conv_params.in_strides[0] >= 1ul << 18);
-  bool channels_large = (conv_params.C + conv_params.O) >= 512;
-  bool channels_med = (conv_params.C + conv_params.O) >= 256;
-
   if (groups > 1) {
     const int C_per_group = conv_params.C / groups;
     const int O_per_group = conv_params.O / groups;
@@ -764,10 +763,13 @@ void conv_2D_gpu(
   }
 
   // Direct to winograd conv
+  bool inp_large =
+      (conv_params.N * conv_params.iS[0] * conv_params.iS[1]) >= 1ul << 12;
+  bool channels_large = (conv_params.C + conv_params.O) >= 256;
   if (!flip && is_stride_one && is_kdil_one && is_idil_one &&
       conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
-      conv_params.C % 32 == 0 && conv_params.O % 32 == 0 &&
-      (channels_large || (channels_med && inp_large))) {
+      conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && inp_large &&
+      channels_large) {
     return winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
   }
 
@@ -913,12 +915,8 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         "[Convolution::eval_gpu] Only supports 1D, 2D or 3D convolutions.");
   }
 
-  // Clear copies
-  if (copies.size() > 0) {
-    auto command_buffer = d.get_command_buffer(s.index);
-    command_buffer->addCompletedHandler(
-        [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
-  }
+  // Record copies
+  d.add_temporaries(std::move(copies), s.index);
 }
 
 } // namespace mlx::core

mlx/backend/metal/copy.cpp

@@ -74,44 +74,46 @@ void copy_gpu_inplace(
       };
   auto [shape, strides_in_, strides_out_] = maybe_collapse();
   int ndim = shape.size();
-
-  bool use_2d = out.data_size() > UINT32_MAX;
+  bool large;
+  if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
+    // Allow for negative strides
+    large = out.data_size() > INT32_MAX;
+  } else {
+    large = out.data_size() > UINT32_MAX;
+  }
   auto& d = metal::device(s.device);
   int work_per_thread = 1;
   std::string kernel_name;
-  {
-    std::ostringstream kname;
-    switch (ctype) {
-      case CopyType::Scalar:
-        kname << (use_2d ? "s2" : "s");
-        break;
-      case CopyType::Vector:
-        kname << (use_2d ? "v2" : "v");
-        break;
-      case CopyType::General:
-        kname << "g";
-        break;
-      case CopyType::GeneralGeneral:
-        kname << "gg";
-        break;
-    }
-    if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
-      if (shape.size() <= MAX_COPY_SPECIALIZED_DIMS) {
-        kname << shape.size();
-      } else if (shape[ndim - 1] >= 4) {
-        work_per_thread = 4;
-        kname << "n4";
-      }
-    }
-    kname << "_copy";
-    kname << type_to_name(in) << type_to_name(out);
-    kernel_name = kname.str();
+  switch (ctype) {
+    case CopyType::Scalar:
+      kernel_name = (large ? "s2" : "s");
+      break;
+    case CopyType::Vector:
+      kernel_name = (large ? "v2" : "v");
+      break;
+    case CopyType::General:
+      kernel_name = "g";
+      break;
+    case CopyType::GeneralGeneral:
+      kernel_name = "gg";
+      break;
   }
-
+  if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
+    if (shape.size() <= MAX_COPY_SPECIALIZED_DIMS) {
+      kernel_name += std::to_string(shape.size());
+    } else {
+      work_per_thread = large ? 4 : 2;
+      concatenate(kernel_name, "n", std::to_string(work_per_thread));
+    }
+    if (large) {
+      kernel_name += "large";
+    }
+  }
+  concatenate(kernel_name, "_copy", type_to_name(in), type_to_name(out));
   auto kernel = get_copy_kernel(d, kernel_name, in, out);
 
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
   bool donate_in = in.data_shared_ptr() == nullptr;
 
   inp_offset *= size_of(in.dtype());
@@ -120,15 +122,16 @@ void copy_gpu_inplace(
   compute_encoder.set_input_array(donate_in ? out : in, 0, inp_offset);
   compute_encoder.set_output_array(out, 1, out_offset);
 
+  auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
   if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
     std::vector<int64_t> strides_in{strides_in_.begin(), strides_in_.end()};
     std::vector<int64_t> strides_out{strides_out_.begin(), strides_out_.end()};
     if (ndim > 3) {
-      set_vector_bytes(compute_encoder, shape, ndim, 2);
+      compute_encoder.set_vector_bytes(shape, ndim, 2);
     }
-    set_vector_bytes(compute_encoder, strides_in, ndim, 3);
+    compute_encoder.set_vector_bytes(strides_in, ndim, 3);
     if (ctype == CopyType::GeneralGeneral) {
-      set_vector_bytes(compute_encoder, strides_out, ndim, 4);
+      compute_encoder.set_vector_bytes(strides_out, ndim, 4);
     }
 
     int dim0 = ndim > 0 ? shape[ndim - 1] : 1;
@@ -140,29 +143,27 @@ void copy_gpu_inplace(
     int rest = data_size / (dim0 * dim1);
 
     if (ndim > MAX_COPY_SPECIALIZED_DIMS) {
-      compute_encoder->setBytes(&ndim, sizeof(int), 5);
+      compute_encoder.set_bytes(ndim, 5);
       dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     }
 
     // NB assuming thread_group_size is a power of 2 larger than 32 x 32
-    NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
     if (thread_group_size != 1024) {
       throw std::runtime_error("[Metal::copy] Must use 1024 sized block");
     }
 
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     size_t nthreads = out.data_size();
-    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                                 : MTL::Size(nthreads, 1, 1);
-    NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
+                                : MTL::Size(nthreads, 1, 1);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }
 
@@ -194,26 +195,26 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
     return;
   }
   out.set_data(allocator::malloc_or_wait(out.nbytes()));
-  bool use_2d = out.data_size() > UINT32_MAX;
+  bool large = out.data_size() > UINT32_MAX;
   auto& d = metal::device(s.device);
-  std::string kernel_name = std::string(use_2d ? "s2" : "s") + "_copy" +
+  std::string kernel_name = std::string(large ? "s2" : "s") + "_copy" +
       type_to_name(val) + type_to_name(out);
   auto kernel = get_copy_kernel(d, kernel_name, val, out);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(val, 0);
   compute_encoder.set_output_array(out, 1);
 
+  auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
   size_t nthreads = out.data_size();
-  MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                               : MTL::Size(nthreads, 1, 1);
-  NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
   if (thread_group_size > nthreads) {
     thread_group_size = nthreads;
   }
   MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
+                              : MTL::Size(nthreads, 1, 1);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 } // namespace mlx::core

mlx/backend/metal/custom_kernel.cpp

@@ -32,20 +32,18 @@ void CustomKernel::eval_gpu(
       return copies.back();
     }
   };
-  std::vector<const array> checked_inputs;
+  std::vector<array> checked_inputs;
   for (const array& in : inputs) {
     checked_inputs.push_back(check_input(in));
   }
 
   auto& d = metal::device(s.device);
   const auto& lib_name = name_;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    lib = d.get_library(lib_name, metal::utils() + source_);
-  }
+  auto lib =
+      d.get_library(lib_name, [this] { return metal::utils() + source_; });
   auto kernel = d.get_kernel(name_, lib);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
   int index = 0;
   for (int i = 0; i < checked_inputs.size(); i++) {
     const array& in = checked_inputs[i];
@@ -55,15 +53,15 @@ void CustomKernel::eval_gpu(
     if (in.ndim() > 0) {
       int ndim = in.ndim();
       if (shape_info.shape) {
-        set_vector_bytes(compute_encoder, in.shape(), ndim, index);
+        compute_encoder.set_vector_bytes(in.shape(), ndim, index);
         index++;
       }
       if (shape_info.strides) {
-        set_vector_bytes(compute_encoder, in.strides(), ndim, index);
+        compute_encoder.set_vector_bytes(in.strides(), ndim, index);
         index++;
       }
       if (shape_info.ndim) {
-        compute_encoder->setBytes(&ndim, sizeof(int), index);
+        compute_encoder.set_bytes(ndim, index);
         index++;
       }
     }
@@ -74,15 +72,13 @@ void CustomKernel::eval_gpu(
   }
 
   const auto [tx, ty, tz] = threadgroup_;
-  MTL::Size group_dims = MTL::Size(tx, ty, tz);
   const auto [gx, gy, gz] = grid_;
+  MTL::Size group_dims =
+      MTL::Size(std::min(tx, gx), std::min(ty, gy), std::min(tz, gz));
   MTL::Size grid_dims = MTL::Size(gx, gy, gz);
-  compute_encoder->dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 
-  if (!copies.empty()) {
-    d.get_command_buffer(s.index)->addCompletedHandler(
-        [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
-  }
+  d.add_temporaries(std::move(copies), s.index);
 }
 
 } // namespace mlx::core::fast

mlx/backend/metal/device.cpp

@@ -20,18 +20,21 @@ namespace {
 
 // TODO nicer way to set this or possibly expose as an environment variable
 constexpr int MAX_BUFFERS_PER_QUEUE = 12;
-constexpr int MAX_DISPATCHES_PER_ENCODER = 2;
 
 constexpr const char* default_mtllib_path = METAL_PATH;
 
-constexpr auto get_metal_version() {
-#if (MLX_METAL_VERSION >= 320)
-  return MTL::LanguageVersion3_2;
-#elif (MLX_METAL_VERSION >= 310)
-  return MTL::LanguageVersion3_1;
-#else
-  return MTL::LanguageVersion3_0;
-#endif
+auto get_metal_version() {
+  auto get_metal_version_ = []() {
+    if (__builtin_available(macOS 15, iOS 18, tvOS 18, visionOS 2, *)) {
+      return MTL::LanguageVersion3_2;
+    } else if (__builtin_available(macOS 14, iOS 17, tvOS 17, visionOS 1, *)) {
+      return MTL::LanguageVersion3_1;
+    } else {
+      return MTL::LanguageVersion3_0;
+    }
+  };
+  static auto metal_version_ = get_metal_version_();
+  return metal_version_;
 }
 
 auto load_device() {
@@ -121,33 +124,29 @@ MTL::Library* load_library(
 
 } // namespace
 
-CommandEncoder::CommandEncoder(MTL::CommandBuffer* cbuf) : cbuf(cbuf) {
-  enc = cbuf->computeCommandEncoder(MTL::DispatchTypeConcurrent);
-  enc->retain();
+CommandEncoder::CommandEncoder(MTL::CommandBuffer* cbuf) {
+  enc_ = cbuf->computeCommandEncoder(MTL::DispatchTypeConcurrent);
+  enc_->retain();
 }
 
 CommandEncoder::~CommandEncoder() {
-  enc->endEncoding();
-  enc->release();
+  enc_->endEncoding();
+  enc_->release();
 }
 
 void CommandEncoder::set_input_array(
     const array& a,
     int idx,
     int64_t offset /* = 0 */) {
+  all_inputs_.insert(a.buffer().ptr());
   auto r_buf = static_cast<MTL::Resource*>(const_cast<void*>(a.buffer().ptr()));
-  if (auto it = outputs.find(r_buf); it != outputs.end()) {
-    // Insert a barrier
-    enc->memoryBarrier(&r_buf, 1);
-
-    // Remove the output
-    outputs.erase(it);
-  }
+  needs_barrier_ =
+      needs_barrier_ | (prev_outputs_.find(r_buf) != prev_outputs_.end());
   auto a_buf = static_cast<const MTL::Buffer*>(a.buffer().ptr());
   auto base_offset = a.data<char>() -
       static_cast<char*>(const_cast<MTL::Buffer*>(a_buf)->contents());
   base_offset += offset;
-  enc->setBuffer(a_buf, base_offset, idx);
+  enc_->setBuffer(a_buf, base_offset, idx);
 }
 
 void CommandEncoder::set_output_array(
@@ -156,55 +155,49 @@ void CommandEncoder::set_output_array(
     int64_t offset /* = 0 */) {
   // Add barriers before adding the output to the output set
   set_input_array(a, idx, offset);
+  all_outputs_.insert(a.buffer().ptr());
   auto buf = static_cast<MTL::Resource*>(a.buffer().ptr());
-  if (concurrent) {
-    concurrent_outputs.insert(buf);
+  if (concurrent_) {
+    concurrent_outputs_.insert(buf);
   } else {
-    outputs.insert(buf);
+    next_outputs_.insert(buf);
   }
 }
 
-void CommandEncoder::dispatchThreadgroups(
-    MTL::Size grid_dims,
-    MTL::Size group_dims) {
-  num_dispatches++;
-  enc->dispatchThreadgroups(grid_dims, group_dims);
-  maybe_split();
-}
-
-void CommandEncoder::dispatchThreads(
-    MTL::Size grid_dims,
-    MTL::Size group_dims) {
-  num_dispatches++;
-  enc->dispatchThreads(grid_dims, group_dims);
-  maybe_split();
-}
-
-void CommandEncoder::maybe_split() {
-  if (num_dispatches > MAX_DISPATCHES_PER_ENCODER && !concurrent) {
-    enc->endEncoding();
-    enc->release();
-    num_dispatches = 0;
-    outputs.clear();
-    enc = cbuf->computeCommandEncoder(MTL::DispatchTypeConcurrent);
-    enc->retain();
+void CommandEncoder::maybeInsertBarrier() {
+  if (needs_barrier_) {
+    enc_->memoryBarrier(MTL::BarrierScopeBuffers);
+    needs_barrier_ = false;
+    prev_outputs_ = std::move(next_outputs_);
+  } else {
+    prev_outputs_.insert(next_outputs_.begin(), next_outputs_.end());
   }
+  next_outputs_.clear();
+}
+
+void CommandEncoder::dispatch_threadgroups(
+    MTL::Size grid_dims,
+    MTL::Size group_dims) {
+  maybeInsertBarrier();
+  enc_->dispatchThreadgroups(grid_dims, group_dims);
+}
+
+void CommandEncoder::dispatch_threads(
+    MTL::Size grid_dims,
+    MTL::Size group_dims) {
+  maybeInsertBarrier();
+  enc_->dispatchThreads(grid_dims, group_dims);
 }
 
 Device::Device() {
   auto pool = new_scoped_memory_pool();
   device_ = load_device();
   library_map_ = {{"mlx", load_library(device_)}};
+  arch_ = std::string(device_->architecture()->name()->utf8String());
 }
 
 Device::~Device() {
   auto pool = new_scoped_memory_pool();
-  for (auto& q : queue_map_) {
-    q.second->release();
-  }
-  for (auto& b : buffer_map_) {
-    b.second.second->release();
-  }
   for (auto& k : kernel_map_) {
     k.second->release();
   }
@@ -219,69 +212,134 @@ void Device::new_queue(int index) {
 
   // Multiple threads can ask the device for queues
   // We lock this as a critical section for safety
-  const std::lock_guard<std::mutex> lock(mtx_);
   auto q = device_->newCommandQueue(MAX_BUFFERS_PER_QUEUE);
   debug_set_stream_queue_label(q, index);
   if (!q) {
     throw std::runtime_error(
         "[metal::Device] Failed to make new command queue.");
   }
-  queue_map_.insert({index, q});
+  stream_map_.emplace(index, q);
+  if (residency_set_ != nullptr) {
+    q->addResidencySet(residency_set_);
+  }
 }
 
 int Device::get_command_buffer_ops(int index) {
-  auto bit = buffer_map_.find(index);
-  return bit->second.first;
+  return get_stream_(index).buffer_ops;
 }
 
 void Device::increment_command_buffer_ops(int index) {
-  auto bit = buffer_map_.find(index);
-  bit->second.first++;
+  get_stream_(index).buffer_ops++;
 }
 
 MTL::CommandBuffer* Device::get_command_buffer(int index) {
-  auto bit = buffer_map_.find(index);
-  if (bit == buffer_map_.end()) {
-    auto qit = queue_map_.find(index);
-    if (qit == queue_map_.end()) {
-      throw std::runtime_error(
-          "[metal::Device] Attempting to get command buffer for invalid queue.");
-    }
-
-    auto cb = qit->second->commandBufferWithUnretainedReferences();
-
-    if (!cb) {
+  auto& stream = get_stream_(index);
+  if (stream.buffer == nullptr) {
+    stream.buffer = stream.queue->commandBufferWithUnretainedReferences();
+    if (!stream.buffer) {
       throw std::runtime_error(
           "[metal::Device] Unable to create new command buffer");
     }
-
     // Increment ref count so the buffer is not garbage collected
-    cb->retain();
-
-    bit = buffer_map_.insert({index, {0, cb}}).first;
+    stream.buffer->retain();
   }
-  return bit->second.second;
+  return stream.buffer;
 }
 
 void Device::commit_command_buffer(int index) {
-  auto bit = buffer_map_.find(index);
-  bit->second.second->commit();
-  bit->second.second->release();
-  buffer_map_.erase(bit);
+  auto& stream = get_stream_(index);
+  stream.buffer->commit();
+  stream.buffer->release();
+  stream.buffer = nullptr;
+  stream.buffer_ops = 0;
+}
+
+void Device::add_temporary(array arr, int index) {
+  get_stream_(index).temporaries.push_back(std::move(arr));
+}
+
+void Device::add_temporaries(std::vector<array> arrays, int index) {
+  if (arrays.empty()) {
+    return;
+  }
+  auto& stream = get_stream_(index);
+  stream.temporaries.insert(
+      stream.temporaries.end(),
+      std::make_move_iterator(arrays.begin()),
+      std::make_move_iterator(arrays.end()));
 }
 
 void Device::end_encoding(int index) {
-  encoder_map_.erase(index);
+  auto& stream = get_stream_(index);
+  if (stream.encoder != nullptr) {
+    // Each command encoder has a unique fence. We also store a map of
+    // all previous outputs of command encoders to their corresponding fence.
+    // - The command encoder records its inputs and outputs.
+    // - Wait on a fence if any inputs in the encoder are outputs of a previous
+    //   encoder.
+    // - Update the map of outputs to include this command encoder's outputs.
+    // - Always signal this command encoders fence.
+    // - Add a completion handler for this command encoder that removes outputs
+    //   from the map to limit the growth of the map and avoid unecessary waits
+    // - Temporaries are a special case as they do not cross command encoder
+    //   boundaries. These can be removed early from the encoders inputs and
+    //   outputs since they don't need synchronization.
+    auto& enc = *stream.encoder;
+    // Remove temporaries from inputs and outputs
+    for (auto& t : stream.temporaries) {
+      if (t.data<void>() != nullptr) {
+        enc.outputs().erase(t.buffer().ptr());
+        enc.inputs().erase(t.buffer().ptr());
+      }
+    }
+
+    // Keep references to the fences we waited on and put them
+    // in the completion handler so they are not prematurely released
+    std::unordered_set<std::shared_ptr<Fence>> waiting_on;
+    {
+      std::lock_guard<std::mutex> lk(stream.fence_mtx);
+      for (auto in : enc.inputs()) {
+        if (auto it = stream.outputs.find(in); it != stream.outputs.end()) {
+          // If we've already waited on a fence, don't wait on it again.
+          if (waiting_on.find(it->second) == waiting_on.end()) {
+            enc.wait_for_fence(it->second->fence);
+            waiting_on.insert(it->second);
+          }
+        }
+      }
+      for (auto out : enc.outputs()) {
+        stream.outputs[out] = stream.fence;
+      }
+    }
+    enc.update_fence(stream.fence->fence);
+    stream.buffer->addCompletedHandler(
+        [&stream,
+         waiting_on = std::move(waiting_on),
+         fence = std::move(stream.fence),
+         outputs = std::move(enc.outputs()),
+         temporaries =
+             std::move(stream.temporaries)](MTL::CommandBuffer*) mutable {
+          temporaries.clear();
+          std::lock_guard<std::mutex> lk(stream.fence_mtx);
+          for (auto o : outputs) {
+            if (auto it = stream.outputs.find(o); it != stream.outputs.end()) {
+              if (it->second == fence) {
+                stream.outputs.erase(it);
+              }
+            }
+          }
+        });
+  }
+  stream.encoder = nullptr;
 }
 
 CommandEncoder& Device::get_command_encoder(int index) {
-  auto eit = encoder_map_.find(index);
-  if (eit == encoder_map_.end()) {
-    auto cb = get_command_buffer(index);
-    eit =
-        encoder_map_.emplace(index, std::make_unique<CommandEncoder>(cb)).first;
+  auto& stream = get_stream_(index);
+  if (stream.encoder == nullptr) {
+    stream.encoder = std::make_unique<CommandEncoder>(stream.buffer);
+    stream.fence = std::make_shared<Fence>(device_->newFence());
   }
-  return *(eit->second);
+  return *stream.encoder;
 }
 
 void Device::register_library(
@@ -293,20 +351,7 @@ void Device::register_library(
   }
 }
 
-MTL::Library* Device::get_library_cache_(const std::string& lib_name) {
-  // Search for cached metal lib
-  MTL::Library* mtl_lib;
-  if (auto it = library_map_.find(lib_name); it != library_map_.end()) {
-    mtl_lib = it->second;
-  } else { // Look for metallib alongside library
-    register_library(lib_name, get_colocated_mtllib_path(lib_name));
-    mtl_lib = library_map_[lib_name];
-  }
-
-  return mtl_lib;
-}
-
-MTL::Library* Device::get_library_(const std::string& source_string) {
+MTL::Library* Device::build_library_(const std::string& source_string) {
   auto pool = new_scoped_memory_pool();
 
   auto ns_code =
@@ -322,26 +367,7 @@ MTL::Library* Device::get_library_(const std::string& source_string) {
   // Throw error if unable to compile library
   if (!mtl_lib) {
     std::ostringstream msg;
-    msg << "[metal::Device] Unable to build metal library from source" << "\n";
-    if (error) {
-      msg << error->localizedDescription()->utf8String() << "\n";
-    }
-    throw std::runtime_error(msg.str());
-  }
-
-  return mtl_lib;
-}
-
-MTL::Library* Device::get_library_(const MTL::StitchedLibraryDescriptor* desc) {
-  auto pool = new_scoped_memory_pool();
-
-  NS::Error* error = nullptr;
-  auto mtl_lib = device_->newLibrary(desc, &error);
-
-  // Throw error if unable to compile library
-  if (!mtl_lib) {
-    std::ostringstream msg;
-    msg << "[metal::Device] Unable to build stitched metal library" << "\n";
+    msg << "[metal::Device] Unable to build metal library from source\n";
     if (error) {
       msg << error->localizedDescription()->utf8String() << "\n";
     }
@@ -465,68 +491,32 @@ MTL::ComputePipelineState* Device::get_kernel_(
   return kernel;
 }
 
-MTL::Library* Device::get_library(const std::string& name) {
+MTL::Library* Device::get_library_(const std::string& name) {
+  std::shared_lock lock(library_mtx_);
   auto it = library_map_.find(name);
   return (it != library_map_.end()) ? it->second : nullptr;
 }
 
 MTL::Library* Device::get_library(
     const std::string& name,
-    const std::string& source,
-    bool cache /* = true */) {
-  if (cache) {
+    const std::function<std::string(void)>& builder) {
+  {
+    std::shared_lock rlock(library_mtx_);
     if (auto it = library_map_.find(name); it != library_map_.end()) {
       return it->second;
     }
   }
 
-  auto mtl_lib = get_library_(source);
-
-  if (cache) {
-    library_map_.insert({name, mtl_lib});
+  std::unique_lock wlock(library_mtx_);
+  if (auto it = library_map_.find(name); it != library_map_.end()) {
+    return it->second;
   }
 
+  auto mtl_lib = build_library_(builder());
+  library_map_.insert({name, mtl_lib});
   return mtl_lib;
 }
 
-MTL::Library* Device::get_library(
-    const std::string& name,
-    const MTL::StitchedLibraryDescriptor* desc,
-    bool cache /* = true */) {
-  if (cache) {
-    if (auto it = library_map_.find(name); it != library_map_.end()) {
-      return it->second;
-    }
-  }
-
-  auto mtl_lib = get_library_(desc);
-
-  if (cache) {
-    library_map_.insert({name, mtl_lib});
-  }
-
-  return mtl_lib;
-}
-
-MTL::Function* Device::get_function(
-    const std::string& base_name,
-    MTL::Library* mtl_lib,
-    const std::string& specialized_name /* = "" */,
-    const MTLFCList& func_consts /* = {} */) {
-  return get_function_(base_name, specialized_name, func_consts, mtl_lib);
-}
-
-MTL::Function* Device::get_function(
-    const std::string& base_name,
-    const std::string& lib_name /* = "mlx" */,
-    const std::string& specialized_name /*  = "" */,
-    const MTLFCList& func_consts /* = {} */) {
-  // Search for cached metal lib
-  MTL::Library* mtl_lib = get_library_cache_(lib_name);
-
-  return get_function(base_name, mtl_lib, specialized_name, func_consts);
-}
-
 MTL::LinkedFunctions* Device::get_linked_functions_(
     const std::vector<MTL::Function*>& funcs) {
   if (funcs.empty()) {
@@ -547,34 +537,55 @@ MTL::LinkedFunctions* Device::get_linked_functions_(
   return lfuncs;
 }
 
+MTL::ComputePipelineState* Device::get_kernel_(
+    const std::string& base_name,
+    MTL::Library* mtl_lib,
+    const std::string& hash_name,
+    const MTLFCList& func_consts /* = {} */,
+    const std::vector<MTL::Function*>& linked_functions /* = {} */) {
+  // Single writer allowed
+  std::unique_lock wlock(kernel_mtx_);
+
+  // Try loading again to avoid loading twice
+  if (auto it = kernel_map_.find(hash_name); it != kernel_map_.end()) {
+    return it->second;
+  }
+
+  auto pool = new_scoped_memory_pool();
+
+  // Pull kernel from library
+  auto mtl_function = get_function_(base_name, hash_name, func_consts, mtl_lib);
+
+  // Compile kernel to compute pipeline
+  auto mtl_linked_funcs = get_linked_functions_(linked_functions);
+  auto kernel = get_kernel_(hash_name, mtl_function, mtl_linked_funcs);
+
+  mtl_function->release();
+  mtl_linked_funcs->release();
+
+  // Add kernel to cache
+  auto inserted = kernel_map_.insert({hash_name, kernel});
+
+  return kernel;
+}
+
 MTL::ComputePipelineState* Device::get_kernel(
     const std::string& base_name,
     MTL::Library* mtl_lib,
     const std::string& hash_name /* = "" */,
     const MTLFCList& func_consts /* = {} */,
     const std::vector<MTL::Function*>& linked_functions /* = {} */) {
-  auto pool = new_scoped_memory_pool();
-
-  // Look for cached kernel
   const auto& kname = hash_name.empty() ? base_name : hash_name;
-  if (auto it = kernel_map_.find(kname); it != kernel_map_.end()) {
-    return it->second;
+  {
+    // Multiple readers allowed
+    std::shared_lock lock(kernel_mtx_);
+
+    // Look for cached kernel
+    if (auto it = kernel_map_.find(kname); it != kernel_map_.end()) {
+      return it->second;
+    }
   }
-
-  // Pull kernel from library
-  auto mtl_function = get_function_(base_name, kname, func_consts, mtl_lib);
-
-  // Compile kernel to compute pipeline
-  auto mtl_linked_funcs = get_linked_functions_(linked_functions);
-  auto kernel = get_kernel_(kname, mtl_function, mtl_linked_funcs);
-
-  mtl_function->release();
-  mtl_linked_funcs->release();
-
-  // Add kernel to cache
-  kernel_map_.insert({kname, kernel});
-
-  return kernel;
+  return get_kernel_(base_name, mtl_lib, kname, func_consts, linked_functions);
 }
 
 MTL::ComputePipelineState* Device::get_kernel(
@@ -583,16 +594,34 @@ MTL::ComputePipelineState* Device::get_kernel(
     const std::string& hash_name /*  = "" */,
     const MTLFCList& func_consts /*  = {} */,
     const std::vector<MTL::Function*>& linked_functions /*  = {} */) {
-  // Look for cached kernel
   const auto& kname = hash_name.size() == 0 ? base_name : hash_name;
-  if (auto it = kernel_map_.find(kname); it != kernel_map_.end()) {
-    return it->second;
+  {
+    // Multiple readers allowed
+    std::shared_lock lock(kernel_mtx_);
+
+    // Look for cached kernel
+    if (auto it = kernel_map_.find(kname); it != kernel_map_.end()) {
+      return it->second;
+    }
   }
-
   // Search for cached metal lib
-  MTL::Library* mtl_lib = get_library_cache_(lib_name);
+  MTL::Library* mtl_lib = get_library_(lib_name);
+  return get_kernel_(base_name, mtl_lib, kname, func_consts, linked_functions);
+}
 
-  return get_kernel(base_name, mtl_lib, kname, func_consts, linked_functions);
+void Device::set_residency_set(const MTL::ResidencySet* residency_set) {
+  if (residency_set_ != nullptr) {
+    throw std::runtime_error(
+        "[Device::set_residency_set] Can only be set once.");
+  }
+  if (residency_set == nullptr) {
+    return;
+  }
+  residency_set_ = residency_set;
+  // Attach residency set to existing command queues
+  for (auto& [_, stream] : stream_map_) {
+    stream.queue->addResidencySet(residency_set_);
+  }
 }
 
 Device& device(mlx::core::Device) {

mlx/backend/metal/device.h

@@ -7,6 +7,7 @@
 #include <filesystem>
 #include <functional>
 #include <mutex>
+#include <shared_mutex>
 #include <string>
 #include <unordered_map>
 #include <unordered_set>
@@ -44,43 +45,114 @@ struct CommandEncoder {
 
   struct ConcurrentContext {
     ConcurrentContext(CommandEncoder& enc) : enc(enc) {
-      enc.concurrent = true;
+      enc.concurrent_ = true;
     }
     ~ConcurrentContext() {
-      enc.concurrent = false;
-      enc.outputs.insert(
-          enc.concurrent_outputs.begin(), enc.concurrent_outputs.end());
-      enc.concurrent_outputs.clear();
+      enc.concurrent_ = false;
+      enc.prev_outputs_.insert(
+          enc.concurrent_outputs_.begin(), enc.concurrent_outputs_.end());
+      enc.concurrent_outputs_.clear();
     }
 
    private:
     CommandEncoder& enc;
   };
 
-  MTL::ComputeCommandEncoder* operator->() {
-    return enc;
-  }
-
   void set_input_array(const array& a, int idx, int64_t offset = 0);
   void set_output_array(array& a, int idx, int64_t offset = 0);
-  void dispatchThreadgroups(MTL::Size grid_dims, MTL::Size group_dims);
-  void dispatchThreads(MTL::Size grid_dims, MTL::Size group_dims);
+  void dispatch_threadgroups(MTL::Size grid_dims, MTL::Size group_dims);
+  void dispatch_threads(MTL::Size grid_dims, MTL::Size group_dims);
+  void maybeInsertBarrier();
+
+  void set_compute_pipeline_state(MTL::ComputePipelineState* kernel) {
+    enc_->setComputePipelineState(kernel);
+  }
+
+  void wait_for_fence(MTL::Fence* fence) {
+    enc_->waitForFence(fence);
+  }
+
+  void update_fence(MTL::Fence* fence) {
+    enc_->updateFence(fence);
+  }
+
+  template <typename T>
+  void set_vector_bytes(const std::vector<T>& vec, size_t nelems, int idx) {
+    enc_->setBytes(vec.data(), nelems * sizeof(T), idx);
+  }
+  template <typename T>
+  void set_vector_bytes(const std::vector<T>& vec, int idx) {
+    return set_vector_bytes(vec, vec.size(), idx);
+  }
+
+  template <typename T>
+  void set_bytes(const T* v, int n, int idx) {
+    return enc_->setBytes(v, n * sizeof(T), idx);
+  }
+
+  template <typename T>
+  void set_bytes(const T& v, int idx) {
+    return enc_->setBytes(&v, sizeof(T), idx);
+  }
 
   ConcurrentContext start_concurrent() {
     return ConcurrentContext(*this);
   }
-
   ~CommandEncoder();
 
- private:
-  void maybe_split();
+  // Inputs to all kernels in the encoder including temporaries
+  std::unordered_set<const void*>& inputs() {
+    return all_inputs_;
+  };
 
-  int num_dispatches{0};
-  MTL::CommandBuffer* cbuf;
-  MTL::ComputeCommandEncoder* enc;
-  bool concurrent{false};
-  std::unordered_set<MTL::Resource*> outputs;
-  std::unordered_set<MTL::Resource*> concurrent_outputs;
+  // Outputs of all kernels in the encoder including temporaries
+  std::unordered_set<const void*> outputs() {
+    return all_outputs_;
+  };
+
+ private:
+  MTL::ComputeCommandEncoder* enc_;
+  bool needs_barrier_{false};
+  bool concurrent_{false};
+  std::unordered_set<MTL::Resource*> prev_outputs_;
+  std::unordered_set<MTL::Resource*> next_outputs_;
+  std::unordered_set<MTL::Resource*> concurrent_outputs_;
+  std::unordered_set<const void*> all_inputs_;
+  std::unordered_set<const void*> all_outputs_;
+};
+
+struct Fence {
+  Fence(MTL::Fence* fence) : fence(fence) {}
+  ~Fence() {
+    fence->release();
+  }
+  MTL::Fence* fence;
+};
+
+struct DeviceStream {
+  DeviceStream(MTL::CommandQueue* queue) : queue(queue) {};
+  ~DeviceStream() {
+    queue->release();
+    if (buffer != nullptr) {
+      buffer->release();
+    }
+  };
+  MTL::CommandQueue* queue;
+  // A map of prior command encoder outputs to their corresponding fence
+  std::unordered_map<const void*, std::shared_ptr<Fence>> outputs;
+  // Used to allow thread-safe access to the outputs map
+  std::mutex fence_mtx;
+
+  // The buffer and buffer op count are updated
+  // between command buffers
+  MTL::CommandBuffer* buffer{nullptr};
+  int buffer_ops{0};
+
+  // The command encoder, fence, and temporaries are updated between command
+  // encoders
+  std::unique_ptr<CommandEncoder> encoder{nullptr};
+  std::shared_ptr<Fence> fence;
+  std::vector<array> temporaries;
 };
 
 class Device {
@@ -94,6 +166,10 @@ class Device {
     return device_;
   };
 
+  const std::string& get_architecture() {
+    return arch_;
+  }
+
   void new_queue(int index);
   MTL::CommandBuffer* get_command_buffer(int index);
   int get_command_buffer_ops(int index);
@@ -114,29 +190,9 @@ class Device {
     }
   }
 
-  MTL::Library* get_library(const std::string& name);
-
   MTL::Library* get_library(
       const std::string& name,
-      const std::string& source_string,
-      bool cache = true);
-
-  MTL::Library* get_library(
-      const std::string& name,
-      const MTL::StitchedLibraryDescriptor* desc,
-      bool cache = true);
-
-  MTL::Function* get_function(
-      const std::string& base_name,
-      MTL::Library* mtl_lib,
-      const std::string& specialized_name = "",
-      const MTLFCList& func_consts = {});
-
-  MTL::Function* get_function(
-      const std::string& base_name,
-      const std::string& lib_name = "mlx",
-      const std::string& specialized_name = "",
-      const MTLFCList& func_consts = {});
+      const std::function<std::string(void)>& builder);
 
   MTL::ComputePipelineState* get_kernel(
       const std::string& base_name,
@@ -155,11 +211,20 @@ class Device {
   MTL::ArgumentEncoder* argument_encoder(
       const std::vector<MTL::ArgumentDescriptor*>& arg_descs) const;
 
+  // Record temporary arrays for the given stream index
+  void add_temporary(array arr, int index);
+  void add_temporaries(std::vector<array> arrays, int index);
+
+  void set_residency_set(const MTL::ResidencySet* residency_set);
+
  private:
+  DeviceStream& get_stream_(int index) {
+    return stream_map_.find(index)->second;
+  }
   MTL::Library* get_library_cache_(const std::string& name);
 
-  MTL::Library* get_library_(const std::string& source_string);
-  MTL::Library* get_library_(const MTL::StitchedLibraryDescriptor* desc);
+  MTL::Library* get_library_(const std::string& name);
+  MTL::Library* build_library_(const std::string& source_string);
 
   MTL::Function* get_function_(const std::string& name, MTL::Library* mtl_lib);
 
@@ -181,13 +246,23 @@ class Device {
       const MTL::Function* mtl_function,
       const MTL::LinkedFunctions* linked_functions);
 
+  MTL::ComputePipelineState* get_kernel_(
+      const std::string& base_name,
+      MTL::Library* mtl_lib,
+      const std::string& hash_name,
+      const MTLFCList& func_consts = {},
+      const std::vector<MTL::Function*>& linked_functions = {});
+
   MTL::Device* device_;
-  std::unordered_map<int32_t, MTL::CommandQueue*> queue_map_;
-  std::unordered_map<int32_t, std::pair<int, MTL::CommandBuffer*>> buffer_map_;
-  std::unordered_map<int32_t, std::unique_ptr<CommandEncoder>> encoder_map_;
+  std::unordered_map<int32_t, DeviceStream> stream_map_;
+
+  std::shared_mutex kernel_mtx_;
   std::unordered_map<std::string, MTL::ComputePipelineState*> kernel_map_;
+
+  std::shared_mutex library_mtx_;
   std::unordered_map<std::string, MTL::Library*> library_map_;
-  std::mutex mtx_;
+  const MTL::ResidencySet* residency_set_{nullptr};
+  std::string arch_;
 };
 
 Device& device(mlx::core::Device);

mlx/backend/metal/event.cpp

@@ -27,4 +27,9 @@ void Event::signal() {
   static_cast<MTL::SharedEvent*>(raw_event().get())->setSignaledValue(value());
 }
 
+bool Event::is_signaled() const {
+  return static_cast<MTL::SharedEvent*>(raw_event().get())->signaledValue() >=
+      value();
+}
+
 } // namespace mlx::core

mlx/backend/metal/fft.cpp

@@ -575,8 +575,7 @@ void fft_op(
   auto plan = plan_fft(n);
   if (plan.four_step) {
     four_step_fft(in, out, axis, inverse, real, plan, copies, s);
-    d.get_command_buffer(s.index)->addCompletedHandler(
-        [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
+    d.add_temporaries(std::move(copies), s.index);
     return;
   }
 
@@ -700,7 +699,7 @@ void fft_op(
     auto kernel =
         get_fft_kernel(d, base_name, hash_name, func_consts, template_def);
 
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(in_contiguous, 0);
     compute_encoder.set_output_array(out, 1);
 
@@ -712,9 +711,9 @@ void fft_op(
 
       compute_encoder.set_input_array(w_q, 2); // w_q
       compute_encoder.set_input_array(w_k, 3); // w_k
-      compute_encoder->setBytes(&n, sizeof(int), 4);
-      compute_encoder->setBytes(&plan.bluestein_n, sizeof(int), 5);
-      compute_encoder->setBytes(&total_batch_size, sizeof(int), 6);
+      compute_encoder.set_bytes(n, 4);
+      compute_encoder.set_bytes(plan.bluestein_n, 5);
+      compute_encoder.set_bytes(total_batch_size, 6);
     } else if (plan.rader_n > 1) {
       auto [b_q, g_q, g_minus_q] = compute_raders_constants(plan.rader_n, s);
       copies.push_back(b_q);
@@ -724,25 +723,25 @@ void fft_op(
       compute_encoder.set_input_array(b_q, 2);
       compute_encoder.set_input_array(g_q, 3);
       compute_encoder.set_input_array(g_minus_q, 4);
-      compute_encoder->setBytes(&n, sizeof(int), 5);
-      compute_encoder->setBytes(&total_batch_size, sizeof(int), 6);
-      compute_encoder->setBytes(&plan.rader_n, sizeof(int), 7);
+      compute_encoder.set_bytes(n, 5);
+      compute_encoder.set_bytes(total_batch_size, 6);
+      compute_encoder.set_bytes(plan.rader_n, 7);
     } else if (four_step_params.required) {
-      compute_encoder->setBytes(&four_step_params.n1, sizeof(int), 2);
-      compute_encoder->setBytes(&four_step_params.n2, sizeof(int), 3);
-      compute_encoder->setBytes(&total_batch_size, sizeof(int), 4);
+      compute_encoder.set_bytes(four_step_params.n1, 2);
+      compute_encoder.set_bytes(four_step_params.n2, 3);
+      compute_encoder.set_bytes(total_batch_size, 4);
     } else {
-      compute_encoder->setBytes(&n, sizeof(int), 2);
-      compute_encoder->setBytes(&total_batch_size, sizeof(int), 3);
+      compute_encoder.set_bytes(n, 2);
+      compute_encoder.set_bytes(total_batch_size, 3);
     }
 
     auto group_dims = MTL::Size(1, threadgroup_batch_size, threads_per_fft);
     auto grid_dims =
         MTL::Size(batch_size, threadgroup_batch_size, threads_per_fft);
-    compute_encoder->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
-  d.get_command_buffer(s.index)->addCompletedHandler(
-      [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
+
+  d.add_temporaries(std::move(copies), s.index);
 }
 
 void fft_op(
@@ -785,8 +784,7 @@ void nd_fft_op(
   }
 
   auto& d = metal::device(s.device);
-  d.get_command_buffer(s.index)->addCompletedHandler(
-      [temp_arrs](MTL::CommandBuffer*) mutable { temp_arrs.clear(); });
+  d.add_temporaries(std::move(temp_arrs), s.index);
 }
 
 void FFT::eval_gpu(const std::vector<array>& inputs, array& out) {

mlx/backend/metal/hadamard.cpp

@@ -60,32 +60,6 @@ std::string gen_hadamard_codelet(int m) {
   return source.str();
 }
 
-void launch_hadamard(
-    const array& in,
-    array& out,
-    int batch_size,
-    int threads_per,
-    const std::string kernel_name,
-    float scale,
-    const Stream& s) {
-  auto& d = metal::device(s.device);
-
-  const auto& lib_name = kernel_name.substr(1);
-  auto lib = d.get_library(lib_name);
-  auto kernel = d.get_kernel(kernel_name, lib);
-  assert(threads_per <= kernel->maxTotalThreadsPerThreadgroup());
-
-  auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
-  compute_encoder.set_input_array(in, 0);
-  compute_encoder.set_output_array(out, 1);
-  compute_encoder->setBytes(&scale, sizeof(float), 2);
-
-  MTL::Size group_dims = MTL::Size(1, threads_per, 1);
-  MTL::Size grid_dims = MTL::Size(batch_size, threads_per, 1);
-  compute_encoder->dispatchThreads(grid_dims, group_dims);
-}
-
 void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto& s = stream();
 
@@ -113,7 +87,8 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
     out.set_data(allocator::malloc_or_wait(out.nbytes()));
   }
 
-  auto [n, m] = decompose_hadamard(in.shape(axis));
+  int n, m;
+  std::tie(n, m) = decompose_hadamard(in.shape(axis));
 
   if (n * (int)size_of(in.dtype()) > MAX_HADAMARD_BYTES) {
     throw std::invalid_argument(
@@ -129,8 +104,7 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto kernel_name = kname.str();
   auto& d = metal::device(s.device);
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     auto codelet = gen_hadamard_codelet(m);
     kernel_source << metal::utils() << codelet << metal::hadamard();
@@ -148,12 +122,31 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
         n,
         m,
         read_width);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
 
   int batch_size = in.size() / n;
   int threads_per = n / max_radix;
 
+  auto& compute_encoder = d.get_command_encoder(s.index);
+
+  auto launch_hadamard = [&](const array& in,
+                             array& out,
+                             const std::string& kernel_name,
+                             float scale) {
+    auto kernel = d.get_kernel(kernel_name, lib);
+    assert(threads_per <= kernel->maxTotalThreadsPerThreadgroup());
+
+    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(scale, 2);
+
+    MTL::Size group_dims = MTL::Size(1, threads_per, 1);
+    MTL::Size grid_dims = MTL::Size(batch_size, threads_per, 1);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
+  };
+
   if (m > 1) {
     // When m is greater than 1, we decompose the
     // computation into two uploads to the GPU:
@@ -171,33 +164,17 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
     temp.set_data(allocator::malloc_or_wait(temp.nbytes()));
     copies.push_back(temp);
 
-    launch_hadamard(
-        in_contiguous,
-        temp,
-        batch_size,
-        threads_per,
-        "n" + kernel_name,
-        1.0,
-        s);
+    launch_hadamard(in_contiguous, temp, "n" + kernel_name, 1.0);
 
     // Metal sometimes reports 256 max threads per group for hadamard_m kernel
     threads_per = std::min(n / read_width, MAX_HADAMARD_THREADS_PER_GROUP);
     batch_size = in.size() / m / read_width / threads_per;
-    launch_hadamard(
-        temp, out, batch_size, threads_per, "m" + kernel_name, scale_, s);
+    launch_hadamard(temp, out, "m" + kernel_name, scale_);
   } else {
-    launch_hadamard(
-        in_contiguous,
-        out,
-        batch_size,
-        threads_per,
-        "n" + kernel_name,
-        scale_,
-        s);
+    launch_hadamard(in_contiguous, out, "n" + kernel_name, scale_);
   }
 
-  d.get_command_buffer(s.index)->addCompletedHandler(
-      [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
+  d.add_temporaries(std::move(copies), s.index);
 }
 
 } // namespace mlx::core

mlx/backend/metal/indexing.cpp

@@ -53,28 +53,31 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   int idx_ndim = nidx ? inputs[1].ndim() : 0;
   size_t ndim = src.ndim();
 
-  std::string lib_name;
-  std::string kernel_name;
-  std::string idx_type_name = nidx ? type_to_name(inputs[1]) : "";
-  {
-    std::ostringstream kname;
-    kname << "gather" << type_to_name(out) << idx_type_name << "_" << nidx
-          << "_" << idx_ndim;
-    lib_name = kname.str();
-    kernel_name = lib_name;
-  }
+  bool large_index = nidx && inputs[1].size() > UINT32_MAX;
+  bool large_src = src.size() > UINT32_MAX;
+  bool large_out = out.size() > UINT32_MAX;
+  bool large = large_index || large_src || large_out;
 
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::gather();
+  std::string idx_type_name = nidx ? type_to_name(inputs[1]) : "";
+  std::string kernel_name = fmt::format(
+      "gather{0}{1}_{2}_{3}_{4}",
+      type_to_name(out),
+      idx_type_name,
+      nidx,
+      idx_ndim,
+      large ? "size_t" : "uint");
+  std::string lib_name = kernel_name;
+
+  auto lib = d.get_library(lib_name, [&]() {
+    std::string kernel_source = metal::utils();
+    kernel_source += metal::gather();
     std::string out_type_str = get_type_string(out.dtype());
     std::string idx_type_str =
         nidx ? get_type_string(inputs[1].dtype()) : "bool";
     auto [idx_args, idx_arr] = make_index_args(idx_type_str, nidx);
 
     // Index dimension specializations
-    kernel_source << fmt::format(
+    kernel_source += fmt::format(
         gather_kernels,
         type_to_name(out) + idx_type_name,
         out_type_str,
@@ -82,13 +85,14 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
         nidx,
         idx_args,
         idx_arr,
-        idx_ndim);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+        idx_ndim,
+        large ? "size_t" : "uint");
+    return kernel_source;
+  });
 
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kernel_name, lib);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   size_t slice_size = 1;
   for (auto s : slice_sizes_) {
@@ -114,17 +118,17 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   // Collect all idx shapes and strides into one place
   std::vector<int> idx_shapes;
   std::vector<size_t> idx_strides;
-
+  std::vector<char> idx_contigs;
   for (int i = 0; i < nidx; ++i) {
     idx_shapes.insert(
         idx_shapes.end(),
         inputs[i + 1].shape().begin(),
         inputs[i + 1].shape().end());
-
     idx_strides.insert(
         idx_strides.end(),
         inputs[i + 1].strides().begin(),
         inputs[i + 1].strides().end());
+    idx_contigs.push_back(inputs[i + 1].flags().row_contiguous);
   }
 
   // Set all the buffers
@@ -132,21 +136,20 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_output_array(out, 1);
 
   // Set source info
-  compute_encoder->setBytes(src.shape().data(), ndim * sizeof(int), 2);
-  compute_encoder->setBytes(src.strides().data(), ndim * sizeof(size_t), 3);
-  compute_encoder->setBytes(&ndim, sizeof(size_t), 4);
-  compute_encoder->setBytes(slice_sizes_.data(), ndim * sizeof(int), 5);
-  compute_encoder->setBytes(axes_.data(), nidx * sizeof(int), 6);
+  compute_encoder.set_vector_bytes(src.shape(), 2);
+  compute_encoder.set_vector_bytes(src.strides(), 3);
+  compute_encoder.set_bytes(ndim, 4);
+  compute_encoder.set_vector_bytes(slice_sizes_, 5);
+  compute_encoder.set_vector_bytes(axes_, 6);
 
   // Set index info
   //
   // We don't need to check for empty idx_shapes because gather has a
   // idx_ndim == 0 specialization
-  compute_encoder->setBytes(
-      idx_shapes.data(), idx_shapes.size() * sizeof(int), 7);
-  compute_encoder->setBytes(
-      idx_strides.data(), idx_strides.size() * sizeof(size_t), 8);
-  compute_encoder->setBytes(&idx_ndim, sizeof(int), 9);
+  compute_encoder.set_vector_bytes(idx_shapes, 7);
+  compute_encoder.set_vector_bytes(idx_strides, 8);
+  compute_encoder.set_vector_bytes(idx_contigs, 9);
+  compute_encoder.set_bytes(idx_ndim, 10);
 
   // Set index buffers
   for (int i = 0; i < nidx; ++i) {
@@ -154,7 +157,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 
   // Launch grid
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -173,12 +176,20 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 
   // Copy src into out
-  auto copy_type =
-      inputs[0].data_size() == 1 ? CopyType::Scalar : CopyType::General;
+  CopyType copy_type;
+  if (inputs[0].data_size() == 1) {
+    copy_type = CopyType::Scalar;
+  } else if (inputs[0].flags().row_contiguous) {
+    copy_type = CopyType::Vector;
+  } else {
+    copy_type = CopyType::General;
+  }
   copy_gpu(inputs[0], out, copy_type);
 
+  auto& upd = inputs.back();
+
   // Empty update
-  if (inputs.back().size() == 0) {
+  if (upd.size() == 0) {
     return;
   }
 
@@ -187,23 +198,22 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto& d = metal::device(s.device);
 
   int idx_ndim = nidx ? inputs[1].ndim() : 0;
-  bool index_nd1_specialization = (idx_ndim == 1);
+  size_t idx_size = nidx ? inputs[1].size() : 1;
 
-  // Bail from fast path (1d index specialization) if scatter dims aren't
-  // the outermost dims and contiguous since update access won't be raster
-  // order.
-  for (auto i = 0; i < axes_.size() && index_nd1_specialization; i++) {
-    index_nd1_specialization &= (axes_[i] == i);
+  auto idx_to_out = idx_size / out.size();
+  int nwork;
+  if (idx_ndim <= 1 || idx_to_out < 1) {
+    nwork = 1;
+  } else if (idx_to_out <= 4) {
+    nwork = 4;
+  } else if (idx_to_out < 16) {
+    nwork = 8;
+  } else if (idx_to_out < 32) {
+    nwork = 16;
+  } else {
+    nwork = 32;
   }
 
-  // Bail from fast path (1d index specialization) if any of the dims are
-  // broadcasted, since we can't rely on linear indexing in that case.
-  for (int i = 1; i < inputs.size() && index_nd1_specialization; i++) {
-    index_nd1_specialization &= inputs[i].flags().row_contiguous;
-  }
-
-  std::string lib_name;
-  std::string kernel_name;
   std::string idx_type_name = nidx ? type_to_name(inputs[1]) : "";
   std::string op_name;
   switch (reduce_type_) {
@@ -223,24 +233,25 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
       op_name = "min";
       break;
   }
+  auto upd_contig = upd.flags().row_contiguous;
+  bool large_out = out.size() > UINT32_MAX;
+  bool large_idx = nidx && (inputs[1].size() > UINT32_MAX);
+  bool large_upd = upd.size() > UINT32_MAX;
+  bool large = large_out || large_idx || large_upd;
+  std::string kernel_name = fmt::format(
+      "scatter{0}{1}_{2}_{3}_{4}_nwork{5}_{6}",
+      type_to_name(out),
+      idx_type_name,
+      op_name,
+      nidx,
+      upd_contig ? "updc_true" : "updc_false",
+      nwork,
+      large ? "size_t" : "uint");
+  std::string lib_name = kernel_name;
 
-  {
-    std::ostringstream kname;
-    if (index_nd1_specialization) {
-      kname << "scatter_1d_index" << type_to_name(out) << idx_type_name;
-    } else {
-      kname << "scatter" << type_to_name(out) << idx_type_name;
-    }
-    kname << "_" << op_name << "_" << nidx;
-    lib_name = kname.str();
-    kernel_name = kname.str();
-  }
-
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::reduce_utils()
-                  << metal::scatter();
+  auto lib = d.get_library(lib_name, [&]() {
+    std::string kernel_source = metal::utils();
+    concatenate(kernel_source, metal::reduce_utils(), metal::scatter());
 
     std::string out_type_str = get_type_string(out.dtype());
     std::string idx_type_str =
@@ -264,11 +275,11 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
         break;
     }
     if (reduce_type_ != Scatter::None) {
-      op_type = fmt::format(op_type, out_type_str);
+      op_type = fmt::format(fmt::runtime(op_type), out_type_str);
     }
     auto [idx_args, idx_arr] = make_index_args(idx_type_str, nidx);
 
-    kernel_source << fmt::format(
+    kernel_source += fmt::format(
         scatter_kernels,
         type_to_name(out) + idx_type_name + "_" + op_name,
         out_type_str,
@@ -276,126 +287,105 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
         op_type,
         nidx,
         idx_args,
-        idx_arr);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+        idx_arr,
+        upd_contig,
+        nwork,
+        large ? "size_t" : "uint");
+    return kernel_source;
+  });
 
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kernel_name, lib);
 
-  auto& upd = inputs.back();
   size_t nthreads = upd.size();
 
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Set all the buffers
   compute_encoder.set_input_array(upd, 1);
   compute_encoder.set_output_array(out, 2);
 
   // Set update info
-  uint upd_ndim = upd.ndim();
+  size_t upd_ndim = upd.ndim();
   size_t upd_size = 1;
   for (int i = idx_ndim; i < upd.ndim(); ++i) {
     upd_size *= upd.shape(i);
   }
-  if (index_nd1_specialization) {
-    compute_encoder->setBytes(
-        out.shape().data(), out.shape().size() * sizeof(int), 3);
-    compute_encoder->setBytes(
-        out.strides().data(), out.strides().size() * sizeof(size_t), 4);
-
-    size_t out_ndim = out.ndim();
-    compute_encoder->setBytes(&out_ndim, sizeof(out_ndim), 5);
-    if (upd_ndim <= 1) {
-      // Placeholder so Metal doesn't compalain
-      int shape_ = 0;
-      compute_encoder->setBytes(&shape_, sizeof(int), 6);
-    } else {
-      compute_encoder->setBytes(upd.shape().data(), upd_ndim * sizeof(int), 6);
-    }
-    compute_encoder->setBytes(&upd_ndim, sizeof(size_t), 7);
-    compute_encoder->setBytes(&upd_size, sizeof(size_t), 8);
-
-    // Set index buffers
-    for (int i = 0; i < nidx; ++i) {
-      compute_encoder.set_input_array(inputs[i + 1], 20 + i);
-    }
-
-    // Launch grid
-    MTL::Size grid_dims = MTL::Size(upd_size, nthreads / upd_size, 1);
-    MTL::Size group_dims = get_block_dims(upd_size, nthreads / upd_size, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
-
-  } else {
-    // Collect all idx shapes and strides into one place
-    std::vector<int> idx_shapes;
-    std::vector<size_t> idx_strides;
-
-    for (int i = 0; i < nidx; ++i) {
-      idx_shapes.insert(
-          idx_shapes.end(),
-          inputs[i + 1].shape().begin(),
-          inputs[i + 1].shape().end());
-
-      idx_strides.insert(
-          idx_strides.end(),
-          inputs[i + 1].strides().begin(),
-          inputs[i + 1].strides().end());
-    }
-
-    if (upd_ndim == 0) {
-      // Need placeholders so Metal doesn't compalain
-      int shape_ = 0;
-      size_t stride_ = 0;
-      compute_encoder->setBytes(&shape_, sizeof(int), 3);
-      compute_encoder->setBytes(&stride_, sizeof(size_t), 4);
-    } else {
-      compute_encoder->setBytes(upd.shape().data(), upd_ndim * sizeof(int), 3);
-      compute_encoder->setBytes(
-          upd.strides().data(), upd_ndim * sizeof(size_t), 4);
-    }
-    compute_encoder->setBytes(&upd_ndim, sizeof(size_t), 5);
-    compute_encoder->setBytes(&upd_size, sizeof(size_t), 6);
-
-    // Set output info
-    size_t out_ndim = out.ndim();
-    if (out_ndim == 0) {
-      // Need placeholders so Metal doesn't compalain
-      int shape_ = 0;
-      size_t stride_ = 0;
-      compute_encoder->setBytes(&shape_, sizeof(int), 7);
-      compute_encoder->setBytes(&stride_, sizeof(size_t), 8);
-    } else {
-      compute_encoder->setBytes(out.shape().data(), out_ndim * sizeof(int), 7);
-      compute_encoder->setBytes(
-          out.strides().data(), out_ndim * sizeof(size_t), 8);
-    }
-    compute_encoder->setBytes(&out_ndim, sizeof(size_t), 9);
-    compute_encoder->setBytes(axes_.data(), axes_.size() * sizeof(int), 10);
-
-    // Set index info
-    if (idx_ndim == 0) {
-      // Add a 0 in idx_shapes and strides to avoid the missing buffer binding
-      // error in the metal API.
-      idx_shapes.push_back(0);
-      idx_strides.push_back(0);
-    }
-    compute_encoder->setBytes(
-        idx_shapes.data(), idx_shapes.size() * sizeof(int), 11);
-    compute_encoder->setBytes(
-        idx_strides.data(), idx_strides.size() * sizeof(size_t), 12);
-    compute_encoder->setBytes(&idx_ndim, sizeof(int), 13);
-
-    // Set index buffers
-    for (int i = 0; i < nidx; ++i) {
-      compute_encoder.set_input_array(inputs[i + 1], 20 + i);
-    }
-
-    // Launch grid
-    MTL::Size grid_dims = MTL::Size(upd_size, nthreads / upd_size, 1);
-    MTL::Size group_dims = get_block_dims(upd_size, nthreads / upd_size, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+  // Collect all idx shapes and strides into one place
+  std::vector<int> idx_shapes;
+  std::vector<size_t> idx_strides;
+  // To access .data() use char instead of bool
+  // bool is 1 byte in Metal so this is safe
+  std::vector<char> idx_contigs;
+  for (int i = 0; i < nidx; ++i) {
+    idx_shapes.insert(
+        idx_shapes.end(),
+        inputs[i + 1].shape().begin(),
+        inputs[i + 1].shape().end());
+    idx_strides.insert(
+        idx_strides.end(),
+        inputs[i + 1].strides().begin(),
+        inputs[i + 1].strides().end());
+    idx_contigs.push_back(inputs[i + 1].flags().row_contiguous);
   }
+
+  if (upd_ndim == 0) {
+    // Need placeholders so Metal doesn't compalain
+    int shape_ = 0;
+    size_t stride_ = 0;
+    compute_encoder.set_bytes(shape_, 3);
+    compute_encoder.set_bytes(stride_, 4);
+  } else {
+    compute_encoder.set_vector_bytes(upd.shape(), 3);
+    compute_encoder.set_vector_bytes(upd.strides(), 4);
+  }
+  compute_encoder.set_bytes(upd_ndim, 5);
+  compute_encoder.set_bytes(upd_size, 6);
+
+  // Set output info
+  size_t out_ndim = out.ndim();
+  if (out_ndim == 0) {
+    // Need placeholders so Metal doesn't compalain
+    int shape_ = 0;
+    size_t stride_ = 0;
+    compute_encoder.set_bytes(shape_, 7);
+    compute_encoder.set_bytes(stride_, 8);
+  } else {
+    compute_encoder.set_vector_bytes(out.shape(), 7);
+    compute_encoder.set_vector_bytes(out.strides(), 8);
+  }
+  compute_encoder.set_bytes(out_ndim, 9);
+  compute_encoder.set_vector_bytes(axes_, 10);
+
+  // Set index info
+  if (idx_ndim == 0) {
+    // Add a 0 in idx_shapes and strides to avoid the missing buffer binding
+    // error in the metal API.
+    idx_shapes.push_back(0);
+    idx_strides.push_back(0);
+    idx_contigs.push_back(false);
+  }
+  compute_encoder.set_vector_bytes(idx_shapes, 11);
+  compute_encoder.set_vector_bytes(idx_strides, 12);
+  compute_encoder.set_vector_bytes(idx_contigs, 13);
+  compute_encoder.set_bytes(idx_ndim, 14);
+  compute_encoder.set_bytes(idx_size, 15);
+
+  // Set index buffers
+  for (int i = 0; i < nidx; ++i) {
+    compute_encoder.set_input_array(inputs[i + 1], 20 + i);
+  }
+
+  // Launch grid
+  auto grid_y = (nthreads / upd_size);
+  grid_y = (grid_y + nwork - 1) / nwork;
+  MTL::Size grid_dims = MTL::Size(upd_size, grid_y, 1);
+  auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
+  if (thread_group_size != 1024) {
+    throw std::runtime_error("[Scatter::eval_gpu] Invalid number of threads");
+  }
+  MTL::Size group_dims = get_block_dims(upd_size, grid_y, 1);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 } // namespace mlx::core

mlx/backend/metal/jit/indexing.h

@@ -1,7 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 
 constexpr std::string_view gather_kernels = R"(
-[[kernel]] void gather{0}_{3}_{6}(
+[[kernel]] void gather{0}_{3}_{6}_{7}(
     const device {1}* src [[buffer(0)]],
     device {1}* out [[buffer(1)]],
     const constant int* src_shape [[buffer(2)]],
@@ -11,14 +11,15 @@ constexpr std::string_view gather_kernels = R"(
     const constant int* axes [[buffer(6)]],
     const constant int* idx_shapes [[buffer(7)]],
     const constant size_t* idx_strides [[buffer(8)]],
-    const constant int& idx_ndim [[buffer(9)]],
+    const constant bool* idx_contigs [[buffer(9)]],
+    const constant int& idx_ndim [[buffer(10)]],
     {4}
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {{
   Indices<{2}, {3}> idxs{{
-    {{ {5} }}, idx_shapes, idx_strides, idx_ndim}};
+    {{ {5} }}, idx_shapes, idx_strides, idx_contigs, idx_ndim}};
 
-  return gather_impl<{1}, {2}, {3}, {6}>(
+  return gather_impl<{1}, {2}, {3}, {6}, {7}>(
       src,
       out,
       src_shape,
@@ -33,32 +34,7 @@ constexpr std::string_view gather_kernels = R"(
 )";
 
 constexpr std::string_view scatter_kernels = R"(
-[[kernel]] void scatter_1d_index{0}_{4}(
-    const device {1}* updates [[buffer(1)]],
-    device mlx_atomic<{1}>* out [[buffer(2)]],
-    const constant int* out_shape [[buffer(3)]],
-    const constant size_t* out_strides [[buffer(4)]],
-    const constant size_t& out_ndim [[buffer(5)]],
-    const constant int* upd_shape [[buffer(6)]],
-    const constant size_t& upd_ndim [[buffer(7)]],
-    const constant size_t& upd_size [[buffer(8)]],
-    {5}
-    uint2 gid [[thread_position_in_grid]]) {{
-  const array<const device {2}*, {4}> idx_buffers = {{ {6} }};
-  return scatter_1d_index_impl<{1}, {2}, {3}, {4}>(
-      updates,
-      out,
-      out_shape,
-      out_strides,
-      out_ndim,
-      upd_shape,
-      upd_ndim,
-      upd_size,
-      idx_buffers,
-      gid);
-}}
-
-[[kernel]] void scatter{0}_{4}(
+[[kernel]] void scatter{0}_{4}_updc_{7}_nwork{8}_{9}(
     const device {1}* updates [[buffer(1)]],
     device mlx_atomic<{1}>* out [[buffer(2)]],
     const constant int* upd_shape [[buffer(3)]],
@@ -71,12 +47,14 @@ constexpr std::string_view scatter_kernels = R"(
     const constant int* axes [[buffer(10)]],
     const constant int* idx_shapes [[buffer(11)]],
     const constant size_t* idx_strides [[buffer(12)]],
-    const constant int& idx_ndim [[buffer(13)]],
+    const constant bool* idx_contigs [[buffer(13)]],
+    const constant int& idx_ndim [[buffer(14)]],
+    const constant size_t& idx_size [[buffer(15)]],
     {5}
     uint2 gid [[thread_position_in_grid]]) {{
-  Indices<{2}, {4}> idxs{{ {{ {6} }}, idx_shapes, idx_strides, idx_ndim}};
+  Indices<{2}, {4}> idxs{{ {{ {6} }}, idx_shapes, idx_strides, idx_contigs, idx_ndim}};
 
-  return scatter_impl<{1}, {2}, {3}, {4}>(
+  return scatter_impl<{1}, {2}, {3}, {4}, {7}, {8}, {9}>(
       updates,
       out,
       upd_shape,
@@ -87,6 +65,7 @@ constexpr std::string_view scatter_kernels = R"(
       out_strides,
       out_ndim,
       axes,
+      idx_size,
       idxs,
       gid);
 }}

mlx/backend/metal/jit/scan.h

@@ -1,26 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-constexpr std::string_view scan_kernels = R"(
-template [[host_name("contig_{0}")]] [[kernel]] void
-contiguous_scan<{1}, {2}, {3}<{2}>, 4, {4}, {5}>(
-    const device {1}* in [[buffer(0)]],
-    device {2}* out [[buffer(1)]],
-    const constant size_t& axis_size [[buffer(2)]],
-    uint gid [[thread_position_in_grid]],
-    uint lid [[thread_position_in_threadgroup]],
-    uint lsize [[threads_per_threadgroup]],
-    uint simd_size [[threads_per_simdgroup]],
-    uint simd_lane_id [[thread_index_in_simdgroup]],
-    uint simd_group_id [[simdgroup_index_in_threadgroup]]);
-
-template [[host_name("strided_{0}")]] [[kernel]] void
-strided_scan<{1}, {2}, {3}<{2}>, 4, {4}, {5}>(
-    const device {1}* in [[buffer(0)]],
-    device {2}* out [[buffer(1)]],
-    const constant size_t& axis_size [[buffer(2)]],
-    const constant size_t& stride [[buffer(3)]],
-    uint2 gid [[thread_position_in_grid]],
-    uint2 lid [[thread_position_in_threadgroup]],
-    uint2 lsize [[threads_per_threadgroup]],
-    uint simd_size [[threads_per_simdgroup]]);
-)";

mlx/backend/metal/jit_kernels.cpp

@@ -1,10 +1,8 @@
 // Copyright © 2024 Apple Inc.
-
 #include "mlx/backend/common/compiled.h"
 #include "mlx/backend/metal/jit/arange.h"
 #include "mlx/backend/metal/jit/gemv_masked.h"
 #include "mlx/backend/metal/jit/includes.h"
-#include "mlx/backend/metal/jit/scan.h"
 #include "mlx/backend/metal/jit/softmax.h"
 #include "mlx/backend/metal/jit/steel_conv.h"
 #include "mlx/backend/metal/jit/steel_gemm.h"
@@ -25,48 +23,50 @@ MTL::ComputePipelineState* get_arange_kernel(
     metal::Device& d,
     const std::string& kernel_name,
     const array& out) {
-  const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(kernel_name, [&]() {
     std::ostringstream kernel_source;
-    kernel_source
-        << metal::utils() << metal::arange()
-        << fmt::format(arange_kernels, lib_name, get_type_string(out.dtype()));
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    kernel_source << metal::utils() << metal::arange()
+                  << fmt::format(
+                         arange_kernels,
+                         kernel_name,
+                         get_type_string(out.dtype()));
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
 MTL::ComputePipelineState* get_unary_kernel(
     metal::Device& d,
     const std::string& kernel_name,
+    Dtype in_type,
     Dtype out_type,
     const std::string op) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::unary_ops() << metal::unary();
-    kernel_source << get_template_definition(
-        "v_" + lib_name, "unary_v", get_type_string(out_type), op);
-    kernel_source << get_template_definition(
-        "v2_" + lib_name, "unary_v2", get_type_string(out_type), op);
-    kernel_source << get_template_definition(
-        "g_" + lib_name, "unary_g", get_type_string(out_type), op);
-    kernel_source << get_template_definition(
-        "gn4_" + lib_name, "unary_g", get_type_string(out_type), op, 4);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+  auto lib = d.get_library(lib_name, [&]() {
+    auto in_t = get_type_string(in_type);
+    auto out_t = get_type_string(out_type);
+    std::string kernel_source = metal::utils();
+    concatenate(kernel_source, metal::unary_ops(), metal::unary());
+    kernel_source +=
+        get_template_definition("v_" + lib_name, "unary_v", in_t, out_t, op);
+    kernel_source +=
+        get_template_definition("v2_" + lib_name, "unary_v2", in_t, out_t, op);
+    kernel_source += get_template_definition(
+        "gn1_" + lib_name, "unary_g", in_t, out_t, op, 1, "uint");
+    kernel_source += get_template_definition(
+        "gn4large_" + lib_name, "unary_g", in_t, out_t, op, 4);
+    return kernel_source;
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
-void add_binary_kernels(
+void append_binary_kernels(
     const std::string lib_name,
     Dtype in_type,
     Dtype out_type,
     const std::string op,
-    std::ostringstream& kernel_source) {
-  const std::array<std::pair<std::string, std::string>, 11> kernel_types = {{
+    std::string& kernel_source) {
+  const std::array<std::pair<std::string, std::string>, 10> kernel_types = {{
       {"ss", "binary_ss"},
       {"vs", "binary_vs"},
       {"sv", "binary_sv"},
@@ -75,27 +75,24 @@ void add_binary_kernels(
       {"sv2", "binary_sv2"},
       {"vv2", "binary_vv2"},
       {"g1", "binary_g_nd1"},
-      {"g2", "binary_g_nd2"},
-      {"g3", "binary_g_nd3"},
-      {"gn", "binary_g"},
+      {"g2large", "binary_g_nd2"},
+      {"g3large", "binary_g_nd3"},
   }};
+  auto in_t = get_type_string(in_type);
+  auto out_t = get_type_string(out_type);
+
   for (auto& [name, func] : kernel_types) {
-    std::string template_def;
-    template_def = get_template_definition(
-        name + "_" + lib_name,
-        func,
-        get_type_string(in_type),
-        get_type_string(out_type),
-        op);
-    kernel_source << template_def;
+    kernel_source +=
+        get_template_definition(name + "_" + lib_name, func, in_t, out_t, op);
   }
-  kernel_source << get_template_definition(
-      "gn4_" + lib_name,
-      "binary_g",
-      get_type_string(in_type),
-      get_type_string(out_type),
-      op,
-      4);
+  kernel_source += get_template_definition(
+      "g2_" + lib_name, "binary_g_nd2", in_t, out_t, op, "uint");
+  kernel_source += get_template_definition(
+      "g3_" + lib_name, "binary_g_nd3", in_t, out_t, op, "uint");
+  kernel_source += get_template_definition(
+      "gn2_" + lib_name, "binary_g", in_t, out_t, op, 2, "uint");
+  kernel_source += get_template_definition(
+      "gn4large_" + lib_name, "binary_g", in_t, out_t, op, 4);
 }
 
 MTL::ComputePipelineState* get_binary_kernel(
@@ -105,13 +102,13 @@ MTL::ComputePipelineState* get_binary_kernel(
     Dtype out_type,
     const std::string op) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::binary_ops() << metal::binary();
-    add_binary_kernels(lib_name, in_type, out_type, op, kernel_source);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+  auto lib = d.get_library(lib_name, [&]() {
+    std::string kernel_source;
+    kernel_source = metal::utils();
+    concatenate(kernel_source, metal::binary_ops(), metal::binary());
+    append_binary_kernels(lib_name, in_type, out_type, op, kernel_source);
+    return kernel_source;
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -122,14 +119,12 @@ MTL::ComputePipelineState* get_binary_two_kernel(
     Dtype out_type,
     const std::string op) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::binary_ops()
-                  << metal::binary_two();
-    add_binary_kernels(lib_name, in_type, out_type, op, kernel_source);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+  auto lib = d.get_library(lib_name, [&]() {
+    std::string kernel_source = metal::utils();
+    concatenate(kernel_source, metal::binary_ops(), metal::binary_two());
+    append_binary_kernels(lib_name, in_type, out_type, op, kernel_source);
+    return kernel_source;
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -139,28 +134,31 @@ MTL::ComputePipelineState* get_ternary_kernel(
     Dtype type,
     const std::string op) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
-    const std::array<std::pair<std::string, std::string>, 6> kernel_types = {{
+  auto lib = d.get_library(lib_name, [&]() {
+    auto t_str = get_type_string(type);
+    std::string kernel_source = metal::utils();
+    concatenate(kernel_source, metal::ternary_ops(), metal::ternary());
+    const std::array<std::pair<std::string, std::string>, 5> kernel_types = {{
         {"v", "ternary_v"},
         {"v2", "ternary_v2"},
-        {"g", "ternary_g"},
         {"g1", "ternary_g_nd1"},
-        {"g2", "ternary_g_nd2"},
-        {"g3", "ternary_g_nd3"},
+        {"g2large", "ternary_g_nd2"},
+        {"g3large", "ternary_g_nd3"},
     }};
-    kernel_source << metal::utils() << metal::ternary_ops() << metal::ternary();
     for (auto& [name, func] : kernel_types) {
-      std::string template_def;
-      template_def = get_template_definition(
-          name + "_" + lib_name, func, get_type_string(type), op);
-      kernel_source << template_def;
+      kernel_source +=
+          get_template_definition(name + "_" + lib_name, func, t_str, op);
     }
-    kernel_source << get_template_definition(
-        "gn4_" + lib_name, "ternary_g", get_type_string(type), op, 4);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    kernel_source += get_template_definition(
+        "g2_" + lib_name, "ternary_g_nd2", t_str, op, "uint");
+    kernel_source += get_template_definition(
+        "g3_" + lib_name, "ternary_g_nd3", t_str, op, "uint");
+    kernel_source += get_template_definition(
+        "gn2_" + lib_name, "ternary_g", t_str, op, 2, "uint");
+    kernel_source += get_template_definition(
+        "gn4large_" + lib_name, "ternary_g", t_str, op, 4);
+    return kernel_source;
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -170,36 +168,45 @@ MTL::ComputePipelineState* get_copy_kernel(
     const array& in,
     const array& out) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
+  auto lib = d.get_library(lib_name, [&]() {
+    std::string kernel_source = metal::utils();
+    kernel_source += metal::copy();
     auto in_type = get_type_string(in.dtype());
     auto out_type = get_type_string(out.dtype());
-    kernel_source
-        << metal::utils() << metal::copy()
-        << get_template_definition("s_" + lib_name, "copy_s", in_type, out_type)
-        << get_template_definition("v_" + lib_name, "copy_v", in_type, out_type)
-        << get_template_definition(
-               "g1_" + lib_name, "copy_g_nd1", in_type, out_type)
-        << get_template_definition(
-               "g2_" + lib_name, "copy_g_nd2", in_type, out_type)
-        << get_template_definition(
-               "g3_" + lib_name, "copy_g_nd3", in_type, out_type)
-        << get_template_definition("g_" + lib_name, "copy_g", in_type, out_type)
-        << get_template_definition(
-               "gn4_" + lib_name, "copy_g", in_type, out_type, 4)
-        << get_template_definition(
-               "gg1_" + lib_name, "copy_gg_nd1", in_type, out_type)
-        << get_template_definition(
-               "gg2_" + lib_name, "copy_gg_nd2", in_type, out_type)
-        << get_template_definition(
-               "gg3_" + lib_name, "copy_gg_nd3", in_type, out_type)
-        << get_template_definition(
-               "gg_" + lib_name, "copy_gg", in_type, out_type)
-        << get_template_definition(
-               "ggn4_" + lib_name, "copy_gg", in_type, out_type, 4);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    kernel_source +=
+        get_template_definition("s_" + lib_name, "copy_s", in_type, out_type);
+    kernel_source +=
+        get_template_definition("v_" + lib_name, "copy_v", in_type, out_type);
+    kernel_source += get_template_definition(
+        "g1_" + lib_name, "copy_g_nd1", in_type, out_type);
+    kernel_source += get_template_definition(
+        "g2_" + lib_name, "copy_g_nd2", in_type, out_type, "int");
+    kernel_source += get_template_definition(
+        "g3_" + lib_name, "copy_g_nd3", in_type, out_type, "int");
+    kernel_source += get_template_definition(
+        "gn2_" + lib_name, "copy_g", in_type, out_type, 2, "int");
+    kernel_source += get_template_definition(
+        "gg1_" + lib_name, "copy_gg_nd1", in_type, out_type);
+    kernel_source += get_template_definition(
+        "gg2_" + lib_name, "copy_gg_nd2", in_type, out_type, "int");
+    kernel_source += get_template_definition(
+        "gg3_" + lib_name, "copy_gg_nd3", in_type, out_type, "int");
+    kernel_source += get_template_definition(
+        "ggn2_" + lib_name, "copy_gg", in_type, out_type, 2, "int");
+    kernel_source += get_template_definition(
+        "g2large_" + lib_name, "copy_g_nd2", in_type, out_type);
+    kernel_source += get_template_definition(
+        "g3large_" + lib_name, "copy_g_nd3", in_type, out_type);
+    kernel_source += get_template_definition(
+        "gn4large_" + lib_name, "copy_g", in_type, out_type, 4);
+    kernel_source += get_template_definition(
+        "gg2large_" + lib_name, "copy_gg_nd2", in_type, out_type);
+    kernel_source += get_template_definition(
+        "gg3large_" + lib_name, "copy_gg_nd3", in_type, out_type);
+    kernel_source += get_template_definition(
+        "ggn4large_" + lib_name, "copy_gg", in_type, out_type, 4);
+    return kernel_source;
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -209,8 +216,7 @@ MTL::ComputePipelineState* get_softmax_kernel(
     bool precise,
     const array& out) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&] {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::softmax()
                   << fmt::format(
@@ -218,8 +224,8 @@ MTL::ComputePipelineState* get_softmax_kernel(
                          lib_name,
                          get_type_string(out.dtype()),
                          get_type_string(precise ? float32 : out.dtype()));
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -232,22 +238,29 @@ MTL::ComputePipelineState* get_scan_kernel(
     const array& in,
     const array& out) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
-    std::string op_name = "Cum" + reduce_type;
-    op_name[3] = toupper(op_name[3]);
+  auto lib = d.get_library(lib_name, [&]() {
+    auto out_type = get_type_string(out.dtype());
+    std::string op = "Cum" + reduce_type + "<" + out_type + ">";
+    op[3] = toupper(op[3]);
     std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::scan()
-                  << fmt::format(
-                         scan_kernels,
-                         lib_name,
-                         get_type_string(in.dtype()),
-                         get_type_string(out.dtype()),
-                         op_name,
-                         inclusive,
-                         reverse);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    kernel_source << metal::utils() << metal::scan();
+    const std::array<std::pair<std::string, std::string>, 2> scan_kernels = {{
+        {"contig_", "contiguous_scan"},
+        {"strided_", "strided_scan"},
+    }};
+    for (auto& [prefix, kernel] : scan_kernels) {
+      kernel_source << get_template_definition(
+          prefix + lib_name,
+          kernel,
+          get_type_string(in.dtype()),
+          get_type_string(out.dtype()),
+          op,
+          in.itemsize() <= 4 ? 4 : 2,
+          inclusive,
+          reverse);
+    }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -259,8 +272,7 @@ MTL::ComputePipelineState* get_sort_kernel(
     int bn,
     int tn) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     auto in_type = get_type_string(in.dtype());
     auto out_type = get_type_string(out.dtype());
@@ -285,8 +297,8 @@ MTL::ComputePipelineState* get_sort_kernel(
           bn,
           tn);
     }
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -298,8 +310,7 @@ MTL::ComputePipelineState* get_mb_sort_kernel(
     int bn,
     int tn) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::sort();
     std::array<std::pair<std::string, std::string>, 3> kernel_types = {
@@ -316,27 +327,28 @@ MTL::ComputePipelineState* get_mb_sort_kernel(
           bn,
           tn);
     }
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
 MTL::ComputePipelineState* get_reduce_init_kernel(
     metal::Device& d,
     const std::string& kernel_name,
-    const array& out) {
-  auto lib = d.get_library(kernel_name);
-  if (lib == nullptr) {
-    std::ostringstream kernel_source;
-    std::string op_type = op_name(out);
-    op_type[0] = std::toupper(op_name(out)[0]);
-    auto out_type = get_type_string(out.dtype());
-    std::string op = op_type + "<" + out_type + ">";
-    kernel_source << metal::utils() << metal::reduce_utils() << metal::reduce();
-    kernel_source << get_template_definition(
-        kernel_name, "init_reduce", out_type, op);
-    lib = d.get_library(kernel_name, kernel_source.str());
-  }
+    const std::string& func_name,
+    const std::string& op_name,
+    const Dtype& out_type) {
+  auto lib = d.get_library(kernel_name, [&]() {
+    std::string op_type = op_name;
+    op_type[0] = std::toupper(op_name[0]);
+    auto out_t = get_type_string(out_type);
+    std::string op = op_type + "<" + out_t + ">";
+    std::string kernel_source = metal::utils();
+    kernel_source += metal::reduce_utils();
+    kernel_source += metal::reduce();
+    kernel_source += get_template_definition(kernel_name, func_name, out_t, op);
+    return kernel_source;
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -345,32 +357,32 @@ MTL::ComputePipelineState* get_reduce_kernel(
     const std::string& kernel_name,
     const std::string& func_name,
     const std::string& op_name,
-    const array& in,
-    const array& out,
+    const Dtype& in_type,
+    const Dtype& out_type,
+    const std::string& idx_t,
     int ndim /* = -1 */,
     int bm /* = -1 */,
     int bn /* = -1 */) {
-  auto lib = d.get_library(kernel_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(kernel_name, [&]() {
     std::string op_type = op_name;
     op_type[0] = std::toupper(op_name[0]);
-    std::ostringstream kernel_source;
-    auto in_type = get_type_string(in.dtype());
-    auto out_type = get_type_string(out.dtype());
-    std::string op = op_type + "<" + out_type + ">";
-    kernel_source << metal::utils() << metal::reduce_utils() << metal::reduce();
+    auto in_t = get_type_string(in_type);
+    auto out_t = get_type_string(out_type);
+    std::string op = op_type + "<" + out_t + ">";
+    std::string kernel_source = metal::utils();
+    concatenate(kernel_source, metal::reduce_utils(), metal::reduce());
     if (bm >= 0) {
-      kernel_source << get_template_definition(
-          kernel_name, func_name, in_type, out_type, op, ndim, bm, bn);
+      kernel_source += get_template_definition(
+          kernel_name, func_name, in_t, out_t, op, idx_t, ndim, bm, bn);
     } else if (ndim >= 0) {
-      kernel_source << get_template_definition(
-          kernel_name, func_name, in_type, out_type, op, ndim);
+      kernel_source += get_template_definition(
+          kernel_name, func_name, in_t, out_t, op, idx_t, ndim);
     } else {
-      kernel_source << get_template_definition(
-          kernel_name, func_name, in_type, out_type, op);
+      kernel_source += get_template_definition(
+          kernel_name, func_name, in_t, out_t, op, idx_t);
     }
-    lib = d.get_library(kernel_name, kernel_source.str());
-  }
+    return kernel_source;
+  });
   auto st = d.get_kernel(kernel_name, lib);
   return st;
 }
@@ -389,8 +401,7 @@ MTL::ComputePipelineState* get_steel_gemm_fused_kernel(
     int wm,
     int wn) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::gemm()
                   << metal::steel_gemm_fused()
@@ -405,8 +416,8 @@ MTL::ComputePipelineState* get_steel_gemm_fused_kernel(
                          "wn"_a = wn,
                          "trans_a"_a = transpose_a,
                          "trans_b"_a = transpose_b);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib, hash_name, func_consts);
 }
 
@@ -425,8 +436,7 @@ MTL::ComputePipelineState* get_steel_gemm_splitk_kernel(
     bool mn_aligned,
     bool k_aligned) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::gemm()
                   << metal::steel_gemm_splitk()
@@ -444,8 +454,8 @@ MTL::ComputePipelineState* get_steel_gemm_splitk_kernel(
                          "trans_b"_a = transpose_b,
                          "mn_aligned"_a = mn_aligned,
                          "k_aligned"_a = k_aligned);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -456,19 +466,19 @@ MTL::ComputePipelineState* get_steel_gemm_splitk_accum_kernel(
     const array& out,
     bool axbpy) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::gemm()
                   << metal::steel_gemm_splitk()
                   << fmt::format(
-                         axbpy ? steel_gemm_splitk_accum_axbpy_kernels
-                               : steel_gemm_splitk_accum_kernels,
+                         fmt::runtime(
+                             axbpy ? steel_gemm_splitk_accum_axbpy_kernels
+                                   : steel_gemm_splitk_accum_kernels),
                          "name"_a = lib_name,
                          "atype"_a = get_type_string(in.dtype()),
                          "otype"_a = get_type_string(out.dtype()));
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -488,8 +498,7 @@ MTL::ComputePipelineState* get_steel_gemm_masked_kernel(
     bool mn_aligned,
     bool k_aligned) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     auto out_mask_type = mask_out.has_value()
         ? get_type_string((*mask_out).dtype())
@@ -513,8 +522,8 @@ MTL::ComputePipelineState* get_steel_gemm_masked_kernel(
                          "trans_b"_a = transpose_b,
                          "mn_aligned"_a = mn_aligned,
                          "k_aligned"_a = k_aligned);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -533,8 +542,7 @@ MTL::ComputePipelineState* get_gemv_masked_kernel(
     int tn,
     bool contiguous) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     auto out_mask_type = mask_out.has_value()
         ? get_type_string((*mask_out).dtype())
@@ -556,8 +564,8 @@ MTL::ComputePipelineState* get_gemv_masked_kernel(
                          "tn"_a = tn,
                          "trans"_a = transpose_mat ? "t_" : "",
                          "nc"_a = contiguous ? "0" : "1");
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -573,8 +581,7 @@ MTL::ComputePipelineState* get_steel_conv_kernel(
     int n_channel_specialization,
     bool small_filter) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::conv() << metal::steel_conv()
                   << fmt::format(
@@ -588,8 +595,8 @@ MTL::ComputePipelineState* get_steel_conv_kernel(
                          "wn"_a = wn,
                          "n_channels"_a = n_channel_specialization,
                          "small_filter"_a = small_filter);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -603,8 +610,7 @@ MTL::ComputePipelineState* get_steel_conv_general_kernel(
     int wm,
     int wn) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::conv()
                   << metal::steel_conv_general()
@@ -617,8 +623,8 @@ MTL::ComputePipelineState* get_steel_conv_general_kernel(
                          "bk"_a = bk,
                          "wm"_a = wm,
                          "wn"_a = wn);
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 
@@ -629,13 +635,12 @@ MTL::ComputePipelineState* get_fft_kernel(
     const metal::MTLFCList& func_consts,
     const std::string& template_def) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     std::string kernel_string;
     kernel_source << metal::fft() << template_def;
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib, hash_name, func_consts);
 }
 
@@ -644,13 +649,12 @@ MTL::ComputePipelineState* get_quantized_kernel(
     const std::string& kernel_name,
     const std::string& template_def) {
   const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name);
-  if (lib == nullptr) {
+  auto lib = d.get_library(lib_name, [&]() {
     std::ostringstream kernel_source;
     kernel_source << metal::utils() << metal::gemm() << metal::quantized()
                   << template_def;
-    lib = d.get_library(lib_name, kernel_source.str());
-  }
+    return kernel_source.str();
+  });
   return d.get_kernel(kernel_name, lib);
 }
 

mlx/backend/metal/kernels.h

@@ -15,6 +15,7 @@ MTL::ComputePipelineState* get_arange_kernel(
 MTL::ComputePipelineState* get_unary_kernel(
     metal::Device& d,
     const std::string& kernel_name,
+    Dtype in_type,
     Dtype out_type,
     const std::string op);
 
@@ -78,15 +79,18 @@ MTL::ComputePipelineState* get_mb_sort_kernel(
 MTL::ComputePipelineState* get_reduce_init_kernel(
     metal::Device& d,
     const std::string& kernel_name,
-    const array& out);
+    const std::string& func_name,
+    const std::string& op_name,
+    const Dtype& out_type);
 
 MTL::ComputePipelineState* get_reduce_kernel(
     metal::Device& d,
     const std::string& kernel_name,
     const std::string& func_name,
     const std::string& op_name,
-    const array& in,
-    const array& out,
+    const Dtype& in_type,
+    const Dtype& out_type,
+    const std::string& idx_t,
     int ndim = -1,
     int bm = -1,
     int bn = -1);
@@ -208,10 +212,10 @@ get_template_definition(std::string name, std::string func, Args... args) {
   };
   (add_arg(args), ...);
   s << ">";
-  std::string base_string = R"(
-template [[host_name("{0}")]] [[kernel]] decltype({1}) {1};
-  )";
-  return fmt::format(base_string, name, s.str());
+  return fmt::format(
+      "\ntemplate [[host_name(\"{0}\")]] [[kernel]] decltype({1}) {1};\n",
+      name,
+      s.str());
 }
 
 } // namespace mlx::core

mlx/backend/metal/kernels/CMakeLists.txt

@@ -1,13 +1,27 @@
-set(BASE_HEADERS bf16.h bf16_math.h complex.h defines.h expm1f.h utils.h)
+set(BASE_HEADERS
+    metal_3_1/bf16.h
+    metal_3_0/bf16.h
+    bf16_math.h
+    complex.h
+    defines.h
+    expm1f.h
+    utils.h)
 
 function(build_kernel_base TARGET SRCFILE DEPS)
   set(METAL_FLAGS -Wall -Wextra -fno-fast-math)
   if(MLX_METAL_DEBUG)
     set(METAL_FLAGS ${METAL_FLAGS} -gline-tables-only -frecord-sources)
   endif()
+  if(MLX_METAL_VERSION GREATER_EQUAL 310)
+    set(VERSION_INCLUDES
+        ${PROJECT_SOURCE_DIR}/mlx/backend/metal/kernels/metal_3_1)
+  else()
+    set(VERSION_INCLUDES
+        ${PROJECT_SOURCE_DIR}/mlx/backend/metal/kernels/metal_3_0)
+  endif()
   add_custom_command(
     COMMAND xcrun -sdk macosx metal ${METAL_FLAGS} -c ${SRCFILE}
-            -I${PROJECT_SOURCE_DIR} -o ${TARGET}.air
+            -I${PROJECT_SOURCE_DIR} -I${VERSION_INCLUDES} -o ${TARGET}.air
     DEPENDS ${SRCFILE} ${DEPS} ${BASE_HEADERS}
     OUTPUT ${TARGET}.air
     COMMENT "Building ${TARGET}.air"
@@ -30,8 +44,7 @@ build_kernel(layer_norm)
 build_kernel(random)
 build_kernel(rms_norm)
 build_kernel(rope)
-build_kernel(scaled_dot_product_attention scaled_dot_product_attention_params.h
-             steel/defines.h steel/gemm/transforms.h steel/utils.h)
+build_kernel(scaled_dot_product_attention sdpa_vector.h)
 
 set(STEEL_HEADERS
     steel/defines.h
@@ -49,7 +62,27 @@ set(STEEL_HEADERS
     steel/gemm/transforms.h
     steel/gemm/kernels/steel_gemm_fused.h
     steel/gemm/kernels/steel_gemm_masked.h
-    steel/gemm/kernels/steel_gemm_splitk.h)
+    steel/gemm/kernels/steel_gemm_splitk.h
+    steel/utils/type_traits.h
+    steel/utils/integral_constant.h)
+
+set(STEEL_ATTN_HEADERS
+    steel/defines.h
+    steel/utils.h
+    steel/gemm/gemm.h
+    steel/gemm/mma.h
+    steel/gemm/loader.h
+    steel/gemm/transforms.h
+    steel/utils/type_traits.h
+    steel/utils/integral_constant.h
+    steel/attn/attn.h
+    steel/attn/loader.h
+    steel/attn/mma.h
+    steel/attn/params.h
+    steel/attn/transforms.h
+    steel/attn/kernels/steel_attention.h)
+
+build_kernel(steel/attn/kernels/steel_attention ${STEEL_ATTN_HEADERS})
 
 if(NOT MLX_METAL_JIT)
   build_kernel(arange arange.h)

mlx/backend/metal/kernels/arange.metal

@@ -1,7 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 
 // clang-format off
-#include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/arange.h"
 
 #define instantiate_arange(tname, type)                                 \

mlx/backend/metal/kernels/bf16_math.h

@@ -2,8 +2,6 @@
 
 #pragma once
 
-#include "mlx/backend/metal/kernels/bf16.h"
-
 ///////////////////////////////////////////////////////////////////////////////
 // Metal math for bfloat16
 ///////////////////////////////////////////////////////////////////////////////
@@ -369,18 +367,6 @@ instantiate_metal_math_funcs(
     return static_cast<otype>(__metal_simd_xor(static_cast<ctype>(data)));     \
   }
 
-#if (MLX_METAL_VERSION >= 310) || (__METAL_VERSION__ >= 310)
-
-#define bfloat16_to_uint16(x) as_type<uint16_t>(x)
-#define uint16_to_bfloat16(x) as_type<bfloat16_t>(x)
-
-#else
-
-#define bfloat16_to_uint16(x) x.bits_
-#define uint16_to_bfloat16(x) _MLX_BFloat16(x, _MLX_BFloat16::bits_to_bfloat())
-
-#endif
-
 namespace metal {
 
 instantiate_metal_simd_comm_funcs(

mlx/backend/metal/kernels/binary.h

@@ -77,12 +77,12 @@ template <typename T, typename U, typename Op>
     constant const size_t& a_stride,
     constant const size_t& b_stride,
     uint index [[thread_position_in_grid]]) {
-  auto a_idx = elem_to_loc_1(index, a_stride);
-  auto b_idx = elem_to_loc_1(index, b_stride);
+  auto a_idx = elem_to_loc_1<size_t, uint>(index, a_stride);
+  auto b_idx = elem_to_loc_1<size_t, uint>(index, b_stride);
   c[index] = Op()(a[a_idx], b[b_idx]);
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, typename IdxT = size_t>
 [[kernel]] void binary_g_nd2(
     device const T* a,
     device const T* b,
@@ -91,13 +91,13 @@ template <typename T, typename U, typename Op>
     constant const size_t b_strides[2],
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto a_idx = elem_to_loc_2(index, a_strides);
-  auto b_idx = elem_to_loc_2(index, b_strides);
-  size_t out_idx = index.x + size_t(grid_dim.x) * index.y;
+  auto a_idx = elem_to_loc_2<size_t, IdxT>(index, a_strides);
+  auto b_idx = elem_to_loc_2<size_t, IdxT>(index, b_strides);
+  IdxT out_idx = index.x + IdxT(grid_dim.x) * index.y;
   c[out_idx] = Op()(a[a_idx], b[b_idx]);
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, typename IdxT = size_t>
 [[kernel]] void binary_g_nd3(
     device const T* a,
     device const T* b,
@@ -106,14 +106,18 @@ template <typename T, typename U, typename Op>
     constant const size_t b_strides[3],
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto a_idx = elem_to_loc_3(index, a_strides);
-  auto b_idx = elem_to_loc_3(index, b_strides);
-  size_t out_idx =
-      index.x + grid_dim.x * (index.y + size_t(grid_dim.y) * index.z);
+  auto a_idx = elem_to_loc_3<size_t, IdxT>(index, a_strides);
+  auto b_idx = elem_to_loc_3<size_t, IdxT>(index, b_strides);
+  IdxT out_idx = index.x + grid_dim.x * (index.y + IdxT(grid_dim.y) * index.z);
   c[out_idx] = Op()(a[a_idx], b[b_idx]);
 }
 
-template <typename T, typename U, typename Op, int N = 1>
+template <
+    typename T,
+    typename U,
+    typename Op,
+    int N = 1,
+    typename IdxT = size_t>
 [[kernel]] void binary_g(
     device const T* a,
     device const T* b,
@@ -124,13 +128,12 @@ template <typename T, typename U, typename Op, int N = 1>
     constant const int& ndim,
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto idx = elem_to_loc_2_nd(
+  auto idx = elem_to_loc_2_nd<size_t, IdxT>(
       {N * index.x, index.y, index.z}, shape, a_strides, b_strides, ndim);
   auto xshape = shape[ndim - 1];
-  size_t out_idx =
-      N * index.x + xshape * (index.y + size_t(grid_dim.y) * index.z);
-  auto a_xstride = a_strides[ndim - 1];
-  auto b_xstride = b_strides[ndim - 1];
+  IdxT out_idx = N * index.x + xshape * (index.y + IdxT(grid_dim.y) * index.z);
+  IdxT a_xstride = a_strides[ndim - 1];
+  IdxT b_xstride = b_strides[ndim - 1];
   for (int i = 0; i < N && (int(N * index.x) + i) < xshape; ++i) {
     c[out_idx++] = Op()(a[idx.x], b[idx.y]);
     idx.x += a_xstride;

mlx/backend/metal/kernels/binary.metal

@@ -9,19 +9,21 @@
 #include "mlx/backend/metal/kernels/binary_ops.h"
 #include "mlx/backend/metal/kernels/binary.h"
 
-#define instantiate_binary_all(op, tname, itype, otype)                  \
-  instantiate_kernel("ss_" #op #tname, binary_ss, itype, otype, op)      \
-  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op)      \
-  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op)      \
-  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op)      \
-  instantiate_kernel("sv2_" #op #tname, binary_sv2, itype, otype, op)    \
-  instantiate_kernel("vs2_" #op #tname, binary_vs2, itype, otype, op)    \
-  instantiate_kernel("vv2_" #op #tname, binary_vv2, itype, otype, op)    \
-  instantiate_kernel("gn_" #op #tname, binary_g, itype, otype, op)       \
-  instantiate_kernel("gn4_" #op #tname, binary_g, itype, otype, op, 4)   \
-  instantiate_kernel("g1_" #op #tname, binary_g_nd1, itype, otype, op)   \
-  instantiate_kernel("g2_" #op #tname, binary_g_nd2, itype, otype, op)   \
-  instantiate_kernel("g3_" #op #tname, binary_g_nd3, itype, otype, op)   \
+#define instantiate_binary_all(op, tname, itype, otype)                        \
+  instantiate_kernel("ss_" #op #tname, binary_ss, itype, otype, op)            \
+  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op)            \
+  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op)            \
+  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op)            \
+  instantiate_kernel("sv2_" #op #tname, binary_sv2, itype, otype, op)          \
+  instantiate_kernel("vs2_" #op #tname, binary_vs2, itype, otype, op)          \
+  instantiate_kernel("vv2_" #op #tname, binary_vv2, itype, otype, op)          \
+  instantiate_kernel("gn2_" #op #tname, binary_g, itype, otype, op, 2, uint)   \
+  instantiate_kernel("gn4large_" #op #tname, binary_g, itype, otype, op, 4)    \
+  instantiate_kernel("g1_" #op #tname, binary_g_nd1, itype, otype, op)         \
+  instantiate_kernel("g2_" #op #tname, binary_g_nd2, itype, otype, op, uint)   \
+  instantiate_kernel("g2large_" #op #tname, binary_g_nd2, itype, otype, op)    \
+  instantiate_kernel("g3_" #op #tname, binary_g_nd3, itype, otype, op, uint)   \
+  instantiate_kernel("g3large_" #op #tname, binary_g_nd3, itype, otype, op)
 
 #define instantiate_binary_integer(op)                   \
   instantiate_binary_all(op, uint8, uint8_t, uint8_t)    \

mlx/backend/metal/kernels/binary_ops.h

@@ -217,7 +217,7 @@ struct Power {
 
   template <>
   complex64_t operator()(complex64_t x, complex64_t y) {
-    auto x_theta = metal::atan(x.imag / x.real);
+    auto x_theta = metal::atan2(x.imag, x.real);
     auto x_ln_r = 0.5 * metal::log(x.real * x.real + x.imag * x.imag);
     auto mag = metal::exp(y.real * x_ln_r - y.imag * x_theta);
     auto phase = y.imag * x_ln_r + y.real * x_theta;

mlx/backend/metal/kernels/binary_two.h

@@ -99,14 +99,14 @@ template <typename T, typename U, typename Op>
     constant const size_t& a_stride,
     constant const size_t& b_stride,
     uint index [[thread_position_in_grid]]) {
-  auto a_idx = elem_to_loc_1(index, a_stride);
-  auto b_idx = elem_to_loc_1(index, b_stride);
+  auto a_idx = elem_to_loc_1<size_t, uint>(index, a_stride);
+  auto b_idx = elem_to_loc_1<size_t, uint>(index, b_stride);
   auto out = Op()(a[a_idx], b[b_idx]);
   c[index] = out[0];
   d[index] = out[1];
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, typename IdxT = size_t>
 [[kernel]] void binary_g_nd2(
     device const T* a,
     device const T* b,
@@ -116,15 +116,15 @@ template <typename T, typename U, typename Op>
     constant const size_t b_strides[2],
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto a_idx = elem_to_loc_2(index, a_strides);
-  auto b_idx = elem_to_loc_2(index, b_strides);
-  size_t out_idx = index.x + size_t(grid_dim.x) * index.y;
+  auto a_idx = elem_to_loc_2<size_t, IdxT>(index, a_strides);
+  auto b_idx = elem_to_loc_2<size_t, IdxT>(index, b_strides);
+  IdxT out_idx = index.x + IdxT(grid_dim.x) * index.y;
   auto out = Op()(a[a_idx], b[b_idx]);
   c[out_idx] = out[0];
   d[out_idx] = out[1];
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, typename IdxT = size_t>
 [[kernel]] void binary_g_nd3(
     device const T* a,
     device const T* b,
@@ -134,16 +134,20 @@ template <typename T, typename U, typename Op>
     constant const size_t b_strides[3],
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto a_idx = elem_to_loc_3(index, a_strides);
-  auto b_idx = elem_to_loc_3(index, b_strides);
-  size_t out_idx =
-      index.x + grid_dim.x * (index.y + size_t(grid_dim.y) * index.z);
+  auto a_idx = elem_to_loc_3<size_t, IdxT>(index, a_strides);
+  auto b_idx = elem_to_loc_3<size_t, IdxT>(index, b_strides);
+  IdxT out_idx = index.x + grid_dim.x * (index.y + IdxT(grid_dim.y) * index.z);
   auto out = Op()(a[a_idx], b[b_idx]);
   c[out_idx] = out[0];
   d[out_idx] = out[1];
 }
 
-template <typename T, typename U, typename Op, int N = 1>
+template <
+    typename T,
+    typename U,
+    typename Op,
+    int N = 1,
+    typename IdxT = size_t>
 [[kernel]] void binary_g(
     device const T* a,
     device const T* b,
@@ -155,13 +159,12 @@ template <typename T, typename U, typename Op, int N = 1>
     constant const int& ndim,
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto idx = elem_to_loc_2_nd(
+  auto idx = elem_to_loc_2_nd<size_t, IdxT>(
       {N * index.x, index.y, index.z}, shape, a_strides, b_strides, ndim);
   auto xshape = shape[ndim - 1];
-  size_t out_idx =
-      N * index.x + xshape * (index.y + size_t(grid_dim.y) * index.z);
-  auto a_xstride = a_strides[ndim - 1];
-  auto b_xstride = b_strides[ndim - 1];
+  IdxT out_idx = N * index.x + xshape * (index.y + IdxT(grid_dim.y) * index.z);
+  IdxT a_xstride = a_strides[ndim - 1];
+  IdxT b_xstride = b_strides[ndim - 1];
   for (int i = 0; i < N && (int(N * index.x) + i) < xshape; ++i) {
     auto out = Op()(a[idx.x], b[idx.y]);
     c[out_idx] = out[0];

mlx/backend/metal/kernels/binary_two.metal

@@ -7,19 +7,21 @@
 #include "mlx/backend/metal/kernels/binary_ops.h"
 #include "mlx/backend/metal/kernels/binary_two.h"
 
-#define instantiate_binary_all(op, tname, itype, otype)                  \
-  instantiate_kernel("ss_" #op #tname, binary_ss, itype, otype, op)      \
-  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op)      \
-  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op)      \
-  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op)      \
-  instantiate_kernel("sv2_" #op #tname, binary_sv2, itype, otype, op)    \
-  instantiate_kernel("vs2_" #op #tname, binary_vs2, itype, otype, op)    \
-  instantiate_kernel("vv2_" #op #tname, binary_vv2, itype, otype, op)    \
-  instantiate_kernel("gn_" #op #tname, binary_g, itype, otype, op)       \
-  instantiate_kernel("gn4_" #op #tname, binary_g, itype, otype, op, 4)   \
-  instantiate_kernel("g1_" #op #tname, binary_g_nd1, itype, otype, op)   \
-  instantiate_kernel("g2_" #op #tname, binary_g_nd2, itype, otype, op)   \
-  instantiate_kernel("g3_" #op #tname, binary_g_nd3, itype, otype, op)   \
+#define instantiate_binary_all(op, tname, itype, otype)                      \
+  instantiate_kernel("ss_" #op #tname, binary_ss, itype, otype, op)          \
+  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op)          \
+  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op)          \
+  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op)          \
+  instantiate_kernel("sv2_" #op #tname, binary_sv2, itype, otype, op)        \
+  instantiate_kernel("vs2_" #op #tname, binary_vs2, itype, otype, op)        \
+  instantiate_kernel("vv2_" #op #tname, binary_vv2, itype, otype, op)        \
+  instantiate_kernel("gn2_" #op #tname, binary_g, itype, otype, op, 2, uint) \
+  instantiate_kernel("gn4large_" #op #tname, binary_g, itype, otype, op, 4)  \
+  instantiate_kernel("g1_" #op #tname, binary_g_nd1, itype, otype, op)       \
+  instantiate_kernel("g2_" #op #tname, binary_g_nd2, itype, otype, op, uint) \
+  instantiate_kernel("g3_" #op #tname, binary_g_nd3, itype, otype, op, uint) \
+  instantiate_kernel("g2large_" #op #tname, binary_g_nd2, itype, otype, op)  \
+  instantiate_kernel("g3large_" #op #tname, binary_g_nd3, itype, otype, op)
 
 #define instantiate_binary_float(op)                \
   instantiate_binary_all(op, float16, half, half)   \

mlx/backend/metal/kernels/conv.metal

@@ -4,8 +4,8 @@
 #include <metal_simdgroup_matrix>
 #include <metal_stdlib>
 
-#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/steel/conv/params.h"
+#include "mlx/backend/metal/kernels/utils.h"
 
 #define MLX_MTL_CONST static constant constexpr const
 
@@ -113,6 +113,7 @@ template <typename T, int N>
   for (int i = N - 1; i >= 0; --i) {
     int os_ = (oS % params->oS[i]);
     int ws_ = (wS % params->wS[i]);
+    out += ws_ * kernel_stride;
 
     ws_ = params->flip ? params->wS[i] - ws_ - 1 : ws_;
 
@@ -126,7 +127,6 @@ template <typename T, int N>
     oS /= params->oS[i];
     wS /= params->wS[i];
 
-    out += ws_ * kernel_stride;
     kernel_stride *= params->wS[i];
   }
 

mlx/backend/metal/kernels/copy.h

@@ -42,36 +42,36 @@ template <typename T, typename U>
     device U* dst [[buffer(1)]],
     constant const int64_t& src_stride [[buffer(3)]],
     uint index [[thread_position_in_grid]]) {
-  auto src_idx = elem_to_loc_1(index, src_stride);
+  auto src_idx = elem_to_loc_1<int64_t, int>(index, src_stride);
   dst[index] = static_cast<U>(src[src_idx]);
 }
 
-template <typename T, typename U>
+template <typename T, typename U, typename IdxT = int64_t>
 [[kernel]] void copy_g_nd2(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant const int64_t* src_strides [[buffer(3)]],
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto src_idx = elem_to_loc_2(index, src_strides);
-  int64_t dst_idx = index.x + (int64_t)grid_dim.x * index.y;
+  auto src_idx = elem_to_loc_2<int64_t, IdxT>(index, src_strides);
+  IdxT dst_idx = index.x + IdxT(grid_dim.x) * index.y;
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
-template <typename T, typename U>
+template <typename T, typename U, typename IdxT = int64_t>
 [[kernel]] void copy_g_nd3(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant const int64_t* src_strides [[buffer(3)]],
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto src_idx = elem_to_loc_3(index, src_strides);
-  int64_t dst_idx =
-      index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
+  auto src_idx = elem_to_loc_3<int64_t, IdxT>(index, src_strides);
+  IdxT dst_idx =
+      index.x + IdxT(grid_dim.x) * (index.y + IdxT(grid_dim.y) * index.z);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
-template <typename T, typename U, int N = 1>
+template <typename T, typename U, int N = 1, typename IdxT = int64_t>
 [[kernel]] void copy_g(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
@@ -80,17 +80,16 @@ template <typename T, typename U, int N = 1>
     constant const int& ndim [[buffer(5)]],
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto src_idx = elem_to_loc(
+  auto src_idx = elem_to_loc<int64_t, IdxT>(
       {N * index.x, index.y, index.z}, src_shape, src_strides, ndim);
   if (N == 1) {
-    int64_t dst_idx =
-        index.x + grid_dim.x * (index.y + int64_t(grid_dim.y) * index.z);
+    IdxT dst_idx =
+        index.x + grid_dim.x * (index.y + IdxT(grid_dim.y) * index.z);
     dst[dst_idx] = static_cast<U>(src[src_idx]);
     return;
   }
   auto xshape = src_shape[ndim - 1];
-  int64_t dst_idx =
-      N * index.x + xshape * (index.y + int64_t(grid_dim.y) * index.z);
+  IdxT dst_idx = N * index.x + xshape * (index.y + IdxT(grid_dim.y) * index.z);
   auto src_xstride = src_strides[ndim - 1];
   for (int i = 0; i < N && (int(N * index.x) + i) < xshape; ++i) {
     dst[dst_idx + i] = static_cast<U>(src[src_idx]);
@@ -105,36 +104,36 @@ template <typename T, typename U>
     constant const int64_t& src_stride [[buffer(3)]],
     constant const int64_t& dst_stride [[buffer(4)]],
     uint index [[thread_position_in_grid]]) {
-  auto src_idx = elem_to_loc_1(index, src_stride);
-  auto dst_idx = elem_to_loc_1(index, dst_stride);
+  auto src_idx = elem_to_loc_1<int64_t, int>(index, src_stride);
+  auto dst_idx = elem_to_loc_1<int64_t, int>(index, dst_stride);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
-template <typename T, typename U>
+template <typename T, typename U, typename IdxT = int64_t>
 [[kernel]] void copy_gg_nd2(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant const int64_t* src_strides [[buffer(3)]],
     constant const int64_t* dst_strides [[buffer(4)]],
     uint2 index [[thread_position_in_grid]]) {
-  auto src_idx = elem_to_loc_2(index, src_strides);
-  auto dst_idx = elem_to_loc_2(index, dst_strides);
+  auto src_idx = elem_to_loc_2<int64_t, IdxT>(index, src_strides);
+  auto dst_idx = elem_to_loc_2<int64_t, IdxT>(index, dst_strides);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
-template <typename T, typename U>
+template <typename T, typename U, typename IdxT = int64_t>
 [[kernel]] void copy_gg_nd3(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant const int64_t* src_strides [[buffer(3)]],
     constant const int64_t* dst_strides [[buffer(4)]],
     uint3 index [[thread_position_in_grid]]) {
-  auto src_idx = elem_to_loc_3(index, src_strides);
-  auto dst_idx = elem_to_loc_3(index, dst_strides);
+  auto src_idx = elem_to_loc_3<int64_t, IdxT>(index, src_strides);
+  auto dst_idx = elem_to_loc_3<int64_t, IdxT>(index, dst_strides);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
-template <typename T, typename U, int N = 1>
+template <typename T, typename U, int N = 1, typename IdxT = int64_t>
 [[kernel]] void copy_gg(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
@@ -143,7 +142,7 @@ template <typename T, typename U, int N = 1>
     constant const int64_t* dst_strides [[buffer(4)]],
     constant const int& ndim [[buffer(5)]],
     uint3 index [[thread_position_in_grid]]) {
-  auto idx = elem_to_loc_2_nd(
+  auto idx = elem_to_loc_2_nd<int64_t, IdxT>(
       {N * index.x, index.y, index.z},
       src_shape,
       src_strides,
@@ -153,8 +152,8 @@ template <typename T, typename U, int N = 1>
     dst[idx.y] = static_cast<U>(src[idx.x]);
     return;
   }
-  auto src_xstride = src_strides[ndim - 1];
-  auto dst_xstride = dst_strides[ndim - 1];
+  IdxT src_xstride = src_strides[ndim - 1];
+  IdxT dst_xstride = dst_strides[ndim - 1];
   auto xshape = src_shape[ndim - 1];
   for (int i = 0; i < N && (int(N * index.x) + i) < xshape; ++i) {
     dst[idx.y] = static_cast<U>(src[idx.x]);

mlx/backend/metal/kernels/copy.metal

@@ -2,24 +2,27 @@
 
 // clang-format off
 #include "mlx/backend/metal/kernels/utils.h"
-#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/copy.h"
 
-#define instantiate_copy_all(tname, itype, otype)    \
-  instantiate_kernel("s_copy" #tname, copy_s, itype, otype) \
-  instantiate_kernel("v_copy" #tname, copy_v, itype, otype) \
-  instantiate_kernel("s2_copy" #tname, copy_s2, itype, otype) \
-  instantiate_kernel("v2_copy" #tname, copy_v2, itype, otype) \
-  instantiate_kernel("g1_copy" #tname, copy_g_nd1, itype, otype) \
-  instantiate_kernel("g2_copy" #tname, copy_g_nd2, itype, otype) \
-  instantiate_kernel("g3_copy" #tname, copy_g_nd3, itype, otype) \
-  instantiate_kernel("gg1_copy" #tname, copy_gg_nd1, itype, otype) \
-  instantiate_kernel("gg2_copy" #tname, copy_gg_nd2, itype, otype) \
-  instantiate_kernel("gg3_copy" #tname, copy_gg_nd3, itype, otype) \
-  instantiate_kernel("g_copy" #tname, copy_g, itype, otype) \
-  instantiate_kernel("gn4_copy" #tname, copy_g, itype, otype, 4) \
-  instantiate_kernel("gg_copy" #tname, copy_gg, itype, otype) \
-  instantiate_kernel("ggn4_copy" #tname, copy_gg, itype, otype, 4)
+#define instantiate_copy_all(tname, itype, otype)                       \
+  instantiate_kernel("s_copy" #tname, copy_s, itype, otype)             \
+  instantiate_kernel("v_copy" #tname, copy_v, itype, otype)             \
+  instantiate_kernel("s2_copy" #tname, copy_s2, itype, otype)           \
+  instantiate_kernel("v2_copy" #tname, copy_v2, itype, otype)           \
+  instantiate_kernel("g1_copy" #tname, copy_g_nd1, itype, otype)        \
+  instantiate_kernel("g2_copy" #tname, copy_g_nd2, itype, otype, int)   \
+  instantiate_kernel("g3_copy" #tname, copy_g_nd3, itype, otype, int)   \
+  instantiate_kernel("gg1_copy" #tname, copy_gg_nd1, itype, otype)      \
+  instantiate_kernel("gg2_copy" #tname, copy_gg_nd2, itype, otype, int) \
+  instantiate_kernel("gg3_copy" #tname, copy_gg_nd3, itype, otype, int) \
+  instantiate_kernel("gn2_copy" #tname, copy_g, itype, otype, 2, int)   \
+  instantiate_kernel("ggn2_copy" #tname, copy_gg, itype, otype, 2, int) \
+  instantiate_kernel("g2large_copy" #tname, copy_g_nd2, itype, otype)   \
+  instantiate_kernel("g3large_copy" #tname, copy_g_nd3, itype, otype)   \
+  instantiate_kernel("gg2large_copy" #tname, copy_gg_nd2, itype, otype) \
+  instantiate_kernel("gg3large_copy" #tname, copy_gg_nd3, itype, otype) \
+  instantiate_kernel("gn4large_copy" #tname, copy_g, itype, otype, 4)   \
+  instantiate_kernel("ggn4large_copy" #tname, copy_gg, itype, otype, 4)
 
 #define instantiate_copy_itype(itname, itype)                \
   instantiate_copy_all(itname ##bool_, itype, bool)          \

mlx/backend/metal/kernels/gather.h

@@ -4,7 +4,7 @@
 
 #include "mlx/backend/metal/kernels/indexing.h"
 
-template <typename T, typename IdxT, int NIDX, int IDX_NDIM>
+template <typename T, typename IdxT, int NIDX, int IDX_NDIM, typename LocT>
 METAL_FUNC void gather_impl(
     const device T* src [[buffer(0)]],
     device T* out [[buffer(1)]],
@@ -16,34 +16,36 @@ METAL_FUNC void gather_impl(
     const thread Indices<IdxT, NIDX>& indices,
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  size_t src_idx = 0;
+  LocT src_idx = 0;
   for (int i = 0; i < NIDX; ++i) {
-    size_t idx_loc;
+    LocT idx_loc;
     if (IDX_NDIM == 0) {
       idx_loc = 0;
     } else if (IDX_NDIM == 1) {
-      idx_loc = index.x * indices.strides[indices.ndim * i];
+      idx_loc = index.x * static_cast<LocT>(indices.strides[indices.ndim * i]);
     } else {
-      idx_loc = index.x * indices.strides[indices.ndim * i];
-      idx_loc += elem_to_loc(
-          index.y,
-          &indices.shapes[indices.ndim * i + 1],
-          &indices.strides[indices.ndim * i + 1],
-          indices.ndim - 1);
+      idx_loc = index.x * static_cast<LocT>(indices.strides[indices.ndim * i]);
+      idx_loc += indices.row_contiguous[i]
+          ? index.y
+          : elem_to_loc<size_t, LocT>(
+                index.y,
+                &indices.shapes[indices.ndim * i + 1],
+                &indices.strides[indices.ndim * i + 1],
+                indices.ndim - 1);
     }
     auto ax = axes[i];
     auto idx_val = offset_neg_idx(indices.buffers[i][idx_loc], src_shape[ax]);
-    src_idx += idx_val * src_strides[ax];
+    src_idx += static_cast<LocT>(idx_val) * static_cast<LocT>(src_strides[ax]);
   }
 
-  auto src_offset = elem_to_loc(index.z, slice_sizes, src_strides, src_ndim);
+  auto src_offset =
+      elem_to_loc<size_t, LocT>(index.z, slice_sizes, src_strides, src_ndim);
 
-  size_t out_idx = index.z;
+  LocT out_idx = index.z;
   if (IDX_NDIM == 1) {
-    out_idx += static_cast<size_t>(grid_dim.z) * index.x;
+    out_idx += static_cast<LocT>(grid_dim.z) * index.x;
   } else if (IDX_NDIM >= 2) {
-    out_idx +=
-        grid_dim.z * (index.x * static_cast<size_t>(grid_dim.y) + index.y);
+    out_idx += grid_dim.z * (index.x * static_cast<LocT>(grid_dim.y) + index.y);
   }
   out[out_idx] = src[src_offset + src_idx];
 }

mlx/backend/metal/kernels/gemv.metal

@@ -3,8 +3,6 @@
 #include <metal_simdgroup>
 #include <metal_stdlib>
 
-#include "mlx/backend/metal/kernels/bf16.h"
-#include "mlx/backend/metal/kernels/defines.h"
 #include "mlx/backend/metal/kernels/utils.h"
 
 #include "mlx/backend/metal/kernels/steel/utils.h"
@@ -912,4 +910,4 @@ template <
 // clang-format off
 instantiate_gemv_t_bs_blocks(float32, float);
 instantiate_gemv_t_bs_blocks(float16, half);
-instantiate_gemv_t_bs_blocks(bfloat16, bfloat16_t); // clang-format on
+instantiate_gemv_t_bs_blocks(bfloat16, bfloat16_t); // clang-format on

mlx/backend/metal/kernels/gemv_masked.metal

@@ -4,8 +4,6 @@
 #include <metal_simdgroup>
 #include <metal_stdlib>
 
-#include "mlx/backend/metal/kernels/bf16.h"
-#include "mlx/backend/metal/kernels/defines.h"
 #include "mlx/backend/metal/kernels/utils.h"
 
 #include "mlx/backend/metal/kernels/gemv_masked.h"

mlx/backend/metal/kernels/indexing.h

@@ -9,11 +9,12 @@ struct Indices {
   const array<const device IdxT*, NIDX> buffers;
   const constant int* shapes;
   const constant size_t* strides;
+  const constant bool* row_contiguous;
   const int ndim;
 };
 
 template <typename IdxT>
-METAL_FUNC size_t offset_neg_idx(IdxT idx, size_t size) {
+METAL_FUNC size_t offset_neg_idx(IdxT idx, int size) {
   if (is_unsigned_v<IdxT>) {
     return idx;
   } else {

mlx/backend/metal/kernels/jit/bf16.h

@@ -0,0 +1,16 @@
+// Copyright © 2024 Apple Inc.
+
+// clang-format off
+#define jit_if #if
+#define jit_else #else
+#define jit_endif #endif
+
+jit_if (__METAL_VERSION__ >= 310)
+
+#include "mlx/backend/metal/kernels/metal_3_1/bf16.h"
+
+jit_else
+
+#include "mlx/backend/metal/kernels/metal_3_0/bf16.h"
+
+jit_endif // clang-format on

mlx/backend/metal/kernels/layer_norm.metal

@@ -3,8 +3,6 @@
 #include <metal_common>
 #include <metal_simdgroup>
 
-#include "mlx/backend/metal/kernels/bf16.h"
-#include "mlx/backend/metal/kernels/defines.h"
 #include "mlx/backend/metal/kernels/utils.h"
 
 using namespace metal;

mlx/backend/metal/kernels/metal_3_0/bf16.h

@@ -6,12 +6,6 @@
 
 using namespace metal;
 
-#if (MLX_METAL_VERSION >= 310) || (__METAL_VERSION__ >= 310)
-
-typedef bfloat bfloat16_t;
-
-#else
-
 /////////////////////////////////////////////////////////////////////////////
 // Helpers
 /////////////////////////////////////////////////////////////////////////////
@@ -311,7 +305,10 @@ METAL_FUNC bool isnan(_MLX_BFloat16 x) {
 } // namespace metal
 
 #pragma METAL internals : disable
+inline uint16_t bfloat16_to_uint16(const bfloat16_t x) {
+  return x.bits_;
+}
 
-#endif
-
-#include "mlx/backend/metal/kernels/bf16_math.h"
+inline bfloat16_t uint16_to_bfloat16(const uint16_t x) {
+  return _MLX_BFloat16(x, _MLX_BFloat16::bits_to_bfloat());
+}

mlx/backend/metal/kernels/metal_3_1/bf16.h

@@ -0,0 +1,16 @@
+// Copyright © 2023 Apple Inc.
+
+#pragma once
+
+#include <metal_stdlib>
+
+using namespace metal;
+
+typedef bfloat bfloat16_t;
+inline uint16_t bfloat16_to_uint16(const bfloat16_t x) {
+  return as_type<uint16_t>(x);
+}
+
+inline bfloat16_t uint16_to_bfloat16(const uint16_t x) {
+  return as_type<bfloat16_t>(x);
+}

mlx/backend/metal/kernels/quantized.metal

@@ -5,67 +5,119 @@
 #include "mlx/backend/metal/kernels/steel/gemm/gemm.h"
 #include "mlx/backend/metal/kernels/quantized.h"
 
-#define instantiate_quantized(name, type, group_size, bits) \
-  instantiate_kernel(                                       \
-      #name "_" #type "_gs_" #group_size "_b_" #bits,       \
-      name,                                                 \
-      type,                                                 \
-      group_size,                                           \
+#define instantiate_quantized(name, type, group_size, bits)     \
+  instantiate_kernel(                                                    \
+      #name "_" #type "_gs_" #group_size "_b_" #bits,                    \
+      name,                                                              \
+      type,                                                              \
+      group_size,                                                        \
       bits)
 
-#define instantiate_quantized_types(name, group_size, bits) \
-  instantiate_quantized(name, float, group_size, bits)      \
-  instantiate_quantized(name, float16_t, group_size, bits)  \
-  instantiate_quantized(name, bfloat16_t, group_size, bits)
+#define instantiate_quantized_batched(name, type, group_size, bits, batched)     \
+  instantiate_kernel(                                                    \
+      #name "_" #type "_gs_" #group_size "_b_" #bits "_batch_" #batched, \
+      name,                                                              \
+      type,                                                              \
+      group_size,                                                        \
+      bits,                                                              \
+      batched)
 
-#define instantiate_quantized_groups(name, bits) \
-  instantiate_quantized_types(name, 128, bits)   \
-  instantiate_quantized_types(name, 64, bits)    \
-  instantiate_quantized_types(name, 32, bits)
-
-#define instantiate_quantized_all(name) \
-  instantiate_quantized_groups(name, 2) \
-  instantiate_quantized_groups(name, 4) \
-  instantiate_quantized_groups(name, 8)
-
-instantiate_quantized_all(qmv_fast)
-instantiate_quantized_all(qmv)
-instantiate_quantized_all(qvm)
-instantiate_quantized_all(qmm_n)
-instantiate_quantized_all(bs_qmv_fast)
-instantiate_quantized_all(bs_qmv)
-instantiate_quantized_all(bs_qvm)
-instantiate_quantized_all(bs_qmm_n)
-instantiate_quantized_all(affine_quantize)
-instantiate_quantized_all(affine_quantize_scales_biases)
-instantiate_quantized_all(affine_dequantize)
-
-#define instantiate_quantized_aligned(name, type, group_size, bits, aligned) \
-  instantiate_kernel(                                                        \
-      #name "_" #type "_gs_" #group_size "_b_" #bits "_alN_" #aligned,       \
+#define instantiate_quantized_aligned(name, type, group_size, bits, aligned)     \
+  instantiate_kernel(                                                                     \
+      #name "_" #type "_gs_" #group_size "_b_" #bits "_alN_" #aligned, \
       name,                                                                  \
       type,                                                                  \
       group_size,                                                            \
       bits,                                                                  \
       aligned)
 
-#define instantiate_quantized_types_aligned(name, group_size, bits)       \
-  instantiate_quantized_aligned(name, float, group_size, bits, true)      \
-  instantiate_quantized_aligned(name, float16_t, group_size, bits, true)  \
-  instantiate_quantized_aligned(name, bfloat16_t, group_size, bits, true) \
-  instantiate_quantized_aligned(name, float, group_size, bits, false)     \
-  instantiate_quantized_aligned(name, float16_t, group_size, bits, false) \
-  instantiate_quantized_aligned(name, bfloat16_t, group_size, bits, false)
+#define instantiate_quantized_aligned_batched(name, type, group_size, bits, aligned, batched)     \
+  instantiate_kernel(                                                                     \
+      #name "_" #type "_gs_" #group_size "_b_" #bits "_alN_" #aligned "_batch_" #batched, \
+      name,                                                                  \
+      type,                                                                  \
+      group_size,                                                            \
+      bits,                                                                  \
+      aligned,                                                               \
+      batched)
 
-#define instantiate_quantized_groups_aligned(name, bits) \
-  instantiate_quantized_types_aligned(name, 128, bits)   \
-  instantiate_quantized_types_aligned(name, 64, bits)    \
-  instantiate_quantized_types_aligned(name, 32, bits)
+#define instantiate_quantized_quad(name, type, group_size, bits, D, batched)     \
+  instantiate_kernel(                                                            \
+      #name "_" #type "_gs_" #group_size "_b_" #bits "_d_" #D "_batch_" #batched, \
+      name,                                                         \
+      type,                                                         \
+      group_size,                                                   \
+      bits,                                                         \
+      D,                                                            \
+      batched)
 
-#define instantiate_quantized_all_aligned(name) \
-  instantiate_quantized_groups_aligned(name, 2) \
-  instantiate_quantized_groups_aligned(name, 4) \
-  instantiate_quantized_groups_aligned(name, 8) \
+#define instantiate_quantized_split_k(name, type, group_size, bits, split_k)     \
+  instantiate_kernel(                                                            \
+      #name "_" #type "_gs_" #group_size "_b_" #bits "_spk_" #split_k, \
+      name,                                                         \
+      type,                                                         \
+      group_size,                                                   \
+      bits,                                                         \
+      split_k)
 
-instantiate_quantized_all_aligned(qmm_t)
-instantiate_quantized_all_aligned(bs_qmm_t) // clang-format on
+#define instantiate_quantized_batched_wrap(name, type, group_size, bits) \
+  instantiate_quantized_batched(name, type, group_size, bits, 1)      \
+  instantiate_quantized_batched(name, type, group_size, bits, 0)
+
+#define instantiate_quantized_all_batched(type, group_size, bits) \
+  instantiate_quantized_batched_wrap(qmv_fast, type, group_size, bits)     \
+  instantiate_quantized_batched_wrap(qmv, type, group_size, bits)     \
+  instantiate_quantized_batched_wrap(qvm, type, group_size, bits)     \
+  instantiate_quantized_batched_wrap(qmm_n, type, group_size, bits)
+
+#define instantiate_quantized_all_single(type, group_size, bits) \
+  instantiate_quantized(affine_quantize, type, group_size, bits) \
+  instantiate_quantized(affine_dequantize, type, group_size, bits)     \
+  instantiate_quantized(bs_qmv_fast, type, group_size, bits)     \
+  instantiate_quantized(bs_qmv, type, group_size, bits)     \
+  instantiate_quantized(bs_qvm, type, group_size, bits)     \
+  instantiate_quantized(bs_qmm_n, type, group_size, bits)
+
+#define instantiate_quantized_all_aligned(type, group_size, bits)   \
+  instantiate_quantized_aligned(bs_qmm_t, type, group_size, bits, true) \
+  instantiate_quantized_aligned(bs_qmm_t, type, group_size, bits, false) \
+  instantiate_quantized_aligned_batched(qmm_t, type, group_size, bits, true, 1) \
+  instantiate_quantized_aligned_batched(qmm_t, type, group_size, bits, true, 0) \
+  instantiate_quantized_aligned_batched(qmm_t, type, group_size, bits, false, 1) \
+  instantiate_quantized_aligned_batched(qmm_t, type, group_size, bits, false, 0)
+
+#define instantiate_quantized_all_quad(type, group_size, bits)   \
+  instantiate_quantized_quad(qmv_quad, type, group_size, bits, 64, 1)   \
+  instantiate_quantized_quad(qmv_quad, type, group_size, bits, 64, 0)  \
+  instantiate_quantized_quad(qmv_quad, type, group_size, bits, 128, 1)  \
+  instantiate_quantized_quad(qmv_quad, type, group_size, bits, 128, 0)
+
+#define instantiate_quantized_all_splitk(type, group_size, bits)   \
+  instantiate_quantized_split_k(qvm_split_k, type, group_size, bits, 8)   \
+  instantiate_quantized_split_k(qvm_split_k, type, group_size, bits, 32)
+
+#define instantiate_quantized_funcs(type, group_size, bits) \
+  instantiate_quantized_all_single(type, group_size, bits)  \
+  instantiate_quantized_all_batched(type, group_size, bits) \
+  instantiate_quantized_all_aligned(type, group_size, bits) \
+  instantiate_quantized_all_quad(type, group_size, bits)    \
+  instantiate_quantized_all_splitk(type, group_size, bits)
+
+#define instantiate_quantized_types(group_size, bits)       \
+  instantiate_quantized_funcs(float, group_size, bits)      \
+  instantiate_quantized_funcs(float16_t, group_size, bits)  \
+  instantiate_quantized_funcs(bfloat16_t, group_size, bits)
+
+#define instantiate_quantized_groups(bits) \
+  instantiate_quantized_types(128, bits)   \
+  instantiate_quantized_types(64, bits)    \
+  instantiate_quantized_types(32, bits)
+
+#define instantiate_quantized_all() \
+  instantiate_quantized_groups(2) \
+  instantiate_quantized_groups(3) \
+  instantiate_quantized_groups(4) \
+  instantiate_quantized_groups(6) \
+  instantiate_quantized_groups(8)
+
+instantiate_quantized_all() // clang-format on

mlx/backend/metal/kernels/random.metal

@@ -34,8 +34,8 @@ rbits threefry2x32_hash(const thread uint2& key, uint2 count) {
 [[kernel]] void rbitsc(
     device const uint32_t* keys,
     device char* out,
-    device const bool& odd,
-    device const uint& bytes_per_key,
+    constant const bool& odd,
+    constant const uint& bytes_per_key,
     uint2 grid_dim [[threads_per_grid]],
     uint2 index [[thread_position_in_grid]]) {
   auto kidx = 2 * index.x;
@@ -67,8 +67,8 @@ rbits threefry2x32_hash(const thread uint2& key, uint2 count) {
 [[kernel]] void rbits(
     device const uint32_t* keys,
     device char* out,
-    device const bool& odd,
-    device const uint& bytes_per_key,
+    constant const bool& odd,
+    constant const uint& bytes_per_key,
     constant const int& ndim,
     constant const int* key_shape,
     constant const size_t* key_strides,

mlx/backend/metal/kernels/reduce.metal

@@ -10,178 +10,156 @@
 #include "mlx/backend/metal/kernels/reduction/ops.h"
 #include "mlx/backend/metal/kernels/reduce.h"
 
-#define instantiate_reduce_helper_floats(inst_f, name, op) \
-  inst_f(name, float16, half, op)                          \
-  inst_f(name, float32, float, op)                         \
-  inst_f(name, bfloat16, bfloat16_t, op)
+#define instantiate_init_reduce(name, tname, type, op) \
+  instantiate_kernel("init_reduce_" #name #tname, init_reduce, type, op<type>)
 
-#define instantiate_reduce_helper_uints(inst_f, name, op)  \
-  inst_f(name, uint8, uint8_t, op)                         \
-  inst_f(name, uint16, uint16_t, op)                       \
-  inst_f(name, uint32, uint32_t, op)
+instantiate_init_reduce(and, bool_, bool, And)
+instantiate_init_reduce(or, bool_, bool, Or)
 
-#define instantiate_reduce_helper_ints(inst_f, name, op) \
-  inst_f(name, int8, int8_t, op)                         \
-  inst_f(name, int16, int16_t, op)                       \
-  inst_f(name, int32, int32_t, op)
+#define instantiate_init_sum_prod(name, op)                 \
+  instantiate_init_reduce(name, int32, int32_t, op)         \
+  instantiate_init_reduce(name, int64, int64_t, op)         \
+  instantiate_init_reduce(name, float16, float16_t, op)     \
+  instantiate_init_reduce(name, bfloat16, bfloat16_t, op)   \
+  instantiate_init_reduce(name, float32, float, op)         \
+  instantiate_init_reduce(name, complex64, complex64_t, op)
 
-#define instantiate_reduce_helper_64b(inst_f, name, op) \
-  inst_f(name, int64, int64_t, op)                      \
-  inst_f(name, uint64, uint64_t, op)                    \
-  inst_f(name, complex64, complex64_t, op)
+instantiate_init_sum_prod(sum, Sum)
+instantiate_init_sum_prod(prod, Prod)
 
-#define instantiate_reduce_helper_types(inst_f, name, op) \
-  instantiate_reduce_helper_floats(inst_f, name, op)      \
-  instantiate_reduce_helper_uints(inst_f, name, op)       \
-  instantiate_reduce_helper_ints(inst_f, name, op)
+#define instantiate_init_min_max(name, op)                   \
+  instantiate_init_reduce(name, bool_, bool, op)             \
+  instantiate_init_reduce(name, int8, int8_t, op)            \
+  instantiate_init_reduce(name, int16, int16_t, op)          \
+  instantiate_init_reduce(name, int32, int32_t, op)          \
+  instantiate_init_reduce(name, int64, int64_t, op)          \
+  instantiate_init_reduce(name, uint8, uint8_t, op)          \
+  instantiate_init_reduce(name, uint16, uint16_t, op)        \
+  instantiate_init_reduce(name, uint32, uint32_t, op)        \
+  instantiate_init_reduce(name, uint64, uint64_t, op)        \
+  instantiate_init_reduce(name, float16, float16_t, op)      \
+  instantiate_init_reduce(name, bfloat16, bfloat16_t, op)    \
+  instantiate_init_reduce(name, float32, float, op)          \
+  instantiate_init_reduce(name, complex64, complex64_t, op)
 
-#define instantiate_reduce_ops(inst_f, type_f) \
-  type_f(inst_f, sum, Sum)                     \
-  type_f(inst_f, prod, Prod)                   \
-  type_f(inst_f, min, Min)                     \
-  type_f(inst_f, max, Max)
-
-// Special case for bool reductions
-#define instantiate_reduce_from_types_helper( \
-    inst_f, name, tname, itype, otype, op)    \
-    inst_f(name##tname, itype, otype, op)
-
-#define instantiate_reduce_from_types(inst_f, name, otype, op)  \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, bool_, bool, otype, op)                       \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, uint8, uint8_t, otype, op)                    \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, uint16, uint16_t, otype, op)                  \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, uint32, uint32_t, otype, op)                  \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, uint64, uint64_t, otype, op)                  \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, int8, int8_t, otype, op)                      \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, int16, int16_t, otype, op)                    \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, int32, int32_t, otype, op)                    \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, int64, int64_t, otype, op)                    \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f, name, float16, half, otype, op)                     \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f,                                                     \
-    name,                                                       \
-    float32,                                                    \
-    float,                                                      \
-    otype,                                                      \
-    op)                                                         \
-  instantiate_reduce_from_types_helper(                         \
-    inst_f,                                                     \
-    name,                                                       \
-    bfloat16,                                                   \
-    bfloat16_t,                                                 \
-    otype,                                                      \
-    op)
-
-#define instantiate_init_reduce(name, otype, op) \
-  instantiate_kernel("init_reduce_" #name,       \
-                     init_reduce,                \
-                     otype, op)
-
-#define instantiate_init_reduce_helper(name, tname, type, op) \
-  instantiate_init_reduce(name##tname, type, op<type>)
-
-instantiate_reduce_ops(instantiate_init_reduce_helper, instantiate_reduce_helper_types)
-instantiate_reduce_ops(instantiate_init_reduce_helper, instantiate_reduce_helper_64b)
-
-instantiate_init_reduce(andbool_, bool, And<bool>)
-instantiate_init_reduce(orbool_, bool, Or<bool>)
+instantiate_init_min_max(min, Min)
+instantiate_init_min_max(max, Max)
 
 #define instantiate_all_reduce(name, itype, otype, op) \
   instantiate_kernel("all_reduce_" #name,              \
                      all_reduce,                       \
                      itype, otype, op)
 
-#define instantiate_same_all_reduce_helper(name, tname, type, op) \
-  instantiate_all_reduce(name##tname, type, type, op<type>)
+#define instantiate_col_reduce_small(name, itype, otype, op, dim)          \
+  instantiate_kernel("col_reduce_small_" #dim "_reduce_" #name,            \
+                     col_reduce_small,                                     \
+                     itype, otype, op, uint, dim)                          \
+  instantiate_kernel("col_reduce_longcolumn_" #dim "_reduce_" #name,       \
+                     col_reduce_longcolumn,                                \
+                     itype, otype, op, uint, dim)                          \
+  instantiate_kernel("col_reduce_small_large_" #dim "_reduce_" #name,      \
+                     col_reduce_small,                                     \
+                     itype, otype, op, size_t, dim)                        \
+  instantiate_kernel("col_reduce_longcolumn_large_" #dim "_reduce_" #name, \
+                     col_reduce_longcolumn,                                \
+                     itype, otype, op, size_t, dim)
 
-instantiate_reduce_ops(instantiate_same_all_reduce_helper, instantiate_reduce_helper_types)
-instantiate_reduce_ops(instantiate_same_all_reduce_helper, instantiate_reduce_helper_64b)
+#define instantiate_col_reduce_looped_tile(name, itype, otype, op, dim, bm, bn)        \
+  instantiate_kernel("col_reduce_looped_" #dim "_" #bm "_" #bn "_reduce_" #name,       \
+                     col_reduce_looped,                                                \
+                     itype, otype, op, uint, dim, bm, bn)                              \
+  instantiate_kernel("col_reduce_looped_large_" #dim "_" #bm "_" #bn "_reduce_" #name, \
+                     col_reduce_looped,                                                \
+                     itype, otype, op, size_t, dim, bm, bn)
 
-instantiate_reduce_from_types(instantiate_all_reduce, and, bool, And<bool>)
-instantiate_reduce_from_types(instantiate_all_reduce, or, bool, Or<bool>)
-
-// special case bool with larger output type
-instantiate_all_reduce(sumbool_, bool, uint32_t, Sum<uint32_t>)
-
-#define instantiate_col_reduce_small(name, itype, otype, op, dim) \
-  instantiate_kernel("col_reduce_small_" #dim "_reduce_" #name,   \
-                     col_reduce_small,                            \
-                     itype, otype, op, dim)
-
-#define instantiate_col_reduce_looped_tile(name, itype, otype, op, dim, bm, bn)  \
-  instantiate_kernel("col_reduce_looped_" #dim "_" #bm "_" #bn "_reduce_" #name, \
-                     col_reduce_looped,                                          \
-                     itype, otype, op, dim, bm, bn)
+#define instantiate_col_reduce_2pass_tile(name, itype, otype, op, dim, bm, bn)        \
+  instantiate_kernel("col_reduce_2pass_" #dim "_" #bm "_" #bn "_reduce_" #name,       \
+                     col_reduce_2pass,                                                \
+                     itype, otype, op, uint, dim, bm, bn)                             \
+  instantiate_kernel("col_reduce_2pass_large_" #dim "_" #bm "_" #bn "_reduce_" #name, \
+                     col_reduce_2pass,                                                \
+                     itype, otype, op, size_t, dim, bm, bn)
 
 #define instantiate_col_reduce_looped(name, itype, otype, op, dim)        \
-  instantiate_col_reduce_looped_tile(name, itype, otype, op, dim, 8, 128) \
-  instantiate_col_reduce_looped_tile(name, itype, otype, op, dim, 32, 32)
+  instantiate_col_reduce_looped_tile(name, itype, otype, op, dim, 32, 32) \
+  instantiate_col_reduce_2pass_tile(name, itype, otype, op, dim, 32, 32)
 
 #define instantiate_col_reduce_general(name, itype, otype, op) \
-  instantiate_col_reduce_small(name, itype, otype, op, 0)      \
   instantiate_col_reduce_small(name, itype, otype, op, 1)      \
   instantiate_col_reduce_small(name, itype, otype, op, 2)      \
-  instantiate_col_reduce_small(name, itype, otype, op, 3)      \
-  instantiate_col_reduce_small(name, itype, otype, op, 4)      \
-  instantiate_col_reduce_looped(name, itype, otype, op, 0)     \
+  instantiate_col_reduce_small(name, itype, otype, op, 5)      \
   instantiate_col_reduce_looped(name, itype, otype, op, 1)     \
   instantiate_col_reduce_looped(name, itype, otype, op, 2)     \
-  instantiate_col_reduce_looped(name, itype, otype, op, 3)     \
-  instantiate_col_reduce_looped(name, itype, otype, op, 4)
+  instantiate_col_reduce_looped(name, itype, otype, op, 5)
 
-#define instantiate_same_col_reduce_helper(name, tname, type, op)  \
-  instantiate_col_reduce_general(name##tname, type, type, op<type>)
+#define instantiate_row_reduce_small(name, itype, otype, op, dim)     \
+  instantiate_kernel("row_reduce_small_" #dim "_reduce_" #name,       \
+                     row_reduce_small,                                \
+                     itype, otype, op, uint, dim)                     \
+  instantiate_kernel("row_reduce_small_large_" #dim "_reduce_" #name, \
+                     row_reduce_small,                                \
+                     itype, otype, op, size_t, dim)
 
-instantiate_reduce_ops(instantiate_same_col_reduce_helper, instantiate_reduce_helper_types)
-instantiate_reduce_ops(instantiate_same_col_reduce_helper, instantiate_reduce_helper_64b)
-
-instantiate_col_reduce_general(sumbool_, bool, uint32_t, Sum<uint32_t>)
-instantiate_reduce_from_types(instantiate_col_reduce_general, and, bool, And<bool>)
-instantiate_reduce_from_types(instantiate_col_reduce_general, or, bool, Or<bool>)
-
-#define instantiate_row_reduce_small(name, itype, otype, op, dim) \
-  instantiate_kernel("row_reduce_small_" #dim "_reduce_" #name,   \
-                     row_reduce_small,                            \
-                     itype, otype, op, dim)
-
-#define instantiate_row_reduce_looped(name, itype, otype, op, dim) \
-  instantiate_kernel("row_reduce_looped_" #dim "_reduce_" #name,   \
-                     row_reduce_looped, \
-                     itype, otype, op, dim)
+#define instantiate_row_reduce_looped(name, itype, otype, op, dim)       \
+  instantiate_kernel("row_reduce_looped_" #dim "_reduce_" #name,         \
+                     row_reduce_looped,                                  \
+                     itype, otype, op, uint, dim)                        \
+  instantiate_kernel("row_reduce_looped_large_" #dim "_reduce_" #name,   \
+                     row_reduce_looped,                                  \
+                     itype, otype, op, size_t, dim)
 
 #define instantiate_row_reduce_general(name, itype, otype, op) \
-  instantiate_row_reduce_small(name, itype, otype, op, 0)      \
   instantiate_row_reduce_small(name, itype, otype, op, 1)      \
   instantiate_row_reduce_small(name, itype, otype, op, 2)      \
-  instantiate_row_reduce_small(name, itype, otype, op, 3)      \
-  instantiate_row_reduce_small(name, itype, otype, op, 4)      \
-  instantiate_row_reduce_looped(name, itype, otype, op, 0)     \
+  instantiate_row_reduce_small(name, itype, otype, op, 5)      \
   instantiate_row_reduce_looped(name, itype, otype, op, 1)     \
   instantiate_row_reduce_looped(name, itype, otype, op, 2)     \
-  instantiate_row_reduce_looped(name, itype, otype, op, 3)     \
-  instantiate_row_reduce_looped(name, itype, otype, op, 4)     \
+  instantiate_row_reduce_looped(name, itype, otype, op, 5)     \
   instantiate_kernel("row_reduce_simple_" #name,               \
                      row_reduce_simple,                        \
                      itype, otype, op)
 
-#define instantiate_same_row_reduce_helper(name, tname, type, op)  \
-  instantiate_row_reduce_general(name##tname, type, type, op<type>)
+#define instantiate_reduce_functions(name, tname, itype, otype, op)    \
+  instantiate_all_reduce(name##tname, itype, otype, op<otype>)         \
+  instantiate_row_reduce_general(name##tname, itype, otype, op<otype>) \
+  instantiate_col_reduce_general(name##tname, itype, otype, op<otype>)
 
-instantiate_reduce_ops(instantiate_same_row_reduce_helper, instantiate_reduce_helper_types)
-instantiate_reduce_ops(instantiate_same_row_reduce_helper, instantiate_reduce_helper_64b)
+#define instantiate_and_or(name, op)                           \
+  instantiate_reduce_functions(name, bool_, bool, bool, op)   \
+  instantiate_reduce_functions(name, int16, int16_t, bool, op) \
+  instantiate_reduce_functions(name, int32, int32_t, bool, op) \
+  instantiate_reduce_functions(name, int64, int64_t, bool, op)
 
-instantiate_reduce_from_types(instantiate_row_reduce_general, and, bool, And<bool>)
-instantiate_reduce_from_types(instantiate_row_reduce_general, or, bool, Or<bool>)
+instantiate_and_or(and, And)
+instantiate_and_or(or, Or)
 
-instantiate_row_reduce_general(sumbool_, bool, uint32_t, Sum<uint32_t>)
+#define instantiate_sum_prod(name, op)                                       \
+  instantiate_reduce_functions(name, int8, int8_t, int32_t, op)              \
+  instantiate_reduce_functions(name, int16, int16_t, int32_t, op)            \
+  instantiate_reduce_functions(name, int32, int32_t, int32_t, op)            \
+  instantiate_reduce_functions(name, int64, int64_t, int64_t, op)            \
+  instantiate_reduce_functions(name, float16, float16_t, float16_t, op)      \
+  instantiate_reduce_functions(name, bfloat16, bfloat16_t, bfloat16_t, op)   \
+  instantiate_reduce_functions(name, float32, float, float, op)              \
+  instantiate_reduce_functions(name, complex64, complex64_t, complex64_t, op)
+
+instantiate_sum_prod(sum, Sum)
+instantiate_sum_prod(prod, Prod)
+
+#define instantiate_min_max(name, op)                                        \
+  instantiate_reduce_functions(name, int8, int8_t, int8_t, op)               \
+  instantiate_reduce_functions(name, int16, int16_t, int16_t, op)            \
+  instantiate_reduce_functions(name, int32, int32_t, int32_t, op)            \
+  instantiate_reduce_functions(name, int64, int64_t, int64_t, op)            \
+  instantiate_reduce_functions(name, uint8, uint8_t, uint8_t, op)            \
+  instantiate_reduce_functions(name, uint16, uint16_t, uint16_t, op)         \
+  instantiate_reduce_functions(name, uint32, uint32_t, uint32_t, op)         \
+  instantiate_reduce_functions(name, uint64, uint64_t, uint64_t, op)         \
+  instantiate_reduce_functions(name, float16, float16_t, float16_t, op)      \
+  instantiate_reduce_functions(name, bfloat16, bfloat16_t, bfloat16_t, op)   \
+  instantiate_reduce_functions(name, float32, float, float, op)              \
+  instantiate_reduce_functions(name, complex64, complex64_t, complex64_t, op)
+
+instantiate_min_max(min, Min)
+instantiate_min_max(max, Max)
     // clang-format on