Enable attn build outside of jit

* start of reduce specializations

* fix all reduce

* fix many dims

* fix

* non-jit tests clear

* cleanup instantiations

* cpu merges

* change dim specializations

* optimize

* fix jit

* fix jit

* use higher precision for integer sum+prod

* fixes

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 0.19.3)
+  set(MLX_VERSION 0.20.0)
 endif()
 
 # --------------------- Processor tests -------------------------
@@ -89,25 +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)
@@ -115,8 +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)

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

@@ -4,42 +4,51 @@ import math
 import mlx.core as mx
 from time_utils import time_fn
 
-L = 1024
+L = 16384
 H = 32
-H_k = 32 // 4
+H_k = H // 4
 D = 128
+dtype = mx.float16
+loops = 10
 
 
 def attention(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)
+    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):
-    return mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0)
+    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))
-    k = mx.random.uniform(shape=(1, H_k, L, D))
-    v = mx.random.uniform(shape=(1, H_k, L, D))
+    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))
-    k = mx.random.uniform(shape=(1, H_k, L, D))
-    v = mx.random.uniform(shape=(1, H_k, L, D))
+    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)
 

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

@@ -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
 ^^^^^^^^^^^^^^^

docs/src/usage/function_transforms.rst

@@ -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.

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);

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

@@ -214,6 +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();
@@ -292,6 +294,14 @@ array::ArrayDesc::~ArrayDesc() {
     auto top = std::move(for_deletion.back());
     for_deletion.pop_back();
     append_deletable_inputs(*top);
+
+    // 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/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

@@ -279,7 +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;

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];
@@ -606,7 +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/quantized.cpp

@@ -2,7 +2,9 @@
 
 #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"
 
 namespace mlx::core {
@@ -404,4 +406,103 @@ void GatherQMM::eval(const std::vector<array>& inputs, array& out) {
       transpose_);
 }
 
+template <typename T>
+void quantize(
+    const array& w_,
+    array& out_,
+    array& scales_,
+    array& biases_,
+    int bits,
+    int group_size,
+    bool compute_scale_bias) {
+  const T* w = w_.data<T>();
+  T* scales = scales_.data<T>();
+  T* biases = biases_.data<T>();
+  auto out = out_.data<uint32_t>();
+
+  T n_bins = (1 << bits) - 1;
+  T eps = 1e-7;
+  int el_per_int = 32 / bits;
+  int int_per_group = group_size / 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;
+    if (compute_scale_bias) {
+      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; ++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);
+      }
+      out[out_idx + j] = out_el;
+    }
+  }
+}
+
+void fast::AffineQuantize::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  bool compute_scale_bias = inputs.size() == 1;
+
+  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 =
+      compute_scale_bias ? outputs[1] : const_cast<array&>(inputs[1]);
+  auto& biases =
+      compute_scale_bias ? outputs[2] : const_cast<array&>(inputs[2]);
+  if (compute_scale_bias) {
+    scales.set_data(allocator::malloc_or_wait(scales.nbytes()));
+    biases.set_data(allocator::malloc_or_wait(biases.nbytes()));
+  }
+  if (w.dtype() == float16) {
+    quantize<float16_t>(
+        w, out, scales, biases, bits_, group_size_, compute_scale_bias);
+  } else if (w.dtype() == bfloat16) {
+    quantize<bfloat16_t>(
+        w, out, scales, biases, bits_, group_size_, compute_scale_bias);
+  } else if (w.dtype() == float32) {
+    quantize<float>(
+        w, out, scales, biases, bits_, group_size_, compute_scale_bias);
+  } 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/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

@@ -178,4 +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

@@ -14,14 +14,21 @@ 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)

mlx/backend/metal/allocator.cpp

@@ -242,6 +242,9 @@ 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();

mlx/backend/metal/binary.cpp

@@ -22,37 +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(
@@ -81,18 +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) ? 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
@@ -117,19 +122,15 @@ 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++);
     }
 
     if (thread_group_size != 1024) {
@@ -137,7 +138,7 @@ void binary_op_gpu_inplace(
     }
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     // Launch a 1D or 2D grid of threads
     size_t nthreads = out.data_size();
@@ -145,9 +146,9 @@ void binary_op_gpu_inplace(
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                                 : MTL::Size(nthreads, 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"
@@ -11,12 +12,12 @@
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
+using namespace fmt::literals;
+
 namespace mlx::core {
 
-constexpr int WORK_PER_THREAD = 4;
-
 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,
@@ -41,8 +42,8 @@ inline void build_kernel(
   int cnt = 0;
 
   // Start the kernel
-  os << "[[host_name(\"" << kernel_name << "\")]]\n"
-     << "[[kernel]] void " << kernel_name << "(\n";
+  os += fmt::format(
+      "[[host_name(\"{0}\")]]\n[[kernel]] void {0}(\n", kernel_name);
 
   // Add the input arguments
   for (auto& x : inputs) {
@@ -54,51 +55,61 @@ 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++ << ")]],\n";
-    } else {
+    if (!is_scalar(x) && !contiguous) {
       add_indices = true;
-      os << "    device const " << get_type_string(x.dtype()) << "* " << xname
-         << " [[buffer(" << cnt++ << ")]],\n";
     }
+    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++ << ")]],\n";
+    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++
-       << ")]],\n"
-       << "    constant const int* output_shape [[buffer(" << cnt++ << ")]],\n";
+    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++ << ")]],\n";
+    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]],\n"
-     << "    uint3 grid [[threads_per_grid]]) {\n";
+  os += "    uint3 pos [[thread_position_in_grid]],\n";
+  os += "    uint3 grid [[threads_per_grid]]) {\n";
 
-  if (use_big_index) {
+  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);\n";
+    os += "  size_t index = pos.x + grid.x * size_t(pos.y);\n";
   } else if (work_per_thread > 1) {
-    os << "  constexpr int N_ = " << std::to_string(work_per_thread) << ";\n"
-       << "  int xshape = output_shape["
-       << (dynamic_dims ? "ndim - 1" : std::to_string(ndim - 1)) << "];\n"
-       << "  size_t index = N_ * pos.x + xshape * (pos.y + size_t(grid.y) * pos.z);\n";
+    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 << " size_t index = pos.x + grid.x * (pos.y + size_t(grid.y) * pos.z);\n";
+    os += fmt::format(
+        "  {0} index = pos.x + grid.x * (pos.y + {0}(grid.y) * pos.z);\n",
+        idx_type);
   }
 
   // Read constant / contiguous inputs in tmps
@@ -109,16 +120,19 @@ inline void build_kernel(
 
     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 << ");\n";
+      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];\n";
+      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];\n";
+      os += fmt::format(
+          "  {0} tmp_{1} = {1}[index];\n", get_type_string(x.dtype()), xname);
     } else {
       nc_inputs.push_back(x);
     }
@@ -127,83 +141,98 @@ inline void build_kernel(
   // 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 << "  size_t index_" << xname << " = elem_to_loc_1(pos.x, "
-         << "in_strides[" << offset << "]);\n";
+      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 << "  size_t index_" << xname << " = elem_to_loc_2({pos.x, pos.y}, "
-         << "in_strides + " << offset << ");\n";
+      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 << "  size_t index_" << xname << " = elem_to_loc_3(pos, "
-         << "in_strides + " << offset << ");\n";
+      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 * ndim;
-      os << "  size_t index_" << xname << " = N_ * pos.x * in_strides["
-         << offset + ndim - 1 << "]"
-         << " + pos.y * in_strides[" << offset + ndim - 2 << "];\n";
+      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 << "  size_t index_" << xname << " = N_ * pos.x * in_strides[ndim * "
-         << i << " + ndim - 1]"
-         << " + pos.y * in_strides[ndim * " << i << " + ndim - 2];\n";
+      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";
+    os += "  uint zpos = pos.z;\n";
     if (dynamic_dims) {
-      os << "  for (int d = ndim - 3; d >= 0; --d) {\n";
+      os += "  for (int d = ndim - 3; d >= 0; --d) {\n";
     } else {
-      os << "  for (int d = " << ndim - 3 << "; d >= 0; --d) {\n";
+      os += fmt::format("  for (int d = {0}; d >= 0; --d) {{\n", ndim - 3);
     }
-    os << "    uint l = zpos % output_shape[d];\n";
+    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 << "    index_" << xname << " += ";
+      os += fmt::format("    index_{0} += ", xname);
       if (dynamic_dims) {
-        os << "l * in_strides[" << i << " * ndim + d];\n";
+        os +=
+            fmt::format("l * {0}(in_strides[{1} * ndim + d]);\n", idx_type, i);
       } else {
-        os << "l * in_strides[" << i * ndim << " + d];\n";
+        os +=
+            fmt::format("l * {0}(in_strides[{1} + d]);\n", idx_type, i * ndim);
       }
     }
-    os << "    zpos /= output_shape[d];\n  }\n";
+    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";
+    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 << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
-       << xname << "[index_" << xname << "];\n";
+    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]) << ");\n";
+      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()) << ");\n";
+      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)
-       << ";\n";
+    os += fmt::format("  {0}[index] = tmp_{0};\n", namer.get_name(x));
   }
   // Increment indices and close per thread loop
   if (work_per_thread > 1) {
@@ -211,18 +240,18 @@ inline void build_kernel(
       auto& x = nc_inputs[i];
       auto& xname = namer.get_name(x);
       if (!dynamic_dims) {
-        os << "  index_" << xname << " += "
-           << "in_strides[" << i * ndim + ndim - 1 << "];\n";
+        os += fmt::format(
+            "  index_{0} += in_strides[{1}];\n", xname, i * ndim + ndim - 1);
       } else {
-        os << "  index_" << xname << " += "
-           << "in_strides[" << i << " * ndim + ndim - 1];\n";
+        os += fmt::format(
+            "  index_{0} += in_strides[{1} * ndim + ndim - 1];\n", xname, i);
       }
     }
-    os << "  index++;\n  }\n";
+    os += "  index++;\n  }\n";
   }
 
   // Finish the kernel
-  os << "}\n";
+  os += "}\n";
 
   if (cnt > 31) {
     std::ostringstream msg;
@@ -246,9 +275,9 @@ void Compiled::eval_gpu(
   auto& s = stream();
   auto& d = metal::device(s.device);
   auto lib = d.get_library(kernel_lib_, [&]() {
-    std::ostringstream kernel;
-    kernel << metal::utils() << metal::unary_ops() << metal::binary_ops()
-           << metal::ternary_ops();
+    std::string kernel = metal::utils();
+    concatenate(
+        kernel, metal::unary_ops(), metal::binary_ops(), metal::ternary_ops());
     build_kernel(
         kernel,
         kernel_lib_ + "_contiguous",
@@ -261,7 +290,7 @@ void Compiled::eval_gpu(
         /* dynamic_dims = */ false);
     build_kernel(
         kernel,
-        kernel_lib_ + "_contiguous_big",
+        kernel_lib_ + "_contiguous_large",
         inputs_,
         outputs_,
         tape_,
@@ -282,7 +311,21 @@ void Compiled::eval_gpu(
           /* ndim = */ i,
           /* dynamic_dims = */ false,
           /* use_big_index = */ false,
-          /* work_per_thread = */ i > 3 ? WORK_PER_THREAD : 1);
+          /* 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,
@@ -295,13 +338,25 @@ void Compiled::eval_gpu(
         /* ndim = */ 0,
         /* dynamic_dims = */ true,
         /* use_big_index = */ false,
-        /* work_per_thread = */ WORK_PER_THREAD);
-    return kernel.str();
+        /* 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.
@@ -349,13 +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
@@ -368,12 +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;
@@ -394,8 +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(
@@ -408,14 +469,13 @@ 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
@@ -424,15 +484,15 @@ void Compiled::eval_gpu(
     MTL::Size group_dims(
         std::min(nthreads, kernel->maxTotalThreadsPerThreadgroup()), 1, 1);
 
-    MTL::Size grid_dims = use_2d
+    MTL::Size grid_dims = large
         ? get_2d_grid_dims(outputs[0].shape(), outputs[0].strides())
         : MTL::Size(nthreads, 1, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    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 ? WORK_PER_THREAD : 1;
+    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();
     int pow2;
@@ -445,7 +505,7 @@ void Compiled::eval_gpu(
     }
     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};
@@ -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.
@@ -237,7 +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];
 
@@ -252,8 +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(
@@ -352,7 +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;
@@ -368,11 +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(
@@ -506,7 +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;
@@ -523,17 +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(
@@ -622,18 +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
@@ -650,18 +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
@@ -698,18 +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);
   }
 }
 

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 {
-        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());
@@ -125,11 +127,11 @@ void copy_gpu_inplace(
     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;
@@ -141,7 +143,7 @@ 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;
     }
 
@@ -152,16 +154,16 @@ void copy_gpu_inplace(
 
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     size_t nthreads = out.data_size();
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                                 : MTL::Size(nthreads, 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);
   }
 }
 
@@ -193,13 +195,13 @@ 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);
@@ -210,9 +212,9 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
     thread_group_size = nthreads;
   }
   MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-  MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                               : MTL::Size(nthreads, 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

@@ -43,7 +43,7 @@ void CustomKernel::eval_gpu(
       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];
@@ -53,15 +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++;
       }
     }
@@ -72,10 +72,11 @@ 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);
 
   d.add_temporaries(std::move(copies), s.index);
 }

mlx/backend/metal/device.cpp

@@ -23,14 +23,18 @@ constexpr int MAX_BUFFERS_PER_QUEUE = 12;
 
 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() {
@@ -171,14 +175,14 @@ void CommandEncoder::maybeInsertBarrier() {
   next_outputs_.clear();
 }
 
-void CommandEncoder::dispatchThreadgroups(
+void CommandEncoder::dispatch_threadgroups(
     MTL::Size grid_dims,
     MTL::Size group_dims) {
   maybeInsertBarrier();
   enc_->dispatchThreadgroups(grid_dims, group_dims);
 }
 
-void CommandEncoder::dispatchThreads(
+void CommandEncoder::dispatch_threads(
     MTL::Size grid_dims,
     MTL::Size group_dims) {
   maybeInsertBarrier();
@@ -298,7 +302,7 @@ void Device::end_encoding(int index) {
         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->waitForFence(it->second->fence);
+            enc.wait_for_fence(it->second->fence);
             waiting_on.insert(it->second);
           }
         }
@@ -307,7 +311,7 @@ void Device::end_encoding(int index) {
         stream.outputs[out] = stream.fence;
       }
     }
-    enc->updateFence(stream.fence->fence);
+    enc.update_fence(stream.fence->fence);
     stream.buffer->addCompletedHandler(
         [&stream,
          waiting_on = std::move(waiting_on),

mlx/backend/metal/device.h

@@ -58,16 +58,43 @@ struct CommandEncoder {
     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);
   }

mlx/backend/metal/fft.cpp

@@ -699,7 +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);
 
@@ -711,9 +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);
@@ -723,22 +723,22 @@ 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.add_temporaries(std::move(copies), s.index);

mlx/backend/metal/hadamard.cpp

@@ -137,14 +137,14 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
     auto kernel = d.get_kernel(kernel_name, lib);
     assert(threads_per <= kernel->maxTotalThreadsPerThreadgroup());
 
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(in, 0);
     compute_encoder.set_output_array(out, 1);
-    compute_encoder->setBytes(&scale, sizeof(float), 2);
+    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->dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   };
 
   if (m > 1) {

mlx/backend/metal/indexing.cpp

@@ -53,27 +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;
+  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;
+
   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;
-  }
+  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::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::gather();
+    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,
@@ -81,13 +85,14 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
         nidx,
         idx_args,
         idx_arr,
-        idx_ndim);
-    return 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_) {
@@ -131,20 +136,20 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_output_array(out, 1);
 
   // Set source info
-  set_vector_bytes(compute_encoder, src.shape(), 2);
-  set_vector_bytes(compute_encoder, src.strides(), 3);
-  compute_encoder->setBytes(&ndim, sizeof(size_t), 4);
-  set_vector_bytes(compute_encoder, slice_sizes_, 5);
-  set_vector_bytes(compute_encoder, axes_, 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
-  set_vector_bytes(compute_encoder, idx_shapes, 7);
-  set_vector_bytes(compute_encoder, idx_strides, 8);
-  set_vector_bytes(compute_encoder, idx_contigs, 9);
-  compute_encoder->setBytes(&idx_ndim, sizeof(int), 10);
+  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) {
@@ -152,7 +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) {
@@ -209,8 +214,6 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     nwork = 32;
   }
 
-  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_) {
@@ -231,18 +234,24 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
       break;
   }
   auto upd_contig = upd.flags().row_contiguous;
-  {
-    std::ostringstream kname;
-    kname << "scatter" << type_to_name(out) << idx_type_name;
-    kname << "_" << op_name << "_" << nidx << "_"
-          << (upd_contig ? "updc_true" : "updc_false") << "_nwork" << nwork;
-    lib_name = kname.str();
-    kernel_name = kname.str();
-  }
+  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;
+
   auto lib = d.get_library(lib_name, [&]() {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::reduce_utils()
-                  << metal::scatter();
+    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 =
@@ -270,7 +279,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     }
     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,
@@ -280,8 +289,9 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
         idx_args,
         idx_arr,
         upd_contig,
-        nwork);
-    return kernel_source.str();
+        nwork,
+        large ? "size_t" : "uint");
+    return kernel_source;
   });
 
   auto& compute_encoder = d.get_command_encoder(s.index);
@@ -289,7 +299,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   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);
@@ -323,14 +333,14 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     // 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);
+    compute_encoder.set_bytes(shape_, 3);
+    compute_encoder.set_bytes(stride_, 4);
   } else {
-    set_vector_bytes(compute_encoder, upd.shape(), 3);
-    set_vector_bytes(compute_encoder, upd.strides(), 4);
+    compute_encoder.set_vector_bytes(upd.shape(), 3);
+    compute_encoder.set_vector_bytes(upd.strides(), 4);
   }
-  compute_encoder->setBytes(&upd_ndim, sizeof(size_t), 5);
-  compute_encoder->setBytes(&upd_size, sizeof(size_t), 6);
+  compute_encoder.set_bytes(upd_ndim, 5);
+  compute_encoder.set_bytes(upd_size, 6);
 
   // Set output info
   size_t out_ndim = out.ndim();
@@ -338,14 +348,14 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     // 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);
+    compute_encoder.set_bytes(shape_, 7);
+    compute_encoder.set_bytes(stride_, 8);
   } else {
-    set_vector_bytes(compute_encoder, out.shape(), 7);
-    set_vector_bytes(compute_encoder, out.strides(), 8);
+    compute_encoder.set_vector_bytes(out.shape(), 7);
+    compute_encoder.set_vector_bytes(out.strides(), 8);
   }
-  compute_encoder->setBytes(&out_ndim, sizeof(size_t), 9);
-  compute_encoder->setBytes(axes_.data(), axes_.size() * sizeof(int), 10);
+  compute_encoder.set_bytes(out_ndim, 9);
+  compute_encoder.set_vector_bytes(axes_, 10);
 
   // Set index info
   if (idx_ndim == 0) {
@@ -355,11 +365,11 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     idx_strides.push_back(0);
     idx_contigs.push_back(false);
   }
-  set_vector_bytes(compute_encoder, idx_shapes, 11);
-  set_vector_bytes(compute_encoder, idx_strides, 12);
-  set_vector_bytes(compute_encoder, idx_contigs, 13);
-  compute_encoder->setBytes(&idx_ndim, sizeof(int), 14);
-  compute_encoder->setBytes(&idx_size, sizeof(size_t), 15);
+  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) {
@@ -375,7 +385,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     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.dispatchThreads(grid_dims, group_dims);
+  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)]],
@@ -19,7 +19,7 @@ constexpr std::string_view gather_kernels = R"(
   Indices<{2}, {3}> idxs{{
     {{ {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,
@@ -34,7 +34,7 @@ constexpr std::string_view gather_kernels = R"(
 )";
 
 constexpr std::string_view scatter_kernels = R"(
-[[kernel]] void scatter{0}_{4}_updc_{7}_nwork{8}(
+[[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)]],
@@ -54,7 +54,7 @@ constexpr std::string_view scatter_kernels = R"(
     uint2 gid [[thread_position_in_grid]]) {{
   Indices<{2}, {4}> idxs{{ {{ {6} }}, idx_shapes, idx_strides, idx_contigs, idx_ndim}};
 
-  return scatter_impl<{1}, {2}, {3}, {4}, {7}, {8}>(
+  return scatter_impl<{1}, {2}, {3}, {4}, {7}, {8}, {9}>(
       updates,
       out,
       upd_shape,

mlx/backend/metal/jit_kernels.cpp

@@ -1,5 +1,4 @@
 // 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"
@@ -46,25 +45,27 @@ MTL::ComputePipelineState* get_unary_kernel(
   auto lib = d.get_library(lib_name, [&]() {
     auto in_t = get_type_string(in_type);
     auto out_t = get_type_string(out_type);
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << 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(
-        "gn4_" + lib_name, "unary_g", in_t, out_t, op, 4);
-    return kernel_source.str();
+    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) {
+    std::string& kernel_source) {
   const std::array<std::pair<std::string, std::string>, 10> kernel_types = {{
       {"ss", "binary_ss"},
       {"vs", "binary_vs"},
@@ -74,26 +75,24 @@ void add_binary_kernels(
       {"sv2", "binary_sv2"},
       {"vv2", "binary_vv2"},
       {"g1", "binary_g_nd1"},
-      {"g2", "binary_g_nd2"},
-      {"g3", "binary_g_nd3"},
+      {"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(
@@ -104,10 +103,11 @@ MTL::ComputePipelineState* get_binary_kernel(
     const std::string op) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
   auto lib = d.get_library(lib_name, [&]() {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::binary_ops() << metal::binary();
-    add_binary_kernels(lib_name, in_type, out_type, op, kernel_source);
-    return kernel_source.str();
+    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);
 }
@@ -120,11 +120,10 @@ MTL::ComputePipelineState* get_binary_two_kernel(
     const std::string op) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
   auto lib = d.get_library(lib_name, [&]() {
-    std::ostringstream kernel_source;
-    kernel_source << metal::utils() << metal::binary_ops()
-                  << metal::binary_two();
-    add_binary_kernels(lib_name, in_type, out_type, op, kernel_source);
-    return kernel_source.str();
+    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);
 }
@@ -136,24 +135,29 @@ MTL::ComputePipelineState* get_ternary_kernel(
     const std::string op) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
   auto lib = d.get_library(lib_name, [&]() {
-    std::ostringstream kernel_source;
+    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"},
         {"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);
-    return 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);
 }
@@ -165,31 +169,43 @@ MTL::ComputePipelineState* get_copy_kernel(
     const array& out) {
   std::string lib_name = kernel_name.substr(kernel_name.find("_") + 1);
   auto lib = d.get_library(lib_name, [&]() {
-    std::ostringstream kernel_source;
+    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(
-                         "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(
-                         "ggn4_" + lib_name, "copy_gg", in_type, out_type, 4);
-    return 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);
 }
@@ -321,17 +337,17 @@ MTL::ComputePipelineState* get_reduce_init_kernel(
     const std::string& kernel_name,
     const std::string& func_name,
     const std::string& op_name,
-    const array& out) {
+    const Dtype& out_type) {
   auto lib = d.get_library(kernel_name, [&]() {
-    std::ostringstream kernel_source;
     std::string op_type = op_name;
     op_type[0] = std::toupper(op_name[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, func_name, out_type, op);
-    return kernel_source.str();
+    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);
 }
@@ -341,30 +357,31 @@ 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, [&]() {
     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);
     }
-    return kernel_source.str();
+    return kernel_source;
   });
   auto st = d.get_kernel(kernel_name, lib);
   return st;

mlx/backend/metal/kernels.h

@@ -81,15 +81,16 @@ MTL::ComputePipelineState* get_reduce_init_kernel(
     const std::string& kernel_name,
     const std::string& func_name,
     const std::string& op_name,
-    const array& out);
+    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);

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"
@@ -30,9 +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
-  sdpa_vector.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
@@ -54,6 +66,24 @@ set(STEEL_HEADERS
     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)
   build_kernel(binary binary.h binary_ops.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

@@ -9,18 +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("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_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

@@ -7,18 +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("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
 

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

@@ -2,22 +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("gn4_copy" #tname, copy_g, itype, otype, 4) \
-  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)]],
@@ -16,18 +16,18 @@ 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 = index.x * static_cast<LocT>(indices.strides[indices.ndim * i]);
       idx_loc += indices.row_contiguous[i]
           ? index.y
-          : elem_to_loc(
+          : elem_to_loc<size_t, LocT>(
                 index.y,
                 &indices.shapes[indices.ndim * i + 1],
                 &indices.strides[indices.ndim * i + 1],
@@ -35,17 +35,17 @@ METAL_FUNC void gather_impl(
     }
     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

@@ -14,7 +14,7 @@ struct Indices {
 };
 
 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.h

@@ -564,18 +564,21 @@ METAL_FUNC void qmv_impl(
         static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
         0,
         values_per_thread);
-    U sum =
-        load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
+    if (remaining > 0) {
+      U sum = load_vector_safe<T, U, values_per_thread, bits>(
+          x, x_thread, remaining);
 
-    for (int row = 0; out_row + row < out_vec_size; row++) {
-      const device uint8_t* wl =
-          (const device uint8_t*)(w + row * in_vec_size_w);
-      const device T* sl = scales + row * in_vec_size_g;
-      const device T* bl = biases + row * in_vec_size_g;
+      for (int row = 0; out_row + row < out_vec_size; row++) {
+        const device uint8_t* wl =
+            (const device uint8_t*)(w + row * in_vec_size_w);
+        const device T* sl = scales + row * in_vec_size_g;
+        const device T* bl = biases + row * in_vec_size_g;
 
-      U s = sl[0];
-      U b = bl[0];
-      result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
+        U s = sl[0];
+        U b = bl[0];
+        result[row] +=
+            qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
+      }
     }
 
     for (int row = 0; out_row + row < out_vec_size; row++) {
@@ -619,21 +622,22 @@ METAL_FUNC void qmv_impl(
         static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
         0,
         values_per_thread);
-    U sum =
-        load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
+    if (remaining > 0) {
+      U sum = load_vector_safe<T, U, values_per_thread, bits>(
+          x, x_thread, remaining);
 
-    for (int row = 0; row < results_per_simdgroup; row++) {
-      const device uint8_t* wl =
-          (const device uint8_t*)(w + row * in_vec_size_w);
-      const device T* sl = scales + row * in_vec_size_g;
-      const device T* bl = biases + row * in_vec_size_g;
+      for (int row = 0; row < results_per_simdgroup; row++) {
+        const device uint8_t* wl =
+            (const device uint8_t*)(w + row * in_vec_size_w);
+        const device T* sl = scales + row * in_vec_size_g;
+        const device T* bl = biases + row * in_vec_size_g;
 
-      U s = sl[0];
-      U b = bl[0];
-      result[row] += qdot_safe<U, values_per_thread, bits>(
-          wl, x_thread, s, b, sum, remaining);
+        U s = sl[0];
+        U b = bl[0];
+        result[row] += qdot_safe<U, values_per_thread, bits>(
+            wl, x_thread, s, b, sum, remaining);
+      }
     }
-
     for (int row = 0; row < results_per_simdgroup; row++) {
       result[row] = simd_sum(result[row]);
       if (simd_lid == 0) {

mlx/backend/metal/kernels/reduce.metal

@@ -10,186 +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)                            \
-  instantiate_kernel("col_reduce_longcolumn_" #dim "_reduce_" #name,   \
-                     col_reduce_longcolumn,                            \
-                     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, 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, 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

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

@@ -1,6 +1,11 @@
 // Copyright © 2023-2024 Apple Inc.
 
-template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
+template <
+    typename T,
+    typename U,
+    typename Op,
+    typename IdxT = int64_t,
+    int N_READS = REDUCE_N_READS>
 [[kernel]] void all_reduce(
     const device T* in [[buffer(0)]],
     device U* out [[buffer(1)]],
@@ -16,10 +21,10 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
   threadgroup U shared_vals[simd_size];
 
   U total = Op::init;
-  int64_t start_idx = gid.y * row_size;
-  int64_t actual_row =
+  IdxT start_idx = gid.y * IdxT(row_size);
+  IdxT actual_row =
       (start_idx + row_size <= in_size) ? row_size : in_size - start_idx;
-  int64_t blocks = actual_row / (lsize.x * N_READS);
+  IdxT blocks = actual_row / (lsize.x * N_READS);
   int extra = actual_row - blocks * (lsize.x * N_READS);
   extra -= lid.x * N_READS;
   start_idx += lid.x * N_READS;
@@ -30,7 +35,7 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
     extra = 0;
   }
 
-  for (int64_t b = 0; b < blocks; b++) {
+  for (IdxT b = 0; b < blocks; b++) {
     for (int i = 0; i < N_READS; i++) {
       total = op(static_cast<U>(in[i]), total);
     }

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

@@ -1,6 +1,6 @@
 // Copyright © 2023-2024 Apple Inc.
 
-template <typename T, typename U, typename Op, int NDIMS>
+template <typename T, typename U, typename Op, typename IdxT, int NDIMS>
 [[kernel]] void col_reduce_small(
     const device T* in [[buffer(0)]],
     device U* out [[buffer(1)]],
@@ -19,7 +19,7 @@ template <typename T, typename U, typename Op, int NDIMS>
     uint3 lsize [[threads_per_threadgroup]]) {
   constexpr int n_reads = 4;
   Op op;
-  looped_elem_to_loc<NDIMS> loop;
+  LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
   const device T* row;
 
   U totals[n_reads];
@@ -27,20 +27,20 @@ template <typename T, typename U, typename Op, int NDIMS>
     totals[i] = Op::init;
   }
 
-  size_t column = size_t(gid.x) * lsize.x * n_reads + lid.x * n_reads;
+  IdxT column = IdxT(gid.x) * lsize.x * n_reads + lid.x * n_reads;
   if (column >= reduction_stride) {
     return;
   }
   bool safe = column + n_reads <= reduction_stride;
 
-  size_t out_idx = gid.y + gsize.y * size_t(gid.z);
-  size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
+  IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
+  IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
   in += in_idx + column;
 
-  size_t total_rows = non_col_reductions * reduction_size;
+  IdxT total_rows = IdxT(non_col_reductions) * IdxT(reduction_size);
   loop.next(lid.y, reduce_shape, reduce_strides);
-  for (size_t r = lid.y; r < total_rows; r += lsize.y) {
-    row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
+  for (IdxT r = lid.y; r < total_rows; r += lsize.y) {
+    row = in + loop.location();
     if (safe) {
       for (int i = 0; i < n_reads; i++) {
         totals[i] = op(static_cast<U>(row[i]), totals[i]);
@@ -80,7 +80,7 @@ template <typename T, typename U, typename Op, int NDIMS>
   }
 
   if (lid.y == 0) {
-    out += out_idx * reduction_stride + column;
+    out += out_idx * IdxT(reduction_stride) + column;
     if (safe) {
       for (int i = 0; i < n_reads; i++) {
         out[i] = totals[i];
@@ -93,7 +93,7 @@ template <typename T, typename U, typename Op, int NDIMS>
   }
 }
 
-template <typename T, typename U, typename Op, int NDIMS>
+template <typename T, typename U, typename Op, typename IdxT, int NDIMS>
 [[kernel]] void col_reduce_longcolumn(
     const device T* in [[buffer(0)]],
     device U* out [[buffer(1)]],
@@ -112,19 +112,19 @@ template <typename T, typename U, typename Op, int NDIMS>
     uint3 lid [[thread_position_in_threadgroup]],
     uint3 lsize [[threads_per_threadgroup]]) {
   Op op;
-  looped_elem_to_loc<NDIMS> loop;
+  LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
   const device T* row;
 
-  size_t out_idx = gid.x + gsize.x * size_t(gid.y);
-  size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
+  IdxT out_idx = gid.x + gsize.x * IdxT(gid.y);
+  IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
   in += in_idx + lid.x;
 
   U total = Op::init;
-  size_t total_rows = non_col_reductions * reduction_size;
+  IdxT total_rows = IdxT(non_col_reductions) * IdxT(reduction_size);
   loop.next(gid.z * lsize.y + lid.y, reduce_shape, reduce_strides);
-  for (size_t r = gid.z * lsize.y + lid.y; r < total_rows;
+  for (IdxT r = gid.z * lsize.y + lid.y; r < total_rows;
        r += lsize.y * gsize.z) {
-    row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
+    row = in + loop.location();
     total = op(static_cast<U>(*row), total);
     loop.next(lsize.y * gsize.z, reduce_shape, reduce_strides);
   }
@@ -136,7 +136,8 @@ template <typename T, typename U, typename Op, int NDIMS>
     for (uint i = 1; i < lsize.y; i++) {
       total = op(total, shared_vals[i * lsize.x + lid.x]);
     }
-    out[gid.z * out_size + out_idx * reduction_stride + lid.x] = total;
+    out[gid.z * IdxT(out_size) + out_idx * IdxT(reduction_stride) + lid.x] =
+        total;
   }
 }
 
@@ -151,7 +152,14 @@ template <typename T, typename U, typename Op, int NDIMS>
  *     totals with a loop.
  *  7. Write them to the output
  */
-template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
+template <
+    typename T,
+    typename U,
+    typename Op,
+    typename IdxT,
+    int NDIMS,
+    int BM,
+    int BN>
 [[kernel]] void col_reduce_looped(
     const device T* in [[buffer(0)]],
     device U* out [[buffer(1)]],
@@ -176,7 +184,7 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
 
   threadgroup U shared_vals[BN * BM];
   U totals[n_reads];
-  looped_elem_to_loc<NDIMS> loop;
+  LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
   const device T* row;
 
   for (int i = 0; i < n_reads; i++) {
@@ -185,17 +193,17 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
 
   short lid = simd_group_id * simd_size + simd_lane_id;
   short2 offset((lid % n_read_blocks) * n_reads, lid / n_read_blocks);
-  size_t column = BN * gid.x + offset.x;
+  IdxT column = BN * gid.x + offset.x;
   bool safe = column + n_reads <= reduction_stride;
 
-  size_t out_idx = gid.y + gsize.y * size_t(gid.z);
-  size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
+  IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
+  IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
   in += in_idx + column;
 
-  size_t total = non_col_reductions * reduction_size;
+  IdxT total = IdxT(non_col_reductions) * IdxT(reduction_size);
   loop.next(offset.y, reduce_shape, reduce_strides);
-  for (size_t r = offset.y; r < total; r += BM) {
-    row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
+  for (IdxT r = offset.y; r < total; r += BM) {
+    row = in + loop.location();
 
     if (safe) {
       for (int i = 0; i < n_reads; i++) {
@@ -235,8 +243,8 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
 
     // Write the output.
     if (simd_lane_id == 0) {
-      size_t out_column = BN * gid.x + out_offset.x;
-      out += out_idx * reduction_stride + out_column;
+      IdxT out_column = BN * gid.x + out_offset.x;
+      out += out_idx * IdxT(reduction_stride) + out_column;
       if (out_column + n_outputs <= reduction_stride) {
         for (int i = 0; i < n_outputs; i++) {
           out[i] = totals[i];
@@ -269,7 +277,7 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
 
     // Write the output.
     if (offset.y == 0) {
-      out += out_idx * reduction_stride + column;
+      out += out_idx * IdxT(reduction_stride) + column;
       if (safe) {
         for (int i = 0; i < n_reads; i++) {
           out[i] = totals[i];
@@ -283,7 +291,14 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
   }
 }
 
-template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
+template <
+    typename T,
+    typename U,
+    typename Op,
+    typename IdxT,
+    int NDIMS,
+    int BM,
+    int BN>
 [[kernel]] void col_reduce_2pass(
     const device T* in [[buffer(0)]],
     device U* out [[buffer(1)]],
@@ -312,7 +327,7 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
 
   threadgroup U shared_vals[BN * BM];
   U totals[n_reads];
-  looped_elem_to_loc<NDIMS> loop;
+  LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
   const device T* row;
 
   for (int i = 0; i < n_reads; i++) {
@@ -321,20 +336,19 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
 
   short lid = simd_group_id * simd_size + simd_lane_id;
   short2 offset((lid % n_read_blocks) * n_reads, lid / n_read_blocks);
-  size_t column = BN * gid.x + offset.x;
+  IdxT column = BN * gid.x + offset.x;
   bool safe = column + n_reads <= reduction_stride;
 
-  size_t full_idx = gid.y + gsize.y * size_t(gid.z);
-  size_t block_idx = full_idx / out_size;
-  size_t out_idx = full_idx % out_size;
-  size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
+  IdxT full_idx = gid.y + gsize.y * IdxT(gid.z);
+  IdxT block_idx = full_idx / IdxT(out_size);
+  IdxT out_idx = full_idx % IdxT(out_size);
+  IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
   in += in_idx + column;
 
-  size_t total = non_col_reductions * reduction_size;
+  IdxT total = IdxT(non_col_reductions) * IdxT(reduction_size);
   loop.next(offset.y + block_idx * BM, reduce_shape, reduce_strides);
-  for (size_t r = offset.y + block_idx * BM; r < total;
-       r += outer_blocks * BM) {
-    row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
+  for (IdxT r = offset.y + block_idx * BM; r < total; r += outer_blocks * BM) {
+    row = in + loop.location();
 
     if (safe) {
       for (int i = 0; i < n_reads; i++) {
@@ -369,8 +383,8 @@ template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
 
   // Write the output.
   if (simd_lane_id == 0) {
-    size_t out_column = BN * gid.x + out_offset.x;
-    out += full_idx * reduction_stride + out_column;
+    IdxT out_column = BN * gid.x + out_offset.x;
+    out += full_idx * IdxT(reduction_stride) + out_column;
     if (out_column + n_outputs <= reduction_stride) {
       for (int i = 0; i < n_outputs; i++) {
         out[i] = totals[i];

mlx/backend/metal/kernels/reduction/reduce_row.h

@@ -193,6 +193,7 @@ template <
     typename T,
     typename U,
     typename Op,
+    typename IdxT,
     int NDIMS,
     int N_READS = REDUCE_N_READS>
 [[kernel]] void row_reduce_small(
@@ -214,20 +215,20 @@ template <
   Op op;
 
   U total_val = Op::init;
-  looped_elem_to_loc<NDIMS> loop;
+  LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
 
   // Precompute some row reduction numbers
   const device T* row;
-  int blocks = row_size / N_READS;
-  int extra = row_size % N_READS;
+  int blocks = IdxT(row_size) / N_READS;
+  int extra = IdxT(row_size) % N_READS;
 
   if ((non_row_reductions < 32 && row_size <= 8) || non_row_reductions <= 8) {
     // Simple loop over non_row_reductions and reduce the row in the thread.
-    size_t out_idx = tid.x + tsize.y * size_t(tid.y);
-    in += elem_to_loc(out_idx, shape, strides, ndim);
+    IdxT out_idx = tid.x + tsize.y * IdxT(tid.y);
+    in += elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
 
     for (uint r = 0; r < non_row_reductions; r++) {
-      row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
+      row = in + loop.location();
       thread_reduce<T, U, Op, N_READS>(total_val, row, blocks, extra);
       loop.next(reduce_shape, reduce_strides);
     }
@@ -236,13 +237,13 @@ template <
   } else {
     // Collaboratively reduce over non_row_reductions in the simdgroup. Each
     // thread reduces every 32nd row and then a simple simd reduce.
-    size_t out_idx = gid.y + gsize.y * size_t(gid.z);
-    in += elem_to_loc(out_idx, shape, strides, ndim);
+    IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
+    in += elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
 
     loop.next(simd_lane_id, reduce_shape, reduce_strides);
 
     for (uint r = simd_lane_id; r < non_row_reductions; r += simd_size) {
-      row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
+      row = in + loop.location();
       thread_reduce<T, U, Op, N_READS>(total_val, row, blocks, extra);
       loop.next(simd_size, reduce_shape, reduce_strides);
     }
@@ -259,6 +260,7 @@ template <
     typename T,
     typename U,
     typename Op,
+    typename IdxT = size_t,
     int N_READS = REDUCE_N_READS,
     int N_WRITES = REDUCE_N_WRITES>
 [[kernel]] void row_reduce_simple(
@@ -277,15 +279,15 @@ template <
   U totals[N_WRITES];
 
   // Move to the row
-  size_t out_idx = N_WRITES * (gid.y + gsize.y * size_t(gid.z));
+  IdxT out_idx = N_WRITES * (gid.y + gsize.y * IdxT(gid.z));
   if (out_idx + N_WRITES > out_size) {
     out_idx = out_size - N_WRITES;
   }
-  in += out_idx * reduction_size;
+  in += out_idx * IdxT(reduction_size);
   out += out_idx;
 
   // Each thread reduces across the row
-  int blocks = reduction_size / (lsize.x * N_READS);
+  int blocks = IdxT(reduction_size) / (lsize.x * N_READS);
   int extra = reduction_size - blocks * (lsize.x * N_READS);
   per_thread_row_reduce<T, U, Op, N_READS, N_WRITES>(
       totals, in, reduction_size, blocks, extra, lsize.x, lid.x);
@@ -306,6 +308,7 @@ template <
     typename T,
     typename U,
     typename Op,
+    typename IdxT,
     int NDIMS,
     int N_READS = REDUCE_N_READS>
 [[kernel]] void row_reduce_looped(
@@ -330,19 +333,20 @@ template <
   threadgroup U shared_vals[simd_size];
   U total = Op::init;
 
-  size_t out_idx = gid.y + gsize.y * size_t(gid.z);
+  IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
 
   // lid.x * N_READS breaks the per_thread_row_reduce interface a bit. Maybe it
   // needs a small refactor.
-  in += elem_to_loc(out_idx, shape, strides, ndim) + lid.x * N_READS;
+  in += elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim) +
+      lid.x * N_READS;
 
-  looped_elem_to_loc<NDIMS> loop;
+  LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
   const device T* row;
-  int blocks = row_size / (lsize.x * N_READS);
+  int blocks = IdxT(row_size) / (lsize.x * N_READS);
   int extra = row_size - blocks * (lsize.x * N_READS);
 
-  for (size_t i = 0; i < non_row_reductions; i++) {
-    row = in + loop.location(i, reduce_shape, reduce_strides, reduce_ndim);
+  for (IdxT i = 0; i < non_row_reductions; i++) {
+    row = in + loop.location();
 
     // Each thread reduces across the row
     U row_total;

mlx/backend/metal/kernels/rms_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;
@@ -17,12 +15,15 @@ template <typename T, int N_READS = RMS_N_READS>
     constant float& eps,
     constant uint& axis_size,
     constant uint& w_stride,
-    threadgroup float* local_inv_mean [[threadgroup(0)]],
-    threadgroup float* local_sums [[threadgroup(1)]],
     uint gid [[threadgroup_position_in_grid]],
     uint lid [[thread_position_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  constexpr int SIMD_SIZE = 32;
+
+  threadgroup float local_inv_mean[1];
+  threadgroup float local_sums[SIMD_SIZE];
+
   float acc = 0;
   x += gid * size_t(axis_size) + lid * N_READS;
   w += w_stride * lid * N_READS;
@@ -84,13 +85,15 @@ template <typename T, int N_READS = RMS_N_READS>
     constant float& eps,
     constant uint& axis_size,
     constant uint& w_stride,
-    threadgroup float* local_inv_mean [[threadgroup(0)]],
-    threadgroup float* local_sums [[threadgroup(1)]],
     uint gid [[threadgroup_position_in_grid]],
     uint lid [[thread_position_in_threadgroup]],
     uint lsize [[threads_per_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  constexpr int SIMD_SIZE = 32;
+  threadgroup float local_inv_mean[1];
+  threadgroup float local_sums[SIMD_SIZE];
+
   float acc = 0;
   x += gid * size_t(axis_size) + lid * N_READS;
   w += w_stride * lid * N_READS;
@@ -376,8 +379,6 @@ template <typename T, int N_READS = RMS_N_READS>
       constant float& eps,                                    \
       constant uint& axis_size,                               \
       constant uint& w_stride,                                \
-      threadgroup float* local_inv_mean [[threadgroup(0)]],   \
-      threadgroup float* local_sums [[threadgroup(1)]],       \
       uint gid [[thread_position_in_grid]],                   \
       uint lid [[thread_position_in_threadgroup]],            \
       uint simd_lane_id [[thread_index_in_simdgroup]],        \
@@ -407,8 +408,6 @@ template <typename T, int N_READS = RMS_N_READS>
       constant float& eps,                                       \
       constant uint& axis_size,                                  \
       constant uint& w_stride,                                   \
-      threadgroup float* local_inv_mean [[threadgroup(0)]],      \
-      threadgroup float* local_sums [[threadgroup(1)]],          \
       uint gid [[thread_position_in_grid]],                      \
       uint lid [[thread_position_in_threadgroup]],               \
       uint lsize [[threads_per_threadgroup]],                    \

mlx/backend/metal/kernels/rope.metal

@@ -2,7 +2,6 @@
 
 #include <metal_math>
 
-#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/utils.h"
 template <typename T, bool traditional, bool forward>
 void rope_single_impl(

mlx/backend/metal/kernels/scaled_dot_product_attention.metal

@@ -1,946 +1,16 @@
 #include <metal_stdlib>
 
-#include "mlx/backend/metal/kernels/scaled_dot_product_attention_params.h"
 #include "mlx/backend/metal/kernels/sdpa_vector.h"
-#include "mlx/backend/metal/kernels/steel/defines.h"
-#include "mlx/backend/metal/kernels/steel/gemm/transforms.h"
 #include "mlx/backend/metal/kernels/utils.h"
 
 using namespace metal;
 
-using namespace mlx::steel;
-
-template <
-    typename T,
-    short BROWS,
-    short BCOLS,
-    short dst_ld,
-    short reduction_dim,
-    short tgp_size,
-    short alignment = 1,
-    short n_reads = (BCOLS * BROWS) / (tgp_size),
-    short TCOLS = BCOLS / n_reads,
-    short TROWS = tgp_size / TCOLS>
-struct BlockLoaderFA {
-  STEEL_CONST short n_rows = (BROWS + TROWS - 1) / TROWS;
-  STEEL_CONST short vec_size = n_reads;
-
-  // Leading dimension for src
-  const int src_ld;
-  const int tile_stride;
-
-  // Thread location indices
-  const short thread_idx;
-  const short bi;
-  const short bj;
-
-  // threadgroup and device memory
-  threadgroup T* dst;
-  const device T* src;
-
-  struct alignas(alignment * sizeof(T)) ReadVector {
-    uint8_t v[sizeof(T) * vec_size];
-  };
-
-  /* Constructor */
-  METAL_FUNC BlockLoaderFA(
-      const device T* src_,
-      const int src_ld_,
-      threadgroup T* dst_,
-      ushort simd_group_id [[simdgroup_index_in_threadgroup]],
-      ushort simd_lane_id [[thread_index_in_simdgroup]])
-      : src_ld(src_ld_),
-        tile_stride(reduction_dim ? BCOLS : BROWS * src_ld),
-        thread_idx(simd_group_id * 32 + simd_lane_id),
-        bi(thread_idx / TCOLS),
-        bj(vec_size * (thread_idx % TCOLS)),
-        dst(dst_ + bi * dst_ld + bj),
-        src(src_ + bi * src_ld + bj) {}
-
-  /* Load from device memory into threadgroup memory - without bound checking */
-  METAL_FUNC void load_unsafe() const {
-    STEEL_PRAGMA_UNROLL
-    for (short i = 0; i < BROWS; i += TROWS) {
-      *((threadgroup ReadVector*)(&dst[i * dst_ld])) =
-          *((const device ReadVector*)(&src[i * src_ld]));
-    }
-  }
-
-  /* Load from device memory into threadgroup memory - with bound checking */
-  METAL_FUNC void load_safe(short2 src_tile_dim) const {
-    src_tile_dim = src_tile_dim - short2(bj, bi);
-
-    // Skip loading if thread has no valid reads
-    if (src_tile_dim.x <= 0 || src_tile_dim.y <= 0) {
-      STEEL_PRAGMA_UNROLL
-      for (short i = 0; i < BROWS; i += TROWS) {
-        STEEL_PRAGMA_UNROLL
-        for (short j = 0; j < vec_size; j++) {
-          dst[i * dst_ld + j] = T(0);
-        }
-      }
-      return;
-    }
-
-    // Use fast thread memory for bound checks
-    bool tmp_idx[vec_size];
-    T tmp_val[vec_size];
-
-    STEEL_PRAGMA_UNROLL
-    for (short i = 0; i < BROWS; i += TROWS) {
-      // Make sure tmp_idx only contains valid indices
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < vec_size; j++) {
-        tmp_idx[j] = (i < src_tile_dim.y) && (j < src_tile_dim.x);
-      }
-
-      // Read valid indices into tmp_val
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < vec_size; j++) {
-        tmp_val[j] = src[(tmp_idx[j] ? i * src_ld + j : 0)];
-      }
-
-      // Zero out uneeded values
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < vec_size; j++) {
-        tmp_val[j] = tmp_idx[j] ? tmp_val[j] : T(0);
-      }
-
-      // Copy values to threadgroup memory
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < vec_size; j++) {
-        dst[i * dst_ld + j] = tmp_val[j];
-      }
-    }
-  }
-
-  /* Iteration helper */
-  METAL_FUNC void next() {
-    src += tile_stride;
-  }
-  METAL_FUNC void next(short n) {
-    src += n * tile_stride;
-  }
-};
-
-template <bool M_aligned, bool N_aligned, bool K_aligned>
-struct LoopAlignment {};
-
-template <
-    typename T,
-    typename U,
-    int BM,
-    int BN,
-    int BK,
-    int WM,
-    int WN,
-    bool transpose_a,
-    bool transpose_b,
-    short lda_tgp,
-    short ldb_tgp,
-    typename AccumType = float,
-    typename Epilogue = TransformNone<U, AccumType>>
-struct BlockMMAFA {
-  // Warp tile simdgroup matrix strides along M
-  STEEL_CONST short TM_stride = 8 * WM;
-  // Warp tile simdgroup matrix strides along M
-  STEEL_CONST short TN_stride = 8 * WN;
-
-  // Warp tile size along M
-  STEEL_CONST short TM = BM / TM_stride;
-  // Warp tile size along N
-  STEEL_CONST short TN = BN / TN_stride;
-
-  // Strides of A, B along reduction axis
-  STEEL_CONST short simd_stride_a = {
-      transpose_a ? TM_stride : TM_stride * lda_tgp};
-  STEEL_CONST short simd_stride_b = {
-      transpose_b ? TN_stride * ldb_tgp : TN_stride};
-
-  // Jump between elements
-  STEEL_CONST short jump_a = {transpose_a ? lda_tgp : 1};
-  STEEL_CONST short jump_b = {transpose_b ? ldb_tgp : 1};
-
-  STEEL_CONST short tile_stride_a = {transpose_a ? 8 * lda_tgp : 8};
-  STEEL_CONST short tile_stride_b = {transpose_b ? 8 : 8 * ldb_tgp};
-
-  // Simdgroup matrices
-  simdgroup_matrix<AccumType, 8, 8> Asimd[TM];
-  simdgroup_matrix<AccumType, 8, 8> Bsimd[TN];
-  simdgroup_matrix<AccumType, 8, 8> results[TM * TN] = {
-      simdgroup_matrix<AccumType, 8, 8>(0)};
-
-  // Offsets within threadgroup
-  const short tm;
-  const short tn;
-
-  short sm;
-  short sn;
-
-  ushort sid;
-  ushort slid;
-
-  short As_offset;
-  short Bs_offset;
-
-  /* Constructor */
-  METAL_FUNC BlockMMAFA(
-      ushort simd_group_id [[simdgroup_index_in_threadgroup]],
-      ushort simd_lane_id [[thread_index_in_simdgroup]])
-      : tm(8 * (simd_group_id / WN)), tn(8 * (simd_group_id % WN)) {
-    // Determine thread position in simdgroup matrix
-    short qid = simd_lane_id / 4;
-    slid = simd_lane_id;
-    sid = simd_group_id;
-
-    sm = (qid & 4) + (simd_lane_id / 2) % 4;
-    sn = (qid & 2) * 2 + (simd_lane_id % 2) * 2;
-
-    // Determine thread and simdgroup offset
-    As_offset =
-        transpose_a ? ((sn)*lda_tgp + (tm + sm)) : ((sn) + (tm + sm) * lda_tgp);
-    Bs_offset =
-        transpose_b ? ((tn + sn) * ldb_tgp + (sm)) : ((sm)*ldb_tgp + (tn + sn));
-  }
-
-  /* (BM, BK) X (BK, BN) multiply accumulate function */
-  METAL_FUNC void mma(const threadgroup T* As, const threadgroup T* Bs) {
-    // Adjust for simdgroup and thread location
-    As += As_offset;
-    Bs += Bs_offset;
-
-    // Iterate over BK in blocks of 8
-    STEEL_PRAGMA_UNROLL
-    for (short kk = 0; kk < BK; kk += 8) {
-      simdgroup_barrier(mem_flags::mem_none);
-
-      // Load elements from threadgroup A as simdgroup matrices
-      STEEL_PRAGMA_UNROLL
-      for (short i = 0; i < TM; i++) {
-        Asimd[i].thread_elements()[0] =
-            static_cast<AccumType>(As[i * simd_stride_a + 0]);
-        Asimd[i].thread_elements()[1] =
-            static_cast<AccumType>(As[i * simd_stride_a + jump_a]);
-      }
-
-      simdgroup_barrier(mem_flags::mem_none);
-
-      // Load elements from threadgroup B as simdgroup matrices
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < TN; j++) {
-        Bsimd[j].thread_elements()[0] =
-            static_cast<AccumType>(Bs[j * simd_stride_b + 0]);
-        Bsimd[j].thread_elements()[1] =
-            static_cast<AccumType>(Bs[j * simd_stride_b + jump_b]);
-      }
-
-      simdgroup_barrier(mem_flags::mem_none);
-
-      // Multiply and accumulate into result simdgroup matrices
-      STEEL_PRAGMA_UNROLL
-      for (short i = 0; i < TM; i++) {
-        STEEL_PRAGMA_UNROLL
-        for (short j = 0; j < TN; j++) {
-          short j_serp = (i % 2) ? (TN - 1 - j) : j;
-
-          simdgroup_multiply_accumulate(
-              results[i * TN + j_serp],
-              Asimd[i],
-              Bsimd[j_serp],
-              results[i * TN + j_serp]);
-        }
-      }
-
-      // Progress to next simdgroup tile
-      As += tile_stride_a;
-      Bs += tile_stride_b;
-    }
-  }
-
-  METAL_FUNC void rescale_output(const threadgroup float* Corrections) {
-    // Loop over all simdgroup tiles
-
-    STEEL_PRAGMA_UNROLL
-    for (short i = 0; i < TM; i++) {
-      short row = sm + tm + i * TM_stride;
-      float scale_value = Corrections[row];
-
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < TN; j++) {
-        // Get accumulated result and associated offset in C
-        thread auto& accum = results[i * TN + j].thread_elements();
-        // int offset = (i * TM_stride) * ldc + (j * TN_stride);
-        accum[0] *= scale_value;
-        accum[1] *= scale_value;
-      }
-    }
-  }
-
-  /* Store results from simdgroup_matrix results into device memory */
-  METAL_FUNC void store_result(device U* C, const int ldc) const {
-    // Adjust for simdgroup and thread location
-    C += (sm + tm) * ldc + tn + sn;
-
-    // Loop over all simdgroup tiles
-    STEEL_PRAGMA_UNROLL
-    for (short i = 0; i < TM; i++) {
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < TN; j++) {
-        // Get accumulated result and associated offset in C
-        thread const auto& accum = results[i * TN + j].thread_elements();
-        int offset = (i * TM_stride) * ldc + (j * TN_stride);
-
-        // Apply epilogue
-        U outs[2] = {Epilogue::apply(accum[0]), Epilogue::apply(accum[1])};
-
-        // Write out C
-        C[offset] = outs[0];
-        C[offset + 1] = outs[1];
-      }
-    }
-  }
-
-  METAL_FUNC void store_result_to_tgp_memory(
-      threadgroup U* C,
-      const int ldc,
-      short2 dst_tile_dims) const {
-    // Adjust for simdgroup and thread location
-    C += (sm + tm) * ldc + (tn + sn);
-    dst_tile_dims -= short2(tn + sn, sm + tm);
-
-    STEEL_PRAGMA_UNROLL
-    for (int i = 0; i < TM; i++) {
-      if (i * TM_stride < dst_tile_dims.y) {
-        STEEL_PRAGMA_UNROLL
-        for (int j = 0; j < TN; j++) {
-          // Get accumulated result and associated offset in C
-          thread const auto& accum = results[i * TN + j].thread_elements();
-          int offset = (i * TM_stride) * ldc + (j * TN_stride);
-
-          // Apply epilogue and output C
-          if (j * TN_stride < dst_tile_dims.x) {
-            C[offset] = Epilogue::apply(accum[0]);
-          }
-
-          if (j * TN_stride + 1 < dst_tile_dims.x) {
-            C[offset + 1] = Epilogue::apply(accum[1]);
-          }
-        }
-      }
-    }
-  }
-
-  METAL_FUNC void
-  store_result_safe(device U* C, const int ldc, short2 dst_tile_dims) const {
-    // Adjust for simdgroup and thread location
-    C += (sm + tm) * ldc + (tn + sn);
-    dst_tile_dims -= short2(tn + sn, sm + tm);
-
-    STEEL_PRAGMA_UNROLL
-    for (int i = 0; i < TM; i++) {
-      if (i * TM_stride < dst_tile_dims.y) {
-        STEEL_PRAGMA_UNROLL
-        for (int j = 0; j < TN; j++) {
-          // Get accumulated result and associated offset in C
-          thread const auto& accum = results[i * TN + j].thread_elements();
-          int offset = (i * TM_stride) * ldc + (j * TN_stride);
-
-          // Apply epilogue and output C
-          if (j * TN_stride < dst_tile_dims.x) {
-            C[offset] = Epilogue::apply(accum[0]);
-          }
-
-          if (j * TN_stride + 1 < dst_tile_dims.x) {
-            C[offset + 1] = Epilogue::apply(accum[1]);
-          }
-        }
-      }
-    }
-  }
-
-  /* Store results from simdgroup_matrix results into device memory */
-  METAL_FUNC void store_result(
-      device U* D,
-      const int ldd,
-      const device U* C,
-      const int ldc,
-      const int fdc,
-      thread const Epilogue& epilogue_op) const {
-    // Adjust for simdgroup and thread location
-    C += (sm + tm) * ldc + (tn + sn) * fdc;
-    D += (sm + tm) * ldd + tn + sn;
-
-    // Loop over all simdgroup tiles
-    STEEL_PRAGMA_UNROLL
-    for (short i = 0; i < TM; i++) {
-      STEEL_PRAGMA_UNROLL
-      for (short j = 0; j < TN; j++) {
-        // Get accumulated result and associated offset in C
-        thread const auto& accum = results[i * TN + j].thread_elements();
-        int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
-        int offset_d = (i * TM_stride) * ldd + (j * TN_stride);
-
-        // Apply epilogue
-        U outs[2] = {
-            epilogue_op.apply(accum[0], C[offset_c]),
-            epilogue_op.apply(accum[1], C[offset_c + fdc])};
-
-        // Write out D
-        D[offset_d] = outs[0];
-        D[offset_d + 1] = outs[1];
-      }
-    }
-  }
-
-  METAL_FUNC void store_result_safe(
-      device U* D,
-      const int ldd,
-      const device U* C,
-      const int ldc,
-      const int fdc,
-      short2 dst_tile_dims,
-      thread const Epilogue& epilogue_op) const {
-    // Adjust for simdgroup and thread location
-    C += (sm + tm) * ldc + (tn + sn) * fdc;
-    D += (sm + tm) * ldd + tn + sn;
-    dst_tile_dims -= short2(tn + sn, sm + tm);
-
-    STEEL_PRAGMA_UNROLL
-    for (int i = 0; i < TM; i++) {
-      if (i * TM_stride < dst_tile_dims.y) {
-        STEEL_PRAGMA_UNROLL
-        for (int j = 0; j < TN; j++) {
-          // Get accumulated result and associated offset in C
-          thread const auto& accum = results[i * TN + j].thread_elements();
-          int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
-          int offset_d = (i * TM_stride) * ldd + (j * TN_stride);
-
-          // Apply epilogue and output C
-          if (j * TN_stride < dst_tile_dims.x) {
-            D[offset_d] = epilogue_op.apply(accum[0], C[offset_c]);
-          }
-
-          if (j * TN_stride + 1 < dst_tile_dims.x) {
-            D[offset_d + 1] = epilogue_op.apply(accum[1], C[offset_c + fdc]);
-          }
-        }
-      }
-    }
-  }
-
-  METAL_FUNC void clear_results() {
-    STEEL_PRAGMA_UNROLL
-    for (int i = 0; i < TM; i++) {
-      STEEL_PRAGMA_UNROLL
-      for (int j = 0; j < TN; j++) {
-        results[i * TN + j] = simdgroup_matrix<AccumType, 8, 8>(0);
-      }
-    }
-  }
-};
-
-template <
-    typename T,
-    typename U,
-    int BM,
-    int BN,
-    int BK,
-    int WM,
-    int WN,
-    bool transpose_q,
-    bool transpose_k,
-    bool transpose_v,
-    bool MN_aligned,
-    bool K_aligned,
-    typename AccumType = typename AccumHelper<T>::accum_type,
-    typename Epilogue = TransformNone<U, AccumType>>
-struct FastAttentionKernel {
-  STEEL_CONST short tgp_padding = 16 / sizeof(T);
-  STEEL_CONST short float_padding = 16 / sizeof(float);
-  STEEL_CONST short tgp_mem_size_q =
-      transpose_q ? BK * (BM + tgp_padding) : BM * (BK + tgp_padding);
-  STEEL_CONST short tgp_mem_size_k =
-      transpose_k ? BK * (BN + tgp_padding) : BN * (BK + tgp_padding);
-  STEEL_CONST short tgp_mem_size_v =
-      transpose_v ? BK * (BN + tgp_padding) : BN * (BK + tgp_padding);
-  STEEL_CONST short tgp_mem_size_s = BM * (BN + tgp_padding);
-
-  // maxes, rowsums, rescale
-  STEEL_CONST short tgp_mem_size_corrections =
-      4 * (BM * sizeof(float) + float_padding);
-
-  STEEL_CONST bool share_kv_smem = transpose_k != transpose_v;
-
-  STEEL_CONST short tgp_mem_size = share_kv_smem
-      ? tgp_mem_size_q + tgp_mem_size_k + tgp_mem_size_s +
-          tgp_mem_size_corrections
-      : tgp_mem_size_q + tgp_mem_size_k + tgp_mem_size_s +
-          tgp_mem_size_corrections + tgp_mem_size_v;
-
-  STEEL_CONST short tgp_size = WM * WN * 32;
-
-  static_assert(transpose_q == false, "Expected Q not transposed.");
-  static_assert(transpose_k == true, "Expected K transposed.");
-  static_assert(transpose_v == false, "Expected V not transposed.");
-  static_assert(tgp_mem_size <= 32768, "Excessive tgp memory requested.");
-
-  using loader_q_t = BlockLoaderFA<
-      T,
-      transpose_q ? BK : BM,
-      transpose_q ? BM : BK,
-      transpose_q ? BM + tgp_padding : BK + tgp_padding,
-      !transpose_q,
-      tgp_size>;
-
-  using loader_k_t = BlockLoaderFA<
-      T,
-      transpose_k ? BN : BK,
-      transpose_k ? BK : BN,
-      transpose_k ? BK + tgp_padding : BN + tgp_padding,
-      transpose_k,
-      tgp_size>;
-
-  using loader_v_t = BlockLoaderFA<
-      T,
-      transpose_v ? BK : BN,
-      transpose_v ? BN : BK,
-      transpose_v ? BN + tgp_padding : BK + tgp_padding,
-      transpose_v,
-      tgp_size>;
-
-  using mma_qk_t = BlockMMAFA<
-      T,
-      U,
-      BM,
-      BN,
-      BK,
-      WM,
-      WN,
-      transpose_q,
-      transpose_k,
-      transpose_q ? BM + tgp_padding : BK + tgp_padding,
-      transpose_k ? BK + tgp_padding : BN + tgp_padding,
-      AccumType,
-      Epilogue>;
-
-  using mma_sv_t = BlockMMAFA<
-      T,
-      U,
-      BM,
-      BK,
-      BN,
-      WM,
-      WN,
-      false,
-      transpose_v,
-      BN + tgp_padding,
-      BK + tgp_padding,
-      AccumType,
-      Epilogue>;
-
-  /* Main kernel function */
-  template <bool M_aligned, bool N_aligned, bool K_aligned_>
-  static METAL_FUNC void gemm_loop(
-      threadgroup T* As [[threadgroup(0)]],
-      threadgroup T* Bs [[threadgroup(1)]],
-      const int gemm_k_iterations,
-      thread loader_k_t& loader_b,
-      thread mma_qk_t& mma_op,
-      thread const short& tgp_bm,
-      thread const short& tgp_bn,
-      LoopAlignment<M_aligned, N_aligned, K_aligned_> l = {}) {
-    // Appease the compiler
-    (void)l;
-    (void)tgp_bm;
-
-    short2 tile_dims_B = transpose_k ? short2(BK, tgp_bn) : short2(tgp_bn, BK);
-
-    // not valid for gemm_k_iterations > 1 (so, BK == d_k)
-    for (int k = 0; k < gemm_k_iterations; k++) {
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      if (N_aligned) {
-        loader_b.load_unsafe();
-      } else {
-        loader_b.load_safe(tile_dims_B);
-      }
-
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      // Multiply and accumulate threadgroup elements
-      mma_op.mma(As, Bs);
-    }
-  }
-
-  static METAL_FUNC void initialize_corrections(
-      threadgroup float* C,
-      uint simd_lane_id,
-      uint simd_group_id) {
-    if (simd_group_id == 0) {
-      threadgroup float* maxes = C;
-      threadgroup float* sums = C + (BM + float_padding);
-      threadgroup float* o_rescale = sums + (BM + float_padding);
-      threadgroup float* output_rescale = o_rescale + (BM + float_padding);
-
-      if (simd_lane_id < BM) {
-        maxes[simd_lane_id] = -INFINITY; // m_i
-        sums[simd_lane_id] = 0.f; // l_i
-        o_rescale[simd_lane_id] = 1.f; // li * exp(mi - mi_new)
-        output_rescale[simd_lane_id] = 1.f; // 1.0 / l_i
-      }
-    }
-  }
-
-  static METAL_FUNC void rescale_ss(
-      threadgroup T* Ss,
-      threadgroup float* Corrections,
-      uint simd_group_id,
-      uint simd_lane_id,
-      short2 local_blocks,
-      float alpha) {
-    if (simd_group_id == 0) {
-      short row_offset = BM + float_padding;
-      threadgroup float* maxes = Corrections;
-      threadgroup float* sums = Corrections + row_offset;
-      threadgroup float* o_rescale = sums + row_offset;
-      threadgroup float* output_scales = o_rescale + row_offset;
-
-      if (simd_lane_id < uint(local_blocks.y)) {
-        float m_i_old = maxes[simd_lane_id];
-        float l_i_old = sums[simd_lane_id];
-
-        float m_i_new = m_i_old;
-        float l_i_new = l_i_old;
-
-        short offset = simd_lane_id * (BN + tgp_padding);
-
-        float m_ij = -INFINITY;
-
-        for (short j = 0; j < local_blocks.x; j++) {
-          float val = alpha * float(Ss[offset + j]);
-          m_ij = max(m_ij, val);
-        }
-
-        m_i_new = max(m_ij, m_i_new);
-
-        float rowsum = 0.f; // lij
-
-        for (short j = 0; j < local_blocks.x; j++) {
-          float val = alpha * float(Ss[offset + j]);
-          float P_i_j = exp(val - m_ij);
-          rowsum += P_i_j;
-          P_i_j = P_i_j * exp(m_ij - m_i_new);
-          Ss[offset + j] = T(P_i_j);
-        }
-
-        l_i_new =
-            exp(m_i_old - m_i_new) * l_i_old + exp(m_ij - m_i_new) * rowsum;
-        maxes[simd_lane_id] = m_i_new;
-        sums[simd_lane_id] = l_i_new;
-        float rescale = l_i_old * exp(m_i_old - m_i_new);
-        o_rescale[simd_lane_id] = rescale;
-        output_scales[simd_lane_id] = 1.0 / l_i_new;
-      }
-    }
-  }
-
-  /* Main kernel function */
-  static METAL_FUNC void run(
-      const device T* Q [[buffer(0)]],
-      const device T* K [[buffer(1)]],
-      const device T* V [[buffer(2)]],
-      device U* O [[buffer(3)]],
-      const constant MLXFastAttentionParams* params [[buffer(4)]],
-      threadgroup T* Qs [[threadgroup(0)]],
-      threadgroup T* Ks [[threadgroup(1)]],
-      threadgroup T* Ss [[threadgroup(2)]],
-      threadgroup T* Vs [[threadgroup(3)]],
-      threadgroup float* Corrections [[threadgroup(4)]],
-      uint simd_lane_id [[thread_index_in_simdgroup]],
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],
-      uint3 tid [[threadgroup_position_in_grid]],
-      uint3 lid [[thread_position_in_threadgroup]]) {
-    // Pacifying compiler
-    (void)lid;
-
-    const int tid_y = ((tid.y) << params->swizzle_log) +
-        ((tid.x) & ((1 << params->swizzle_log) - 1));
-    const int tid_x = (tid.x) >> params->swizzle_log;
-
-    if (params->tiles_n <= tid_x || params->tiles_m <= tid_y) {
-      return;
-    }
-
-    threadgroup_barrier(mem_flags::mem_none);
-
-    // Find block in Q, O; and head in K, V.
-    const int c_row = tid_y * BM;
-
-    Q += transpose_q ? c_row : c_row * params->ldq;
-    thread loader_q_t loader_q(Q, params->ldq, Qs, simd_group_id, simd_lane_id);
-
-    short tgp_bm = min(BM, params->M - c_row);
-    short2 tile_dims_Q = transpose_q ? short2(tgp_bm, BK) : short2(BK, tgp_bm);
-
-    loader_q.load_safe(tile_dims_Q);
-
-    initialize_corrections(Corrections, simd_lane_id, simd_group_id);
-
-    O += c_row * params->ldo;
-
-    // Prepare threadgroup mma operation
-    thread mma_qk_t mma_qk_op(simd_group_id, simd_lane_id);
-    thread mma_sv_t mma_softmax_sv_op(simd_group_id, simd_lane_id);
-    thread loader_k_t loader_k(K, params->ldk, Ks, simd_group_id, simd_lane_id);
-    thread loader_v_t loader_v(V, params->ldv, Vs, simd_group_id, simd_lane_id);
-
-    for (short n_block = 0; n_block < params->gemm_n_iterations_aligned;
-         n_block++) {
-      short c_col = BN;
-
-      // Prepare threadgroup loading operations
-      short gemm_k_iterations = params->gemm_k_iterations_aligned;
-      short tgp_bn_qk = min(BN, params->N - c_col * n_block);
-      threadgroup_barrier(mem_flags::mem_none);
-
-      ///////////////////////////////////////////////////////////////////////////////
-      { // Loop over K - unaligned case
-
-        if (tgp_bm == BM && tgp_bn_qk == BN) {
-          gemm_loop<true, true, K_aligned>(
-              Qs,
-              Ks,
-              gemm_k_iterations,
-              loader_k,
-              mma_qk_op,
-              tgp_bm,
-              tgp_bn_qk);
-        } else if (tgp_bn_qk == BN) {
-          gemm_loop<false, true, K_aligned>(
-              Qs,
-              Ks,
-              gemm_k_iterations,
-              loader_k,
-              mma_qk_op,
-              tgp_bm,
-              tgp_bn_qk);
-
-        } else if (tgp_bm == BM) {
-          gemm_loop<true, false, K_aligned>(
-              Qs,
-              Ks,
-              gemm_k_iterations,
-              loader_k,
-              mma_qk_op,
-              tgp_bm,
-              tgp_bn_qk);
-
-        } else {
-          gemm_loop<false, false, K_aligned>(
-              Qs,
-              Ks,
-              gemm_k_iterations,
-              loader_k,
-              mma_qk_op,
-              tgp_bm,
-              tgp_bn_qk);
-        }
-      }
-
-      mma_qk_op.store_result_to_tgp_memory(
-          Ss, BN + tgp_padding, short2(BN, BM));
-
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      rescale_ss(
-          Ss,
-          Corrections,
-          simd_group_id,
-          simd_lane_id,
-          short2(tgp_bn_qk, tgp_bm),
-          params->alpha);
-
-      loader_v.load_safe(short2(BK, tgp_bn_qk));
-
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      threadgroup float* o_scales = Corrections + 2 * (BM + float_padding);
-      mma_softmax_sv_op.rescale_output(o_scales);
-
-      mma_softmax_sv_op.mma(Ss, Vs);
-
-      threadgroup float* final_output_scales =
-          Corrections + 3 * (BM + float_padding);
-
-      mma_softmax_sv_op.rescale_output(final_output_scales);
-
-      loader_v.next();
-      loader_k.next(BN);
-
-      mma_qk_op.clear_results();
-    }
-
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-    mma_softmax_sv_op.store_result_safe(O, params->ldo, short2(BK, tgp_bm));
-  }
-};
-
-template <
-    typename T,
-    int BM,
-    int BN,
-    int BK,
-    int WM,
-    int WN,
-    bool transpose_q,
-    bool transpose_k,
-    bool transpose_v,
-    bool MN_aligned,
-    bool K_aligned>
-[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void attention(
-    const device T* Q [[buffer(0)]],
-    const device T* K [[buffer(1)]],
-    const device T* V [[buffer(2)]],
-    device T* O [[buffer(3)]],
-    const constant MLXFastAttentionParams* params [[buffer(4)]],
-    const constant int* batch_shape [[buffer(6)]],
-    const constant size_t* batch_strides [[buffer(7)]],
-    uint simd_lane_id [[thread_index_in_simdgroup]],
-    uint simd_group_id [[simdgroup_index_in_threadgroup]],
-    uint3 tid [[threadgroup_position_in_grid]],
-    uint3 lid [[thread_position_in_threadgroup]]) {
-  using attention_kernel = FastAttentionKernel<
-      T,
-      T,
-      BM,
-      BN,
-      BK,
-      WM,
-      WN,
-      transpose_q,
-      transpose_k,
-      transpose_v,
-      MN_aligned,
-      K_aligned>;
-
-  // Adjust for batch
-  if (params->batch_ndim > 1) {
-    const constant size_t* Q_bstrides = batch_strides;
-    const constant size_t* KV_bstrides = batch_strides + params->batch_ndim;
-
-    ulong2 batch_offsets = elem_to_loc_broadcast(
-        tid.z, batch_shape, Q_bstrides, KV_bstrides, params->batch_ndim);
-
-    Q += batch_offsets.x;
-    K += batch_offsets.y;
-    V += batch_offsets.y;
-
-  } else {
-    Q += params->batch_stride_q * tid.z;
-    K += params->batch_stride_k * tid.z;
-    V += params->batch_stride_v * tid.z;
-  }
-
-  // same shape as input
-  O += params->batch_stride_o * tid.z;
-  threadgroup T Qs[attention_kernel::tgp_mem_size_q];
-  threadgroup T Ss[attention_kernel::tgp_mem_size_s];
-  threadgroup float Corrections[attention_kernel::tgp_mem_size_corrections];
-
-  if (attention_kernel::share_kv_smem) {
-    threadgroup T Ks[attention_kernel::tgp_mem_size_k];
-    threadgroup T* Vs = Ks; //[attention_kernel::tgp_mem_size_v];
-    attention_kernel::run(
-        Q,
-        K,
-        V,
-        O,
-        params,
-        Qs,
-        Ks,
-        Ss,
-        Vs,
-        Corrections,
-        simd_lane_id,
-        simd_group_id,
-        tid,
-        lid);
-  } else {
-    threadgroup T Ks[attention_kernel::tgp_mem_size_k];
-    threadgroup T Vs[attention_kernel::tgp_mem_size_v];
-    attention_kernel::run(
-        Q,
-        K,
-        V,
-        O,
-        params,
-        Qs,
-        Ks,
-        Ss,
-        Vs,
-        Corrections,
-        simd_lane_id,
-        simd_group_id,
-        tid,
-        lid);
-  }
-}
-
 // clang-format off
-
-// SDPA full instantiations
-#define instantiate_fast_inference_self_attention_kernel(                   \
-    itype, otype, bm, bn, bk, wm, wn)                                       \
-  template [[host_name("steel_gemm_attention_bm_" #bm "_bn_" #bn "_bk_" #bk \
-                       "_itype_" #itype)]] [[kernel]] void                  \
-  attention<itype, bm, bn, bk, wm, wn, false, true, false, false, true>(    \
-      const device itype* Q [[buffer(0)]],                                  \
-      const device itype* K [[buffer(1)]],                                  \
-      const device itype* V [[buffer(2)]],                                  \
-      device otype* O [[buffer(3)]],                                        \
-      const constant MLXFastAttentionParams* params [[buffer(4)]],          \
-      const constant int* batch_shape [[buffer(6)]],                        \
-      const constant size_t* batch_strides [[buffer(7)]],                   \
-      uint simd_lane_id [[thread_index_in_simdgroup]],                      \
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],                \
-      uint3 tid [[threadgroup_position_in_grid]],                           \
-      uint3 lid [[thread_position_in_threadgroup]]);
-
-instantiate_fast_inference_self_attention_kernel(
-    float,
-    float,
-    16,
-    16,
-    64,
-    2,
-    2);
-instantiate_fast_inference_self_attention_kernel(
-    float,
-    float,
-    16,
-    16,
-    128,
-    2,
-    2);
-instantiate_fast_inference_self_attention_kernel(half, half, 16, 16, 64, 2, 2);
-instantiate_fast_inference_self_attention_kernel(half, half, 16, 16, 128, 2, 2);
-
 // SDPA vector instantiations
-#define instantiate_sdpa_vector(type, head_dim)                              \
-  template [[host_name("sdpa_vector_" #type "_" #head_dim)]]                 \
-  [[kernel]] void sdpa_vector<type, head_dim>(                               \
-      const device type* queries [[buffer(0)]],                              \
-      const device type* keys [[buffer(1)]],                                 \
-      const device type* values [[buffer(2)]],                               \
-      device type* out [[buffer(3)]],                                        \
-      const constant int& gqa_factor,                                        \
-      const constant int& N,                                                 \
-      const constant size_t& k_stride,                                       \
-      const constant size_t& v_stride,                                       \
-      const constant float& scale,                                           \
-      uint3 tid [[threadgroup_position_in_grid]],                            \
-      uint simd_gid [[simdgroup_index_in_threadgroup]],                      \
-      uint simd_lid [[thread_index_in_simdgroup]]);
+#define instantiate_sdpa_vector(type, head_dim)                                   \
+  instantiate_kernel("sdpa_vector_" #type "_" #head_dim, sdpa_vector, type, head_dim)          \
+  instantiate_kernel("sdpa_vector_2pass_1_" #type "_" #head_dim, sdpa_vector_2pass_1, type, head_dim)  \
+  instantiate_kernel("sdpa_vector_2pass_2_" #type "_" #head_dim, sdpa_vector_2pass_2, type, head_dim)
 
 #define instantiate_sdpa_vector_heads(type) \
   instantiate_sdpa_vector(type, 64)         \

mlx/backend/metal/kernels/scaled_dot_product_attention_params.h

@@ -1,42 +0,0 @@
-//
-//  scaled_dot_product_attention_params.h
-//  mlx
-
-#pragma once
-
-struct MLXFastAttentionParams {
-  const int M;
-  const int N;
-  const int K;
-
-  const int ldq; // ldq == ldo
-  const int ldk;
-  const int ldv;
-  const int lds;
-  const int ldo;
-
-  const int tiles_n;
-  const int tiles_m;
-
-  const int batch_stride_q;
-  const int batch_stride_k;
-  const int batch_stride_v;
-  const int batch_stride_o;
-
-  const int swizzle_log;
-  const int gemm_n_iterations_aligned;
-  const int gemm_k_iterations_aligned;
-  const int gemm_sv_m_block_iterations;
-
-  const int batch_ndim;
-  const float alpha;
-};
-
-struct MLXScaledDotProductAttentionParams {
-  // Associated dimensions & transposition information
-  const uint QUERY_SEQUENCE_LENGTH = 1;
-  const uint N_Q_HEADS = 32;
-  const uint N_KV_HEADS = 32;
-  const uint KV_TILES = 1;
-  const float INV_ALPHA = 0.08838834764831843f;
-};

mlx/backend/metal/kernels/scatter.h

@@ -10,7 +10,8 @@ template <
     typename Op,
     int NIDX,
     bool UPD_ROW_CONTIG,
-    int NWORK>
+    int NWORK,
+    typename LocT>
 METAL_FUNC void scatter_impl(
     const device T* updates,
     device mlx_atomic<T>* out,
@@ -28,29 +29,31 @@ METAL_FUNC void scatter_impl(
   Op op;
 
   auto ind_idx = gid.y * NWORK;
-  size_t out_offset = 0;
+  LocT out_offset = 0;
   if (upd_size > 1) {
-    out_offset =
-        elem_to_loc(gid.x, upd_shape + indices.ndim, out_strides, out_ndim);
+    out_offset = elem_to_loc<size_t, LocT>(
+        gid.x, upd_shape + indices.ndim, out_strides, out_ndim);
   }
 
   for (int j = 0; j < NWORK && ind_idx < idx_size; ++j, ind_idx++) {
-    size_t out_idx = out_offset;
+    LocT out_idx = out_offset;
     for (int i = 0; i < NIDX; ++i) {
       auto idx_loc = indices.row_contiguous[i]
           ? ind_idx
-          : elem_to_loc(
+          : elem_to_loc<size_t, LocT>(
                 ind_idx,
                 &indices.shapes[indices.ndim * i],
                 &indices.strides[indices.ndim * i],
                 indices.ndim);
       auto ax = axes[i];
       auto idx_val = offset_neg_idx(indices.buffers[i][idx_loc], out_shape[ax]);
-      out_idx += idx_val * out_strides[ax];
+      out_idx +=
+          static_cast<LocT>(idx_val) * static_cast<LocT>(out_strides[ax]);
     }
-    auto upd_idx = ind_idx * upd_size + gid.x;
+    auto upd_idx = ind_idx * static_cast<LocT>(upd_size) + gid.x;
     if constexpr (!UPD_ROW_CONTIG) {
-      upd_idx = elem_to_loc(upd_idx, upd_shape, upd_strides, upd_ndim);
+      upd_idx =
+          elem_to_loc<size_t, LocT>(upd_idx, upd_shape, upd_strides, upd_ndim);
     }
     op.atomic_update(out, updates[upd_idx], out_idx);
   }

mlx/backend/metal/kernels/sdpa_vector.h

@@ -21,8 +21,7 @@ template <typename T, int D>
   constexpr int BN = 32;
   constexpr int BD = 32;
   constexpr int elem_per_thread = D / BD;
-
-  const int stride = BN * D;
+  constexpr int stride = BN * D;
 
   typedef float U;
 
@@ -84,7 +83,6 @@ template <typename T, int D>
     keys += stride;
     values += stride;
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
 
   // Each thread has a partial part of the output so we need to combine them.
 
@@ -114,3 +112,181 @@ template <typename T, int D>
     }
   }
 }
+
+template <typename T, int D>
+[[kernel]] void sdpa_vector_2pass_1(
+    const device T* queries [[buffer(0)]],
+    const device T* keys [[buffer(1)]],
+    const device T* values [[buffer(2)]],
+    device float* out [[buffer(3)]],
+    device float* sums [[buffer(4)]],
+    device float* maxs [[buffer(5)]],
+    const constant int& gqa_factor,
+    const constant int& N,
+    const constant size_t& k_stride,
+    const constant size_t& v_stride,
+    const constant float& scale,
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  constexpr int BN = 8;
+  constexpr int BD = 32;
+  constexpr int elem_per_thread = D / BD;
+  constexpr int stride = BN * D;
+  constexpr int blocks = 32;
+
+  typedef float U;
+
+  thread U q[elem_per_thread];
+  thread U k[elem_per_thread];
+  thread U o[elem_per_thread];
+
+  threadgroup U outputs[BN * BD];
+  threadgroup U max_scores[BN];
+  threadgroup U sum_exp_scores[BN];
+
+  // Adjust positions
+  const int block_idx = tid.z;
+  const int head_idx = tid.y;
+  const int kv_head_idx = head_idx / gqa_factor;
+  queries += head_idx * D + simd_lid * elem_per_thread;
+  keys += kv_head_idx * k_stride + (block_idx * BN + simd_gid) * D +
+      simd_lid * elem_per_thread;
+  values += kv_head_idx * v_stride + (block_idx * BN + simd_gid) * D +
+      simd_lid * elem_per_thread;
+  out += head_idx * blocks * D + block_idx * D + simd_lid * elem_per_thread;
+  sums += head_idx * blocks + block_idx;
+  maxs += head_idx * blocks + block_idx;
+
+  // Read the query and 0 the output accumulator
+  for (int i = 0; i < elem_per_thread; i++) {
+    q[i] = static_cast<U>(scale) * queries[i];
+  }
+  for (int i = 0; i < elem_per_thread; i++) {
+    o[i] = 0;
+  }
+
+  U max_score = -1e9;
+  U sum_exp_score = 0;
+
+  // For each key
+  for (int i = block_idx * BN + simd_gid; i < N; i += blocks * BN) {
+    // Read the key
+    for (int i = 0; i < elem_per_thread; i++) {
+      k[i] = keys[i];
+    }
+
+    // Compute the i-th score
+    U score = 0;
+    for (int i = 0; i < elem_per_thread; i++) {
+      score += q[i] * k[i];
+    }
+    score = simd_sum(score);
+
+    // Update the accumulators
+    U new_max = max(max_score, score);
+    U factor = fast::exp(max_score - new_max);
+    U exp_score = fast::exp(score - new_max);
+
+    max_score = new_max;
+    sum_exp_score = sum_exp_score * factor + exp_score;
+
+    // Update the output accumulator
+    for (int i = 0; i < elem_per_thread; i++) {
+      o[i] = o[i] * factor + exp_score * values[i];
+    }
+
+    // Move the pointers to the next kv
+    keys += blocks * stride;
+    values += blocks * stride;
+  }
+
+  // Each thread has a partial part of the output so we need to combine them.
+
+  // First let's communicate the max and sum_exp
+  if (simd_lid == 0) {
+    max_scores[simd_gid] = max_score;
+    sum_exp_scores[simd_gid] = sum_exp_score;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  max_score = (simd_lid < BN) ? max_scores[simd_lid] : -1e9;
+  U new_max = simd_max(max_score);
+  U factor = fast::exp(max_score - new_max);
+  sum_exp_score = (simd_lid < BN) ? sum_exp_scores[simd_lid] : 0;
+  sum_exp_score = simd_sum(sum_exp_score * factor);
+
+  // Write the sum and new max
+  if (simd_gid == 0) {
+    sums[0] = sum_exp_score;
+    maxs[0] = new_max;
+  }
+
+  // Now we need to aggregate all the outputs
+  for (int i = 0; i < elem_per_thread; i++) {
+    outputs[simd_lid * BN + simd_gid] =
+        o[i] * fast::exp(max_scores[simd_gid] - new_max);
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // And write the output
+    if (simd_gid == 0) {
+      U output = outputs[simd_lid * BN];
+      for (int j = 1; j < BN; j++) {
+        output += outputs[simd_lid * BN + j];
+      }
+      out[i] = static_cast<T>(output);
+    }
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+  }
+}
+
+template <typename T, int D>
+[[kernel]] void sdpa_vector_2pass_2(
+    const device float* partials [[buffer(0)]],
+    const device float* sums [[buffer(1)]],
+    const device float* maxs [[buffer(2)]],
+    device T* out [[buffer(3)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  constexpr int BN = 32;
+  constexpr int BD = 32;
+  constexpr int elem_per_thread = D / BD;
+  constexpr int blocks = 32;
+
+  typedef float U;
+
+  thread U o[elem_per_thread];
+  threadgroup U outputs[BN * BD];
+
+  // Adjust positions
+  const int head_idx = tid.y;
+  partials += head_idx * blocks * D + simd_gid * D + simd_lid * elem_per_thread;
+  sums += head_idx * blocks;
+  maxs += head_idx * blocks;
+  out += head_idx * D + simd_gid * elem_per_thread;
+
+  // First everybody reads the max and sum_exp
+  U max_score = maxs[simd_lid];
+  U new_max = simd_max(max_score);
+  U factor = fast::exp(max_score - new_max);
+  U sum_exp_score = simd_sum(sums[simd_lid] * factor);
+
+  // Now read the block into registers and then use shared memory to transpose
+  // it
+  for (int i = 0; i < elem_per_thread; i++) {
+    o[i] = partials[i];
+  }
+  for (int i = 0; i < elem_per_thread; i++) {
+    outputs[simd_lid * BD + simd_gid] = o[i];
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    o[i] = simd_sum(outputs[simd_gid * BD + simd_lid] * factor) / sum_exp_score;
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+  }
+
+  // And write the output
+  if (simd_lid == 0) {
+    for (int i = 0; i < elem_per_thread; i++) {
+      out[i] = static_cast<T>(o[i]);
+    }
+  }
+}

mlx/backend/metal/kernels/softmax.metal

@@ -6,8 +6,6 @@
 using namespace metal;
 
 // clang-format off
-#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/softmax.h"
 

mlx/backend/metal/kernels/sort.metal

@@ -3,8 +3,6 @@
 #include <metal_stdlib>
 
 // clang-format off
-#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/sort.h"
 

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

@@ -0,0 +1,296 @@
+// Copyright © 2024 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/metal/kernels/steel/attn/loader.h"
+#include "mlx/backend/metal/kernels/steel/attn/mma.h"
+#include "mlx/backend/metal/kernels/steel/attn/params.h"
+#include "mlx/backend/metal/kernels/steel/attn/transforms.h"
+#include "mlx/backend/metal/kernels/steel/gemm/params.h"
+#include "mlx/backend/metal/kernels/steel/utils.h"
+
+using namespace metal;
+
+///////////////////////////////////////////////////////////////////////////////
+// GEMM kernel class
+///////////////////////////////////////////////////////////////////////////////
+
+namespace mlx {
+namespace steel {
+
+template <bool M_aligned, bool N_aligned, bool K_aligned>
+struct LoopAlignment {};
+
+template <
+    typename T,
+    typename U,
+    int BM,
+    int BN,
+    int BK,
+    int WM,
+    int WN,
+    bool transpose_a,
+    bool transpose_b,
+    bool MN_aligned,
+    bool K_aligned,
+    typename AccumType = typename AccumHelper<T>::accum_type,
+    typename Epilogue = TransformNone<U, AccumType>>
+struct GEMMKernel {
+  STEEL_CONST short tgp_padding_a = 16 / sizeof(T);
+  STEEL_CONST short tgp_padding_b = 16 / sizeof(T);
+  STEEL_CONST short tgp_mem_size_a =
+      transpose_a ? BK * (BM + tgp_padding_a) : BM * (BK + tgp_padding_a);
+  STEEL_CONST short tgp_mem_size_b =
+      transpose_b ? BN * (BK + tgp_padding_b) : BK * (BN + tgp_padding_b);
+  STEEL_CONST short tgp_mem_size = tgp_mem_size_a + tgp_mem_size_b;
+
+  STEEL_CONST short tgp_size = WM * WN * 32;
+
+  using loader_a_t = BlockLoader<
+      T,
+      transpose_a ? BK : BM,
+      transpose_a ? BM : BK,
+      transpose_a ? BM + tgp_padding_a : BK + tgp_padding_a,
+      !transpose_a,
+      tgp_size>;
+  using loader_b_t = BlockLoader<
+      T,
+      transpose_b ? BN : BK,
+      transpose_b ? BK : BN,
+      transpose_b ? BK + tgp_padding_b : BN + tgp_padding_b,
+      transpose_b,
+      tgp_size>;
+  using mma_t = BlockMMA<
+      T,
+      U,
+      BM,
+      BN,
+      BK,
+      WM,
+      WN,
+      transpose_a,
+      transpose_b,
+      transpose_a ? BM + tgp_padding_a : BK + tgp_padding_a,
+      transpose_b ? BK + tgp_padding_b : BN + tgp_padding_b,
+      AccumType,
+      Epilogue>;
+
+  /* Main kernel function */
+  template <bool M_aligned, bool N_aligned, bool K_aligned_>
+  static METAL_FUNC void gemm_loop(
+      threadgroup T* As [[threadgroup(0)]],
+      threadgroup T* Bs [[threadgroup(1)]],
+      const int gemm_k_iterations,
+      thread loader_a_t& loader_a,
+      thread loader_b_t& loader_b,
+      thread mma_t& mma_op,
+      thread const short& tgp_bm,
+      thread const short& tgp_bn,
+      thread const short& lbk,
+      LoopAlignment<M_aligned, N_aligned, K_aligned_> l = {}) {
+    // Appease the compiler
+    (void)l;
+
+    short2 tile_dims_A = transpose_a ? short2(tgp_bm, BK) : short2(BK, tgp_bm);
+
+    short2 tile_dims_B = transpose_b ? short2(BK, tgp_bn) : short2(tgp_bn, BK);
+
+    for (int k = 0; k < gemm_k_iterations; k++) {
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+      // Load elements into threadgroup
+      if (M_aligned) {
+        loader_a.load_unsafe();
+      } else {
+        loader_a.load_safe(tile_dims_A);
+      }
+
+      if (N_aligned) {
+        loader_b.load_unsafe();
+      } else {
+        loader_b.load_safe(tile_dims_B);
+      }
+
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+
+      // Multiply and accumulate threadgroup elements
+      mma_op.mma(As, Bs);
+
+      // Prepare for next iteration
+      loader_a.next();
+      loader_b.next();
+    }
+
+    if (!K_aligned_) {
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+
+      short2 tile_dims_A_last =
+          transpose_a ? short2(tgp_bm, lbk) : short2(lbk, tgp_bm);
+      short2 tile_dims_B_last =
+          transpose_b ? short2(lbk, tgp_bn) : short2(tgp_bn, lbk);
+
+      loader_a.load_safe(tile_dims_A_last);
+      loader_b.load_safe(tile_dims_B_last);
+
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+
+      mma_op.mma(As, Bs);
+    }
+  }
+
+  /* Main kernel function */
+  static METAL_FUNC void run(
+      const device T* A [[buffer(0)]],
+      const device T* B [[buffer(1)]],
+      device U* D [[buffer(2)]],
+      const constant GEMMParams* params [[buffer(3)]],
+      threadgroup T* As [[threadgroup(0)]],
+      threadgroup T* Bs [[threadgroup(1)]],
+      uint simd_lane_id [[thread_index_in_simdgroup]],
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],
+      uint3 tid [[threadgroup_position_in_grid]],
+      uint3 lid [[thread_position_in_threadgroup]]) {
+    // Pacifying compiler
+    (void)lid;
+
+    const int tid_y = ((tid.y) << params->swizzle_log) +
+        ((tid.x) & ((1 << params->swizzle_log) - 1));
+    const int tid_x = (tid.x) >> params->swizzle_log;
+
+    if (params->tiles_n <= tid_x || params->tiles_m <= tid_y) {
+      return;
+    }
+
+    threadgroup_barrier(mem_flags::mem_none);
+
+    // Find block in A, B, C
+    const int c_row = tid_y * BM;
+    const int c_col = tid_x * BN;
+    const size_t c_row_long = size_t(c_row);
+    const size_t c_col_long = size_t(c_col);
+
+    A += transpose_a ? c_row_long : c_row_long * params->lda;
+    B += transpose_b ? c_col_long * params->ldb : c_col_long;
+    D += c_row_long * params->ldd + c_col_long;
+
+    // Prepare threadgroup loading operations
+    thread loader_a_t loader_a(A, params->lda, As, simd_group_id, simd_lane_id);
+    thread loader_b_t loader_b(B, params->ldb, Bs, simd_group_id, simd_lane_id);
+
+    // Prepare threadgroup mma operation
+    thread mma_t mma_op(simd_group_id, simd_lane_id);
+
+    int gemm_k_iterations = params->gemm_k_iterations_aligned;
+
+    ///////////////////////////////////////////////////////////////////////////////
+    // MNK aligned loop
+    if (MN_aligned) {
+      for (int k = 0; k < gemm_k_iterations; k++) {
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+        // Load elements into threadgroup
+        loader_a.load_unsafe();
+        loader_b.load_unsafe();
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // Multiply and accumulate threadgroup elements
+        mma_op.mma(As, Bs);
+
+        // Prepare for next iteration
+        loader_a.next();
+        loader_b.next();
+      }
+
+      threadgroup_barrier(mem_flags::mem_none);
+
+      // Loop tail
+      if (!K_aligned) {
+        int lbk = params->K - params->gemm_k_iterations_aligned * BK;
+        short2 tile_dims_A = transpose_a ? short2(BM, lbk) : short2(lbk, BM);
+        short2 tile_dims_B = transpose_b ? short2(lbk, BN) : short2(BN, lbk);
+
+        loader_a.load_safe(tile_dims_A);
+        loader_b.load_safe(tile_dims_B);
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        mma_op.mma(As, Bs);
+      }
+
+      // Store results to device memory
+      mma_op.store_result(D, params->ldd);
+      return;
+
+    }
+    ///////////////////////////////////////////////////////////////////////////////
+    // MN unaligned loop
+    else { // Loop over K - unaligned case
+      short tgp_bm = min(BM, params->M - c_row);
+      short tgp_bn = min(BN, params->N - c_col);
+      short leftover_bk = params->K - params->gemm_k_iterations_aligned * BK;
+
+      if (tgp_bm == BM && tgp_bn == BN) {
+        gemm_loop<true, true, K_aligned>(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            leftover_bk);
+
+        mma_op.store_result(D, params->ldd);
+        return;
+
+      } else if (tgp_bn == BN) {
+        gemm_loop<false, true, K_aligned>(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            leftover_bk);
+
+        mma_op.store_result_safe(D, params->ldd, short2(tgp_bn, tgp_bm));
+        return;
+
+      } else if (tgp_bm == BM) {
+        gemm_loop<true, false, K_aligned>(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            leftover_bk);
+
+        mma_op.store_result_safe(D, params->ldd, short2(tgp_bn, tgp_bm));
+        return;
+
+      } else {
+        gemm_loop<false, false, K_aligned>(
+            As,
+            Bs,
+            gemm_k_iterations,
+            loader_a,
+            loader_b,
+            mma_op,
+            tgp_bm,
+            tgp_bn,
+            leftover_bk);
+
+        mma_op.store_result_safe(D, params->ldd, short2(tgp_bn, tgp_bm));
+        return;
+      }
+    }
+  }
+};
+
+} // namespace steel
+} // namespace mlx

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

@@ -0,0 +1,349 @@
+// Copyright © 2024 Apple Inc.
+
+using namespace mlx::steel;
+
+///////////////////////////////////////////////////////////////////////////////
+// GEMM kernels
+///////////////////////////////////////////////////////////////////////////////
+
+constant bool align_Q [[function_constant(200)]];
+constant bool align_K [[function_constant(201)]];
+
+template <typename T>
+struct TransformScale {
+  T scale;
+  METAL_FUNC TransformScale(T scale_) : scale(scale_) {}
+
+  METAL_FUNC T apply(T x) const {
+    return scale * x;
+  }
+};
+
+struct MaxOp {
+  template <typename T>
+  METAL_FUNC static constexpr T apply(T x, T y) {
+    return metal::max(x, y);
+  }
+};
+
+struct SumOp {
+  template <typename T>
+  METAL_FUNC static constexpr T apply(T x, T y) {
+    return x + y;
+  }
+};
+
+struct MulOp {
+  template <typename T>
+  METAL_FUNC static constexpr T apply(T x, T y) {
+    return x * y;
+  }
+};
+
+struct SubOp {
+  template <typename T>
+  METAL_FUNC static constexpr T apply(T x, T y) {
+    return x - y;
+  }
+};
+
+struct ExpSubOp {
+  template <typename T>
+  METAL_FUNC static constexpr T apply(T x, T y) {
+    return fast::exp(x - y);
+  }
+};
+
+struct DivOp {
+  template <typename T>
+  METAL_FUNC static constexpr T apply(T x, T y) {
+    return x / y;
+  }
+};
+
+// clang-format off
+template <
+    typename T,
+    int BQ,
+    int BK,
+    int BD,
+    int WM,
+    int WN,
+    typename AccumType = float>
+[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void attention(
+    const device T* Q [[buffer(0)]],
+    const device T* K [[buffer(1)]],
+    const device T* V [[buffer(2)]],
+    device T* O [[buffer(3)]],
+    const constant AttnParams* params [[buffer(4)]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]]) { // clang-format on
+
+  // Pacifying compiler
+  (void)lid;
+
+  // Move to correct block
+  ulong3 tidl{tid.x, tid.y, tid.z};
+
+  Q += tidl.z * params->Q_strides[0] + // Batch
+      tidl.y * params->Q_strides[1] + // Head
+      tidl.x * BQ * params->Q_strides[2]; // Seqeunce
+
+  ulong kv_head_idx = int(tid.y) / params->gqa_factor;
+  K += tidl.z * params->K_strides[0] + // Batch
+      kv_head_idx * params->K_strides[1]; // Head
+
+  V += tidl.z * params->V_strides[0] + // Batch
+      kv_head_idx * params->V_strides[1]; // Head
+
+  O += tidl.z * params->O_strides[0] + // Batch
+      tidl.y * params->O_strides[1] + // Head
+      tidl.x * BQ * params->O_strides[2]; // Seqeunce
+
+  // Prepare threadgroup memory
+  constexpr short padQ = 0; // 16 / sizeof(T);
+  constexpr short padK = 0; // 16 / sizeof(T);
+  constexpr short padV = 0; // 16 / sizeof(T);
+
+  constexpr short LDQ_tgp = BD + padQ;
+  constexpr short LDK_tgp = BK + padK;
+  constexpr short LDV_tgp = BD + padV;
+
+  threadgroup T Qs[BQ * (BD + padQ)];
+  threadgroup T Ks[(BK + padK) * BD];
+  threadgroup T Vs[BK * (BD + padV)];
+
+  // Prepare block loaders
+  using QBlockLoader = BlockLoaderT<
+      /* typename T = */ T,
+      /* short BROWS = */ BQ,
+      /* short BCOLS = */ BD,
+      /* short kDstStrRow = */ LDQ_tgp,
+      /* short kDstStrCol = */ 1,
+      /* short reduction_dim = */ 1,
+      /* short tgp_size = */ WM * WN * 32>;
+
+  // K is loaded in transposed
+  using KBlockLoader = BlockLoaderT<
+      /* typename T = */ T,
+      /* short BROWS = */ BK,
+      /* short BCOLS = */ BD,
+      /* short kDstStrRow = */ 1,
+      /* short kDstStrCol = */ LDK_tgp,
+      /* short reduction_dim = */ 0,
+      /* short tgp_size = */ WM * WN * 32>;
+
+  using VBlockLoader = BlockLoaderT<
+      /* typename T = */ T,
+      /* short BROWS = */ BK,
+      /* short BCOLS = */ BD,
+      /* short kDstStrRow = */ LDV_tgp,
+      /* short kDstStrCol = */ 1,
+      /* short reduction_dim = */ 0,
+      /* short tgp_size = */ WM * WN * 32>;
+
+  QBlockLoader loader_q(
+      Q, params->Q_strides[2], Qs, simd_group_id, simd_lane_id);
+  KBlockLoader loader_k(
+      K, params->K_strides[2], Ks, simd_group_id, simd_lane_id);
+  VBlockLoader loader_v(
+      V, params->V_strides[2], Vs, simd_group_id, simd_lane_id);
+
+  TransformScale<T> ts(static_cast<T>(params->scale));
+
+  // Prepare MMA tiles
+  constexpr short kFragSize = 8; // MMAFrag size
+  using MMAFrag_acc_t = BaseMMAFrag<AccumType, kFragSize, kFragSize>;
+
+  constexpr int kNWarps = WM * WN;
+  static_assert(
+      BQ >= (kNWarps * kFragSize) && BQ % (kNWarps * kFragSize) == 0,
+      "Each simdgroup must host atleast 1 simdgroup matrix along Q sequence.");
+
+  // Q seq frags per warp
+  constexpr int TQ = BQ / (kNWarps * kFragSize);
+  // KV sequence frags (all warps load the same frags)
+  constexpr int TK = BK / kFragSize;
+  // HeadDim frags (all warps load the same frags)
+  constexpr int TD = BD / kFragSize;
+
+  static_assert(TQ == 1, "Check TQ");
+
+  MMATile<AccumType, TQ, 1, MMAFrag_acc_t> Qtile;
+  MMATile<AccumType, 1, TK, MMAFrag_acc_t> Ktile;
+  MMATile<AccumType, TQ, TK, MMAFrag_acc_t> Stile;
+  MMATile<AccumType, TK, TD, MMAFrag_acc_t> Vtile;
+  MMATile<AccumType, TQ, TD, MMAFrag_acc_t> Otile;
+
+  Otile.clear();
+
+  // Prepare mma tile offsets
+  const short2 simd_coord = MMAFrag_acc_t::get_coord(simd_lane_id);
+  const short sm = simd_coord.y;
+  const short sn = simd_coord.x;
+  const short tm = kFragSize * TQ * simd_group_id;
+
+  const short Qs_offset = (tm + sm) * LDQ_tgp + sn;
+  const short Ks_offset = sm * LDK_tgp + sn;
+  const short Vs_offset = sm * LDV_tgp + sn;
+
+  constexpr short Qs_tile_stride = kFragSize;
+  constexpr short Ks_tile_stride = kFragSize * LDK_tgp;
+
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  // Load Q blocks apply scale
+  if (!align_Q && int(tid.x) == (params->NQ_aligned)) {
+    loader_q.load_safe(short2(BD, params->qL - params->NQ_aligned * BQ));
+  } else {
+    loader_q.load_unsafe();
+  }
+  loader_q.apply_inplace_op(ts);
+
+  // Init row reduction variables
+  constexpr short kRowsPT = decltype(Stile)::kRowsPerThread;
+
+  AccumType max_score[kRowsPT];
+  AccumType sum_score[kRowsPT] = {0};
+
+  // Init to -Inf
+  STEEL_PRAGMA_UNROLL
+  for (short i = 0; i < kRowsPT; ++i) {
+    max_score[i] = Limits<AccumType>::min;
+  }
+
+  // Loop over KV seq length
+  for (int kb = 0; kb < params->NK; kb++) {
+    // Load K block and apply scale
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    if (!align_K && kb == (params->NK_aligned)) {
+      loader_k.load_safe(short2(BD, params->kL - params->NK_aligned * BK));
+    } else {
+      loader_k.load_unsafe();
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Do S = Q @ K.T
+    Stile.clear();
+
+    for (short dd = 0; dd < TD; dd++) {
+      simdgroup_barrier(mem_flags::mem_none);
+
+      Qtile.template load<T, 1, 1, LDQ_tgp, 1>(
+          &Qs[Qs_offset + dd * Qs_tile_stride]);
+      Ktile.template load<T, 1, 1, LDK_tgp, 1>(
+          &Ks[Ks_offset + dd * Ks_tile_stride]);
+
+      simdgroup_barrier(mem_flags::mem_none);
+
+      tile_matmad(Stile, Qtile, Ktile, Stile);
+    }
+
+    // Mask out of length sequence
+    if (!align_K && kb == (params->NK_aligned)) {
+      using stile_t = decltype(Stile);
+      using selem_t = typename stile_t::elem_type;
+      constexpr auto neg_inf = -metal::numeric_limits<selem_t>::infinity();
+      const short lim = params->kL - params->NK_aligned * BK;
+
+      STEEL_PRAGMA_UNROLL
+      for (short i = 0; i < stile_t::kTileRows; i++) {
+        STEEL_PRAGMA_UNROLL
+        for (short j = 0; j < stile_t::kTileCols; j++) {
+          short col_pos = sn + (j * stile_t::kFragCols);
+          STEEL_PRAGMA_UNROLL
+          for (short jj = 0; jj < stile_t::MMAFrag_t::kElemCols; jj++) {
+            if ((col_pos + jj) >= lim) {
+              Stile.frag_at(i, j)[jj] = neg_inf;
+            }
+          }
+        }
+      }
+    }
+
+    simdgroup_barrier(mem_flags::mem_none);
+
+    // Load V blocks
+    if (!align_K && kb == (params->NK_aligned)) {
+      loader_v.load_safe(short2(BD, params->kL - params->NK_aligned * BK));
+    } else {
+      loader_v.load_unsafe();
+    }
+
+    // Do softmax
+
+    // Temp variables
+    AccumType new_max[kRowsPT];
+    AccumType factor[kRowsPT];
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kRowsPT; ++i) {
+      new_max[i] = max_score[i];
+    }
+
+    // Row max
+    Stile.template row_reduce<MaxOp>(new_max);
+
+    // exp(Si - rowmax(Si))
+    Stile.template row_bin_op<ExpSubOp>(new_max);
+
+    // Factor exp(rowmax(Si) - rowmax(Si-1))
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kRowsPT; ++i) {
+      factor[i] = fast::exp(max_score[i] - new_max[i]);
+    }
+
+    // Save max for next iteration
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kRowsPT; ++i) {
+      max_score[i] = new_max[i];
+    }
+
+    // Row Sum
+    AccumType sum_score_tmp[kRowsPT] = {0};
+    Stile.template row_reduce<SumOp>(sum_score_tmp);
+
+    // Update norm
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kRowsPT; ++i) {
+      sum_score[i] = sum_score[i] * factor[i] + sum_score_tmp[i];
+    }
+
+    // Update O
+    Otile.template row_bin_op<MulOp>(factor);
+
+    // Load V into registers
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    Vtile.template load<T, 1, 1, LDV_tgp, 1>(&Vs[Vs_offset]);
+
+    simdgroup_barrier(mem_flags::mem_none);
+
+    // Do O = S @ V
+    tile_matmad(Otile, Stile, Vtile, Otile);
+
+    // Prepare for next iteration
+    loader_k.next();
+    loader_v.next();
+  }
+
+  // Normalize output
+  Otile.template row_bin_op<DivOp>(sum_score);
+  threadgroup_barrier(mem_flags::mem_none);
+
+  // Store results
+  O += (tm + sm) * params->O_strides[2] + sn;
+
+  if (!align_Q && int(tid.x) == (params->NQ_aligned)) {
+    auto dst_tile_dims =
+        short2(BD - sn, params->qL - BQ * params->NQ_aligned - (tm + sm));
+
+    if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
+      return;
+
+    Otile.template store_safe<T, 1, 1>(O, params->O_strides[2], dst_tile_dims);
+  } else {
+    Otile.template store<T, 1, 1>(O, params->O_strides[2]);
+  }
+}

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

@@ -0,0 +1,31 @@
+// Copyright © 2024 Apple Inc.
+
+// clang-format off
+#include "mlx/backend/metal/kernels/utils.h"
+
+#include "mlx/backend/metal/kernels/steel/attn/attn.h"
+#include "mlx/backend/metal/kernels/steel/attn/kernels/steel_attention.h"
+
+#define instantiate_attn(tname, dtype, bq, bk, bd, wm, wn) \
+  template [[host_name("steel_attention_" #tname "_bq" #bq "_bk" #bk "_bd" #bd "_wm" #wm "_wn" #wn)]] \
+  [[kernel]] void attention<dtype, bq, bk, bd, wm, wn, float>( \
+      const device dtype* Q [[buffer(0)]], \
+      const device dtype* K [[buffer(1)]], \
+      const device dtype* V [[buffer(2)]], \
+      device dtype* O [[buffer(3)]],\
+      const constant AttnParams* params [[buffer(4)]], \
+      uint simd_lane_id [[thread_index_in_simdgroup]], \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]], \
+      uint3 tid [[threadgroup_position_in_grid]], \
+      uint3 lid [[thread_position_in_threadgroup]]);
+
+#define instantiate_attn_shapes_helper(iname, itype) \
+    instantiate_attn(iname, itype, 32, 16, 128, 4, 1) \
+    instantiate_attn(iname, itype, 32, 32,  80, 4, 1) \
+    instantiate_attn(iname, itype, 32, 32,  64, 4, 1)
+
+instantiate_attn_shapes_helper(float16, half);
+instantiate_attn_shapes_helper(bfloat16, bfloat16_t);
+
+instantiate_attn_shapes_helper(float32, float);
+// clang-format on

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

@@ -0,0 +1,264 @@
+// Copyright © 2024 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/metal/kernels/steel/defines.h"
+
+///////////////////////////////////////////////////////////////////////////////
+// Loading helper
+///////////////////////////////////////////////////////////////////////////////
+
+namespace mlx {
+namespace steel {
+
+template <
+    typename T,
+    short BROWS,
+    short BCOLS,
+    short dst_ld,
+    short reduction_dim,
+    short tgp_size,
+    short alignment = 1,
+    short n_reads = (BCOLS * BROWS) / (tgp_size),
+    short TCOLS = BCOLS / n_reads,
+    short TROWS = tgp_size / TCOLS>
+struct BlockLoader {
+  STEEL_CONST short n_rows = (BROWS + TROWS - 1) / TROWS;
+  STEEL_CONST short vec_size = n_reads;
+
+  // Leading dimension for src
+  const int src_ld;
+  const int tile_stride;
+
+  // Thread location indices
+  const short thread_idx;
+  const short bi;
+  const short bj;
+
+  // threadgroup and device memory
+  threadgroup T* dst;
+  const device T* src;
+
+  struct alignas(alignment * sizeof(T)) ReadVector {
+    uint8_t v[sizeof(T) * vec_size];
+  };
+
+  /* Constructor */
+  METAL_FUNC BlockLoader(
+      const device T* src_,
+      const int src_ld_,
+      threadgroup T* dst_,
+      ushort simd_group_id [[simdgroup_index_in_threadgroup]],
+      ushort simd_lane_id [[thread_index_in_simdgroup]])
+      : src_ld(src_ld_),
+        tile_stride(reduction_dim ? BCOLS : BROWS * src_ld),
+        thread_idx(simd_group_id * 32 + simd_lane_id),
+        bi(thread_idx / TCOLS),
+        bj(vec_size * (thread_idx % TCOLS)),
+        dst(dst_ + bi * dst_ld + bj),
+        src(src_ + bi * src_ld + bj) {}
+
+  /* Apply operation to threadgroup without bound checking */
+  template <typename UnaryOp>
+  METAL_FUNC void apply_inplace_op(thread const UnaryOp& op) const {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < BROWS; i += TROWS) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        dst[i * dst_ld + j] = op.apply(dst[i * dst_ld + j]);
+      }
+    }
+  }
+
+  /* Load from device memory into threadgroup memory - without bound checking */
+  METAL_FUNC void load_unsafe() const {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < BROWS; i += TROWS) {
+      *((threadgroup ReadVector*)(&dst[i * dst_ld])) =
+          *((const device ReadVector*)(&src[i * src_ld]));
+    }
+  }
+
+  /* Load from device memory into threadgroup memory - with bound checking */
+  METAL_FUNC void load_safe(short2 src_tile_dim) const {
+    src_tile_dim = src_tile_dim - short2(bj, bi);
+
+    // Skip loading if thread has no valid reads
+    if (src_tile_dim.x <= 0 || src_tile_dim.y <= 0) {
+      STEEL_PRAGMA_UNROLL
+      for (short i = 0; i < BROWS; i += TROWS) {
+        STEEL_PRAGMA_UNROLL
+        for (short j = 0; j < vec_size; j++) {
+          dst[i * dst_ld + j] = T(0);
+        }
+      }
+      return;
+    }
+
+    // Use fast thread memory for bound checks
+    bool tmp_idx[vec_size];
+    T tmp_val[vec_size];
+
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < BROWS; i += TROWS) {
+      // Make sure tmp_idx only contains valid indices
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        tmp_idx[j] = (i < src_tile_dim.y) && (j < src_tile_dim.x);
+      }
+
+      // Read valid indices into tmp_val
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        tmp_val[j] = src[(tmp_idx[j] ? i * src_ld + j : 0)];
+      }
+
+      // Zero out uneeded values
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        tmp_val[j] = tmp_idx[j] ? tmp_val[j] : T(0);
+      }
+
+      // Copy values to threadgroup memory
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        dst[i * dst_ld + j] = tmp_val[j];
+      }
+    }
+  }
+
+  /* Iteration helper */
+  METAL_FUNC void next() {
+    src += tile_stride;
+  }
+};
+
+template <int R, int C>
+struct CShape {
+  STEEL_CONST int kRows = R;
+  STEEL_CONST int kCols = C;
+};
+
+template <
+    typename T,
+    short BROWS,
+    short BCOLS,
+    short kDstStrRow,
+    short kDstStrCol,
+    short reduction_dim,
+    short tgp_size,
+    short n_reads = (BCOLS * BROWS) / (tgp_size),
+    short TCOLS = BCOLS / n_reads,
+    short TROWS = tgp_size / TCOLS>
+struct BlockLoaderT {
+  STEEL_CONST short n_rows = (BROWS + TROWS - 1) / TROWS;
+  STEEL_CONST short vec_size = n_reads;
+
+  // Leading dimension for src
+  const int src_ld;
+  const int tile_stride;
+
+  // Thread location indices
+  const short thread_idx;
+  const short bi;
+  const short bj;
+
+  // threadgroup and device memory
+  threadgroup T* dst;
+  const device T* src;
+
+  /* Constructor */
+  METAL_FUNC BlockLoaderT(
+      const device T* src_,
+      const int src_ld_,
+      threadgroup T* dst_,
+      ushort simd_group_id [[simdgroup_index_in_threadgroup]],
+      ushort simd_lane_id [[thread_index_in_simdgroup]])
+      : src_ld(src_ld_),
+        tile_stride(reduction_dim ? BCOLS : BROWS * src_ld),
+        thread_idx(simd_group_id * 32 + simd_lane_id),
+        bi(thread_idx / TCOLS),
+        bj(vec_size * (thread_idx % TCOLS)),
+        dst(dst_ + bi * kDstStrRow + bj * kDstStrCol),
+        src(src_ + bi * src_ld + bj) {}
+
+  /* Apply operation to threadgroup without bound checking */
+  template <typename UnaryOp>
+  METAL_FUNC void apply_inplace_op(thread const UnaryOp& op) const {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < BROWS; i += TROWS) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        dst[i * kDstStrRow + j * kDstStrCol] =
+            op.apply(dst[i * kDstStrRow + j * kDstStrCol]);
+      }
+    }
+  }
+
+  /* Load from device memory into threadgroup memory - without bound checking */
+  METAL_FUNC void load_unsafe() const {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < BROWS; i += TROWS) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        dst[i * kDstStrRow + j * kDstStrCol] = src[i * src_ld + j];
+      }
+    }
+  }
+
+  /* Load from device memory into threadgroup memory - with bound checking */
+  METAL_FUNC void load_safe(short2 src_tile_dim) const {
+    src_tile_dim = src_tile_dim - short2(bj, bi);
+
+    // Skip loading if thread has no valid reads
+    if (src_tile_dim.x <= 0 || src_tile_dim.y <= 0) {
+      STEEL_PRAGMA_UNROLL
+      for (short i = 0; i < BROWS; i += TROWS) {
+        STEEL_PRAGMA_UNROLL
+        for (short j = 0; j < vec_size; j++) {
+          dst[i * kDstStrRow + j * kDstStrCol] = T(0);
+        }
+      }
+      return;
+    }
+
+    // Use fast thread memory for bound checks
+    bool tmp_idx[vec_size];
+    T tmp_val[vec_size];
+
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < BROWS; i += TROWS) {
+      // Make sure tmp_idx only contains valid indices
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        tmp_idx[j] = (i < src_tile_dim.y) && (j < src_tile_dim.x);
+      }
+
+      // Read valid indices into tmp_val
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        tmp_val[j] = src[(tmp_idx[j] ? i * src_ld + j : 0)];
+      }
+
+      // Zero out uneeded values
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        tmp_val[j] = tmp_idx[j] ? tmp_val[j] : T(0);
+      }
+
+      // Copy values to threadgroup memory
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < vec_size; j++) {
+        dst[i * kDstStrRow + j * kDstStrCol] = tmp_val[j];
+      }
+    }
+  }
+
+  /* Iteration helper */
+  METAL_FUNC void next() {
+    src += tile_stride;
+  }
+};
+
+} // namespace steel
+} // namespace mlx

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

@@ -0,0 +1,726 @@
+// Copyright © 2024 Apple Inc.
+
+#pragma once
+
+#include <metal_simdgroup>
+#include <metal_simdgroup_matrix>
+#include <metal_stdlib>
+
+#include "mlx/backend/metal/kernels/steel/attn/transforms.h"
+#include "mlx/backend/metal/kernels/steel/defines.h"
+#include "mlx/backend/metal/kernels/steel/utils/integral_constant.h"
+
+using namespace metal;
+
+///////////////////////////////////////////////////////////////////////////////
+// MMA helper
+///////////////////////////////////////////////////////////////////////////////
+
+namespace mlx {
+namespace steel {
+
+template <typename RInt, typename CInt>
+struct Shape2D {
+  RInt r;
+  CInt c;
+
+  Shape2D(RInt r_, CInt c_) : r(r_), c(c_) {}
+};
+
+template <typename Shape, typename Layout>
+struct Layout2D {
+  Shape shape;
+  Layout layout;
+};
+
+template <typename T, int kFragRows_, int kFragCols_>
+struct BaseMMAFrag {
+  static_assert(
+      kFragRows_ == 8,
+      "Only 8 x 8 fragment matrices are currently supported");
+  static_assert(
+      kFragCols_ == 8,
+      "Only 8 x 8 fragment matrices are currently supported");
+};
+
+template <typename T>
+struct BaseMMAFrag<T, 8, 8> {
+  STEEL_CONST int kFragRows = 8;
+  STEEL_CONST int kFragCols = 8;
+
+  STEEL_CONST int kElemsPerFrag = (kFragRows * kFragCols) / 32;
+
+  STEEL_CONST int kElemRows = 1;
+  STEEL_CONST int kElemCols = 2;
+
+  static_assert(
+      kElemRows * kElemCols == kElemsPerFrag,
+      "MMAFrag shape is not consistent with MMAFrag size");
+
+  typedef metal::simdgroup_matrix<T, kFragRows, kFragCols> mat_type;
+  typedef metal::vec<T, kElemsPerFrag> frag_type;
+  typedef metal::vec<T, kElemRows> row_frag_type;
+  typedef metal::vec<T, kElemCols> col_frag_type;
+
+  METAL_FUNC static constexpr short2 get_coord(ushort simd_lane_id
+                                               [[thread_index_in_simdgroup]]) {
+    const short qid = simd_lane_id / 4;
+    const short fm = (qid & 4) + ((simd_lane_id / 2) % 4);
+    const short fn = (qid & 2) * 2 + (simd_lane_id % 2) * 2;
+    return short2{fn, fm};
+  }
+
+  template <typename SrcPtrType, typename StrX, typename StrY>
+  METAL_FUNC static constexpr void
+  load(thread frag_type& dst, SrcPtrType src, StrX str_x, StrY str_y) {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kElemRows; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kElemCols; j++) {
+        dst[i * kElemCols + j] = static_cast<T>(src[i * str_x + j * str_y]);
+      }
+    }
+  }
+
+  template <
+      typename SrcPtrType,
+      typename StrX,
+      typename StrY,
+      typename LimX,
+      typename LimY,
+      typename OffX,
+      typename OffY>
+  METAL_FUNC static constexpr void load_safe(
+      thread frag_type& dst,
+      SrcPtrType src,
+      StrX str_x,
+      StrY str_y,
+      LimX lim_x,
+      LimY lim_y,
+      OffX off_x = Int<0>{},
+      OffY off_y = Int<0>{}) {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kElemRows; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kElemCols; j++) {
+        if ((off_x + i) < lim_x && (off_y + j) < lim_y) {
+          dst[i * kElemCols + j] =
+              static_cast<T>(src[(off_x + i) * str_x + (off_x + j) * str_y]);
+        } else {
+          dst[i * kElemCols + j] = T(0);
+        }
+      }
+    }
+  }
+
+  template <typename DstPtrType, typename StrX, typename StrY>
+  METAL_FUNC static constexpr void
+  store(const thread frag_type& src, DstPtrType dst, StrX str_x, StrY str_y) {
+    using U = pointer_element_t<DstPtrType>;
+
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kElemRows; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kElemCols; j++) {
+        dst[i * str_x + j * str_y] = static_cast<U>(src[i * kElemCols + j]);
+      }
+    }
+  }
+
+  template <
+      typename DstPtrType,
+      typename StrX,
+      typename StrY,
+      typename LimX,
+      typename LimY,
+      typename OffX,
+      typename OffY>
+  METAL_FUNC static constexpr void store_safe(
+      const thread frag_type& src,
+      DstPtrType dst,
+      StrX str_x,
+      StrY str_y,
+      LimX lim_x,
+      LimY lim_y,
+      OffX off_x = Int<0>{},
+      OffY off_y = Int<0>{}) {
+    using U = pointer_element_t<DstPtrType>;
+
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kElemRows; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kElemCols; j++) {
+        if ((off_x + i) < lim_x && (off_y + j) < lim_y) {
+          dst[(off_x + i) * str_x + (off_y + j) * str_y] =
+              static_cast<U>(src[i * kElemCols + j]);
+        }
+      }
+    }
+  }
+
+  METAL_FUNC static constexpr void mma(
+      thread frag_type& D,
+      thread frag_type& A,
+      thread frag_type& B,
+      thread frag_type& C) {
+    mat_type D_mat;
+    mat_type A_mat;
+    mat_type B_mat;
+    mat_type C_mat;
+
+    reinterpret_cast<thread frag_type&>(A_mat.thread_elements()) = A;
+    reinterpret_cast<thread frag_type&>(B_mat.thread_elements()) = B;
+    reinterpret_cast<thread frag_type&>(C_mat.thread_elements()) = C;
+
+    mma(D_mat, A_mat, B_mat, C_mat);
+
+    D = reinterpret_cast<thread frag_type&>(D_mat.thread_elements());
+  }
+
+  METAL_FUNC static constexpr void mma(
+      thread mat_type& D,
+      thread mat_type& A,
+      thread mat_type& B,
+      thread mat_type& C) {
+    simdgroup_multiply_accumulate(D, A, B, C);
+  }
+
+  template <typename Op>
+  METAL_FUNC static constexpr void row_reduce(
+      thread const frag_type& inp_vals,
+      thread T* reduced_vals) {
+    T thr_reduce = Op::apply(inp_vals.x, inp_vals.y);
+
+    T qgr_reduce = simd_shuffle_xor(thr_reduce, ushort(1));
+    qgr_reduce = Op::apply(thr_reduce, qgr_reduce);
+
+    T sgr_reduce = simd_shuffle_xor(qgr_reduce, ushort(8));
+    sgr_reduce = Op::apply(qgr_reduce, sgr_reduce);
+
+    reduced_vals[0] = Op::apply(reduced_vals[0], sgr_reduce);
+  }
+
+  template <typename Op>
+  METAL_FUNC static constexpr void row_bin_op(
+      thread frag_type& inp_vals,
+      thread T* row_vals) {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kElemRows; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kElemCols; j++) {
+        inp_vals[i * kElemCols + j] =
+            Op::apply(inp_vals[i * kElemCols + j], row_vals[i]);
+      }
+    }
+  }
+};
+
+template <
+    typename T,
+    int kTileRows_,
+    int kTileCols_,
+    class MMAFrag_ = BaseMMAFrag<T, 8, 8>>
+struct MMATile {
+  using MMAFrag_t = MMAFrag_;
+  using elem_type = T;
+  STEEL_CONST int kFragRows = MMAFrag_t::kFragRows;
+  STEEL_CONST int kFragCols = MMAFrag_t::kFragCols;
+  STEEL_CONST int kElemsPerFrag = MMAFrag_t::kElemsPerFrag;
+
+  STEEL_CONST int kTileRows = kTileRows_;
+  STEEL_CONST int kTileCols = kTileCols_;
+
+  STEEL_CONST int kRows = kTileRows * kFragRows;
+  STEEL_CONST int kCols = kTileCols * kFragCols;
+
+  STEEL_CONST int kNumFrags = kTileRows * kTileCols;
+  STEEL_CONST int kElemsPerTile = kNumFrags * kElemsPerFrag;
+
+  STEEL_CONST int kRowsPerThread = kTileRows * MMAFrag_t::kElemRows;
+  STEEL_CONST int kColsPerThread = kTileCols * MMAFrag_t::kElemCols;
+
+  typedef typename MMAFrag_t::mat_type mat_type;
+  typedef typename MMAFrag_t::frag_type frag_type;
+
+  frag_type val_frags[kNumFrags] = {frag_type(0)};
+
+  METAL_FUNC MMATile() thread {}
+
+  METAL_FUNC constexpr void clear() {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kNumFrags; ++i) {
+      val_frags[i] = frag_type(0);
+    }
+  }
+
+  METAL_FUNC constexpr thread frag_type& frag_at(const short i, const short j) {
+    return val_frags[i * kTileCols + j];
+  }
+
+  METAL_FUNC constexpr const thread frag_type& frag_at(
+      const short i,
+      const short j) const {
+    return val_frags[i * kTileCols + j];
+  }
+
+  METAL_FUNC mat_type mat_at(const short i, const short j) {
+    mat_type val_mat;
+    STEEL_PRAGMA_UNROLL
+    for (short ii = 0; ii < kElemsPerFrag; ++ii) {
+      val_mat.thread_elements()[ii] = frag_at(i, j)[ii];
+    }
+    return val_mat;
+  }
+
+  METAL_FUNC thread elem_type* elems() {
+    return reinterpret_cast<thread elem_type*>(val_frags);
+  }
+
+  METAL_FUNC const thread elem_type* elems() const {
+    return reinterpret_cast<const thread elem_type*>(val_frags);
+  }
+
+  template <typename Op>
+  METAL_FUNC void row_reduce(thread T vals[kRowsPerThread]) const {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::template row_reduce<Op>(
+            frag_at(i, j), &vals[i * MMAFrag_t::kElemRows]);
+      }
+    }
+  }
+
+  template <typename Op>
+  METAL_FUNC void row_bin_op(thread T vals[kRowsPerThread]) {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::template row_bin_op<Op>(
+            frag_at(i, j), &vals[i * MMAFrag_t::kElemRows]);
+      }
+    }
+  }
+
+  template <typename U, int w_x, int w_y, int str_x, int str_y>
+  METAL_FUNC void load(const threadgroup U* src) {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::load(
+            frag_at(i, j),
+            &(
+                src[(i * kFragRows) * w_x * str_x +
+                    (j * kFragCols) * w_y * str_y]),
+            Int<str_x>{},
+            Int<str_y>{});
+      }
+    }
+  }
+
+  template <typename U, int w_x, int w_y, int str_x, int str_y>
+  METAL_FUNC void store(threadgroup U* dst) const {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::store(
+            frag_at(i, j),
+            &(
+                dst[(i * kFragRows) * w_x * str_x +
+                    (j * kFragCols) * w_y * str_y]),
+            Int<str_x>{},
+            Int<str_y>{});
+      }
+    }
+  }
+
+  template <typename U, int w_x, int w_y>
+  METAL_FUNC void load(const device U* src, const int ld) {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::load(
+            frag_at(i, j),
+            &(src[(i * kFragRows) * w_x * ld + (j * kFragCols) * w_y]),
+            ld,
+            Int<1>{});
+      }
+    }
+  }
+
+  template <typename U, int w_x, int w_y>
+  METAL_FUNC void store(device U* dst, const int ld) const {
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::store(
+            frag_at(i, j),
+            &(dst[(i * kFragRows) * w_x * ld + (j * kFragCols) * w_y]),
+            ld,
+            Int<1>{});
+      }
+    }
+  }
+
+  template <typename U, int w_x, int w_y>
+  METAL_FUNC void
+  load_safe(const device U* src, const int ld, const short2 src_tile_dims) {
+    STEEL_PRAGMA_UNROLL
+    for (int i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (int j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::load_safe(
+            frag_at(i, j),
+            src,
+            ld,
+            Int<1>{},
+            src_tile_dims.y,
+            src_tile_dims.x,
+            (i * kFragRows) * w_x,
+            (j * kFragCols) * w_y);
+      }
+    }
+  }
+
+  template <typename U, int w_x, int w_y>
+  METAL_FUNC void
+  store_safe(device U* dst, const int ld, const short2 dst_tile_dims) const {
+    STEEL_PRAGMA_UNROLL
+    for (int i = 0; i < kTileRows; ++i) {
+      STEEL_PRAGMA_UNROLL
+      for (int j = 0; j < kTileCols; ++j) {
+        MMAFrag_t::store_safe(
+            frag_at(i, j),
+            dst,
+            ld,
+            Int<1>{},
+            dst_tile_dims.y,
+            dst_tile_dims.x,
+            (i * kFragRows) * w_x,
+            (j * kFragCols) * w_y);
+      }
+    }
+  }
+};
+
+template <typename T, typename U, int M, int N, int K>
+METAL_FUNC void tile_matmad(
+    thread MMATile<T, M, N>& D,
+    thread MMATile<U, M, K>& A,
+    thread MMATile<U, K, N>& B,
+    thread MMATile<T, M, N>& C) {
+  STEEL_PRAGMA_UNROLL
+  for (short k = 0; k < K; ++k) {
+    STEEL_PRAGMA_UNROLL
+    for (short m = 0; m < M; ++m) {
+      STEEL_PRAGMA_UNROLL
+      for (short n = 0; n < N; ++n) {
+        short n_serp = (m % 2) ? (N - 1 - n) : n;
+        MMATile<T, M, N>::MMAFrag_t::mma(
+            D.frag_at(m, n_serp),
+            A.frag_at(m, k),
+            B.frag_at(k, n_serp),
+            C.frag_at(m, n_serp));
+      }
+    }
+  }
+}
+
+template <
+    typename T,
+    typename U,
+    int BM,
+    int BN,
+    int BK,
+    int WM,
+    int WN,
+    bool transpose_a,
+    bool transpose_b,
+    short lda_tgp,
+    short ldb_tgp,
+    typename AccumType = float,
+    typename Epilogue = TransformNone<U, AccumType>>
+struct BlockMMA {
+  // MMAFrag size
+  STEEL_CONST short kFragSize = 8;
+  using MMAFrag_acc_t = BaseMMAFrag<AccumType, kFragSize, kFragSize>;
+
+  // Warp tile simdgroup matrix strides along M
+  STEEL_CONST short TM_stride = kFragSize * WM;
+  // Warp tile simdgroup matrix strides along M
+  STEEL_CONST short TN_stride = kFragSize * WN;
+
+  // Warp tile size along M
+  STEEL_CONST short TM = BM / TM_stride;
+  // Warp tile size along N
+  STEEL_CONST short TN = BN / TN_stride;
+
+  // Threadgroup A strides
+  STEEL_CONST short A_str_m = transpose_a ? 1 : lda_tgp; // M
+  STEEL_CONST short A_str_k = transpose_a ? lda_tgp : 1; // K
+
+  // Threadgroup B strides
+  STEEL_CONST short B_str_k = transpose_b ? 1 : ldb_tgp; // K
+  STEEL_CONST short B_str_n = transpose_b ? ldb_tgp : 1; // N
+
+  // Threadgroup strides along K
+  STEEL_CONST short tile_stride_a = kFragSize * A_str_k;
+  STEEL_CONST short tile_stride_b = kFragSize * B_str_k;
+
+  // Simdgroup matrices
+  MMATile<AccumType, TM, 1, MMAFrag_acc_t> Atile;
+  MMATile<AccumType, 1, TN, MMAFrag_acc_t> Btile;
+  MMATile<AccumType, TM, TN, MMAFrag_acc_t> Ctile;
+
+  // Offsets within threadgroup
+  short sm;
+  short sn;
+
+  short As_offset;
+  short Bs_offset;
+
+  /* Constructor */
+  METAL_FUNC BlockMMA(
+      ushort simd_group_id [[simdgroup_index_in_threadgroup]],
+      ushort simd_lane_id [[thread_index_in_simdgroup]]) {
+    // Determine thread position in simdgroup matrix
+    short tm = kFragSize * (simd_group_id / WN);
+    short tn = kFragSize * (simd_group_id % WN);
+
+    short2 simd_coord = MMAFrag_acc_t::get_coord(simd_lane_id);
+    sm = simd_coord.y;
+    sn = simd_coord.x;
+
+    // Determine thread and simdgroup offset
+    As_offset = (tm + sm) * A_str_m + (sn)*A_str_k; // M, K
+    Bs_offset = (sm)*B_str_k + (tn + sn) * B_str_n; // K, N
+
+    sm += tm;
+    sn += tn;
+  }
+
+  /* (BM, BK) X (BK, BN) multiply accumulate function */
+  METAL_FUNC void mma(const threadgroup T* As, const threadgroup T* Bs) {
+    // Adjust for simdgroup and thread location
+    As += As_offset;
+    Bs += Bs_offset;
+
+    // Iterate over BK in blocks of kFragSize
+    STEEL_PRAGMA_UNROLL
+    for (short kk = 0; kk < BK; kk += kFragSize) {
+      simdgroup_barrier(mem_flags::mem_none);
+
+      Atile.template load<T, WM, 1, A_str_m, A_str_k>(As);
+
+      simdgroup_barrier(mem_flags::mem_none);
+
+      Btile.template load<T, 1, WN, B_str_k, B_str_n>(Bs);
+
+      simdgroup_barrier(mem_flags::mem_none);
+
+      tile_matmad(Ctile, Atile, Btile, Ctile);
+
+      // Progress to next simdgroup tile
+      As += tile_stride_a;
+      Bs += tile_stride_b;
+    }
+  }
+
+  /* Store results from simdgroup_matrix results into device memory */
+  METAL_FUNC void store_result(device U* D, const int ldd) {
+    // Apply epilogue
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < decltype(Ctile)::kElemsPerTile; i++) {
+      Ctile.elems()[i] = Epilogue::apply(Ctile.elems()[i]);
+    }
+
+    // Adjust for simdgroup and thread location
+    D += sm * ldd + sn;
+
+    Ctile.template store<U, WM, WN>(D, ldd);
+  }
+
+  METAL_FUNC void
+  store_result_safe(device U* D, const int ldd, short2 dst_tile_dims) {
+    // Apply epilogue
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < decltype(Ctile)::kElemsPerTile; i++) {
+      Ctile.elems()[i] = Epilogue::apply(Ctile.elems()[i]);
+    }
+
+    // Adjust for simdgroup and thread location
+    D += sm * ldd + sn;
+    dst_tile_dims -= short2(sn, sm);
+
+    if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
+      return;
+
+    Ctile.template store_safe<U, WM, WN>(D, ldd, dst_tile_dims);
+  }
+
+  /* Apply epilogue */
+  template <typename UnaryEpilogue>
+  METAL_FUNC void apply_epilogue(thread const UnaryEpilogue& epilogue_op) {
+    // Loop over all simdgroup tiles
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < decltype(Ctile)::kElemsPerTile; i++) {
+      Ctile.elems()[i] = epilogue_op.apply(Ctile.elems()[i]);
+    }
+  }
+
+  /* Apply epilogue */
+  template <typename BinaryEpilogue>
+  METAL_FUNC void apply_epilogue(
+      const device U* C,
+      const int ldc,
+      const int fdc,
+      thread const BinaryEpilogue& epilogue_op) {
+    // Adjust for simdgroup and thread location
+    C += (sm)*ldc + (sn)*fdc;
+
+    // Loop over all simdgroup tiles
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < TM; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < TN; j++) {
+        // Get accumulated result and associated offset in C
+        thread auto& accum = Ctile.frag_at(i, j);
+        int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+
+        // Apply epilogue
+        STEEL_PRAGMA_UNROLL
+        for (short k = 0; k < decltype(Ctile)::kElemsPerFrag; k++) {
+          accum[k] = epilogue_op.apply(accum[k], C[offset_c + k * fdc]);
+        }
+      }
+    }
+  }
+
+  /* Apply epilogue */
+  template <typename BinaryEpilogue>
+  METAL_FUNC void apply_epilogue_safe(
+      const device U* C,
+      const int ldc,
+      const int fdc,
+      short2 dst_tile_dims,
+      thread const BinaryEpilogue& epilogue_op) {
+    // Adjust for simdgroup and thread location
+    C += (sm)*ldc + (sn)*fdc;
+    dst_tile_dims -= short2(sn, sm);
+
+    if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
+      return;
+
+    // Loop over all simdgroup tiles
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < TM; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < TN; j++) {
+        // Get accumulated result and associated offset in C
+        thread auto& accum = Ctile.frag_at(i, j);
+        int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+
+        constexpr short kelems = decltype(Ctile)::kElemsPerFrag;
+
+        // Read C
+        U c_elems[kelems] = {0};
+
+        STEEL_PRAGMA_UNROLL
+        for (short k = 0; k < kelems; k++) {
+          if ((j * TN_stride + k) < dst_tile_dims.x) {
+            c_elems[k] = C[offset_c + k * fdc];
+          }
+        }
+
+        // Apply epilogue
+        STEEL_PRAGMA_UNROLL
+        for (short k = 0; k < kelems; k++) {
+          accum[k] = epilogue_op.apply(accum[k], c_elems[k]);
+        }
+      }
+    }
+  }
+
+  /* Store results from simdgroup_matrix results into device memory */
+  METAL_FUNC void store_result(
+      device U* D,
+      const int ldd,
+      const device U* C,
+      const int ldc,
+      const int fdc,
+      thread const Epilogue& epilogue_op) const {
+    // Adjust for simdgroup and thread location
+    C += (sm)*ldc + (sn)*fdc;
+    D += (sm)*ldd + sn;
+
+    constexpr short kelems = decltype(Ctile)::kElemsPerFrag;
+
+    // Loop over all simdgroup tiles
+    STEEL_PRAGMA_UNROLL
+    for (short i = 0; i < TM; i++) {
+      STEEL_PRAGMA_UNROLL
+      for (short j = 0; j < TN; j++) {
+        // Get accumulated result and associated offset in C
+        thread const auto& accum = Ctile.frag_at(i, j);
+        int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+        int offset_d = (i * TM_stride) * ldd + (j * TN_stride);
+
+        // Apply epilogue
+        STEEL_PRAGMA_UNROLL
+        for (short k = 0; k < kelems; k++) {
+          D[offset_d + k] = epilogue_op.apply(accum[k], C[offset_c + k * fdc]);
+        }
+      }
+    }
+  }
+
+  METAL_FUNC void store_result_safe(
+      device U* D,
+      const int ldd,
+      const device U* C,
+      const int ldc,
+      const int fdc,
+      short2 dst_tile_dims,
+      thread const Epilogue& epilogue_op) const {
+    // Adjust for simdgroup and thread location
+    C += (sm)*ldc + (sn)*fdc;
+    D += (sm)*ldd + sn;
+    dst_tile_dims -= short2(sn, sm);
+
+    if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
+      return;
+
+    constexpr short kelems = decltype(Ctile)::kElemsPerFrag;
+
+    STEEL_PRAGMA_UNROLL
+    for (int i = 0; i < TM; i++) {
+      if (i * TM_stride < dst_tile_dims.y) {
+        STEEL_PRAGMA_UNROLL
+        for (int j = 0; j < TN; j++) {
+          // Get accumulated result and associated offset in C
+          thread const auto& accum = Ctile.frag_at(i, j);
+          int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+          int offset_d = (i * TM_stride) * ldd + (j * TN_stride);
+
+          // Apply epilogue
+          STEEL_PRAGMA_UNROLL
+          for (short k = 0; k < kelems; k++) {
+            if ((j * TN_stride + k) < dst_tile_dims.x) {
+              D[offset_d + k] =
+                  epilogue_op.apply(accum[k], C[offset_c + k * fdc]);
+            }
+          }
+        }
+      }
+    }
+  }
+};
+
+} // namespace steel
+} // namespace mlx

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

@@ -0,0 +1,36 @@
+// Copyright © 2024 Apple Inc.
+
+#pragma once
+
+///////////////////////////////////////////////////////////////////////////////
+// Attn param classes
+///////////////////////////////////////////////////////////////////////////////
+
+namespace mlx {
+namespace steel {
+
+struct AttnParams {
+  int B; ///< Batch Size
+  int H; ///< Heads
+  int D; ///< Head Dim
+
+  int qL; ///< Query Sequence Length
+  int kL; ///< Key Sequence Length
+
+  int gqa_factor; ///< Group Query factor
+  float scale; ///< Attention scale
+
+  int NQ; ///< Number of query blocks
+  int NK; ///< Number of key/value blocks
+
+  int NQ_aligned; ///< Number of full query blocks
+  int NK_aligned; ///< Number of full key/value blocks
+
+  size_t Q_strides[3]; ///< Query  strides (B, H, L, D = 1)
+  size_t K_strides[3]; ///< Key    strides (B, H, L, D = 1)
+  size_t V_strides[3]; ///< Value  strides (B, H, L, D = 1)
+  size_t O_strides[3]; ///< Output strides (B, H, L, D = 1)
+};
+
+} // namespace steel
+} // namespace mlx

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

@@ -0,0 +1,71 @@
+// Copyright © 2024 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/metal/kernels/steel/utils.h"
+
+///////////////////////////////////////////////////////////////////////////////
+// Transforms and Epilogues
+///////////////////////////////////////////////////////////////////////////////
+
+namespace mlx {
+namespace steel {
+
+template <typename OutT, typename InT>
+struct TransformNone {
+  static METAL_FUNC OutT apply(InT x) {
+    return static_cast<OutT>(x);
+  }
+
+  static METAL_FUNC OutT apply(InT x, OutT) {
+    return static_cast<OutT>(x);
+  }
+};
+
+template <typename OutT, typename InT>
+struct TransformAdd {
+  TransformAdd(const float, const float) {}
+
+  static METAL_FUNC OutT apply(InT x) {
+    return static_cast<OutT>(x);
+  }
+
+  static METAL_FUNC OutT apply(InT x, OutT c) {
+    return static_cast<OutT>(x) + c;
+  }
+};
+
+template <typename OutT, typename InT>
+struct TransformAxpby {
+  const float alpha;
+  const float beta;
+
+  TransformAxpby(const float alpha_, const float beta_)
+      : alpha(alpha_), beta(beta_) {}
+
+  static METAL_FUNC OutT apply(InT x) {
+    return static_cast<OutT>(x);
+  }
+
+  METAL_FUNC OutT apply(InT x, OutT c) const {
+    return static_cast<OutT>(x * alpha + (beta * c));
+  }
+};
+
+template <typename T>
+struct AccumHelper {
+  typedef float accum_type;
+};
+
+struct BlockSwizzle {
+  static METAL_FUNC int2
+  swizzle(uint3 tid [[threadgroup_position_in_grid]], const int swizzle_log) {
+    const int tid_x = (tid.x) >> swizzle_log;
+    const int tid_y =
+        ((tid.y) << swizzle_log) + ((tid.x) & ((1 << swizzle_log) - 1));
+    return int2(tid_x, tid_y);
+  }
+};
+
+} // namespace steel
+} // namespace mlx

mlx/backend/metal/kernels/steel/conv/kernels/steel_conv.metal

@@ -5,7 +5,7 @@
 // clang-format off
 #include "mlx/backend/metal/kernels/steel/gemm/mma.h"
 
-#include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/steel/conv/conv.h"
 #include "mlx/backend/metal/kernels/steel/conv/params.h"
 #include "mlx/backend/metal/kernels/steel/conv/kernels/steel_conv.h"

mlx/backend/metal/kernels/steel/conv/kernels/steel_conv_general.metal

@@ -5,7 +5,7 @@
 // clang-format off
 #include "mlx/backend/metal/kernels/steel/gemm/mma.h"
 
-#include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/steel/conv/conv.h"
 #include "mlx/backend/metal/kernels/steel/conv/params.h"
 #include "mlx/backend/metal/kernels/steel/utils.h"

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

@@ -1,7 +1,6 @@
 // Copyright © 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/steel/gemm/gemm.h"

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

@@ -1,7 +1,6 @@
 // Copyright © 2024 Apple Inc.
 
 // clang-format off
-#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/steel/gemm/gemm.h"
 #include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_masked.h"

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

@@ -1,7 +1,7 @@
 // Copyright © 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/steel/gemm/gemm.h"
 #include "mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_splitk.h"
 

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

@@ -385,9 +385,9 @@ struct BlockMMA {
   STEEL_CONST short TN_stride = kFragSize * WN;
 
   // Warp tile size along M
-  STEEL_CONST short TM = BM / TM_stride;
+  STEEL_CONST short TM = BM / (kFragSize * WM);
   // Warp tile size along N
-  STEEL_CONST short TN = BN / TN_stride;
+  STEEL_CONST short TN = BN / (kFragSize * WN);
 
   // Threadgroup A strides
   STEEL_CONST short A_str_m = transpose_a ? 1 : lda_tgp; // M

mlx/backend/metal/kernels/ternary.h

@@ -32,13 +32,13 @@ template <typename T, typename Op>
     constant const size_t& b_strides,
     constant const size_t& c_strides,
     uint index [[thread_position_in_grid]]) {
-  auto a_idx = elem_to_loc_1(index, a_strides);
-  auto b_idx = elem_to_loc_1(index, b_strides);
-  auto c_idx = elem_to_loc_1(index, c_strides);
+  auto a_idx = elem_to_loc_1<size_t, uint>(index, a_strides);
+  auto b_idx = elem_to_loc_1<size_t, uint>(index, b_strides);
+  auto c_idx = elem_to_loc_1<size_t, uint>(index, c_strides);
   d[index] = Op()(a[a_idx], b[b_idx], c[c_idx]);
 }
 
-template <typename T, typename Op>
+template <typename T, typename Op, typename IdxT = size_t>
 [[kernel]] void ternary_g_nd2(
     device const bool* a,
     device const T* b,
@@ -49,14 +49,14 @@ template <typename T, typename Op>
     constant const size_t c_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);
-  auto c_idx = elem_to_loc_2(index, c_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);
+  auto c_idx = elem_to_loc_2<size_t, IdxT>(index, c_strides);
+  IdxT out_idx = index.x + IdxT(grid_dim.x) * index.y;
   d[out_idx] = Op()(a[a_idx], b[b_idx], c[c_idx]);
 }
 
-template <typename T, typename Op>
+template <typename T, typename Op, typename IdxT = size_t>
 [[kernel]] void ternary_g_nd3(
     device const bool* a,
     device const T* b,
@@ -67,15 +67,14 @@ template <typename T, typename Op>
     constant const size_t c_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);
-  auto c_idx = elem_to_loc_3(index, c_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);
+  auto c_idx = elem_to_loc_3<size_t, IdxT>(index, c_strides);
+  IdxT out_idx = index.x + grid_dim.x * (index.y + IdxT(grid_dim.y) * index.z);
   d[out_idx] = Op()(a[a_idx], b[b_idx], c[c_idx]);
 }
 
-template <typename T, typename Op, int N = 1>
+template <typename T, typename Op, int N = 1, typename IdxT = size_t>
 [[kernel]] void ternary_g(
     device const bool* a,
     device const T* b,
@@ -88,7 +87,7 @@ template <typename T, 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_3_nd(
+  auto idx = elem_to_loc_3_nd<IdxT>(
       {N * index.x, index.y, index.z},
       shape,
       a_strides,
@@ -96,11 +95,10 @@ template <typename T, typename Op, int N = 1>
       c_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];
-  auto c_xstride = c_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];
+  IdxT c_xstride = c_strides[ndim - 1];
   for (int i = 0; i < N && (int(N * index.x) + i) < xshape; ++i) {
     d[out_idx++] = Op()(a[idx.x], b[idx.y], c[idx.z]);
     idx.x += a_xstride;

mlx/backend/metal/kernels/ternary.metal

@@ -4,18 +4,20 @@
 #include <metal_math>
 
 // clang-format off
-#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/ternary_ops.h"
 #include "mlx/backend/metal/kernels/ternary.h"
 
-#define instantiate_ternary_all(op, tname, type)                  \
-  instantiate_kernel("v_" #op #tname, ternary_v, type, op)        \
-  instantiate_kernel("v2_" #op #tname, ternary_v2, type, op)      \
-  instantiate_kernel("gn4_" #op #tname, ternary_g, type, op, 4)   \
-  instantiate_kernel("g1_" #op #tname, ternary_g_nd1, type, op)   \
-  instantiate_kernel("g2_" #op #tname, ternary_g_nd2, type, op)   \
-  instantiate_kernel("g3_" #op #tname, ternary_g_nd3, type, op)
+#define instantiate_ternary_all(op, tname, type)                       \
+  instantiate_kernel("v_" #op #tname, ternary_v, type, op)             \
+  instantiate_kernel("v2_" #op #tname, ternary_v2, type, op)           \
+  instantiate_kernel("gn2_" #op #tname, ternary_g, type, op, 1, uint)  \
+  instantiate_kernel("g1_" #op #tname, ternary_g_nd1, type, op)        \
+  instantiate_kernel("g2_" #op #tname, ternary_g_nd2, type, op, uint)  \
+  instantiate_kernel("g3_" #op #tname, ternary_g_nd3, type, op, uint)  \
+  instantiate_kernel("g2large_" #op #tname, ternary_g_nd2, type, op)   \
+  instantiate_kernel("g3large_" #op #tname, ternary_g_nd3, type, op)   \
+  instantiate_kernel("gn4large_" #op #tname, ternary_g, type, op, 4)   \
 
 #define instantiate_ternary_types(op)               \
   instantiate_ternary_all(op, bool_, bool)          \

mlx/backend/metal/kernels/unary.h

@@ -18,7 +18,12 @@ template <typename T, typename U, typename Op>
   out[offset] = Op()(in[offset]);
 }
 
-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 unary_g(
     device const T* in,
     device U* out,
@@ -27,12 +32,11 @@ template <typename T, typename U, typename Op, int N = 1>
     device const int& ndim,
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto idx =
-      elem_to_loc({N * index.x, index.y, index.z}, in_shape, in_strides, ndim);
+  auto idx = elem_to_loc<size_t, IdxT>(
+      {N * index.x, index.y, index.z}, in_shape, in_strides, ndim);
   auto xshape = in_shape[ndim - 1];
-  auto xstride = in_strides[ndim - 1];
-  size_t out_idx =
-      N * index.x + xshape * (index.y + size_t(grid_dim.y) * index.z);
+  IdxT xstride = in_strides[ndim - 1];
+  IdxT out_idx = N * index.x + xshape * (index.y + IdxT(grid_dim.y) * index.z);
   for (int i = 0; i < N && (int(N * index.x) + i) < xshape; ++i) {
     out[out_idx++] = Op()(in[idx]);
     idx += xstride;

mlx/backend/metal/kernels/unary.metal

@@ -5,11 +5,13 @@
 #include "mlx/backend/metal/kernels/unary_ops.h"
 #include "mlx/backend/metal/kernels/unary.h"
 
-#define instantiate_unary_all(op, in_tname, out_tname, in_type, out_type)                 \
-  instantiate_kernel("v_" #op #in_tname #out_tname, unary_v, in_type, out_type, op)       \
-  instantiate_kernel("v2_" #op #in_tname #out_tname, unary_v2, in_type, out_type, op)     \
-  instantiate_kernel("gn4_" #op #in_tname #out_tname, unary_g, in_type, out_type, op, 4)
-
+#define instantiate_unary_all(op, in_tname, out_tname, in_type, out_type)             \
+  instantiate_kernel("v_" #op #in_tname #out_tname, unary_v, in_type, out_type, op)   \
+  instantiate_kernel("v2_" #op #in_tname #out_tname, unary_v2, in_type, out_type, op) \
+  instantiate_kernel(                                                                 \
+      "gn1_" #op #in_tname #out_tname, unary_g, in_type, out_type, op, 1, uint)       \
+  instantiate_kernel(                                                                 \
+      "gn4large" #op #in_tname #out_tname, unary_g, in_type, out_type, op, 4)
 
 #define instantiate_unary_all_same(op, tname, type)   \
   instantiate_unary_all(op, tname, tname, type, type)

mlx/backend/metal/kernels/utils.h

@@ -3,7 +3,13 @@
 #pragma once
 
 #include <metal_math>
-#include "mlx/backend/metal/kernels/bf16.h"
+
+// The correct bf16.h is included based on the metal version
+// by giving the correct path to -I during compilation
+// e.g. mlx/backend/metal/kernels/metal_3_0/ for Metal 3.0
+#include "bf16.h"
+
+#include "mlx/backend/metal/kernels/bf16_math.h"
 #include "mlx/backend/metal/kernels/complex.h"
 #include "mlx/backend/metal/kernels/defines.h"
 
@@ -83,44 +89,45 @@ struct Limits<complex64_t> {
 ///////////////////////////////////////////////////////////////////////////////
 // Single Array with generic dims
 
-template <typename stride_t>
-METAL_FUNC stride_t elem_to_loc(
+template <typename StrideT, typename IdxT = StrideT>
+METAL_FUNC IdxT elem_to_loc(
     uint elem,
     constant const int* shape,
-    constant const stride_t* strides,
+    constant const StrideT* strides,
     int ndim) {
-  stride_t loc = 0;
+  IdxT loc = 0;
   for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
-    loc += (elem % shape[i]) * strides[i];
+    loc += (elem % shape[i]) * IdxT(strides[i]);
     elem /= shape[i];
   }
   return loc;
 }
 
-template <typename stride_t>
-METAL_FUNC stride_t elem_to_loc(
-    stride_t elem,
+template <typename StrideT, typename IdxT = StrideT>
+METAL_FUNC IdxT elem_to_loc(
+    StrideT elem,
     constant const int* shape,
-    constant const stride_t* strides,
+    constant const StrideT* strides,
     int ndim) {
-  stride_t loc = 0;
+  IdxT loc = 0;
   for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
-    loc += (elem % shape[i]) * strides[i];
+    loc += (elem % shape[i]) * IdxT(strides[i]);
     elem /= shape[i];
   }
   return loc;
 }
 
 // Non templated version to handle arbitrary dims
-template <typename stride_t>
-METAL_FUNC stride_t elem_to_loc(
+template <typename StrideT, typename IdxT = StrideT>
+METAL_FUNC IdxT elem_to_loc(
     uint3 elem,
     constant const int* shape,
-    constant const stride_t* strides,
+    constant const StrideT* strides,
     int ndim) {
-  stride_t loc = elem.x * strides[ndim - 1] + elem.y * strides[ndim - 2];
+  IdxT loc =
+      elem.x * IdxT(strides[ndim - 1]) + elem.y * IdxT(strides[ndim - 2]);
   for (int d = ndim - 3; d >= 0; --d) {
-    loc += (elem.z % shape[d]) * strides[d];
+    loc += (elem.z % shape[d]) * IdxT(strides[d]);
     elem.z /= shape[d];
   }
   return loc;
@@ -129,61 +136,65 @@ METAL_FUNC stride_t elem_to_loc(
 ///////////////////////////////////////////////////////////////////////////////
 // Single Array with fixed N dims
 
-template <typename stride_t>
-METAL_FUNC stride_t elem_to_loc_1(uint elem, constant const stride_t& stride) {
-  return elem * stride;
+template <typename StrideT, typename IdxT = StrideT>
+METAL_FUNC IdxT elem_to_loc_1(uint elem, constant const StrideT& stride) {
+  return elem * IdxT(stride);
 }
 
-template <typename stride_t>
-METAL_FUNC stride_t
-elem_to_loc_2(uint2 elem, constant const stride_t strides[2]) {
-  return elem.x * strides[1] + elem.y * strides[0];
+template <typename StrideT, typename IdxT = StrideT>
+METAL_FUNC IdxT elem_to_loc_2(uint2 elem, constant const StrideT strides[2]) {
+  return elem.x * IdxT(strides[1]) + elem.y * IdxT(strides[0]);
 }
 
-template <typename stride_t>
-METAL_FUNC stride_t
-elem_to_loc_3(uint3 elem, constant const stride_t strides[3]) {
-  return elem.x * strides[2] + elem.y * strides[1] + elem.z * strides[0];
+template <typename StrideT, typename IdxT = StrideT>
+METAL_FUNC IdxT elem_to_loc_3(uint3 elem, constant const StrideT strides[3]) {
+  return elem.x * IdxT(strides[2]) + elem.y * IdxT(strides[1]) +
+      elem.z * IdxT(strides[0]);
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // Multiple Arrays with generic dims
 
-template <typename stride_t>
-METAL_FUNC ulong2 elem_to_loc_2_nd(
+template <typename StrideT, typename IdxT = StrideT>
+METAL_FUNC vec<IdxT, 2> elem_to_loc_2_nd(
     uint3 elem,
     constant const int* shape,
-    constant const stride_t* a_strides,
-    constant const stride_t* b_strides,
+    constant const StrideT* a_strides,
+    constant const StrideT* b_strides,
     int ndim) {
-  ulong2 loc = {
-      ulong(elem.x * a_strides[ndim - 1] + elem.y * a_strides[ndim - 2]),
-      ulong(elem.x * b_strides[ndim - 1] + elem.y * b_strides[ndim - 2])};
+  vec<IdxT, 2> loc = {
+      IdxT(
+          elem.x * IdxT(a_strides[ndim - 1]) +
+          IdxT(elem.y) * IdxT(a_strides[ndim - 2])),
+      IdxT(
+          elem.x * IdxT(b_strides[ndim - 1]) +
+          elem.y * IdxT(b_strides[ndim - 2]))};
   for (int d = ndim - 3; d >= 0; --d) {
     uint l = elem.z % shape[d];
-    loc.x += l * a_strides[d];
-    loc.y += l * b_strides[d];
+    loc.x += l * IdxT(a_strides[d]);
+    loc.y += l * IdxT(b_strides[d]);
     elem.z /= shape[d];
   }
   return loc;
 }
 
-METAL_FUNC ulong3 elem_to_loc_3_nd(
+template <typename IdxT = size_t>
+METAL_FUNC vec<IdxT, 3> elem_to_loc_3_nd(
     uint3 elem,
     constant const int* shape,
     constant const size_t* a_strides,
     constant const size_t* b_strides,
     constant const size_t* c_strides,
     int ndim) {
-  ulong3 loc = {
-      elem.x * a_strides[ndim - 1] + elem.y * a_strides[ndim - 2],
-      elem.x * b_strides[ndim - 1] + elem.y * b_strides[ndim - 2],
-      elem.x * c_strides[ndim - 1] + elem.y * c_strides[ndim - 2]};
+  vec<IdxT, 3> loc = {
+      elem.x * IdxT(a_strides[ndim - 1]) + elem.y * IdxT(a_strides[ndim - 2]),
+      elem.x * IdxT(b_strides[ndim - 1]) + elem.y * IdxT(b_strides[ndim - 2]),
+      elem.x * IdxT(c_strides[ndim - 1]) + elem.y * IdxT(c_strides[ndim - 2])};
   for (int d = ndim - 3; d >= 0; --d) {
     uint l = elem.z % shape[d];
-    loc.x += l * a_strides[d];
-    loc.y += l * b_strides[d];
-    loc.z += l * c_strides[d];
+    loc.x += l * IdxT(a_strides[d]);
+    loc.y += l * IdxT(b_strides[d]);
+    loc.z += l * IdxT(c_strides[d]);
     elem.z /= shape[d];
   }
   return loc;
@@ -193,16 +204,21 @@ METAL_FUNC ulong3 elem_to_loc_3_nd(
 // Elem to loc in a loop utils
 ///////////////////////////////////////////////////////////////////////////////
 
-template <int dim, typename offset_t = size_t>
-struct looped_elem_to_loc {
-  looped_elem_to_loc<dim - 1, offset_t> inner_looper;
-  offset_t offset{0};
+template <int DIM, typename OffsetT = size_t, bool General = true>
+struct LoopedElemToLoc {
+  int dim;
+  LoopedElemToLoc<DIM - 1, OffsetT, General> inner_looper;
+  OffsetT offset{0};
   int index{0};
 
-  void next(const constant int* shape, const constant size_t* strides) {
-    index++;
-    offset += strides[dim - 1];
+  LoopedElemToLoc(int dim) : dim(dim), inner_looper(dim - 1) {}
 
+  void next(const constant int* shape, const constant size_t* strides) {
+    if (dim == 0) {
+      return;
+    }
+    index++;
+    offset += OffsetT(strides[dim - 1]);
     if (index >= shape[dim - 1]) {
       index = 0;
       inner_looper.next(shape, strides);
@@ -211,13 +227,21 @@ struct looped_elem_to_loc {
   }
 
   void next(int n, const constant int* shape, const constant size_t* strides) {
+    if (dim == 0) {
+      return;
+    }
     index += n;
-    offset += n * strides[dim - 1];
+    offset += n * OffsetT(strides[dim - 1]);
 
     if (index >= shape[dim - 1]) {
       int extra = index - shape[dim - 1];
+      if (extra >= shape[dim - 1]) {
+        inner_looper.next(1 + extra / shape[dim - 1], shape, strides);
+        extra = extra % shape[dim - 1];
+      } else {
+        inner_looper.next(shape, strides);
+      }
       index = 0;
-      inner_looper.next(shape, strides);
       offset = inner_looper.offset;
       if (extra > 0) {
         next(extra, shape, strides);
@@ -225,44 +249,61 @@ struct looped_elem_to_loc {
     }
   }
 
-  offset_t
-  location(offset_t, const constant int*, const constant size_t*, int) {
+  OffsetT location() {
     return offset;
   }
 };
 
-template <typename offset_t>
-struct looped_elem_to_loc<1, offset_t> {
-  offset_t offset{0};
+template <typename OffsetT>
+struct LoopedElemToLoc<1, OffsetT, true> {
+  int dim;
+  OffsetT offset{0};
+  uint index{0};
+
+  LoopedElemToLoc(int dim) : dim(dim) {}
+
+  void next(const constant int* shape, const constant size_t* strides) {
+    index++;
+    if (dim > 1) {
+      offset = elem_to_loc<size_t, OffsetT>(index, shape, strides, dim);
+    } else {
+      offset += OffsetT(strides[0]);
+    }
+  }
+
+  void next(int n, const constant int* shape, const constant size_t* strides) {
+    index += n;
+    if (dim > 1) {
+      offset = elem_to_loc<size_t, OffsetT>(index, shape, strides, dim);
+    } else {
+      offset = index * OffsetT(strides[0]);
+    }
+  }
+
+  OffsetT location() {
+    return offset;
+  }
+};
+
+template <typename OffsetT>
+struct LoopedElemToLoc<1, OffsetT, false> {
+  OffsetT offset{0};
+
+  LoopedElemToLoc(int) {}
 
   void next(const constant int*, const constant size_t* strides) {
-    offset += strides[0];
+    offset += OffsetT(strides[0]);
   }
 
   void next(int n, const constant int*, const constant size_t* strides) {
-    offset += n * strides[0];
+    offset += n * OffsetT(strides[0]);
   }
 
-  offset_t
-  location(offset_t, const constant int*, const constant size_t*, int) {
+  OffsetT location() {
     return offset;
   }
 };
 
-template <typename offset_t>
-struct looped_elem_to_loc<0, offset_t> {
-  void next(const constant int*, const constant size_t*) {}
-  void next(int, const constant int*, const constant size_t*) {}
-
-  offset_t location(
-      offset_t idx,
-      const constant int* shape,
-      const constant size_t* strides,
-      int ndim) {
-    return elem_to_loc(idx, shape, strides, ndim);
-  }
-};
-
 ///////////////////////////////////////////////////////////////////////////////
 // Calculation utils
 ///////////////////////////////////////////////////////////////////////////////

mlx/backend/metal/make_compiled_preamble.sh

@@ -11,12 +11,12 @@ SRC_DIR=$3
 SRC_FILE=$4
 CFLAGS=$5
 SRC_NAME=$(basename -- "${SRC_FILE}")
+JIT_INCLUDES=${SRC_DIR}/mlx/backend/metal/kernels/jit
 INPUT_FILE=${SRC_DIR}/mlx/backend/metal/kernels/${SRC_FILE}.h
 OUTPUT_FILE=${OUTPUT_DIR}/${SRC_NAME}.cpp
 
 mkdir -p "$OUTPUT_DIR"
-
-CONTENT=$($CC -I "$SRC_DIR" -DMLX_METAL_JIT -E -P "$INPUT_FILE" $CFLAGS 2>/dev/null)
+CONTENT=$($CC -I"$SRC_DIR" -I"$JIT_INCLUDES" -DMLX_METAL_JIT -E -P "$INPUT_FILE" $CFLAGS 2>/dev/null)
 
 cat << EOF > "$OUTPUT_FILE"
 namespace mlx::core::metal {

mlx/backend/metal/matmul.cpp

@@ -249,7 +249,7 @@ void steel_matmul_regular(
       wm,
       wn);
 
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Use problem size to determine threadblock swizzle
   int tn = (N + bn - 1) / bn;
@@ -288,12 +288,12 @@ void steel_matmul_regular(
   compute_encoder.set_input_array(b, 1);
   compute_encoder.set_output_array(out, 3);
 
-  compute_encoder->setBytes(&params, sizeof(GEMMParams), 4);
+  compute_encoder.set_bytes(params, 4);
 
-  set_vector_bytes(compute_encoder, batch_shape, 6);
-  set_vector_bytes(compute_encoder, batch_strides, 7);
+  compute_encoder.set_vector_bytes(batch_shape, 6);
+  compute_encoder.set_vector_bytes(batch_strides, 7);
 
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
   // Record copies
   d.add_temporaries(std::move(copies), s.index);
@@ -390,7 +390,7 @@ void steel_matmul(
         wn,
         mn_aligned,
         k_aligned);
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
 
     int tn = (N + bn - 1) / bn;
     int tm = (M + bm - 1) / bm;
@@ -416,34 +416,29 @@ void steel_matmul(
     compute_encoder.set_input_array(b, 1);
     compute_encoder.set_output_array(C_split, 2);
 
-    compute_encoder->setBytes(&params, sizeof(GEMMSpiltKParams), 3);
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.set_bytes(params, 3);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
     // Do accum kernel
     {
-      auto c_split_buf =
-          static_cast<const MTL::Resource*>(C_split.buffer().ptr());
-      const class MTL::Resource* const resources[1] = {c_split_buf};
-      compute_encoder->memoryBarrier(resources, 1);
       auto kernel_name = "steel_gemm_splitk_accum_" + type_to_name(out) + "_" +
           type_to_name(C_split);
 
       auto kernel = get_steel_gemm_splitk_accum_kernel(
           d, kernel_name, C_split, out, false);
-      compute_encoder->setComputePipelineState(kernel);
+      compute_encoder.set_compute_pipeline_state(kernel);
 
       // Set the arguments for the kernel
       compute_encoder.set_input_array(C_split, 0);
       compute_encoder.set_output_array(out, 1);
-      compute_encoder->setBytes(&split_k_partitions, sizeof(int), 2);
-      compute_encoder->setBytes(&split_k_partition_stride, sizeof(int), 3);
-      compute_encoder->setBytes(&N, sizeof(int), 4);
+      compute_encoder.set_bytes(split_k_partitions, 2);
+      compute_encoder.set_bytes(split_k_partition_stride, 3);
+      compute_encoder.set_bytes(N, 4);
 
       // Launch enough thread groups for each output
       MTL::Size grid_dims = MTL::Size(N, M, 1);
-      MTL::Size group_dims = MTL::Size(std::min(1024, N * M), 1, 1);
-
-      compute_encoder.dispatchThreads(grid_dims, group_dims);
+      auto group_dims = get_block_dims(N, M, 1);
+      compute_encoder.dispatch_threads(grid_dims, group_dims);
     }
 
     d.add_temporaries(std::move(copies), s.index);
@@ -625,7 +620,7 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
     // 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);
 
     int n_tgp = (out_vector_len + n_out_per_tgp - 1) / n_out_per_tgp;
     MTL::Size group_dims = MTL::Size(32, bn, bm);
@@ -635,16 +630,16 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
     compute_encoder.set_input_array(vec, 1);
     compute_encoder.set_output_array(out, 3);
 
-    compute_encoder->setBytes(&in_vector_len, sizeof(int), 4);
-    compute_encoder->setBytes(&out_vector_len, sizeof(int), 5);
-    compute_encoder->setBytes(&mat_ld, sizeof(int), 6);
+    compute_encoder.set_bytes(in_vector_len, 4);
+    compute_encoder.set_bytes(out_vector_len, 5);
+    compute_encoder.set_bytes(mat_ld, 6);
 
-    compute_encoder->setBytes(&batch_ndim, sizeof(int), 9);
-    set_vector_bytes(compute_encoder, batch_shape, 10);
-    set_vector_bytes(compute_encoder, batch_strides_vec, 11);
-    set_vector_bytes(compute_encoder, batch_strides_mat, 12);
+    compute_encoder.set_bytes(batch_ndim, 9);
+    compute_encoder.set_vector_bytes(batch_shape, 10);
+    compute_encoder.set_vector_bytes(batch_strides_vec, 11);
+    compute_encoder.set_vector_bytes(batch_strides_mat, 12);
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
     d.add_temporaries(std::move(copies), s.index);
     return;
@@ -822,7 +817,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     // 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);
 
     int n_tgp = (out_vector_len + n_out_per_tgp - 1) / n_out_per_tgp;
     MTL::Size group_dims = MTL::Size(32, bn, bm);
@@ -833,23 +828,23 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     compute_encoder.set_input_array(c, 2);
     compute_encoder.set_output_array(out, 3);
 
-    compute_encoder->setBytes(&in_vector_len, sizeof(int), 4);
-    compute_encoder->setBytes(&out_vector_len, sizeof(int), 5);
-    compute_encoder->setBytes(&mat_ld, sizeof(int), 6);
+    compute_encoder.set_bytes(in_vector_len, 4);
+    compute_encoder.set_bytes(out_vector_len, 5);
+    compute_encoder.set_bytes(mat_ld, 6);
 
-    compute_encoder->setBytes(&alpha_, sizeof(float), 7);
-    compute_encoder->setBytes(&beta_, sizeof(float), 8);
+    compute_encoder.set_bytes(alpha_, 7);
+    compute_encoder.set_bytes(beta_, 8);
 
-    compute_encoder->setBytes(&batch_ndim, sizeof(int), 9);
-    set_vector_bytes(compute_encoder, batch_shape, 10);
-    set_vector_bytes(compute_encoder, batch_strides_vec, 11);
-    set_vector_bytes(compute_encoder, batch_strides_mat, 12);
-    set_vector_bytes(compute_encoder, C_batch_stride, 13);
+    compute_encoder.set_bytes(batch_ndim, 9);
+    compute_encoder.set_vector_bytes(batch_shape, 10);
+    compute_encoder.set_vector_bytes(batch_strides_vec, 11);
+    compute_encoder.set_vector_bytes(batch_strides_mat, 12);
+    compute_encoder.set_vector_bytes(C_batch_stride, 13);
 
     int bias_stride = c.strides()[c.ndim() - 1];
-    compute_encoder->setBytes(&bias_stride, sizeof(int), 14);
+    compute_encoder.set_bytes(bias_stride, 14);
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
     d.add_temporaries(std::move(copies), s.index);
     return;
@@ -907,7 +902,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
         mn_aligned,
         k_aligned);
 
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
 
     int tn = (N + bn - 1) / bn;
     int tm = (M + bm - 1) / bm;
@@ -933,8 +928,8 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     compute_encoder.set_input_array(b, 1);
     compute_encoder.set_output_array(C_split, 2);
 
-    compute_encoder->setBytes(&params, sizeof(GEMMSpiltKParams), 3);
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.set_bytes(params, 3);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
     // Do accum kernel
     {
@@ -943,25 +938,24 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       auto kernel = get_steel_gemm_splitk_accum_kernel(
           d, kernel_name, C_split, out, true);
 
-      compute_encoder->setComputePipelineState(kernel);
+      compute_encoder.set_compute_pipeline_state(kernel);
 
       // Set the arguments for the kernel
       compute_encoder.set_input_array(C_split, 0);
       compute_encoder.set_output_array(out, 1);
-      compute_encoder->setBytes(&split_k_partitions, sizeof(int), 2);
-      compute_encoder->setBytes(&split_k_partition_stride, sizeof(int), 3);
-      compute_encoder->setBytes(&N, sizeof(int), 4);
+      compute_encoder.set_bytes(split_k_partitions, 2);
+      compute_encoder.set_bytes(split_k_partition_stride, 3);
+      compute_encoder.set_bytes(N, 4);
       compute_encoder.set_input_array(c, 5);
-      compute_encoder->setBytes(&ldc, sizeof(int), 6);
-      compute_encoder->setBytes(&fdc, sizeof(int), 7);
-      compute_encoder->setBytes(&alpha_, sizeof(float), 8);
-      compute_encoder->setBytes(&beta_, sizeof(float), 9);
+      compute_encoder.set_bytes(ldc, 6);
+      compute_encoder.set_bytes(fdc, 7);
+      compute_encoder.set_bytes(alpha_, 8);
+      compute_encoder.set_bytes(beta_, 9);
 
       // Launch enough thread groups for each output
       MTL::Size grid_dims = MTL::Size(N, M, 1);
-      MTL::Size group_dims = MTL::Size(std::min(1024, N * M), 1, 1);
-
-      compute_encoder.dispatchThreads(grid_dims, group_dims);
+      auto group_dims = get_block_dims(N, M, 1);
+      compute_encoder.dispatch_threads(grid_dims, group_dims);
     }
 
     d.add_temporaries(std::move(copies), s.index);
@@ -1032,7 +1026,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       wm,
       wn);
 
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   int tn = (N + bn - 1) / bn;
   int tm = (M + bm - 1) / bm;
@@ -1083,13 +1077,13 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_input_array(c, 2);
   compute_encoder.set_output_array(out, 3);
 
-  compute_encoder->setBytes(&gemm_params, sizeof(GEMMParams), 4);
-  compute_encoder->setBytes(&params, sizeof(GEMMAddMMParams), 5);
+  compute_encoder.set_bytes(gemm_params, 4);
+  compute_encoder.set_bytes(params, 5);
 
-  set_vector_bytes(compute_encoder, batch_shape, 6);
-  set_vector_bytes(compute_encoder, batch_strides, 7);
+  compute_encoder.set_vector_bytes(batch_shape, 6);
+  compute_encoder.set_vector_bytes(batch_strides, 7);
 
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
   d.add_temporaries(std::move(copies), s.index);
 }
@@ -1304,7 +1298,7 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) {
         contiguous_kernel);
 
     auto& compute_encoder = d.get_command_encoder(s.index);
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
 
     int n_tgp = (out_vector_len + n_out_per_tgp - 1) / n_out_per_tgp;
     MTL::Size group_dims = MTL::Size(32, bn, bm);
@@ -1372,18 +1366,18 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     compute_encoder.set_input_array(vec, 1);
     compute_encoder.set_output_array(out, 3);
 
-    compute_encoder->setBytes(&in_vector_len, sizeof(int), 4);
-    compute_encoder->setBytes(&out_vector_len, sizeof(int), 5);
-    compute_encoder->setBytes(&mat_ld, sizeof(int), 6);
-    compute_encoder->setBytes(&batch_ndim, sizeof(int), 9);
-    set_vector_bytes(compute_encoder, batch_shape, 10);
-    set_vector_bytes(compute_encoder, batch_strides_vec, 11);
-    set_vector_bytes(compute_encoder, batch_strides_mat, 12);
+    compute_encoder.set_bytes(in_vector_len, 4);
+    compute_encoder.set_bytes(out_vector_len, 5);
+    compute_encoder.set_bytes(mat_ld, 6);
+    compute_encoder.set_bytes(batch_ndim, 9);
+    compute_encoder.set_vector_bytes(batch_shape, 10);
+    compute_encoder.set_vector_bytes(batch_strides_vec, 11);
+    compute_encoder.set_vector_bytes(batch_strides_mat, 12);
 
-    set_vector_bytes(compute_encoder, mask_strides, 23);
-    set_vector_bytes(compute_encoder, mask_batch_strides, 24);
+    compute_encoder.set_vector_bytes(mask_strides, 23);
+    compute_encoder.set_vector_bytes(mask_batch_strides, 24);
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
     d.add_temporaries(std::move(copies), s.index);
     return;
@@ -1423,7 +1417,7 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       wn,
       mn_aligned,
       k_aligned);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Use problem size to determine threadblock swizzle
   int tn = (N + bn - 1) / bn;
@@ -1486,14 +1480,14 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_input_array(b, 1);
   compute_encoder.set_output_array(out, 3);
 
-  compute_encoder->setBytes(&params, sizeof(GEMMParams), 4);
+  compute_encoder.set_bytes(params, 4);
 
-  set_vector_bytes(compute_encoder, batch_shape, 6);
-  set_vector_bytes(compute_encoder, batch_strides, 7);
+  compute_encoder.set_vector_bytes(batch_shape, 6);
+  compute_encoder.set_vector_bytes(batch_strides, 7);
 
-  set_vector_bytes(compute_encoder, mask_strides, 13);
+  compute_encoder.set_vector_bytes(mask_strides, 13);
 
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
   d.add_temporaries(std::move(copies), s.index);
 }
@@ -1687,7 +1681,7 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     // 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);
 
     int n_tgp = (out_vector_len + n_out_per_tgp - 1) / n_out_per_tgp;
     MTL::Size group_dims = MTL::Size(32, bn, bm);
@@ -1697,28 +1691,28 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     compute_encoder.set_input_array(vec, 1);
     compute_encoder.set_output_array(out, 3);
 
-    compute_encoder->setBytes(&in_vector_len, sizeof(int), 4);
-    compute_encoder->setBytes(&out_vector_len, sizeof(int), 5);
-    compute_encoder->setBytes(&mat_ld, sizeof(int), 6);
+    compute_encoder.set_bytes(in_vector_len, 4);
+    compute_encoder.set_bytes(out_vector_len, 5);
+    compute_encoder.set_bytes(mat_ld, 6);
 
-    compute_encoder->setBytes(&batch_ndim, sizeof(int), 9);
-    set_vector_bytes(compute_encoder, batch_shape, 10);
-    set_vector_bytes(compute_encoder, batch_strides, 11);
+    compute_encoder.set_bytes(batch_ndim, 9);
+    compute_encoder.set_vector_bytes(batch_shape, 10);
+    compute_encoder.set_vector_bytes(batch_strides, 11);
 
     int batch_ndim_vec = batch_shape_vec.size();
-    compute_encoder->setBytes(&batch_ndim_vec, sizeof(int), 12);
-    set_vector_bytes(compute_encoder, batch_shape_vec, 13);
-    set_vector_bytes(compute_encoder, batch_strides_vec, 14);
+    compute_encoder.set_bytes(batch_ndim_vec, 12);
+    compute_encoder.set_vector_bytes(batch_shape_vec, 13);
+    compute_encoder.set_vector_bytes(batch_strides_vec, 14);
 
     int batch_ndim_mat = batch_shape_mat.size();
-    compute_encoder->setBytes(&batch_ndim_mat, sizeof(int), 15);
-    set_vector_bytes(compute_encoder, batch_shape_mat, 16);
-    set_vector_bytes(compute_encoder, batch_strides_mat, 17);
+    compute_encoder.set_bytes(batch_ndim_mat, 15);
+    compute_encoder.set_vector_bytes(batch_shape_mat, 16);
+    compute_encoder.set_vector_bytes(batch_strides_mat, 17);
 
     compute_encoder.set_input_array(lhs_indices, 18 + int(!is_b_matrix));
     compute_encoder.set_input_array(rhs_indices, 18 + int(is_b_matrix));
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
     d.add_temporaries(std::move(copies), s.index);
     return;
@@ -1788,7 +1782,7 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       wm,
       wn);
 
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Use problem size to determine threadblock swizzle
   int tn = (N + bn - 1) / bn;
@@ -1827,10 +1821,10 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_input_array(b, 1);
   compute_encoder.set_output_array(out, 3);
 
-  compute_encoder->setBytes(&params, sizeof(GEMMParams), 4);
+  compute_encoder.set_bytes(params, 4);
 
-  set_vector_bytes(compute_encoder, batch_shape, 6);
-  set_vector_bytes(compute_encoder, batch_strides, 7);
+  compute_encoder.set_vector_bytes(batch_shape, 6);
+  compute_encoder.set_vector_bytes(batch_strides, 7);
 
   compute_encoder.set_input_array(lhs_indices, 10);
   compute_encoder.set_input_array(rhs_indices, 11);
@@ -1845,11 +1839,11 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   operand_batch_ndim.push_back(0);
 
-  set_vector_bytes(compute_encoder, operand_shape, 13);
-  set_vector_bytes(compute_encoder, operand_strides, 14);
-  set_vector_bytes(compute_encoder, operand_batch_ndim, 15);
+  compute_encoder.set_vector_bytes(operand_shape, 13);
+  compute_encoder.set_vector_bytes(operand_strides, 14);
+  compute_encoder.set_vector_bytes(operand_batch_ndim, 15);
 
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
   d.add_temporaries(std::move(copies), s.index);
 }

mlx/backend/metal/metal.cpp

@@ -1,11 +1,11 @@
 // Copyright © 2023-2024 Apple Inc.
-#include <cstdlib>
 #include <memory>
 
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/primitives.h"
 #include "mlx/scheduler.h"
+#include "mlx/utils.h"
 
 namespace mlx::core::metal {
 
@@ -13,20 +13,6 @@ bool is_available() {
   return true;
 }
 
-int max_ops_per_buffer() {
-  auto get_val = []() {
-    if (const char* buff_str = std::getenv("MLX_MAX_OPS_PER_BUFFER")) {
-      return atoi(buff_str);
-    } else {
-      return 10;
-    }
-  };
-  static int max_ops_per_buffer_ = get_val();
-  return max_ops_per_buffer_;
-}
-
-#define MAX_OPS_PER_BUFFER max_ops_per_buffer()
-
 inline void check_error(MTL::CommandBuffer* cbuf) {
   if (cbuf->status() == MTL::CommandBufferStatusError) {
     std::ostringstream msg;
@@ -77,7 +63,8 @@ std::function<void()> make_task(array arr, bool signal) {
       out.set_status(array::Status::evaluated);
     }
 
-    if (signal || d.get_command_buffer_ops(s.index) >= MAX_OPS_PER_BUFFER) {
+    if (signal ||
+        d.get_command_buffer_ops(s.index) >= env::max_ops_per_buffer()) {
       d.end_encoding(s.index);
       if (signal) {
         command_buffer->encodeSignalEvent(

mlx/backend/metal/nojit_kernels.cpp

@@ -1,3 +1,4 @@
+// Copyright © 2024 Apple Inc.
 
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/utils.h"
@@ -99,7 +100,7 @@ MTL::ComputePipelineState* get_reduce_init_kernel(
     const std::string& kernel_name,
     const std::string&,
     const std::string&,
-    const array&) {
+    const Dtype&) {
   return d.get_kernel(kernel_name);
 }
 
@@ -108,8 +109,9 @@ MTL::ComputePipelineState* get_reduce_kernel(
     const std::string& kernel_name,
     const std::string&,
     const std::string&,
-    const array&,
-    const array&,
+    const Dtype&,
+    const Dtype&,
+    const std::string&,
     int,
     int,
     int) {

mlx/backend/metal/normalization.cpp

@@ -78,18 +78,15 @@ void RMSNorm::eval_gpu(
     }
 
     uint32_t w_stride = w.strides()[0];
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(
         x.data_shared_ptr() == nullptr ? out : x, 0);
     compute_encoder.set_input_array(w, 1);
     compute_encoder.set_output_array(out, 2);
-    compute_encoder->setBytes(&eps_, sizeof(float), 3);
-    compute_encoder->setBytes(&axis_size, sizeof(int), 4);
-    compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 5);
-    compute_encoder->setThreadgroupMemoryLength(
-        16 * 8, 0); // minimum of 16 bytes
-    compute_encoder->setThreadgroupMemoryLength(simd_size * sizeof(float), 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(eps_, 3);
+    compute_encoder.set_bytes(axis_size, 4);
+    compute_encoder.set_bytes(w_stride, 5);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 
   d.add_temporaries(std::move(copies), s.index);
@@ -183,16 +180,16 @@ void RMSNormVJP::eval_gpu(
     }
 
     uint32_t w_stride = w.strides()[0];
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(x_in_gx ? gx : x, 0);
     compute_encoder.set_input_array(w, 1);
     compute_encoder.set_input_array(g_in_gx ? gx : (g_in_gw ? gw_temp : g), 2);
     compute_encoder.set_output_array(gx, 3);
     compute_encoder.set_output_array(gw_temp, 4);
-    compute_encoder->setBytes(&eps_, sizeof(float), 5);
-    compute_encoder->setBytes(&axis_size, sizeof(int), 6);
-    compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 7);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(eps_, 5);
+    compute_encoder.set_bytes(axis_size, 6);
+    compute_encoder.set_bytes(w_stride, 7);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 
   ReductionPlan plan(
@@ -273,17 +270,17 @@ void LayerNorm::eval_gpu(
 
     uint32_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
     uint32_t b_stride = (b.ndim() == 1) ? b.strides()[0] : 0;
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(
         x.data_shared_ptr() == nullptr ? out : x, 0);
     compute_encoder.set_input_array(w, 1);
     compute_encoder.set_input_array(b, 2);
     compute_encoder.set_output_array(out, 3);
-    compute_encoder->setBytes(&eps_, sizeof(float), 4);
-    compute_encoder->setBytes(&axis_size, sizeof(int), 5);
-    compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 6);
-    compute_encoder->setBytes(&b_stride, sizeof(uint32_t), 7);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(eps_, 4);
+    compute_encoder.set_bytes(axis_size, 5);
+    compute_encoder.set_bytes(w_stride, 6);
+    compute_encoder.set_bytes(b_stride, 7);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 
   d.add_temporaries(std::move(copies), s.index);
@@ -395,16 +392,16 @@ void LayerNormVJP::eval_gpu(
     }
 
     uint32_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(x_in_gx ? gx : x, 0);
     compute_encoder.set_input_array(w, 1);
     compute_encoder.set_input_array(g_in_gx ? gx : (g_in_gw ? gw_temp : g), 2);
     compute_encoder.set_output_array(gx, 3);
     compute_encoder.set_output_array(gw_temp, 4);
-    compute_encoder->setBytes(&eps_, sizeof(float), 5);
-    compute_encoder->setBytes(&axis_size, sizeof(int), 6);
-    compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 7);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(eps_, 5);
+    compute_encoder.set_bytes(axis_size, 6);
+    compute_encoder.set_bytes(w_stride, 7);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 
   if (gw.ndim() == 1 && gw.size() == axis_size) {

mlx/backend/metal/primitives.cpp

@@ -5,6 +5,7 @@
 #include <sstream>
 
 #include "mlx/backend/common/load.h"
+#include "mlx/backend/common/utils.h"
 #include "mlx/backend/metal/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
@@ -17,10 +18,10 @@
 namespace mlx::core {
 
 template <typename T>
-void arange_set_scalars(T start, T next, CommandEncoder& enc) {
-  enc->setBytes(&start, sizeof(T), 0);
+void arange_set_scalars(T start, T next, metal::CommandEncoder& enc) {
+  enc.set_bytes(start, 0);
   T step = next - start;
-  enc->setBytes(&step, sizeof(T), 1);
+  enc.set_bytes(step, 1);
 }
 
 void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -37,7 +38,7 @@ void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
   MTL::Size group_dims = MTL::Size(
       std::min(nthreads, kernel->maxTotalThreadsPerThreadgroup()), 1, 1);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   switch (out.dtype()) {
     case bool_: // unsupported
@@ -80,7 +81,7 @@ void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 
   compute_encoder.set_output_array(out, 2);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -129,25 +130,25 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
     size_t n_threads = out.size() * thread_group_size;
     MTL::Size grid_dims = MTL::Size(n_threads, 1, 1);
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(in, 0);
     compute_encoder.set_output_array(out, 1);
     if (ndim == 0) {
       // Pass place holders so metal doesn't complain
       int shape_ = 0;
       size_t stride_ = 0;
-      compute_encoder->setBytes(&shape_, sizeof(int), 2);
-      compute_encoder->setBytes(&stride_, sizeof(size_t), 3);
-      compute_encoder->setBytes(&stride_, sizeof(size_t), 4);
+      compute_encoder.set_bytes(shape_, 2);
+      compute_encoder.set_bytes(stride_, 3);
+      compute_encoder.set_bytes(stride_, 4);
     } else {
-      compute_encoder->setBytes(shape.data(), ndim * sizeof(int), 2);
-      compute_encoder->setBytes(in_strides.data(), ndim * sizeof(size_t), 3);
-      compute_encoder->setBytes(out_strides.data(), ndim * sizeof(size_t), 4);
+      compute_encoder.set_vector_bytes(shape, 2);
+      compute_encoder.set_vector_bytes(in_strides, 3);
+      compute_encoder.set_vector_bytes(out_strides, 4);
     }
-    compute_encoder->setBytes(&ndim, sizeof(size_t), 5);
-    compute_encoder->setBytes(&axis_stride, sizeof(size_t), 6);
-    compute_encoder->setBytes(&axis_size, sizeof(size_t), 7);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(ndim, 5);
+    compute_encoder.set_bytes(axis_stride, 6);
+    compute_encoder.set_bytes(axis_size, 7);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }
 
@@ -169,6 +170,17 @@ void Concatenate::eval_gpu(const std::vector<array>& inputs, array& out) {
   concatenate_gpu(inputs, out, axis_, stream());
 }
 
+void Contiguous::eval_gpu(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)) {
+    move_or_copy(in, out);
+  } else {
+    copy_gpu(in, out, CopyType::General);
+  }
+}
+
 void Copy::eval_gpu(const std::vector<array>& inputs, array& out) {
   eval(inputs, out);
 }
@@ -273,24 +285,22 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
   // organize into grid nkeys x elem_per_key
   MTL::Size grid_dims = MTL::Size(num_keys, half_size + odd, 1);
   NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
-  MTL::Size group_dims = MTL::Size(1, thread_group_size, 1);
+  auto group_dims = get_block_dims(num_keys, half_size + odd, 1);
   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(keys, 0);
   compute_encoder.set_output_array(out, 1);
-  compute_encoder->setBytes(&odd, sizeof(bool), 2);
-  compute_encoder->setBytes(&bytes_per_key, sizeof(size_t), 3);
+  compute_encoder.set_bytes(odd, 2);
+  compute_encoder.set_bytes(bytes_per_key, 3);
 
   if (!keys.flags().row_contiguous) {
     int ndim = keys.ndim();
-    compute_encoder->setBytes(&ndim, sizeof(int), 4);
-    compute_encoder->setBytes(
-        keys.shape().data(), keys.ndim() * sizeof(int), 5);
-    compute_encoder->setBytes(
-        keys.strides().data(), keys.ndim() * sizeof(size_t), 6);
+    compute_encoder.set_bytes(ndim, 4);
+    compute_encoder.set_vector_bytes(keys.shape(), 5);
+    compute_encoder.set_vector_bytes(keys.strides(), 6);
   }
 
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void Reshape::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -345,7 +355,7 @@ void SliceUpdate::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto& upd = inputs[1];
 
   if (upd.size() == 0) {
-    out.copy_shared_buffer(in);
+    move_or_copy(in, out);
     return;
   }
 
@@ -418,12 +428,12 @@ void View::eval_gpu(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;
     }
-    out.copy_shared_buffer(
-        in, strides, in.flags(), in.data_size() * ibytes / obytes);
+    move_or_copy(
+        in, out, strides, in.flags(), in.data_size() * ibytes / obytes);
   } else {
     auto tmp = array(in.shape(), in.dtype(), nullptr, {});
     tmp.set_data(allocator::malloc_or_wait(tmp.nbytes()));

mlx/backend/metal/quantized.cpp

@@ -101,31 +101,31 @@ void launch_qmm(
   auto& d = metal::device(s.device);
   auto kernel = get_quantized_kernel(d, kname.str(), template_def);
   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(w, 0);
   compute_encoder.set_input_array(scales, 1);
   compute_encoder.set_input_array(biases, 2);
   compute_encoder.set_input_array(x, 3);
   compute_encoder.set_output_array(out, 4);
-  compute_encoder->setBytes(&D, sizeof(int), 5);
-  compute_encoder->setBytes(&O, sizeof(int), 6);
+  compute_encoder.set_bytes(D, 5);
+  compute_encoder.set_bytes(O, 6);
 
   int offset = 7;
   if (matrix) {
-    compute_encoder->setBytes(&B, sizeof(int), 7);
+    compute_encoder.set_bytes(B, 7);
     offset += 1;
   }
 
   if (batched || gather) {
-    compute_encoder->setBytes(&x_batch_ndims, sizeof(int), offset);
-    set_vector_bytes(compute_encoder, x_shape, offset + 1);
-    set_vector_bytes(compute_encoder, x_strides, offset + 2);
-    compute_encoder->setBytes(&w_batch_ndims, sizeof(int), offset + 3);
-    set_vector_bytes(compute_encoder, w_shape, offset + 4);
-    set_vector_bytes(compute_encoder, w_strides, offset + 5);
-    set_vector_bytes(compute_encoder, s_strides, offset + 6);
-    set_vector_bytes(compute_encoder, b_strides, offset + 7);
+    compute_encoder.set_bytes(x_batch_ndims, offset);
+    compute_encoder.set_vector_bytes(x_shape, offset + 1);
+    compute_encoder.set_vector_bytes(x_strides, offset + 2);
+    compute_encoder.set_bytes(w_batch_ndims, offset + 3);
+    compute_encoder.set_vector_bytes(w_shape, offset + 4);
+    compute_encoder.set_vector_bytes(w_strides, offset + 5);
+    compute_encoder.set_vector_bytes(s_strides, offset + 6);
+    compute_encoder.set_vector_bytes(b_strides, offset + 7);
   }
   if (gather) {
     auto& lhs_indices = inputs[4];
@@ -137,15 +137,15 @@ void launch_qmm(
     auto& lhs_strides = lhs_indices.strides();
     auto& rhs_strides = rhs_indices.strides();
 
-    compute_encoder->setBytes(&batch_ndims, sizeof(int), offset + 8);
-    set_vector_bytes(compute_encoder, batch_shape, offset + 9);
+    compute_encoder.set_bytes(batch_ndims, offset + 8);
+    compute_encoder.set_vector_bytes(batch_shape, offset + 9);
     compute_encoder.set_input_array(lhs_indices, offset + 10);
     compute_encoder.set_input_array(rhs_indices, offset + 11);
-    set_vector_bytes(compute_encoder, lhs_strides, offset + 12);
-    set_vector_bytes(compute_encoder, rhs_strides, offset + 13);
+    compute_encoder.set_vector_bytes(lhs_strides, offset + 12);
+    compute_encoder.set_vector_bytes(rhs_strides, offset + 13);
   }
 
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
   d.add_temporaries(std::move(copies), s.index);
 }
 
@@ -236,27 +236,27 @@ void qvm_split_k(
   // Encode and dispatch kernel
   auto kernel = get_quantized_kernel(d, kname.str(), template_def);
   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(w, 0);
   compute_encoder.set_input_array(scales, 1);
   compute_encoder.set_input_array(biases, 2);
   compute_encoder.set_input_array(x, 3);
   compute_encoder.set_output_array(intermediate, 4);
-  compute_encoder->setBytes(&split_D, sizeof(int), 5);
-  compute_encoder->setBytes(&O, sizeof(int), 6);
+  compute_encoder.set_bytes(split_D, 5);
+  compute_encoder.set_bytes(O, 6);
 
-  compute_encoder->setBytes(&x_batch_ndims, sizeof(int), 7);
-  set_vector_bytes(compute_encoder, x_shape, 8);
-  set_vector_bytes(compute_encoder, x_strides, 9);
-  compute_encoder->setBytes(&w_batch_ndims, sizeof(int), 10);
-  set_vector_bytes(compute_encoder, w_shape, 11);
-  set_vector_bytes(compute_encoder, w_strides, 12);
-  set_vector_bytes(compute_encoder, s_strides, 13);
-  set_vector_bytes(compute_encoder, b_strides, 14);
-  compute_encoder->setBytes(&final_block_size, sizeof(int), 15);
+  compute_encoder.set_bytes(x_batch_ndims, 7);
+  compute_encoder.set_vector_bytes(x_shape, 8);
+  compute_encoder.set_vector_bytes(x_strides, 9);
+  compute_encoder.set_bytes(w_batch_ndims, 10);
+  compute_encoder.set_vector_bytes(w_shape, 11);
+  compute_encoder.set_vector_bytes(w_strides, 12);
+  compute_encoder.set_vector_bytes(s_strides, 13);
+  compute_encoder.set_vector_bytes(b_strides, 14);
+  compute_encoder.set_bytes(final_block_size, 15);
 
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
   d.add_temporaries(std::move(copies), s.index);
 
   int axis = intermediate.ndim() - 3;
@@ -447,7 +447,7 @@ void fast::AffineQuantize::eval_gpu(
   auto template_def = get_template_definition(
       kname.str(), kernel_func, type_string, group_size_, bits_);
   auto kernel = get_quantized_kernel(d, kname.str(), template_def);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Treat uint32 as uint8 in kernel
   constexpr int uint8_per_uint32 = 4;
@@ -471,7 +471,7 @@ void fast::AffineQuantize::eval_gpu(
   }
   MTL::Size grid_dims = use_2d ? get_2d_grid_dims(grid_shape, w.strides())
                                : MTL::Size(nthreads, 1, 1);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 
   d.add_temporaries(std::move(copies), s.index);
 }

mlx/backend/metal/reduce.cpp

@@ -2,7 +2,6 @@
 
 #include <algorithm>
 #include <cassert>
-#include <sstream>
 
 #include "mlx/backend/metal/copy.h"
 #include "mlx/backend/metal/device.h"
@@ -67,17 +66,14 @@ struct RowReduceArgs {
       strides.push_back(0);
     }
 
-    compute_encoder->setBytes(&row_size, sizeof(size_t), 2);
-    compute_encoder->setBytes(&non_row_reductions, sizeof(size_t), 3);
-    compute_encoder->setBytes(shape.data(), shape.size() * sizeof(int), 4);
-    compute_encoder->setBytes(
-        strides.data(), strides.size() * sizeof(size_t), 5);
-    compute_encoder->setBytes(&ndim, sizeof(int), 6);
-    compute_encoder->setBytes(
-        reduce_shape.data(), reduce_shape.size() * sizeof(int), 7);
-    compute_encoder->setBytes(
-        reduce_strides.data(), reduce_strides.size() * sizeof(size_t), 8);
-    compute_encoder->setBytes(&reduce_ndim, sizeof(int), 9);
+    compute_encoder.set_bytes(row_size, 2);
+    compute_encoder.set_bytes(non_row_reductions, 3);
+    compute_encoder.set_vector_bytes(shape, 4);
+    compute_encoder.set_vector_bytes(strides, 5);
+    compute_encoder.set_bytes(ndim, 6);
+    compute_encoder.set_vector_bytes(reduce_shape, 7);
+    compute_encoder.set_vector_bytes(reduce_strides, 8);
+    compute_encoder.set_bytes(reduce_ndim, 9);
 
     if (reduce_ndim == 0) {
       reduce_shape.pop_back();
@@ -166,18 +162,15 @@ struct ColReduceArgs {
       strides.push_back(0);
     }
 
-    compute_encoder->setBytes(&reduction_size, sizeof(size_t), 2);
-    compute_encoder->setBytes(&reduction_stride, sizeof(size_t), 3);
-    compute_encoder->setBytes(shape.data(), shape.size() * sizeof(int), 4);
-    compute_encoder->setBytes(
-        strides.data(), strides.size() * sizeof(size_t), 5);
-    compute_encoder->setBytes(&ndim, sizeof(int), 6);
-    compute_encoder->setBytes(
-        reduce_shape.data(), reduce_shape.size() * sizeof(int), 7);
-    compute_encoder->setBytes(
-        reduce_strides.data(), reduce_strides.size() * sizeof(size_t), 8);
-    compute_encoder->setBytes(&reduce_ndim, sizeof(int), 9);
-    compute_encoder->setBytes(&non_col_reductions, sizeof(size_t), 10);
+    compute_encoder.set_bytes(reduction_size, 2);
+    compute_encoder.set_bytes(reduction_stride, 3);
+    compute_encoder.set_vector_bytes(shape, 4);
+    compute_encoder.set_vector_bytes(strides, 5);
+    compute_encoder.set_bytes(ndim, 6);
+    compute_encoder.set_vector_bytes(reduce_shape, 7);
+    compute_encoder.set_vector_bytes(reduce_strides, 8);
+    compute_encoder.set_bytes(reduce_ndim, 9);
+    compute_encoder.set_bytes(non_col_reductions, 10);
 
     if (reduce_ndim == 0) {
       reduce_shape.pop_back();
@@ -208,6 +201,16 @@ inline bool is_64b_dtype(Dtype dtype) {
   return dtype == int64 || dtype == uint64 || dtype == complex64;
 }
 
+inline int get_kernel_reduce_ndim(int reduce_ndim) {
+  if (reduce_ndim <= 1) {
+    return 1;
+  } else if (reduce_ndim == 2) {
+    return 2;
+  } else {
+    return 5;
+  }
+}
+
 inline int threadgroup_size_from_row_size(int row_size) {
   // 1 simdgroup per row smallish rows
   if (row_size <= 512) {
@@ -239,16 +242,51 @@ inline auto output_grid_for_col_reduce(
   return get_2d_grid_dims(out_shape, out_strides);
 }
 
+std::pair<Dtype, Dtype> remap_reduce_types(
+    const array& in,
+    const std::string& op_name) {
+  if (op_name == "sum" || op_name == "prod") {
+    if (issubdtype(in.dtype(), integer)) {
+      switch (in.dtype().size()) {
+        case 1:
+          return {int8, int32};
+        case 2:
+          return {int16, int32};
+        case 4:
+          return {int32, int32};
+        case 8:
+          return {int64, int64};
+      }
+    }
+    if (in.dtype() == bool_) {
+      return {int8, int32};
+    }
+    return {in.dtype(), in.dtype()};
+  } else if (op_name == "and" || op_name == "or") {
+    if (in.dtype().size() == 1) {
+      return {bool_, bool_};
+    } else if (in.dtype().size() == 2) {
+      return {int16, bool_};
+    } else if (in.dtype().size() == 4) {
+      return {int32, bool_};
+    } else {
+      return {int64, bool_};
+    }
+  }
+  return {in.dtype(), in.dtype()};
+}
+
 void init_reduce(
     array& out,
     const std::string& op_name,
     CommandEncoder& compute_encoder,
     metal::Device& d,
     const Stream& s) {
-  std::ostringstream kname;
+  auto [_, out_type] = remap_reduce_types(out, op_name);
   const std::string func_name = "init_reduce";
-  kname << func_name << "_" << op_name << type_to_name(out);
-  auto kernel = get_reduce_init_kernel(d, kname.str(), func_name, op_name, out);
+  std::string kname = func_name;
+  concatenate(kname, "_", op_name, type_to_name(out_type));
+  auto kernel = get_reduce_init_kernel(d, kname, func_name, op_name, out_type);
   size_t nthreads = out.size();
   MTL::Size grid_dims = MTL::Size(nthreads, 1, 1);
   NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
@@ -256,9 +294,9 @@ void init_reduce(
     thread_group_size = nthreads;
   }
   MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
   compute_encoder.set_output_array(out, 0);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void all_reduce_dispatch(
@@ -269,11 +307,13 @@ void all_reduce_dispatch(
     metal::Device& d,
     const Stream& s) {
   // Set the kernel
-  std::ostringstream kname;
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
   const std::string func_name = "all_reduce";
-  kname << func_name << "_" << op_name << type_to_name(in);
-  auto kernel = get_reduce_kernel(d, kname.str(), func_name, op_name, in, out);
-  compute_encoder->setComputePipelineState(kernel);
+  std::string kname = func_name;
+  concatenate(kname, "_", op_name, type_to_name(in_type));
+  auto kernel = get_reduce_kernel(
+      d, kname, func_name, op_name, in_type, out_type, "int64_t");
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   size_t in_size = in.size();
 
@@ -285,9 +325,9 @@ void all_reduce_dispatch(
 
     compute_encoder.set_input_array(in, 0);
     compute_encoder.set_output_array(out, 1);
-    compute_encoder->setBytes(&in_size, sizeof(size_t), 2);
-    compute_encoder->setBytes(&in_size, sizeof(size_t), 3);
-    compute_encoder.dispatchThreads(grid_dims, grid_dims);
+    compute_encoder.set_bytes(in_size, 2);
+    compute_encoder.set_bytes(in_size, 3);
+    compute_encoder.dispatch_threads(grid_dims, grid_dims);
   }
 
   // We need multiple threadgroups so we 'll do it in 2 passes.
@@ -306,7 +346,7 @@ void all_reduce_dispatch(
     }
 
     // Allocate an intermediate tensor to hold results if needed
-    array intermediate({n_rows}, out.dtype(), nullptr, {});
+    array intermediate({n_rows}, out_type, nullptr, {});
     intermediate.set_data(allocator::malloc_or_wait(intermediate.nbytes()));
     d.add_temporary(intermediate, s.index);
 
@@ -319,24 +359,24 @@ void all_reduce_dispatch(
     MTL::Size group_dims(threadgroup_size, 1, 1);
     compute_encoder.set_input_array(in, 0);
     compute_encoder.set_output_array(intermediate, 1);
-    compute_encoder->setBytes(&in_size, sizeof(size_t), 2);
-    compute_encoder->setBytes(&row_size, sizeof(size_t), 3);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(in_size, 2);
+    compute_encoder.set_bytes(row_size, 3);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
 
     // 2nd pass
-    std::ostringstream kname_2nd_pass;
-    kname_2nd_pass << func_name << "_" << op_name << type_to_name(intermediate);
+    std::string kname_2nd_pass = func_name;
+    concatenate(kname_2nd_pass, "_", op_name, type_to_name(intermediate));
     auto kernel_2nd_pass = get_reduce_kernel(
-        d, kname_2nd_pass.str(), func_name, op_name, intermediate, out);
-    compute_encoder->setComputePipelineState(kernel_2nd_pass);
+        d, kname_2nd_pass, func_name, op_name, out_type, out_type, "int64_t");
+    compute_encoder.set_compute_pipeline_state(kernel_2nd_pass);
     size_t intermediate_size = n_rows;
     grid_dims = MTL::Size(threadgroup_2nd_pass, 1, 1);
     group_dims = MTL::Size(threadgroup_2nd_pass, 1, 1);
     compute_encoder.set_input_array(intermediate, 0);
     compute_encoder.set_output_array(out, 1);
-    compute_encoder->setBytes(&intermediate_size, sizeof(size_t), 2);
-    compute_encoder->setBytes(&intermediate_size, sizeof(size_t), 3);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(intermediate_size, 2);
+    compute_encoder.set_bytes(intermediate_size, 3);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }
 
@@ -349,13 +389,31 @@ void row_reduce_small(
     metal::Device& d,
     const Stream& s) {
   // Set the kernel
-  std::ostringstream kname;
-  int n = (args.reduce_ndim < 5) ? std::max(1, args.reduce_ndim) : 0;
+  int n = get_kernel_reduce_ndim(args.reduce_ndim);
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
   const std::string func_name = "row_reduce_small";
-  kname << func_name << "_" << n << "_reduce_" << op_name << type_to_name(in);
-  auto kernel =
-      get_reduce_kernel(d, kname.str(), func_name, op_name, in, out, n);
-  compute_encoder->setComputePipelineState(kernel);
+  std::string kname = func_name;
+  bool large = in.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(
+      kname,
+      "_",
+      std::to_string(n),
+      "_reduce_",
+      op_name,
+      type_to_name(in_type));
+  auto kernel = get_reduce_kernel(
+      d,
+      kname,
+      func_name,
+      op_name,
+      in_type,
+      out_type,
+      large ? "size_t" : "uint",
+      n);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Figure out the grid dims
   MTL::Size grid_dims;
@@ -375,7 +433,7 @@ void row_reduce_small(
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(out, 1);
   args.encode(compute_encoder);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void row_reduce_simple(
@@ -387,11 +445,14 @@ void row_reduce_simple(
     metal::Device& d,
     const Stream& s) {
   // Set the kernel
-  std::ostringstream kname;
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
   const std::string func_name = "row_reduce_simple";
-  kname << func_name << "_" << op_name << type_to_name(in);
-  auto kernel = get_reduce_kernel(d, kname.str(), func_name, op_name, in, out);
-  compute_encoder->setComputePipelineState(kernel);
+  std::string kname = func_name;
+  concatenate(kname, "_", op_name, type_to_name(in_type));
+
+  auto kernel = get_reduce_kernel(
+      d, kname, func_name, op_name, in_type, out_type, "size_t");
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Figure out the grid dims
   size_t row_size = args.row_size;
@@ -410,9 +471,9 @@ void row_reduce_simple(
   // Launch
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(out, 1);
-  compute_encoder->setBytes(&row_size, sizeof(size_t), 2);
-  compute_encoder->setBytes(&out_size, sizeof(size_t), 3);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.set_bytes(row_size, 2);
+  compute_encoder.set_bytes(out_size, 3);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void row_reduce_looped(
@@ -423,14 +484,33 @@ void row_reduce_looped(
     CommandEncoder& compute_encoder,
     metal::Device& d,
     const Stream& s) {
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
+
   // Set the kernel
-  std::ostringstream kname;
-  int n = (args.reduce_ndim < 5) ? std::max(1, args.reduce_ndim) : 0;
+  int n = get_kernel_reduce_ndim(args.reduce_ndim);
   const std::string func_name = "row_reduce_looped";
-  kname << func_name << "_" << n << "_reduce_" << op_name << type_to_name(in);
-  auto kernel =
-      get_reduce_kernel(d, kname.str(), func_name, op_name, in, out, n);
-  compute_encoder->setComputePipelineState(kernel);
+  std::string kname = func_name;
+  bool large = in.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(
+      kname,
+      "_",
+      std::to_string(n),
+      "_reduce_",
+      op_name,
+      type_to_name(in_type));
+  auto kernel = get_reduce_kernel(
+      d,
+      kname,
+      func_name,
+      op_name,
+      in_type,
+      out_type,
+      large ? "size_t" : "uint",
+      n);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Figure out the grid
   auto out_grid_size = get_2d_grid_dims(out.shape(), out.strides());
@@ -443,7 +523,7 @@ void row_reduce_looped(
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(out, 1);
   args.encode(compute_encoder);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void row_reduce_general_dispatch(
@@ -481,6 +561,8 @@ void strided_reduce_small(
     CommandEncoder& compute_encoder,
     metal::Device& d,
     const Stream& s) {
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
+
   // Figure out the grid dims
   MTL::Size grid_dims, group_dims;
 
@@ -489,13 +571,30 @@ void strided_reduce_small(
   args.reduce_strides.push_back(args.reduction_stride);
   args.reduce_ndim++;
 
-  int n = (args.reduce_ndim < 5) ? std::max(1, args.reduce_ndim) : 0;
-  std::ostringstream kname;
+  int n = get_kernel_reduce_ndim(args.reduce_ndim);
   const std::string func_name = "col_reduce_small";
-  kname << func_name << "_" << n << "_reduce_" << op_name << type_to_name(in);
-  auto kernel =
-      get_reduce_kernel(d, kname.str(), func_name, op_name, in, out, n);
-  compute_encoder->setComputePipelineState(kernel);
+  std::string kname = func_name;
+  bool large = in.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(
+      kname,
+      "_",
+      std::to_string(n),
+      "_reduce_",
+      op_name,
+      type_to_name(in_type));
+  auto kernel = get_reduce_kernel(
+      d,
+      kname,
+      func_name,
+      op_name,
+      in_type,
+      out_type,
+      large ? "size_t" : "uint",
+      n);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   const int n_reads = 4;
   size_t reduction_stride_blocks =
@@ -517,7 +616,7 @@ void strided_reduce_small(
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(out, 1);
   args.encode(compute_encoder);
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
 void strided_reduce_longcolumn(
@@ -528,6 +627,7 @@ void strided_reduce_longcolumn(
     CommandEncoder& compute_encoder,
     metal::Device& d,
     const Stream& s) {
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
   size_t total_reduction_size = args.reduction_size * args.non_col_reductions;
   size_t outer_blocks = 32;
   if (total_reduction_size >= 32768) {
@@ -540,7 +640,7 @@ void strided_reduce_longcolumn(
   intermediate_shape.push_back(outer_blocks);
   intermediate_shape.insert(
       intermediate_shape.end(), out.shape().begin(), out.shape().end());
-  array intermediate(std::move(intermediate_shape), out.dtype(), nullptr, {});
+  array intermediate(std::move(intermediate_shape), out_type, nullptr, {});
   intermediate.set_data(allocator::malloc_or_wait(intermediate.nbytes()));
   d.add_temporary(intermediate, s.index);
 
@@ -562,20 +662,37 @@ void strided_reduce_longcolumn(
   MTL::Size group_dims(threadgroup_x, threadgroup_y, 1);
 
   // Set the kernel
-  int n = (args.reduce_ndim < 5) ? std::max(1, args.reduce_ndim) : 0;
-  std::ostringstream kname;
-  const std::string func_name = "col_reduce_longcolumn";
-  kname << func_name << "_" << n << "_reduce_" << op_name << type_to_name(in);
-  auto kernel =
-      get_reduce_kernel(d, kname.str(), func_name, op_name, in, out, n);
-  compute_encoder->setComputePipelineState(kernel);
+  int n = get_kernel_reduce_ndim(args.reduce_ndim);
+  std::string func_name = "col_reduce_longcolumn";
+  std::string kname = func_name;
+  bool large = in.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(
+      kname,
+      "_",
+      std::to_string(n),
+      "_reduce_",
+      op_name,
+      type_to_name(in_type));
+  auto kernel = get_reduce_kernel(
+      d,
+      kname,
+      func_name,
+      op_name,
+      in_type,
+      out_type,
+      large ? "size_t" : "uint",
+      n);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Launch
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(intermediate, 1);
   args.encode(compute_encoder);
-  compute_encoder->setBytes(&out_size, sizeof(size_t), 11);
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.set_bytes(out_size, 11);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
   // Make the 2nd pass arguments and grid_dims
   ColReduceArgs second_args(intermediate);
@@ -587,24 +704,30 @@ void strided_reduce_longcolumn(
   group_dims = MTL::Size(256, 1, 1);
 
   // Set the 2nd kernel
-  const std::string second_kernel = "col_reduce_looped_1_32_32_reduce_" +
-      op_name + type_to_name(intermediate);
+  func_name = "col_reduce_looped";
+  kname = func_name;
+  large = intermediate.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(kname, "_1_32_32_reduce_", op_name, type_to_name(intermediate));
   kernel = get_reduce_kernel(
       d,
-      second_kernel,
-      "col_reduce_looped",
+      kname,
+      func_name,
       op_name,
-      intermediate,
-      out,
+      intermediate.dtype(),
+      out_type,
+      large ? "size_t" : "uint",
       1,
       32,
       32);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(intermediate, 0);
   compute_encoder.set_output_array(out, 1);
   second_args.encode(compute_encoder);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void strided_reduce_looped(
@@ -615,6 +738,8 @@ void strided_reduce_looped(
     CommandEncoder& compute_encoder,
     metal::Device& d,
     const Stream& s) {
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
+
   // Prepare the arguments for the kernel
   args.reduce_shape.push_back(args.reduction_size);
   args.reduce_strides.push_back(args.reduction_stride);
@@ -632,20 +757,42 @@ void strided_reduce_looped(
   MTL::Size group_dims(threadgroup_size, 1, 1);
 
   // Set the kernel
-  int n = (args.reduce_ndim < 5) ? std::max(1, args.reduce_ndim) : 0;
-  std::ostringstream kname;
-  const std::string func_name = "col_reduce_looped";
-  kname << func_name << "_" << n << "_" << BM << "_" << BN << "_reduce_"
-        << op_name << type_to_name(in);
-  auto kernel =
-      get_reduce_kernel(d, kname.str(), func_name, op_name, in, out, n, BM, BN);
-  compute_encoder->setComputePipelineState(kernel);
+  int n = get_kernel_reduce_ndim(args.reduce_ndim);
+  std::string func_name = "col_reduce_looped";
+  std::string kname = func_name;
+  bool large = in.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(
+      kname,
+      "_",
+      std::to_string(n),
+      "_",
+      std::to_string(BM),
+      "_",
+      std::to_string(BN),
+      "_reduce_",
+      op_name,
+      type_to_name(in_type));
+  auto kernel = get_reduce_kernel(
+      d,
+      kname,
+      func_name,
+      op_name,
+      in_type,
+      out_type,
+      large ? "size_t" : "uint",
+      n,
+      BM,
+      BN);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Launch
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(out, 1);
   args.encode(compute_encoder);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void strided_reduce_2pass(
@@ -656,13 +803,15 @@ void strided_reduce_2pass(
     CommandEncoder& compute_encoder,
     metal::Device& d,
     const Stream& s) {
+  auto [in_type, out_type] = remap_reduce_types(in, op_name);
+
   // Prepare the temporary accumulator
   std::vector<int> intermediate_shape;
   intermediate_shape.reserve(out.ndim() + 1);
   intermediate_shape.push_back(32);
   intermediate_shape.insert(
       intermediate_shape.end(), out.shape().begin(), out.shape().end());
-  array intermediate(std::move(intermediate_shape), out.dtype(), nullptr, {});
+  array intermediate(std::move(intermediate_shape), out_type, nullptr, {});
   intermediate.set_data(allocator::malloc_or_wait(intermediate.nbytes()));
   d.add_temporary(intermediate, s.index);
 
@@ -685,21 +834,43 @@ void strided_reduce_2pass(
   MTL::Size group_dims(threadgroup_size, 1, 1);
 
   // Set the kernel
-  int n = (args.reduce_ndim < 5) ? std::max(1, args.reduce_ndim) : 0;
-  std::ostringstream kname;
-  const std::string func_name = "col_reduce_2pass";
-  kname << func_name << "_" << n << "_" << BM << "_" << BN << "_reduce_"
-        << op_name << type_to_name(in);
-  auto kernel =
-      get_reduce_kernel(d, kname.str(), func_name, op_name, in, out, n, BM, BN);
-  compute_encoder->setComputePipelineState(kernel);
+  int n = get_kernel_reduce_ndim(args.reduce_ndim);
+  std::string func_name = "col_reduce_2pass";
+  std::string kname = func_name;
+  bool large = in.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(
+      kname,
+      "_",
+      std::to_string(n),
+      "_",
+      std::to_string(BM),
+      "_",
+      std::to_string(BN),
+      "_reduce_",
+      op_name,
+      type_to_name(in_type));
+  auto kernel = get_reduce_kernel(
+      d,
+      kname,
+      func_name,
+      op_name,
+      in_type,
+      out_type,
+      large ? "size_t" : "uint",
+      n,
+      BM,
+      BN);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Launch
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(intermediate, 1);
   args.encode(compute_encoder);
-  compute_encoder->setBytes(&out_size, sizeof(size_t), 11);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.set_bytes(out_size, 11);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 
   // Make the 2nd pass arguments and grid_dims
   ColReduceArgs second_args(intermediate);
@@ -709,24 +880,30 @@ void strided_reduce_2pass(
   grid_dims = MTL::Size(threadgroup_size * ((out.size() + BN - 1) / BN), 1, 1);
 
   // Set the 2nd kernel
-  const std::string second_kernel = "col_reduce_looped_1_32_32_reduce_" +
-      op_name + type_to_name(intermediate);
+  func_name = "col_reduce_looped";
+  kname = func_name;
+  large = intermediate.size() > UINT32_MAX;
+  if (large) {
+    kname += "_large";
+  }
+  concatenate(kname, "_1_32_32_reduce_", op_name, type_to_name(intermediate));
   kernel = get_reduce_kernel(
       d,
-      second_kernel,
-      "col_reduce_looped",
+      kname,
+      func_name,
       op_name,
-      intermediate,
-      out,
+      intermediate.dtype(),
+      out_type,
+      large ? "size_t" : "uint",
       1,
       32,
       32);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(intermediate, 0);
   compute_encoder.set_output_array(out, 1);
   second_args.encode(compute_encoder);
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 void strided_reduce_general_dispatch(
@@ -786,7 +963,7 @@ void Reduce::eval_gpu(const std::vector<array>& inputs, array& out) {
       op_name = "sum";
       break;
     case Reduce::Prod:
-      op_name = out.dtype() == bool_ ? "and" : "prod";
+      op_name = "prod";
       break;
     case Reduce::Min:
       op_name = out.dtype() == bool_ ? "and" : "min";

mlx/backend/metal/rope.cpp

@@ -75,24 +75,24 @@ void RoPE::eval_gpu(
   auto& compute_encoder = d.get_command_encoder(s.index);
 
   float base = std::log2(base_);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
   compute_encoder.set_input_array(donated ? out : in, 0);
   compute_encoder.set_output_array(out, 1);
-  compute_encoder->setBytes(&offset_, sizeof(int), 2);
-  compute_encoder->setBytes(&scale_, sizeof(float), 3);
+  compute_encoder.set_bytes(offset_, 2);
+  compute_encoder.set_bytes(scale_, 3);
 
   size_t n_batch = in.size() / mat_size;
   MTL::Size group_dims;
   MTL::Size grid_dims;
   if (single) {
-    compute_encoder->setBytes(out_strides, sizeof(size_t), 4);
+    compute_encoder.set_bytes(out_strides, 1, 4);
     uint32_t dim0 = dims_ / 2;
     group_dims = get_block_dims(dim0, n_batch, 1);
     grid_dims = MTL::Size(dim0, n_batch, 1);
   } else {
-    compute_encoder->setBytes(&strides, 3 * sizeof(size_t), 4);
-    compute_encoder->setBytes(&out_strides, 3 * sizeof(size_t), 5);
-    compute_encoder->setBytes(&n_batch, sizeof(size_t), 6);
+    compute_encoder.set_bytes(strides, 3, 4);
+    compute_encoder.set_bytes(out_strides, 3, 5);
+    compute_encoder.set_bytes(n_batch, 6);
     uint32_t dim0 = dims_ / 2;
     uint32_t dim1 = in.shape(-2);
     uint32_t dim2 = (n_batch + n_per_thread - 1) / n_per_thread;
@@ -104,11 +104,11 @@ void RoPE::eval_gpu(
     auto& freqs = inputs[1];
     compute_encoder.set_input_array(freqs, 10);
     auto freq_stride = freqs.strides()[0];
-    compute_encoder->setBytes(&freq_stride, sizeof(size_t), 11);
+    compute_encoder.set_bytes(freq_stride, 11);
   } else {
-    compute_encoder->setBytes(&base, sizeof(float), 10);
+    compute_encoder.set_bytes(base, 10);
   }
-  compute_encoder.dispatchThreads(grid_dims, group_dims);
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
 } // namespace mlx::core::fast

mlx/backend/metal/scaled_dot_product_attention.cpp

@@ -6,8 +6,11 @@
 #include "mlx/backend/common/compiled.h"
 #include "mlx/backend/metal/copy.h"
 #include "mlx/backend/metal/device.h"
-#include "mlx/backend/metal/kernels/scaled_dot_product_attention_params.h"
+
+#include "mlx/backend/metal/kernels/steel/attn/params.h"
+#include "mlx/backend/metal/utils.h"
 #include "mlx/fast_primitives.h"
+#include "mlx/utils.h"
 
 namespace mlx::core::fast {
 
@@ -18,128 +21,92 @@ void sdpa_full_self_attention_metal(
     const array& q,
     const array& k,
     const array& v,
-    const float alpha,
-    array& out) {
-  std::ostringstream kname_self_attention;
-  kname_self_attention << "steel_gemm_attention_";
+    const float scale,
+    array& o) {
+  using namespace mlx::steel;
 
-  constexpr const int bm = 16;
-  constexpr const int bn = 16;
-  const int bk = q.shape(-1); // already forced to be 64 or 128
+  int wm = 4;
+  int wn = 1;
 
-  if (bk != 64 && bk != 128) {
-    throw std::runtime_error(
-        "[ScaledDotProductAttention::eval_gpu]: hidden dim: expected either 64, 128");
-  }
+  int bd = q.shape(-1);
+  int bq = 32;
+  int bk = bd < 128 ? 32 : 16;
 
-  constexpr const int wm = 2;
-  constexpr const int wn = 2;
+  int B = q.shape(0);
+  int H = q.shape(1);
+  int D = q.shape(3);
+  int gqa_factor = q.shape(1) / k.shape(1);
 
-  std::string delimiter = "_";
+  int qL = q.shape(2);
+  int kL = k.shape(2);
 
-  kname_self_attention << "bm_" + std::to_string(bm) + delimiter;
-  kname_self_attention << "bn_" + std::to_string(bn) + delimiter;
-  kname_self_attention << "bk_" + std::to_string(bk) + delimiter;
+  const bool align_Q = (qL % bq) == 0;
+  const bool align_K = (kL % bk) == 0;
 
-  for (const auto& arr : {k, v, out}) {
-    if (arr.dtype() != q.dtype()) {
-      throw std::runtime_error(
-          "[ScaledDotProductAttention::eval_gpu]: expected matching dtypes for q,k,v,o");
-    }
-  }
+  metal::MTLFCList func_consts = {
+      {&align_Q, MTL::DataType::DataTypeBool, 200},
+      {&align_K, MTL::DataType::DataTypeBool, 201},
+  };
 
-  if (q.dtype() == float32) {
-    kname_self_attention << "itype" + delimiter + "float";
-  } else if (q.dtype() == float16) {
-    kname_self_attention << "itype" + delimiter + "half";
-  } else {
-    throw std::runtime_error(
-        "[ScaledDotProductAttention::eval_gpu]: unexpected dtype found for queries: expected either float32 or float16.");
-  }
+  std::ostringstream kname;
+  // clang-format off
+  kname << "steel_attention_" 
+        << type_to_name(q) 
+        << "_bq" << bq 
+        << "_bk" << bk
+        << "_bd" << bd 
+        << "_wm" << wm << "_wn" << wn; // clang-format on
+
+  std::string base_name = kname.str();
+
+  // clang-format off
+  kname << "_align_Q_" << (align_Q ? 't' : 'n') 
+        << "_align_K_" << (align_K ? 't' : 'n'); // clang-format on
+
+  std::string hash_name = kname.str();
 
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(kname_self_attention.str());
-  compute_encoder->setComputePipelineState(kernel);
+  auto kernel = d.get_kernel(base_name, "mlx", hash_name, func_consts);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
-  uint hidden_dim = q.shape(-1);
-  uint qseq = q.shape(-2);
-  uint qheads = q.shape(-3);
+  const int NQ = (qL + bq - 1) / bq;
+  const int NK = (kL + bk - 1) / bk;
 
-  const uint64_t KV_sequence_length = k.shape(-2);
-  const uint query_sequence_length = q.shape(-2);
-  const uint n_q_heads = q.shape(1);
-  const uint n_kv_heads = k.shape(1);
+  const int NQ_aligned = qL / bq;
+  const int NK_aligned = kL / bk;
 
-  const int M = q.shape(-2);
-  const int N = M;
-  const int K = q.shape(-1);
-  const size_t batch_size_out = q.shape(0) * q.shape(1);
+  AttnParams params{
+      /* int B = */ B,
+      /* int H = */ H,
+      /* int D = */ D,
 
-  const std::vector<int> batch_shape = {q.shape(0) * q.shape(1)};
-  const int dk = q.shape(-1);
-  const int ldq = dk;
-  const int ldk = dk;
-  const int ldv = dk;
-  const int lds = bn;
-  const int ldo = dk;
+      /* int qL = */ qL,
+      /* int kL = */ kL,
 
-  int tn = 1;
-  int tm = (M + bm - 1) / bm;
+      /* int gqa_factor = */ gqa_factor,
+      /* float scale = */ scale,
 
-  const int batch_stride_q = dk * query_sequence_length;
-  const int batch_stride_k = dk * query_sequence_length;
-  const int batch_stride_v = dk * query_sequence_length;
-  const int batch_stride_o = dk * query_sequence_length;
-  const int swizzle_log = 0;
-  const int gemm_n_iterations_aligned = (N + bn - 1) / bn;
-  const int gemm_k_iterations_aligned = (K + bk - 1) / bk;
-  const int gemm_sv_m_block_iterations = (M + bm - 1) / bm;
-  const int batch_ndim = int(batch_shape.size());
+      /* int NQ = */ NQ,
+      /* int NK = */ NK,
 
-  MLXFastAttentionParams params{
-      (int)M,
-      (int)N,
-      (int)K,
-      ldq,
-      ldk,
-      ldv,
-      lds,
-      ldo,
-      tn,
-      tm,
-      batch_stride_q,
-      batch_stride_k,
-      batch_stride_v,
-      batch_stride_o,
-      swizzle_log,
-      gemm_n_iterations_aligned,
-      gemm_k_iterations_aligned,
-      gemm_sv_m_block_iterations,
-      batch_ndim,
-      alpha};
+      /* int NQ_aligned = */ NQ_aligned,
+      /* int NK_aligned = */ NK_aligned,
 
-  const std::vector<size_t> batch_strides = {
-      (size_t)batch_stride_q,
-      (size_t)batch_stride_k,
-      (size_t)batch_stride_v,
-      (size_t)batch_stride_o};
+      /* size_t Q_strides[3] = */ {q.strides(0), q.strides(1), q.strides(2)},
+      /* size_t K_strides[3] = */ {k.strides(0), k.strides(1), k.strides(2)},
+      /* size_t V_strides[3] = */ {v.strides(0), v.strides(1), v.strides(2)},
+      /* size_t O_strides[3] = */ {o.strides(0), o.strides(1), o.strides(2)}};
 
   compute_encoder.set_input_array(q, 0);
   compute_encoder.set_input_array(k, 1);
   compute_encoder.set_input_array(v, 2);
-  compute_encoder.set_output_array(out, 3);
+  compute_encoder.set_output_array(o, 3);
+  compute_encoder.set_bytes(params, 4);
 
-  compute_encoder->setBytes(&params, sizeof(MLXFastAttentionParams), 4);
-  compute_encoder->setBytes(
-      batch_shape.data(), sizeof(int) * batch_shape.size(), 6);
-
-  compute_encoder->setBytes(
-      batch_strides.data(), sizeof(size_t) * batch_strides.size(), 7);
-
-  MTL::Size grid_dims = MTL::Size(1, tm, batch_size_out);
+  MTL::Size grid_dims = MTL::Size(NQ, H, B);
   MTL::Size group_dims = MTL::Size(32, wm, wn);
 
-  compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
 void sdpa_vector(
@@ -170,21 +137,109 @@ void sdpa_vector(
   // Get the kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Set its arguments
   compute_encoder.set_input_array(q.data_shared_ptr() == nullptr ? out : q, 0);
   compute_encoder.set_input_array(k, 1);
   compute_encoder.set_input_array(v, 2);
   compute_encoder.set_output_array(out, 3);
-  compute_encoder->setBytes(&gqa_factor, sizeof(int), 4);
-  compute_encoder->setBytes(&N, sizeof(int), 5);
-  compute_encoder->setBytes(&k_stride, sizeof(size_t), 6);
-  compute_encoder->setBytes(&v_stride, sizeof(size_t), 7);
-  compute_encoder->setBytes(&scale, sizeof(float), 8);
+  compute_encoder.set_bytes(gqa_factor, 4);
+  compute_encoder.set_bytes(N, 5);
+  compute_encoder.set_bytes(k_stride, 6);
+  compute_encoder.set_bytes(v_stride, 7);
+  compute_encoder.set_bytes(scale, 8);
 
   // Launch
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+}
+
+void sdpa_vector_2pass(
+    const Stream& s,
+    metal::Device& d,
+    const array& q,
+    const array& k,
+    const array& v,
+    array& out,
+    float scale) {
+  // Set the kernel name
+  std::string kname;
+  kname.reserve(64);
+  kname += "sdpa_vector_2pass_1_";
+  kname += get_type_string(q.dtype());
+  kname += "_";
+  kname += std::to_string(q.shape(-1));
+
+  // Compute the necessary sizes
+  int gqa_factor = q.shape(1) / k.shape(1);
+  int N = k.shape(2);
+  int blocks = 32;
+  int B = q.shape(0) * q.shape(1);
+  size_t k_stride = k.strides()[1];
+  size_t v_stride = v.strides()[1];
+  MTL::Size group_dims(8 * 32, 1, 1);
+  MTL::Size grid_dims(1, B, blocks);
+
+  // Allocate the intermediates
+  std::vector<int> intermediate_shape;
+  intermediate_shape.reserve(out.ndim() + 1);
+  intermediate_shape.insert(
+      intermediate_shape.end(), out.shape().begin(), out.shape().end() - 1);
+  intermediate_shape.push_back(blocks);
+  intermediate_shape.push_back(out.shape().back());
+  array intermediate(intermediate_shape, float32, nullptr, {});
+  intermediate_shape.pop_back();
+  array sums(intermediate_shape, float32, nullptr, {});
+  array maxs(std::move(intermediate_shape), float32, nullptr, {});
+  intermediate.set_data(allocator::malloc_or_wait(intermediate.nbytes()));
+  sums.set_data(allocator::malloc_or_wait(sums.nbytes()));
+  maxs.set_data(allocator::malloc_or_wait(maxs.nbytes()));
+  d.add_temporary(intermediate, s.index);
+  d.add_temporary(sums, s.index);
+  d.add_temporary(maxs, s.index);
+
+  // Get the kernel
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(kname);
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  // Set its arguments
+  compute_encoder.set_input_array(q.data_shared_ptr() == nullptr ? out : q, 0);
+  compute_encoder.set_input_array(k, 1);
+  compute_encoder.set_input_array(v, 2);
+  compute_encoder.set_output_array(intermediate, 3);
+  compute_encoder.set_output_array(sums, 4);
+  compute_encoder.set_output_array(maxs, 5);
+  compute_encoder.set_bytes(gqa_factor, 6);
+  compute_encoder.set_bytes(N, 7);
+  compute_encoder.set_bytes(k_stride, 8);
+  compute_encoder.set_bytes(v_stride, 9);
+  compute_encoder.set_bytes(scale, 10);
+
+  // Launch
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+
+  // Final pass
+  kname.clear();
+  kname += "sdpa_vector_2pass_2_";
+  kname += get_type_string(q.dtype());
+  kname += "_";
+  kname += std::to_string(q.shape(-1));
+
+  // Get the kernel
+  kernel = d.get_kernel(kname);
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  // Set its arguments
+  compute_encoder.set_input_array(intermediate, 0);
+  compute_encoder.set_input_array(sums, 1);
+  compute_encoder.set_input_array(maxs, 2);
+  compute_encoder.set_output_array(out, 3);
+
+  // Launch
+  group_dims = MTL::Size(1024, 1, 1);
+  grid_dims = MTL::Size(1, B, 1);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
 } // namespace
@@ -206,12 +261,14 @@ void ScaledDotProductAttention::eval_gpu(
 
   // Define some copy functions to ensure the layout of the inputs is as
   // expected.
-  auto copy_unless = [&copies, &s](auto predicate, const array& arr) {
+  copies.reserve(3);
+  auto copy_unless = [&copies, &s](
+                         auto predicate, const array& arr) -> const array& {
     if (!predicate(arr)) {
       array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
       copy_gpu(arr, arr_copy, CopyType::General, s);
       copies.push_back(arr_copy);
-      return arr_copy;
+      return copies.back();
     } else {
       return arr;
     }
@@ -240,9 +297,9 @@ void ScaledDotProductAttention::eval_gpu(
 
   // We are in vector mode ie single query
   if (q_pre.shape(2) == 1) {
-    auto q = copy_unless(is_contiguous, q_pre);
-    auto k = copy_unless(is_contiguous_except_seq_len, k_pre);
-    auto v = copy_unless(is_contiguous_except_seq_len, v_pre);
+    const auto& q = copy_unless(is_contiguous, q_pre);
+    const auto& k = copy_unless(is_contiguous_except_seq_len, k_pre);
+    const auto& v = copy_unless(is_contiguous_except_seq_len, v_pre);
 
     // Donate the query if possible
     if (q.is_donatable()) {
@@ -251,15 +308,41 @@ void ScaledDotProductAttention::eval_gpu(
       o.set_data(allocator::malloc_or_wait(o.nbytes()));
     }
 
-    sdpa_vector(s, d, q, k, v, o, scale_);
+    // We route to the 2 pass fused attention if
+    // - The device is large and the sequence length long
+    // - The sequence length is even longer and we have gqa
+    char devc = d.get_architecture().back();
+    if ((devc == 'd' && k.shape(2) >= 1024) ||
+        (k.shape(1) < q.shape(1) && k.shape(2) >= 4096)) {
+      sdpa_vector_2pass(s, d, q, k, v, o, scale_);
+    } else {
+      sdpa_vector(s, d, q, k, v, o, scale_);
+    }
   }
 
   // Full attention mode
   else {
-    auto q = copy_unless(is_matrix_contiguous, q_pre);
-    auto k = copy_unless(is_matrix_contiguous, k_pre);
-    auto v = copy_unless(is_matrix_contiguous, v_pre);
-    o.set_data(allocator::malloc_or_wait(o.nbytes()));
+    const auto& q = copy_unless(is_matrix_contiguous, q_pre);
+    const auto& k = copy_unless(is_matrix_contiguous, k_pre);
+    const auto& v = copy_unless(is_matrix_contiguous, v_pre);
+
+    size_t str_oD = 1;
+    size_t str_oH = o.shape(3);
+    size_t str_oL = o.shape(1) * str_oH;
+    size_t str_oB = o.shape(2) * str_oL;
+    size_t data_size = o.shape(0) * str_oB;
+
+    array::Flags flags{
+        /* bool contiguous = */ 1,
+        /* bool row_contiguous = */ 0,
+        /* bool col_contiguous = */ 0,
+    };
+
+    o.set_data(
+        allocator::malloc_or_wait(o.nbytes()),
+        data_size,
+        {str_oB, str_oH, str_oL, str_oD},
+        flags);
 
     sdpa_full_self_attention_metal(s, d, q, k, v, scale_, o);
   }

mlx/backend/metal/scan.cpp

@@ -68,12 +68,12 @@ void Scan::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   if (contiguous) {
     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(
         in.data_shared_ptr() == nullptr ? out : in, 0);
     compute_encoder.set_output_array(out, 1);
     size_t size = in.shape(axis_);
-    compute_encoder->setBytes(&size, sizeof(size_t), 2);
+    compute_encoder.set_bytes(size, 2);
 
     // Compute the thread grid
     int n_reads = (in.itemsize() <= 4) ? 4 : 2;
@@ -95,10 +95,10 @@ void Scan::eval_gpu(const std::vector<array>& inputs, array& out) {
     MTL::Size grid_dims(
         thread_group_size, tmp_grid_dims.width, tmp_grid_dims.height);
     MTL::Size group_dims(thread_group_size, 1, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     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(
         in.data_shared_ptr() == nullptr ? out : in, 0);
     compute_encoder.set_output_array(out, 1);
@@ -107,9 +107,9 @@ void Scan::eval_gpu(const std::vector<array>& inputs, array& out) {
     int bm = 32;
     int bn = 32;
     size_t stride_blocks = (stride + bn - 1) / bn;
-    compute_encoder->setBytes(&size, sizeof(size_t), 2);
-    compute_encoder->setBytes(&stride, sizeof(size_t), 3);
-    compute_encoder->setBytes(&stride_blocks, sizeof(size_t), 4);
+    compute_encoder.set_bytes(size, 2);
+    compute_encoder.set_bytes(stride, 3);
+    compute_encoder.set_bytes(stride_blocks, 4);
 
     // Compute the thread grid
     int n_reads = (in.itemsize() <= 4) ? 4 : 2;
@@ -125,7 +125,7 @@ void Scan::eval_gpu(const std::vector<array>& inputs, array& out) {
     MTL::Size grid_dims(
         thread_group_size, tmp_grid_dims.width, tmp_grid_dims.height);
     MTL::Size group_dims(thread_group_size, 1, 1);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 
   d.add_temporaries(std::move(copies), s.index);

mlx/backend/metal/softmax.cpp

@@ -81,12 +81,12 @@ void Softmax::eval_gpu(const std::vector<array>& inputs, array& out) {
       group_dims = MTL::Size(threadgroup_size, 1, 1);
     }
 
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(
         in.data_shared_ptr() == nullptr ? out : in, 0);
     compute_encoder.set_output_array(out, 1);
-    compute_encoder->setBytes(&axis_size, sizeof(int), 2);
-    compute_encoder.dispatchThreads(grid_dims, group_dims);
+    compute_encoder.set_bytes(axis_size, 2);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 
   d.add_temporaries(std::move(copies), s.index);

mlx/backend/metal/sort.cpp

@@ -68,29 +68,29 @@ void single_block_sort(
 
   // Prepare command encoder
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
 
   // Set inputs
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(out, 1);
-  compute_encoder->setBytes(&size_sorted_axis, sizeof(int), 2);
-  compute_encoder->setBytes(&in_stride_sorted_axis, sizeof(int), 3);
-  compute_encoder->setBytes(&out_stride_sorted_axis, sizeof(int), 4);
+  compute_encoder.set_bytes(size_sorted_axis, 2);
+  compute_encoder.set_bytes(in_stride_sorted_axis, 3);
+  compute_encoder.set_bytes(out_stride_sorted_axis, 4);
 
   if (contiguous) {
-    compute_encoder->setBytes(&in_stride_segment_axis, sizeof(int), 5);
-    compute_encoder->setBytes(&out_stride_segment_axis, sizeof(int), 6);
+    compute_encoder.set_bytes(in_stride_segment_axis, 5);
+    compute_encoder.set_bytes(out_stride_segment_axis, 6);
   } else {
-    compute_encoder->setBytes(&nc_dim, sizeof(int), 5);
-    compute_encoder->setBytes(nc_shape.data(), nc_dim * sizeof(int), 6);
-    compute_encoder->setBytes(in_nc_str.data(), nc_dim * sizeof(size_t), 7);
-    compute_encoder->setBytes(out_nc_str.data(), nc_dim * sizeof(size_t), 8);
+    compute_encoder.set_bytes(nc_dim, 5);
+    compute_encoder.set_vector_bytes(nc_shape, 6);
+    compute_encoder.set_vector_bytes(in_nc_str, 7);
+    compute_encoder.set_vector_bytes(out_nc_str, 8);
   }
 
   MTL::Size group_dims = MTL::Size(bn, 1, 1);
   MTL::Size grid_dims = MTL::Size(1, n_rows, 1);
 
-  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
 void multi_block_sort(
@@ -152,22 +152,21 @@ void multi_block_sort(
           << type_to_name(dev_idxs_0) << "_bn" << bn << "_tn" << tn;
     auto kernel =
         get_mb_sort_kernel(d, kname.str(), dev_vals_0, dev_idxs_0, bn, tn);
-    compute_encoder->setComputePipelineState(kernel);
+    compute_encoder.set_compute_pipeline_state(kernel);
 
     compute_encoder.set_input_array(in, 0);
     compute_encoder.set_output_array(dev_vals_0, 1);
     compute_encoder.set_output_array(dev_idxs_0, 2);
-    compute_encoder->setBytes(&size_sorted_axis, sizeof(int), 3);
-    compute_encoder->setBytes(&stride_sorted_axis, sizeof(int), 4);
-    compute_encoder->setBytes(&nc_dim, sizeof(int), 5);
-    compute_encoder->setBytes(
-        nc_shape.data(), nc_shape.size() * sizeof(int), 6);
-    compute_encoder->setBytes(nc_str.data(), nc_str.size() * sizeof(size_t), 7);
+    compute_encoder.set_bytes(size_sorted_axis, 3);
+    compute_encoder.set_bytes(stride_sorted_axis, 4);
+    compute_encoder.set_bytes(nc_dim, 5);
+    compute_encoder.set_vector_bytes(nc_shape, 6);
+    compute_encoder.set_vector_bytes(nc_str, 7);
 
     MTL::Size group_dims = MTL::Size(bn, 1, 1);
     MTL::Size grid_dims = MTL::Size(n_blocks, n_rows, 1);
 
-    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
   }
 
   // Do merges
@@ -194,19 +193,19 @@ void multi_block_sort(
 
       auto kernel =
           get_mb_sort_kernel(d, kname.str(), dev_vals_0, dev_idxs_0, bn, tn);
-      compute_encoder->setComputePipelineState(kernel);
+      compute_encoder.set_compute_pipeline_state(kernel);
 
       compute_encoder.set_output_array(block_partitions, 0);
       compute_encoder.set_input_array(dev_vals_in, 1);
       compute_encoder.set_input_array(dev_idxs_in, 2);
-      compute_encoder->setBytes(&size_sorted_axis, sizeof(int), 3);
-      compute_encoder->setBytes(&merge_tiles, sizeof(int), 4);
-      compute_encoder->setBytes(&n_blocks, sizeof(int), 5);
+      compute_encoder.set_bytes(size_sorted_axis, 3);
+      compute_encoder.set_bytes(merge_tiles, 4);
+      compute_encoder.set_bytes(n_blocks, 5);
 
       MTL::Size group_dims = MTL::Size(n_thr_per_group, 1, 1);
       MTL::Size grid_dims = MTL::Size(1, n_rows, 1);
 
-      compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+      compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
     }
 
     // Do merge
@@ -217,21 +216,21 @@ void multi_block_sort(
 
       auto kernel =
           get_mb_sort_kernel(d, kname.str(), dev_vals_0, dev_idxs_0, bn, tn);
-      compute_encoder->setComputePipelineState(kernel);
+      compute_encoder.set_compute_pipeline_state(kernel);
 
       compute_encoder.set_input_array(block_partitions, 0);
       compute_encoder.set_input_array(dev_vals_in, 1);
       compute_encoder.set_input_array(dev_idxs_in, 2);
       compute_encoder.set_output_array(dev_vals_out, 3);
       compute_encoder.set_output_array(dev_idxs_out, 4);
-      compute_encoder->setBytes(&size_sorted_axis, sizeof(int), 5);
-      compute_encoder->setBytes(&merge_tiles, sizeof(int), 6);
-      compute_encoder->setBytes(&n_blocks, sizeof(int), 7);
+      compute_encoder.set_bytes(size_sorted_axis, 5);
+      compute_encoder.set_bytes(merge_tiles, 6);
+      compute_encoder.set_bytes(n_blocks, 7);
 
       MTL::Size group_dims = MTL::Size(bn, 1, 1);
       MTL::Size grid_dims = MTL::Size(n_blocks, n_rows, 1);
 
-      compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
+      compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
     }
   }
 

mlx/backend/metal/ternary.cpp

@@ -36,34 +36,38 @@ void ternary_op_gpu_inplace(
   };
   auto [shape, strides_a, strides_b, strides_c, strides_out] = maybe_collapse();
 
-  bool use_2d = out.data_size() > UINT_MAX;
+  bool large = out.data_size() > UINT_MAX;
   auto ndim = shape.size();
-  int work_per_thread = (topt == TernaryOpType::General) ? 4 : 1;
-  std::string kernel_name;
-  {
-    std::ostringstream kname;
-    if (topt == TernaryOpType::General) {
-      kname << "g";
-      if (shape.size() <= 3) {
-        kname << shape.size();
-      } else if (work_per_thread > 1) {
-        kname << "n" << work_per_thread;
-      }
-    } else if (use_2d) {
-      kname << "v2";
-    } else {
-      kname << "v";
-    }
-    kname << "_" << op << type_to_name(b);
-    kernel_name = kname.str();
+  int work_per_thread;
+  if (topt == TernaryOpType::General) {
+    work_per_thread = large ? 4 : 2;
+  } else {
+    work_per_thread = 1;
   }
+  std::string kernel_name;
+  if (topt == TernaryOpType::General) {
+    kernel_name = "g";
+    if (shape.size() <= 3) {
+      kernel_name += std::to_string(shape.size());
+    } else if (work_per_thread > 1) {
+      concatenate(kernel_name, "n", std::to_string(work_per_thread));
+    }
+    if (large) {
+      kernel_name += "large";
+    }
+  } else if (large) {
+    kernel_name = "v2";
+  } else {
+    kernel_name = "v";
+  }
+  concatenate(kernel_name, "_", op, type_to_name(b));
 
   auto& d = metal::device(s.device);
 
   auto kernel = get_ternary_kernel(d, kernel_name, out.dtype(), op);
 
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder->setComputePipelineState(kernel);
+  compute_encoder.set_compute_pipeline_state(kernel);
   bool donate_a = a.data_shared_ptr() == nullptr;
   bool donate_b = b.data_shared_ptr() == nullptr;
   bool donate_c = c.data_shared_ptr() == nullptr;
@@ -80,18 +84,18 @@ void ternary_op_gpu_inplace(
     size_t rest = out.size() / (dim0 * dim1);
 
     if (ndim > 3) {
-      compute_encoder->setBytes(shape.data(), ndim * sizeof(int), 4);
-      compute_encoder->setBytes(strides_a.data(), ndim * sizeof(size_t), 5);
-      compute_encoder->setBytes(strides_b.data(), ndim * sizeof(size_t), 6);
-      compute_encoder->setBytes(strides_c.data(), ndim * sizeof(size_t), 7);
+      compute_encoder.set_vector_bytes(shape, 4);
+      compute_encoder.set_vector_bytes(strides_a, 5);
+      compute_encoder.set_vector_bytes(strides_b, 6);
+      compute_encoder.set_vector_bytes(strides_c, 7);
 
-      compute_encoder->setBytes(&ndim, sizeof(int), 8);
+      compute_encoder.set_bytes(ndim, 8);
       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), 4);
-      compute_encoder->setBytes(strides_b.data(), ndim * sizeof(size_t), 5);
-      compute_encoder->setBytes(strides_c.data(), ndim * sizeof(size_t), 6);
+      compute_encoder.set_vector_bytes(strides_a, 4);
+      compute_encoder.set_vector_bytes(strides_b, 5);
+      compute_encoder.set_vector_bytes(strides_c, 6);
     }
 
     if (thread_group_size != 1024) {
@@ -99,7 +103,7 @@ void ternary_op_gpu_inplace(
     }
     MTL::Size 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();
@@ -107,9 +111,9 @@ void ternary_op_gpu_inplace(
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                                 : MTL::Size(nthreads, 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/unary.cpp

@@ -35,21 +35,24 @@ void unary_op_gpu_inplace(
   };
   auto [shape, strides] = maybe_collapse();
   int ndim = shape.size();
-  int work_per_thread = !contig ? 4 : 1;
   size_t nthreads = contig ? in.data_size() : in.size();
-  bool use_2d = nthreads > UINT32_MAX;
+  bool large = nthreads > UINT32_MAX;
+  int work_per_thread = !contig && large ? 4 : 1;
   std::string kernel_name;
   if (contig) {
-    kernel_name = (use_2d ? "v2" : "v");
+    kernel_name = (large ? "v2" : "v");
   } else {
-    kernel_name = (work_per_thread == 4 ? "gn4" : "g");
+    kernel_name = "gn" + std::to_string(work_per_thread);
+    if (large) {
+      kernel_name += "_large";
+    }
   }
-  kernel_name += "_" + op + type_to_name(in) + type_to_name(out);
+  concatenate(kernel_name, "_", op, type_to_name(in), type_to_name(out));
   auto kernel = get_unary_kernel(d, kernel_name, in.dtype(), out.dtype(), op);
 
   auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
   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(
       in.data_shared_ptr() == nullptr ? out : in, 0);
   compute_encoder.set_output_array(out, 1);
@@ -58,24 +61,24 @@ void unary_op_gpu_inplace(
     size_t dim0 = ndim > 0 ? shape[ndim - 1] : 1;
     size_t dim1 = ndim > 1 ? shape[ndim - 2] : 1;
     size_t rest = out.size() / (dim0 * dim1);
-    compute_encoder->setBytes(shape.data(), ndim * sizeof(int), 2);
-    compute_encoder->setBytes(strides.data(), ndim * sizeof(size_t), 3);
-    compute_encoder->setBytes(&ndim, sizeof(int), 4);
+    compute_encoder.set_vector_bytes(shape, 2);
+    compute_encoder.set_vector_bytes(strides, 3);
+    compute_encoder.set_bytes(ndim, 4);
     if (thread_group_size != 1024) {
       throw std::runtime_error("[Metal::unary] Must use 1024 sized block");
     }
     dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     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 {
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
-                                 : MTL::Size(nthreads, 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);
   }
 }
 
@@ -161,7 +164,7 @@ void Round::eval_gpu(const std::vector<array>& inputs, array& out) {
     unary_op_gpu(inputs, out, get_primitive_string(this));
   } else {
     // No-op integer types
-    out.copy_shared_buffer(in);
+    move_or_copy(in, out);
   }
 }
 

mlx/backend/metal/utils.cpp

@@ -6,9 +6,9 @@ using namespace mlx;
 
 namespace mlx::core {
 
-std::string type_to_name(const array& a) {
+std::string type_to_name(const Dtype& t) {
   std::string tname;
-  switch (a.dtype()) {
+  switch (t) {
     case bool_:
       tname = "bool_";
       break;
@@ -52,6 +52,10 @@ std::string type_to_name(const array& a) {
   return tname;
 }
 
+std::string type_to_name(const array& a) {
+  return type_to_name(a.dtype());
+}
+
 MTL::Size get_block_dims(int dim0, int dim1, int dim2, int pow2 /* = 10 */) {
   int pows[3] = {0, 0, 0};
   int sum = 0;

mlx/backend/metal/utils.h

@@ -8,23 +8,7 @@
 
 namespace mlx::core {
 
-using metal::CommandEncoder;
-
-template <typename T>
-inline void set_vector_bytes(
-    CommandEncoder& enc,
-    const std::vector<T>& vec,
-    size_t nelems,
-    int idx) {
-  enc->setBytes(vec.data(), nelems * sizeof(T), idx);
-}
-
-template <typename T>
-inline void
-set_vector_bytes(CommandEncoder& enc, const std::vector<T>& vec, int idx) {
-  return set_vector_bytes(enc, vec, vec.size(), idx);
-}
-
+std::string type_to_name(const Dtype& t);
 std::string type_to_name(const array& a);
 
 // Compute the thread block dimensions which fit the given
@@ -78,4 +62,15 @@ inline void debug_set_primitive_buffer_label(
 
 std::string get_primitive_string(Primitive* primitive);
 
+template <typename T>
+void concatenate(std::string& acc, T first) {
+  acc += first;
+}
+
+template <typename T, typename... Args>
+void concatenate(std::string& acc, T first, Args... args) {
+  acc += first;
+  concatenate(acc, args...);
+}
+
 } // namespace mlx::core