Fix packaging of gguflib (#435)

* tests clear metal validation

* add cpp test with metal validation to circleci

* nit

.circleci/config.yml

@@ -62,6 +62,7 @@ jobs:
             pip install --upgrade pybind11[global]
             pip install numpy
             pip install torch
+            pip install tensorflow
             pip install unittest-xml-reporting
       - run:
           name: Build python package
@@ -79,6 +80,13 @@ jobs:
             DEVICE=gpu python -m xmlrunner discover -v python/tests -o test-results/gpu
       - store_test_results:
           path: test-results
+      - run:
+          name: Build CPP only
+          command: |
+            mkdir -p build && cd build && cmake .. && make -j
+      - run:
+          name: Run CPP tests
+          command: METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 ./build/tests/tests
 
   build_release:
     machine: true
@@ -104,7 +112,7 @@ jobs:
             pip install numpy
             pip install twine
       - run:
-          name: Build pacakge
+          name: Build package
           command: |
             eval "$(conda shell.bash hook)"
             conda activate runner-env
@@ -140,7 +148,7 @@ jobs:
             pip install numpy
             pip install twine
       - run:
-          name: Build pacakge
+          name: Build package
           command: |
             eval "$(conda shell.bash hook)"
             conda activate runner-env
@@ -176,7 +184,7 @@ jobs:
             pip install numpy
             pip install twine
       - run:
-          name: Build pacakge
+          name: Build package
           command: |
             eval "$(conda shell.bash hook)"
             conda activate runner-env
@@ -203,7 +211,7 @@ workflows:
               ignore: /.*/
           matrix:
             parameters:
-              python_version: ["3.8", "3.9", "3.10", "3.11"]
+              python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
               macos_version: ["13", "14"]
   nightly_build:
     when: << pipeline.parameters.nightly_build >>
@@ -211,7 +219,7 @@ workflows:
       - build_package:
           matrix:
             parameters:
-              python_version: ["3.8", "3.9", "3.10", "3.11"]
+              python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
               macos_version: ["13", "14"]
   weekly_build:
     when: << pipeline.parameters.weekly_build >>
@@ -219,5 +227,5 @@ workflows:
       - build_dev_release:
           matrix:
             parameters:
-              python_version: ["3.8", "3.9", "3.10", "3.11"]
+              python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
               macos_version: ["13", "14"]

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,28 @@
+---
+name: Bug report
+about: Create a report about an issue you've encountered
+title: "[BUG] "
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+
+Include code snippet
+```python
+
+```
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Desktop (please complete the following information):**
+ - OS Version: [e.g. MacOS 14.1.2]
+ - Version [e.g. 0.7.0]
+
+**Additional context**
+Add any other context about the problem here.

.github/pull_request_template.md

@@ -0,0 +1,12 @@
+## Proposed changes
+
+Please include a description of the problem or feature this PR is addressing. If there is a corresponding issue, include the issue #.
+
+## Checklist
+
+Put an `x` in the boxes that apply.
+
+- [ ] I have read the [CONTRIBUTING](https://github.com/ml-explore/mlx/blob/main/CONTRIBUTING.md) document
+- [ ] I have run `pre-commit run --all-files` to format my code / installed pre-commit prior to committing changes
+- [ ] I have added tests that prove my fix is effective or that my feature works
+- [ ] I have updated the necessary documentation (if needed)

.github/workflows/pull_request.yml

@@ -0,0 +1,20 @@
+on:
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  check_lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
+        with:
+          python-version: 3.8
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pre-commit black isort clang-format
+      - name: Run lint
+        run: |
+          pre-commit run --all-files

.gitignore

@@ -6,10 +6,16 @@ __pycache__/
 # C extensions
 *.so
 
+# tensor files
+*.safe
+*.safetensors
+
 # Metal libraries
 *.metallib
+venv/
 
 # Distribution / packaging
+python/mlx/core
 python/mlx/share
 python/mlx/include
 .Python
@@ -73,3 +79,6 @@ build/
 # VSCode 
 .vscode/
 .DS_Store
+
+# Jetbrains
+.cache

.pre-commit-config.yaml

@@ -1,9 +1,16 @@
 repos:
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v14.0.6
+    rev: v17.0.6
     hooks:
     -   id: clang-format
--   repo: https://github.com/psf/black
-    rev: 22.10.0
+# Using this mirror lets us use mypyc-compiled black, which is about 2x faster
+-   repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 23.12.1
     hooks:
     -   id: black
+-   repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+    -   id: isort
+        args:
+            - --profile=black

ACKNOWLEDGMENTS.md

@@ -1,3 +1,24 @@
+# Individual Contributors
+
+If you wish to be acknowledged for your contributions, please list your name
+with a short description of your contribution(s) below. For example:
+
+- Jane Smith: Added the `foo` and `bar` ops.
+
+MLX was developed with contributions from the following individuals:
+
+- Nripesh Niketan: Added `softsign`, `softmax`, `hardswish`, `logsoftmax` activation functions. Added `dropout3d` ops. Added `LogicalAnd` and `LogicalOR` ops.
+- Juarez Bochi: Fixed bug in cross attention.
+- Justin Deschenaux: Sine, Cosine, arange, randint, truncated normal, bernoulli, lion optimizer, Dropout2d, linear and logistic regression python example.
+- Diogo Da Cruz: Added `tri`, `tril`, `triu`, `tensordot`, `inner`, `outer` and safetensor support
+- Gabrijel Boduljak: Added `mlx.core.linalg`, implemented `norm` method and `InstanceNorm` layer.
+
+<a href="https://github.com/ml-explore/mlx/graphs/contributors">
+  <img class="dark-light" src="https://contrib.rocks/image?repo=ml-explore/mlx&anon=0&columns=20&max=100&r=true" />
+</a>
+
+# Third-Party Software
+
 MLX leverages several third-party software, listed here together with
 their license copied verbatim.
 
@@ -231,4 +252,4 @@ Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
-limitations under the License.
+limitations under the License.

CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required(VERSION 3.24)
 
-project(mlx LANGUAGES CXX)
+project(mlx LANGUAGES C CXX)
 
 # ----------------------------- Setup -----------------------------
 set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
@@ -18,7 +18,28 @@ option(MLX_BUILD_METAL "Build metal backend" ON)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
 
 if(NOT MLX_VERSION)
-  set(MLX_VERSION 0.0.1)
+  set(MLX_VERSION 0.0.9)
+endif()
+
+# --------------------- Processor tests -------------------------
+
+message(STATUS "Building MLX for ${CMAKE_HOST_SYSTEM_PROCESSOR} processor on ${CMAKE_SYSTEM_NAME}")
+
+set(MLX_BUILD_ARM OFF)
+
+if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+
+  if (${CMAKE_HOST_SYSTEM_PROCESSOR} MATCHES "x86_64")
+    message(WARNING 
+      "Building for x86_64 on macOS is not supported." 
+      " If you are on an Apple silicon system, "
+      " make sure you are building for arm64.")
+  elseif(${CMAKE_HOST_SYSTEM_PROCESSOR} MATCHES "arm64")
+    set(MLX_BUILD_ARM ON)
+  endif()
+
+else()
+  message(WARNING "MLX is prioritised for Apple silicon systems using macOS.")
 endif()
 
 # ----------------------------- Lib -----------------------------
@@ -37,20 +58,26 @@ endif()
 
 if (MLX_BUILD_METAL AND NOT METAL_LIB)
   message(STATUS "Metal not found. Unable to build GPU")
+  set(MLX_BUILD_METAL OFF)
 elseif (MLX_BUILD_METAL)
   message(STATUS "Building METAL sources")
   add_compile_definitions(_METAL_)
 
-  execute_process(COMMAND zsh "-c" "/usr/bin/sw_vers | cut -f2- -d: | sed -n 2p | grep -Eo '[0-9]+.[0-9]+'"
-                  OUTPUT_VARIABLE MACOS_VERSION)
+  # 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)
 
-  message(STATUS "Detected macOS version ${MACOS_VERSION}")
-  if (${MACOS_VERSION} GREATER_EQUAL 14.0)
+  message(STATUS "Building with SDK for macOS version ${MACOS_VERSION}")
+  
+  if (${MACOS_VERSION} GREATER_EQUAL 14.2)
+    set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS14.2_iOS17.2.zip)
+  elseif (${MACOS_VERSION} GREATER_EQUAL 14.0)
     set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS14_iOS17-beta.zip)
   elseif (${MACOS_VERSION} GREATER_EQUAL 13.3)
     set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS13.3_iOS16.4.zip)
   else()
-    set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS13_iOS16.zip)
+    message(FATAL_ERROR "MLX requires macOS >= 13.4 to be built with MLX_BUILD_METAL=ON" )
   endif()
 
   FetchContent_Declare(
@@ -72,13 +99,13 @@ elseif (MLX_BUILD_METAL)
 endif()
 
 find_library(ACCELERATE_LIBRARY Accelerate)
-if (ACCELERATE_LIBRARY)
+if (MLX_BUILD_ARM AND ACCELERATE_LIBRARY)
   message(STATUS "Accelerate found ${ACCELERATE_LIBRARY}")
   set(MLX_BUILD_ACCELERATE ON)
   target_link_libraries(mlx ${ACCELERATE_LIBRARY})
   add_compile_definitions(ACCELERATE_NEW_LAPACK)
 else()
-  message(STATUS "Accelerate not found, using default backend.")
+  message(STATUS "Accelerate or arm neon not found, using default backend.")
   set(MLX_BUILD_ACCELERATE OFF)
   #set(BLA_VENDOR Generic)
   find_package(BLAS REQUIRED)
@@ -125,6 +152,8 @@ if (MLX_BUILD_BENCHMARKS)
   add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/benchmarks/cpp)
 endif()
 
+
+
 # ----------------------------- Installation -----------------------------
 include(GNUInstallDirs)
 
@@ -194,4 +223,4 @@ install(
 install(
   DIRECTORY ${CMAKE_MODULE_PATH}/
   DESTINATION ${MLX_CMAKE_INSTALL_MODULE_DIR}
-)
+)

README.md

@@ -2,39 +2,41 @@
 
 [**Quickstart**](#quickstart) | [**Installation**](#installation) |
 [**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |
-[**Examples**](#examples)
+[**Examples**](#examples) 
+
+[![CircleCI](https://circleci.com/gh/ml-explore/mlx.svg?style=svg)](https://circleci.com/gh/ml-explore/mlx)
 
 MLX is an array framework for machine learning on Apple silicon, brought to you
 by Apple machine learning research.
 
 Some key features of MLX include:
 
- - **Familiar APIs**: MLX has a Python API which closely follows NumPy.
-   MLX also has a fully featured C++ API which closely mirrors the Python API. 
-   MLX has higher level packages like `mlx.nn` and `mlx.optimizers` with APIs
+ - **Familiar APIs**: MLX has a Python API that closely follows NumPy.
+   MLX also has a fully featured C++ API, which closely mirrors the Python API. 
+   MLX has higher-level packages like `mlx.nn` and `mlx.optimizers` with APIs
    that closely follow PyTorch to simplify building more complex models.
 
- - **Composable function transformations**: MLX has composable function
+ - **Composable function transformations**: MLX supports composable function
    transformations for automatic differentiation, automatic vectorization,
    and computation graph optimization.
 
  - **Lazy computation**: Computations in MLX are lazy. Arrays are only
    materialized when needed.
 
- - **Dynamic graph construction**: Computation graphs in MLX are built
+ - **Dynamic graph construction**: Computation graphs in MLX are constructed
    dynamically. Changing the shapes of function arguments does not trigger
    slow compilations, and debugging is simple and intuitive.
 
  - **Multi-device**: Operations can run on any of the supported devices
-   (currently the CPU and GPU).
+   (currently the CPU and the GPU).
 
- - **Unified memory**: A noteable difference from MLX and other frameworks
+ - **Unified memory**: A notable difference from MLX and other frameworks
    is the *unified memory model*. Arrays in MLX live in shared memory.
    Operations on MLX arrays can be performed on any of the supported
-   device types without moving data.
+   device types without transferring data.
 
 MLX is designed by machine learning researchers for machine learning
-researchers. The framework is intended to be user friendly, but still efficient
+researchers. The framework is intended to be user-friendly, but still efficient
 to train and deploy models. The design of the framework itself is also
 conceptually simple. We intend to make it easy for researchers to extend and
 improve MLX with the goal of quickly exploring new ideas. 
@@ -47,11 +49,11 @@ The design of MLX is inspired by frameworks like
 ## Examples
 
 The [MLX examples repo](https://github.com/ml-explore/mlx-examples) has a
-variety of examples including:
+variety of examples, including:
 
 - [Transformer language model](https://github.com/ml-explore/mlx-examples/tree/main/transformer_lm) training.
-- Large scale text generation with
-  [LLaMA](https://github.com/ml-explore/mlx-examples/tree/main/llama) and
+- Large-scale text generation with
+  [LLaMA](https://github.com/ml-explore/mlx-examples/tree/main/llms/llama) and
   finetuning with [LoRA](https://github.com/ml-explore/mlx-examples/tree/main/lora).
 - Generating images with [Stable Diffusion](https://github.com/ml-explore/mlx-examples/tree/main/stable_diffusion).
 - Speech recognition with [OpenAI's Whisper](https://github.com/ml-explore/mlx-examples/tree/main/whisper).
@@ -59,12 +61,12 @@ variety of examples including:
 ## Quickstart
 
 See the [quick start
-guide](https://ml-explore.github.io/mlx/build/html/quick_start.html)
+guide](https://ml-explore.github.io/mlx/build/html/usage/quick_start.html)
 in the documentation.
 
 ## Installation
 
-MLX is available on [PyPi](https://pypi.org/project/mlx/). To install the Python API run:
+MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:
 
 ```
 pip install mlx
@@ -77,4 +79,28 @@ for more information on building the C++ and Python APIs from source.
 ## Contributing 
 
 Check out the [contribution guidelines](CONTRIBUTING.md) for more information
-on contributing to MLX.
+on contributing to MLX. See the
+[docs](https://ml-explore.github.io/mlx/build/html/install.html) for more
+information on building from source, and running tests.
+
+We are grateful for all of [our
+contributors](ACKNOWLEDGMENTS.md#Individual-Contributors). If you contribute
+to MLX and wish to be acknowledged, please add your name to the list in your
+pull request.
+
+## Citing MLX
+
+The MLX software suite was initially developed with equal contribution by Awni
+Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
+MLX useful in your research and wish to cite it, please use the following
+BibTex entry:
+
+```
+@software{mlx2023,
+  author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
+  title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
+  url = {https://github.com/ml-explore},
+  version = {0.0},
+  year = {2023},
+}
+```

benchmarks/cpp/single_ops.cpp

@@ -233,6 +233,20 @@ void time_gather_scatter() {
   TIME(single_element_add);
 }
 
+void time_divmod() {
+  auto a = random::normal({1000});
+  auto b = random::normal({1000});
+  eval({a, b});
+
+  auto divmod_fused = [&a, &b]() { return divmod(a, b); };
+  TIME(divmod_fused);
+
+  auto divmod_separate = [&a, &b]() {
+    return std::vector<array>{floor_divide(a, b), remainder(a, b)};
+  };
+  TIME(divmod_separate);
+}
+
 int main() {
   std::cout << "Benchmarks for " << default_device() << std::endl;
   time_creation_ops();
@@ -246,4 +260,5 @@ int main() {
   time_matmul();
   time_reductions();
   time_gather_scatter();
+  time_divmod();
 }

benchmarks/numpy/single_ops.py

@@ -1,7 +1,6 @@
 # Copyright © 2023 Apple Inc.
 
 import numpy as np
-
 from time_utils import time_fn
 
 

benchmarks/python/batch_matmul_bench.py

@@ -1,8 +1,8 @@
 # Copyright © 2023 Apple Inc.
 
 import argparse
-import mlx.core as mx
 
+import mlx.core as mx
 from time_utils import time_fn
 
 B = 8
@@ -30,7 +30,7 @@ def time_batch_matmul():
     time_fn(batch_vjp_second)
 
 
-def time_unbatch_matmul(key):
+def time_unbatch_matmul():
     mx.random.seed(3)
     a = mx.random.uniform(shape=(B * T, D))
     b = mx.random.uniform(shape=(D, D))

benchmarks/python/blas/bench_gemm.py

@@ -1,13 +1,14 @@
 # Copyright © 2023 Apple Inc.
 
-import numpy as np
 import argparse
-import mlx.core as mx
-import time
-import torch
-import os
 import math
+import os
 import subprocess
+import time
+
+import mlx.core as mx
+import numpy as np
+import torch
 
 device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
 device_name = device_name.decode("utf-8").strip("\n")

benchmarks/python/blas/bench_gemv.py

@@ -1,14 +1,14 @@
 # Copyright © 2023 Apple Inc.
 
-import matplotlib.pyplot as plt
-import numpy as np
 import argparse
-import mlx.core as mx
-import time
-import torch
 import os
 import subprocess
+import time
 
+import matplotlib.pyplot as plt
+import mlx.core as mx
+import numpy as np
+import torch
 
 results_dir = "./results"
 
@@ -133,7 +133,7 @@ def get_gbyte_size(in_vec_len, out_vec_len, np_dtype):
     return float(N_iter_bench * N_iter_func * n_elem * item_size) / float(1024**3)
 
 
-def bench_with_in_len(ax, in_vec_len, out_vector_lens, dtype, tranpose):
+def bench_with_in_len(ax, in_vec_len, out_vector_lens, dtype, transpose):
     np_dtype = getattr(np, dtype)
     mlx_gb_s = []
     mlx_gflops = []
@@ -164,7 +164,7 @@ def bench_with_in_len(ax, in_vec_len, out_vector_lens, dtype, tranpose):
     ax.legend()
 
 
-def bench_with_out_len(ax, out_vec_len, in_vector_lens, dtype, tranpose):
+def bench_with_out_len(ax, out_vec_len, in_vector_lens, dtype, transpose):
     np_dtype = getattr(np, dtype)
     mlx_gb_s = []
     mlx_gflops = []

benchmarks/python/comparative/bench_mlx.py

@@ -4,8 +4,10 @@ import argparse
 import math
 import os
 import time
+from functools import partial
 
 import mlx.core as mx
+import mlx.nn as nn
 
 
 def int_or_list(x):
@@ -22,6 +24,16 @@ def none_or_list(x):
         return [int(xi) for xi in x.split(",")]
 
 
+def dtype_from_str(x):
+    if x == "":
+        return mx.float32
+    else:
+        dt = getattr(mx, x)
+        if not isinstance(dt, mx.Dtype):
+            raise ValueError(f"{x} is not an mlx dtype")
+        return dt
+
+
 def bench(f, *args):
     for i in range(10):
         f(*args)
@@ -48,6 +60,23 @@ def matmul(x, y):
     mx.eval(ys)
 
 
+def _quant_matmul(x, w, s, b, group_size, bits):
+    ys = []
+    for i in range(10):
+        ys.append(mx.quantized_matmul(x, w, s, b, group_size=group_size, bits=bits))
+    mx.eval(ys)
+
+
+quant_matmul = {
+    "quant_matmul_64_2": partial(_quant_matmul, group_size=64, bits=2),
+    "quant_matmul_64_4": partial(_quant_matmul, group_size=64, bits=4),
+    "quant_matmul_64_8": partial(_quant_matmul, group_size=64, bits=8),
+    "quant_matmul_128_2": partial(_quant_matmul, group_size=128, bits=2),
+    "quant_matmul_128_4": partial(_quant_matmul, group_size=128, bits=4),
+    "quant_matmul_128_8": partial(_quant_matmul, group_size=128, bits=8),
+}
+
+
 def conv1d(x, y):
     ys = []
     for i in range(10):
@@ -95,7 +124,77 @@ def softmax_fused(axis, x):
 def relu(x):
     y = x
     for i in range(100):
-        y = mx.maximum(y, 0)
+        y = nn.relu(y)
+    mx.eval(y)
+
+
+def leaky_relu(x: mx.array):
+    y = x
+    for i in range(100):
+        y = nn.leaky_relu(y)
+    mx.eval(y)
+
+
+def prelu(x: mx.array):
+    y = x
+    for i in range(100):
+        y = nn.prelu(y, mx.ones(1))
+    mx.eval(y)
+
+
+def softplus(x: mx.array):
+    y = x
+    for i in range(100):
+        y = nn.softplus(y)
+    mx.eval(y)
+
+
+def mish(x: mx.array):
+    y = x
+    for i in range(100):
+        y = nn.mish(y)
+    mx.eval(y)
+
+
+def leaky_relu(x):
+    y = x
+    for i in range(100):
+        y = nn.leaky_relu(y)
+    mx.eval(y)
+
+
+def elu(x):
+    y = x
+    for i in range(100):
+        y = nn.elu(y)
+    mx.eval(y)
+
+
+def relu6(x):
+    y = x
+    for i in range(100):
+        y = nn.relu6(y)
+    mx.eval(y)
+
+
+def softplus(x):
+    y = x
+    for i in range(100):
+        y = nn.softplus(y)
+    mx.eval(y)
+
+
+def celu(x):
+    y = x
+    for i in range(100):
+        y = nn.celu(y)
+    mx.eval(y)
+
+
+def log_sigmoid(x):
+    y = x
+    for i in range(100):
+        y = nn.log_sigmoid(y)
     mx.eval(y)
 
 
@@ -180,6 +279,20 @@ def topk(axis, x):
     mx.eval(ys)
 
 
+def step_function(x):
+    y = x
+    for i in range(100):
+        y = nn.step(x)
+    mx.eval(y)
+
+
+def selu(x):
+    y = x
+    for i in range(100):
+        y = nn.selu(x)
+    mx.eval(y)
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("benchmark", help="Choose the benchmark to run")
@@ -211,9 +324,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--fused", action="store_true", help="Use fused functions where possible"
     )
-    parser.add_argument(
-        "--dtype", choices=["float32", "float16", "bfloat16"], default="float32"
-    )
+    parser.add_argument("--dtype", type=dtype_from_str, default=[], action="append")
 
     args = parser.parse_args()
 
@@ -230,11 +341,15 @@ if __name__ == "__main__":
         mx.set_default_device(mx.cpu)
     else:
         mx.set_default_device(mx.gpu)
-    dtype = dict(float32=mx.float32, float16=mx.float16, bfloat16=mx.bfloat16)[
-        args.dtype
-    ]
+
+    types = args.dtype
+    if not types:
+        types = [mx.float32]
+    if len(types) < len(args.size):
+        types = types + [types[0]] * (len(args.size) - len(types))
+
     xs = []
-    for size in args.size:
+    for size, dtype in zip(args.size, types):
         xs.append(mx.random.normal(size).astype(dtype))
     for i, t in enumerate(args.transpose):
         if t is None:
@@ -250,6 +365,9 @@ if __name__ == "__main__":
     elif args.benchmark == "matmul":
         print(bench(matmul, *xs))
 
+    elif args.benchmark.startswith("quant_matmul"):
+        print(bench(quant_matmul[args.benchmark], *xs))
+
     elif args.benchmark == "linear":
         print(bench(linear, *xs))
 
@@ -277,6 +395,26 @@ if __name__ == "__main__":
     elif args.benchmark == "relu":
         print(bench(relu, x))
 
+    elif args.benchmark == "elu":
+        print(bench(elu, x))
+
+    elif args.benchmark == "relu6":
+        print(bench(relu6, x))
+
+    elif args.benchmark == "celu":
+        print(bench(celu, x))
+
+    elif args.benchmark == "log_sigmoid":
+        print(bench(log_sigmoid, x))
+
+    elif args.benchmark == "leaky_relu":
+        print(bench(leaky_relu, x))
+    elif args.benchmark == "prelu":
+        print(bench(prelu, x))
+    elif args.benchmark == "softplus":
+        print(bench(softplus, x))
+    elif args.benchmark == "mish":
+        print(bench(mish, x))
     elif args.benchmark == "scalar_mul":
         print(bench(scalar_mult, x))
 
@@ -311,5 +449,11 @@ if __name__ == "__main__":
     elif args.benchmark == "topk":
         print(bench(topk, axis, x))
 
+    elif args.benchmark == "step":
+        print(bench(step_function, x))
+
+    elif args.benchmark == "selu":
+        print(bench(selu, x))
+
     else:
         raise ValueError("Unknown benchmark")

benchmarks/python/comparative/bench_torch.py

@@ -22,6 +22,16 @@ def none_or_list(x):
         return [int(xi) for xi in x.split(",")]
 
 
+def dtype_from_str(x):
+    if x == "":
+        return torch.float32
+    else:
+        dt = getattr(torch, x)
+        if not isinstance(dt, torch.dtype):
+            raise ValueError(f"{x} is not a torch dtype")
+        return dt
+
+
 def bench(f, *args):
     for i in range(10):
         f(*args)
@@ -115,6 +125,70 @@ def relu(x):
     sync_if_needed(x)
 
 
+@torch.no_grad()
+def leaky_relu(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.leaky_relu(y)
+    sync_if_needed(x)
+
+
+@torch.no_grad()
+def elu(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.elu(y)
+    sync_if_needed(x)
+
+
+@torch.no_grad()
+def celu(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.celu(y)
+    sync_if_needed(x)
+
+
+@torch.no_grad()
+def relu6(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.relu6(y)
+    sync_if_needed(x)
+
+
+@torch.no_grad()
+def softplus(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.softplus(y)
+    sync_if_needed(x)
+
+
+@torch.no_grad()
+def log_sigmoid(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.logsigmoid(y)
+    sync_if_needed(x)
+
+
+@torch.no_grad()
+def prelu(x: torch.Tensor) -> torch.Tensor:
+    y = x
+    for _ in range(100):
+        y = torch.nn.functional.prelu(y, torch.ones(1).to(y.device))
+    sync_if_needed(x)
+
+
+@torch.no_grad()
+def mish(x: torch.Tensor) -> torch.Tensor:
+    y = x
+    for _ in range(100):
+        return torch.nn.functional.mish(y)
+    sync_if_needed(x)
+
+
 @torch.no_grad()
 def scalar_mult(x):
     y = x
@@ -209,6 +283,14 @@ def topk(axis, x):
     sync_if_needed(x)
 
 
+@torch.no_grad()
+def selu(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.selu(y)
+    sync_if_needed(x)
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("benchmark", help="Choose the benchmark to run")
@@ -240,7 +322,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--fused", action="store_true", help="Use fused functions where possible"
     )
-    parser.add_argument("--dtype", choices=["float32", "float16"], default="float32")
+    parser.add_argument("--dtype", type=dtype_from_str, default=[], action="append")
 
     args = parser.parse_args()
 
@@ -255,9 +337,15 @@ if __name__ == "__main__":
 
     torch.set_num_threads(1)
     device = "cpu" if args.cpu else "mps"
-    dtype = dict(float32=torch.float32, float16=torch.float16)[args.dtype]
+
+    types = args.dtype
+    if not types:
+        types = [torch.float32]
+    if len(types) < len(args.size):
+        types = types + [types[0]] * (len(args.size) - len(types))
+
     xs = []
-    for size in args.size:
+    for size, dtype in zip(args.size, types):
         xs.append(torch.randn(*size).to(device).to(dtype))
     for i, t in enumerate(args.transpose):
         if t is None:
@@ -302,6 +390,28 @@ if __name__ == "__main__":
     elif args.benchmark == "relu":
         print(bench(relu, x))
 
+    elif args.benchmark == "leaky_relu":
+        print(bench(leaky_relu, x))
+
+    elif args.benchmark == "elu":
+        print(bench(elu, x))
+
+    elif args.benchmark == "relu6":
+        print(bench(relu6, x))
+
+    elif args.benchmark == "softplus":
+        print(bench(softplus, x))
+
+    elif args.benchmark == "celu":
+        print(bench(celu, x))
+
+    elif args.benchmark == "log_sigmoid":
+        print(bench(log_sigmoid, x))
+
+    elif args.benchmark == "prelu":
+        print(bench(prelu, x))
+    elif args.benchmark == "mish":
+        print(bench(mish, x))
     elif args.benchmark == "scalar_mul":
         print(bench(scalar_mult, x))
 

benchmarks/python/comparative/compare.py

@@ -62,7 +62,7 @@ def make_predicate(positive_filter, negative_filter):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run comparisons agains PyTorch")
+    parser = argparse.ArgumentParser(description="Run comparisons against PyTorch")
     parser.add_argument(
         "--filter", "-f", help="Regex filter to select benchmarks", nargs="+"
     )
@@ -125,6 +125,14 @@ if __name__ == "__main__":
     compare_filtered("sum_axis --size 16x128x1024 --axis 1")
     compare_filtered("sum_axis --size 16x128x1024 --axis 0 --cpu")
     compare_filtered("sum_axis --size 16x128x1024 --axis 0")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,1 --cpu")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,1")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,2 --cpu")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,2")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,1 --transpose 0,2,1 --cpu")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,1 --transpose 0,2,1")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,2 --transpose 0,2,1 --cpu")
+    compare_filtered("sum_axis --size 16x128x1024 --axis 0,2 --transpose 0,2,1")
     compare_filtered("argmax --size 10x1024x128 --axis 1 --cpu")
     compare_filtered("argmax --size 10x1024x128 --axis 1")
     compare_filtered("argmax --size 10x1024x128 --axis 2 --cpu")
@@ -193,6 +201,27 @@ if __name__ == "__main__":
     compare_filtered("softmax --size 2x1024x1024 --axis 1 --fused --cpu")
     compare_filtered("relu --size 32x16x1024")
     compare_filtered("relu --size 32x16x1024 --cpu")
+    compare_filtered("leaky_relu --size 32x16x1024")
+    compare_filtered("leaky_relu --size 32x16x1024 --cpu")
+    compare_filtered("elu --size 32x16x1024")
+    compare_filtered("elu --size 32x16x1024 --cpu")
+    compare_filtered("relu6 --size 32x16x1024")
+    compare_filtered("relu6 --size 32x16x1024 --cpu")
+    compare_filtered("softplus --size 32x16x1024")
+    compare_filtered("softplus --size 32x16x1024 --cpu")
+    compare_filtered("celu --size 32x16x1024")
+    compare_filtered("celu --size 32x16x1024 --cpu")
+    compare_filtered("log_sigmoid --size 32x16x1024")
+    compare_filtered("log_sigmoid --size 32x16x1024 --cpu")
+    compare_filtered("step --size 32x16x1024")
+    compare_filtered("step --size 32x16x1024 --cpu")
+    compare_filtered("selu --size 32x16x1024")
+    compare_filtered("selu --size 32x16x1024 --cpu")
+    # compare_filtered("mish --size 32x16x1024") NOTE: Torch does not implement Mish in MPS atm
+    compare_filtered("mish --size 32x16x1024 --cpu")
+    compare_filtered("prelu --size 32x16x1024")
+    compare_filtered("prelu --size 32x16x1024 --cpu")
+
     compare_filtered("scalar_mul --size 32x16x1024")
     compare_filtered("scalar_mul --size 32x16x1024 --cpu")
     compare_filtered("cross_entropy --size 256x1024")

benchmarks/python/llama_torch_bench.py

@@ -4,8 +4,8 @@ import math
 import time
 
 import torch
-import torch.nn as nn
 import torch.mps
+import torch.nn as nn
 
 
 def sync_if_needed(x):

benchmarks/python/single_ops.py

@@ -1,8 +1,8 @@
 # Copyright © 2023 Apple Inc.
 
 import argparse
-import mlx.core as mx
 
+import mlx.core as mx
 from time_utils import time_fn
 
 

cmake/extension.cmake

@@ -12,7 +12,7 @@ include(CMakeParseArguments)
 #     OUTPUT_DIRECTORY: Where to place ${TITLE}.metallib
 #     SOURCES: List of source files
 #     INCLUDE_DIRS: List of include dirs
-#     DEPS: List of depedency files (like headers)
+#     DEPS: List of dependency files (like headers)
 #
 macro(mlx_build_metallib)
   # Parse args
@@ -32,7 +32,7 @@ macro(mlx_build_metallib)
   # Collect compile options 
   set(MTLLIB_COMPILE_OPTIONS -Wall -Wextra -fno-fast-math)
 
-  # Prepare metllib build command
+  # Prepare metallib build command
   add_custom_command(
     OUTPUT ${MTLLIB_BUILD_TARGET}
     COMMAND xcrun -sdk macosx metal 

docs/.gitignore

@@ -1 +1,2 @@
 src/python/_autosummary*/
+src/python/nn/_autosummary*/

docs/README.md

@@ -26,7 +26,7 @@ python -m http.server <port>
 
 and point your browser to `http://localhost:<port>`.
 
-### Push to Github Pages
+### Push to GitHub Pages
 
 Check-out the `gh-pages` branch (`git switch gh-pages`) and build
 the docs. Then force add the `build/html` directory:

docs/src/_templates/module-base-class.rst

@@ -0,0 +1,33 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. add toctree option to make autodoc generate the pages
+
+.. autoclass:: {{ objname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: Attributes
+
+   .. autosummary::
+      :toctree: .
+   {% for item in attributes %}
+      ~{{ fullname }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block methods %}
+   {% if methods %}
+   .. rubric:: Methods
+
+   .. autosummary::
+      :toctree: .
+   {% for item in methods %}
+      {%- if item not in inherited_members and item != '__init__' %}
+      ~{{ fullname }}.{{ item }}
+      {%- endif -%}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}

docs/src/conf.py

@@ -10,8 +10,8 @@ import subprocess
 project = "MLX"
 copyright = "2023, MLX Contributors"
 author = "MLX Contributors"
-version = "0.0.3"
-release = "0.0.3"
+version = "0.0.9"
+release = "0.0.9"
 
 # -- General configuration ---------------------------------------------------
 

docs/src/dev/extensions.rst

@@ -15,7 +15,7 @@ Introducing the Example
 -----------------------
 
 Let's say that you would like an operation that takes in two arrays, 
-``x`` and ``y``, scales them both by some coefficents ``alpha`` and ``beta``
+``x`` and ``y``, scales them both by some coefficients ``alpha`` and ``beta``
 respectively, and then adds them together to get the result 
 ``z = alpha * x + beta * y``. Well, you can very easily do that by just 
 writing out a function as follows:
@@ -69,7 +69,7 @@ C++ API:
 .. code-block:: C++
 
     /**
-    *  Scale and sum two vectors elementwise
+    *  Scale and sum two vectors element-wise
     *  z = alpha * x + beta * y
     *
     *  Follow numpy style broadcasting between x and y
@@ -131,7 +131,7 @@ back and go to our example to give ourselves a more concrete image.
         * A primitive must know how to evaluate itself on the CPU/GPU
         * for the given inputs and populate the output array.
         *
-        * To avoid unecessary allocations, the evaluation function
+        * To avoid unnecessary allocations, the evaluation function
         * is responsible for allocating space for the array.
         */
         void eval_cpu(const std::vector<array>& inputs, array& out) override;
@@ -150,7 +150,7 @@ back and go to our example to give ourselves a more concrete image.
             const std::vector<int>& argnums) override;
 
         /**
-        * The primitive must know how to vectorize itself accross
+        * The primitive must know how to vectorize itself across
         * the given axes. The output is a pair containing the array
         * representing the vectorized computation and the axis which
         * corresponds to the output vectorized dimension.
@@ -230,7 +230,7 @@ Let's re-implement our operation now in terms of our :class:`Axpby` primitive.
 
 This operation now handles the following:
 
-#. Upcast inputs and resolve the the output data type.
+#. Upcast inputs and resolve the output data type.
 #. Broadcast the inputs and resolve the output shape.
 #. Construct the primitive :class:`Axpby` using the given stream, ``alpha``, and ``beta``.
 #. Construct the output :class:`array` using the primitive and the inputs.
@@ -284,14 +284,14 @@ pointwise. This is captured in the templated function :meth:`axpby_impl`.
         T alpha = static_cast<T>(alpha_);
         T beta = static_cast<T>(beta_);
 
-        // Do the elementwise operation for each output
+        // Do the element-wise operation for each output
         for (size_t out_idx = 0; out_idx < out.size(); out_idx++) {
             // Map linear indices to offsets in x and y
             auto x_offset = elem_to_loc(out_idx, x.shape(), x.strides());
             auto y_offset = elem_to_loc(out_idx, y.shape(), y.strides());
 
             // We allocate the output to be contiguous and regularly strided
-            // (defaults to row major) and hence it doesn't need additonal mapping
+            // (defaults to row major) and hence it doesn't need additional mapping
             out_ptr[out_idx] = alpha * x_ptr[x_offset] + beta * y_ptr[y_offset];
         }
     }
@@ -305,7 +305,7 @@ if we encounter an unexpected type.
 
     /** Fall back implementation for evaluation on CPU */
     void Axpby::eval(const std::vector<array>& inputs, array& out) {
-        // Check the inputs (registered in the op while contructing the out array)
+        // Check the inputs (registered in the op while constructing the out array)
         assert(inputs.size() == 2);
         auto& x = inputs[0];
         auto& y = inputs[1];
@@ -485,7 +485,7 @@ each data type.
 
     instantiate_axpby(float32, float);
     instantiate_axpby(float16, half);
-    instantiate_axpby(bflot16, bfloat16_t);
+    instantiate_axpby(bfloat16, bfloat16_t);
     instantiate_axpby(complex64, complex64_t);
 
 This kernel will be compiled into a metal library ``mlx_ext.metallib`` as we 
@@ -537,7 +537,7 @@ below.
         compute_encoder->setComputePipelineState(kernel);
 
         // Kernel parameters are registered with buffer indices corresponding to
-        // those in the kernel decelaration at axpby.metal
+        // those in the kernel declaration at axpby.metal
         int ndim = out.ndim();
         size_t nelem = out.size();
 
@@ -568,7 +568,7 @@ below.
         // Fix the 3D size of the launch grid (in terms of threads)
         MTL::Size grid_dims = MTL::Size(nelem, 1, 1);
 
-        // Launch the grid with the given number of threads divded among
+        // Launch the grid with the given number of threads divided among
         // the given threadgroups
         compute_encoder->dispatchThreads(grid_dims, group_dims);
     }
@@ -581,7 +581,7 @@ to give us the active metal compute command encoder instead of building a
 new one and calling :meth:`compute_encoder->end_encoding` at the end. 
 MLX keeps adding kernels (compute pipelines) to the active command encoder 
 until some specified limit is hit or the compute encoder needs to be flushed 
-for synchronization. MLX also handles enqueuing and commiting the associated 
+for synchronization. MLX also handles enqueuing and committing the associated 
 command buffers as needed. We suggest taking a deeper dive into 
 :class:`metal::Device` if you would like to study this routine further.
 
@@ -601,8 +601,8 @@ us the following :meth:`Axpby::jvp` and :meth:`Axpby::vjp` implementations.
             const std::vector<array>& tangents,
             const std::vector<int>& argnums) {
         // Forward mode diff that pushes along the tangents
-        // The jvp transform on the the primitive can built with ops
-        // that are scheduled on the same stream as the primtive
+        // The jvp transform on the primitive can built with ops
+        // that are scheduled on the same stream as the primitive
 
         // If argnums = {0}, we only push along x in which case the
         // jvp is just the tangent scaled by alpha
@@ -642,7 +642,7 @@ own :class:`Primitive`.
 
 .. code-block:: C++
 
-    /** Vectorize primitve along given axis */
+    /** Vectorize primitive along given axis */
     std::pair<array, int> Axpby::vmap(
             const std::vector<array>& inputs,
             const std::vector<int>& axes) {
@@ -666,7 +666,7 @@ Let's look at the overall directory structure first.
 | └── setup.py
 
 * ``extensions/axpby/`` defines the C++ extension library
-* ``extensions/mlx_sample_extensions`` sets out the strucutre for the 
+* ``extensions/mlx_sample_extensions`` sets out the structure for the 
   associated python package
 * ``extensions/bindings.cpp`` provides python bindings for our operation
 * ``extensions/CMakeLists.txt`` holds CMake rules to build the library and 
@@ -697,7 +697,7 @@ are already provided, adding our :meth:`axpby` becomes very simple!
             py::kw_only(),
             "stream"_a = py::none(),
             R"pbdoc(
-                Scale and sum two vectors elementwise
+                Scale and sum two vectors element-wise
                 ``z = alpha * x + beta * y``
                 
                 Follows numpy style broadcasting between ``x`` and ``y``
@@ -840,7 +840,7 @@ This will result in a directory structure as follows:
 | ...
 
 When you try to install using the command ``python -m pip install .`` 
-(in ``extensions/``), the package will be installed with the same strucutre as 
+(in ``extensions/``), the package will be installed with the same structure as 
 ``extensions/mlx_sample_extensions`` and the C++ and metal library will be 
 copied along with the python binding since they are specified as ``package_data``.
 
@@ -945,4 +945,4 @@ Scripts
 .. _Metal-cpp: https://developer.apple.com/metal/cpp/
 .. _`Metal Specification`: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
 .. _`Metal Example`: https://developer.apple.com/documentation/metal/performing_calculations_on_a_gpu?language=objc
-.. _PyBind11: https://pybind11.readthedocs.io/en/stable/
+.. _PyBind11: https://pybind11.readthedocs.io/en/stable/

docs/src/examples/llama-inference.rst

@@ -321,7 +321,7 @@ which can then be used to update the model. Note that the method above incurs
 several unnecessary copies from disk to numpy and then from numpy to MLX. It
 will be replaced in the future with direct loading to MLX.
 
-You can download the full example code in `mlx-examples <code>`_. Assuming, the
+You can download the full example code in `mlx-examples`_. Assuming, the
 existence of ``weights.pth`` and ``tokenizer.model`` in the current working
 directory we can play around with our inference script as follows (the timings
 are representative of an M1 Ultra and the 7B parameter Llama model):
@@ -369,9 +369,9 @@ Scripts
 
 .. admonition:: Download the code
 
-   The full example code is available in `mlx-examples <code>`_.
+   The full example code is available in `mlx-examples`_.
 
-.. code: `https://github.com/ml-explore/mlx-examples/tree/main/llama`_
+.. _mlx-examples: https://github.com/ml-explore/mlx-examples/tree/main/llms/llama
 
 .. [1] Su, J., Lu, Y., Pan, S., Murtadha, A., Wen, B. and Liu, Y., 2021.
    Roformer: Enhanced transformer with rotary position embedding. arXiv

docs/src/examples/mlp.rst

@@ -61,7 +61,10 @@ set:
   def eval_fn(model, X, y):
       return mx.mean(mx.argmax(model(X), axis=1) == y)
 
-Next, setup the problem parameters and load the data:
+Next, setup the problem parameters and load the data. To load the data, you need our
+`mnist data loader
+<https://github.com/ml-explore/mlx-examples/blob/main/mnist/mnist.py>`_, which
+we will import as `mnist`.
 
 .. code-block:: python
 
@@ -127,5 +130,5 @@ Finally, we put it all together by instantiating the model, the
   This should not be confused with :func:`mlx.core.value_and_grad`.
 
 The model should train to a decent accuracy (about 95%) after just a few passes
-over the training set. The `full example <https://github.com/ml-explore/mlx-examples/tree/main/mlp>`_
+over the training set. The `full example <https://github.com/ml-explore/mlx-examples/tree/main/mnist>`_
 is available in the MLX GitHub repo.

docs/src/index.rst

@@ -19,7 +19,7 @@ The main differences between MLX and NumPy are:
 
 The design of MLX is inspired by frameworks like `PyTorch
 <https://pytorch.org/>`_, `Jax <https://github.com/google/jax>`_, and
-`ArrayFire <https://arrayfire.org/>`_. A noteable difference from these
+`ArrayFire <https://arrayfire.org/>`_. A notable difference from these
 frameworks and MLX is the *unified memory model*. Arrays in MLX live in shared
 memory. Operations on MLX arrays can be performed on any of the supported
 device types without performing data copies. Currently supported device types
@@ -35,8 +35,13 @@ are the CPU and GPU.
    :caption: Usage 
    :maxdepth: 1
 
-   quick_start
-   using_streams
+   usage/quick_start
+   usage/lazy_evaluation
+   usage/unified_memory
+   usage/indexing
+   usage/saving_and_loading
+   usage/numpy
+   usage/using_streams
 
 .. toctree::
    :caption: Examples
@@ -56,6 +61,7 @@ are the CPU and GPU.
    python/random
    python/transforms
    python/fft
+   python/linalg
    python/nn
    python/optimizers
    python/tree_utils

docs/src/install.rst

@@ -11,6 +11,33 @@ silicon computer is
 
     pip install mlx
 
+To install from PyPI you must meet the following requirements:
+
+- Using an M series chip (Apple silicon)
+- Using a native Python >= 3.8
+- macOS >= 13.3
+
+.. note::
+    MLX is only available on devices running macOS >= 13.3 
+    It is highly recommended to use macOS 14 (Sonoma)
+
+Troubleshooting
+^^^^^^^^^^^^^^^
+
+*My OS and Python versions are in the required range but pip still does not find
+a matching distribution.*
+
+Probably you are using a non-native Python. The output of
+
+.. code-block:: shell
+
+  python -c "import platform; print(platform.processor())"
+
+should be ``arm``. If it is ``i386`` (and you have M series machine) then you
+are using a non-native Python. Switch your Python to a native Python. A good
+way to do this is with `Conda <https://stackoverflow.com/q/65415996>`_.
+
+
 Build from source
 -----------------
 
@@ -19,7 +46,11 @@ Build Requirements
 
 - A C++ compiler with C++17 support (e.g. Clang >= 5.0)
 - `cmake <https://cmake.org/>`_ -- version 3.24 or later, and ``make``
+- Xcode >= 14.3 (Xcode >= 15.0 for macOS 14 and above)
 
+.. note::
+   Ensure your shell environment is native ``arm``, not ``x86`` via Rosetta. If
+   the output of ``uname -p`` is ``x86``, see the :ref:`troubleshooting section <build shell>` below.
 
 Python API
 ^^^^^^^^^^
@@ -56,8 +87,16 @@ To make sure the install is working run the tests with:
 
 .. code-block:: shell
 
+  pip install ".[testing]"
   python -m unittest discover python/tests
 
+Optional: Install stubs to enable auto completions and type checking from your IDE:
+
+.. code-block:: shell
+
+  pip install ".[dev]"
+  python setup.py generate_stubs
+
 C++ API
 ^^^^^^^
 
@@ -111,3 +150,66 @@ should point to the path to the built metal library.
      - ON
    * - MLX_BUILD_PYTHON_BINDINGS
      - OFF
+
+
+.. note::
+
+    If you have multiple Xcode installations and wish to use 
+    a specific one while building, you can do so by adding the 
+    following environment variable before building 
+
+    .. code-block:: shell
+
+      export DEVELOPER_DIR="/path/to/Xcode.app/Contents/Developer/"
+
+    Further, you can use the following command to find out which 
+    macOS SDK will be used
+
+    .. code-block:: shell
+
+      xcrun -sdk macosx --show-sdk-version
+
+Troubleshooting
+^^^^^^^^^^^^^^^
+
+
+Metal not found
+~~~~~~~~~~~~~~~
+
+You see the following error when you try to build:
+
+.. code-block:: shell
+
+  error: unable to find utility "metal", not a developer tool or in PATH
+
+To fix this, first make sure you have Xcode installed:
+
+.. code-block:: shell
+
+  xcode-select --install
+
+Then set the active developer directory:
+
+.. code-block:: shell
+
+  sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer
+
+x86 Shell 
+~~~~~~~~~
+
+.. _build shell:
+
+If the ouptut of ``uname -p``  is ``x86`` then your shell is running as x86 via
+Rosetta instead of natively.
+
+To fix this, find the application in Finder (``/Applications`` for iTerm,
+``/Applications/Utilities`` for Terminal), right-click, and click “Get Info”.
+Uncheck “Open using Rosetta”, close the “Get Info” window, and restart your
+terminal.
+
+Verify the terminal is now running natively the following command:
+
+.. code-block:: shell
+
+  $ uname -p
+  arm

docs/src/python/array.rst

@@ -34,6 +34,7 @@ Array
     array.prod
     array.reciprocal
     array.reshape
+    array.round
     array.rsqrt
     array.sin
     array.split

docs/src/python/data_types.rst

@@ -29,9 +29,9 @@ The default floating point type is ``float32`` and the default integer type is
    * - ``uint32``
      - 4 
      - 32-bit unsigned integer 
-   * - ``uint32``
+   * - ``uint64``
      - 8 
-     - 32-bit unsigned integer 
+     - 64-bit unsigned integer 
    * - ``int8``
      - 1 
      - 8-bit signed integer 

docs/src/python/linalg.rst

@@ -0,0 +1,11 @@
+.. _linalg:
+
+Linear Algebra
+==============
+
+.. currentmodule:: mlx.core.linalg
+
+.. autosummary:: 
+   :toctree: _autosummary 
+
+    norm

docs/src/python/nn.rst

@@ -64,7 +64,6 @@ Quick Start with Neural Networks
     # gradient with respect to `mlp.trainable_parameters()`
     loss_and_grad = nn.value_and_grad(mlp, l2_loss)
 
-
 .. _module_class:
 
 The Module Class
@@ -86,20 +85,58 @@ name should not start with ``_``). It can be arbitrarily nested in other
 :meth:`Module.parameters` can be used to extract a nested dictionary with all
 the parameters of a module and its submodules.
 
-A :class:`Module` can also keep track of "frozen" parameters.
-:meth:`Module.trainable_parameters` returns only the subset of
-:meth:`Module.parameters` that is not frozen. When using
-:meth:`mlx.nn.value_and_grad` the gradients returned will be with respect to these
-trainable parameters.
+A :class:`Module` can also keep track of "frozen" parameters. See the
+:meth:`Module.freeze` method for more details. :meth:`mlx.nn.value_and_grad`
+the gradients returned will be with respect to these trainable parameters.
 
-Updating the parameters
+
+Updating the Parameters
 ^^^^^^^^^^^^^^^^^^^^^^^
 
 MLX modules allow accessing and updating individual parameters. However, most
 times we need to update large subsets of a module's parameters. This action is
-performed by :meth:`Module.update`. 
+performed by :meth:`Module.update`.
 
-Value and grad
+
+Inspecting Modules
+^^^^^^^^^^^^^^^^^^
+
+The simplest way to see the model architecture is to print it. Following along with
+the above example, you can print the ``MLP`` with:
+
+.. code-block:: python
+
+  print(mlp)
+
+This will display:
+
+.. code-block:: shell
+
+  MLP(
+    (layers.0): Linear(input_dims=2, output_dims=128, bias=True)
+    (layers.1): Linear(input_dims=128, output_dims=128, bias=True)
+    (layers.2): Linear(input_dims=128, output_dims=10, bias=True)
+  )
+
+To get more detailed information on the arrays in a :class:`Module` you can use
+:func:`mlx.utils.tree_map` on the parameters. For example, to see the shapes of
+all the parameters in a :class:`Module` do:
+
+.. code-block:: python
+
+   from mlx.utils import tree_map
+   shapes = tree_map(lambda p: p.shape, mlp.parameters())
+
+As another example, you can count the number of parameters in a :class:`Module`
+with:
+
+.. code-block:: python
+
+   from mlx.utils import tree_flatten
+   num_params = sum(v.size for _, v in tree_flatten(mlp.parameters()))
+
+
+Value and Grad
 --------------
 
 Using a :class:`Module` does not preclude using MLX's high order function
@@ -137,36 +174,9 @@ In detail:
 
    value_and_grad
 
-Neural Network Layers
----------------------
+.. toctree::
 
-.. autosummary::
-   :toctree: _autosummary
-   :template: nn-module-template.rst
-
-   Embedding
-   ReLU
-   GELU
-   SiLU
-   Linear
-   Conv1d
-   Conv2d
-   LayerNorm
-   RMSNorm
-   GroupNorm
-   RoPE
-   MultiHeadAttention
-   Sequential
-
-Layers without parameters (e.g. activation functions) are also provided as
-simple functions.
-
-.. autosummary::
-   :toctree: _autosummary_functions
-   :template: nn-module-template.rst
-
-   gelu
-   gelu_approx
-   gelu_fast_approx
-   relu
-   silu
+   nn/module
+   nn/layers
+   nn/functions
+   nn/losses

docs/src/python/nn/functions.rst

@@ -0,0 +1,23 @@
+.. _nn_functions:
+
+.. currentmodule:: mlx.nn
+
+Functions
+---------
+
+Layers without parameters (e.g. activation functions) are also provided as
+simple functions.
+
+.. autosummary::
+   :toctree: _autosummary_functions
+   :template: nn-module-template.rst
+
+   gelu
+   gelu_approx
+   gelu_fast_approx
+   relu
+   prelu
+   silu
+   step
+   selu
+   mish

docs/src/python/nn/layers.rst

@@ -0,0 +1,37 @@
+.. _layers:
+
+.. currentmodule:: mlx.nn
+
+Layers
+------
+
+.. autosummary::
+   :toctree: _autosummary
+   :template: nn-module-template.rst
+
+   Sequential
+   ReLU
+   PReLU
+   GELU
+   SiLU
+   Step
+   SELU
+   Mish
+   Embedding
+   Linear
+   QuantizedLinear
+   Conv1d
+   Conv2d
+   BatchNorm
+   LayerNorm
+   RMSNorm
+   GroupNorm
+   InstanceNorm
+   Dropout
+   Dropout2d
+   Dropout3d
+   Transformer
+   MultiHeadAttention
+   ALiBi
+   RoPE
+   SinusoidalPositionalEncoding

docs/src/python/nn/losses.rst

@@ -0,0 +1,23 @@
+.. _losses:
+
+.. currentmodule:: mlx.nn.losses
+
+Loss Functions
+--------------
+
+.. autosummary::
+   :toctree: _autosummary_functions
+   :template: nn-module-template.rst
+
+   binary_cross_entropy
+   cross_entropy
+   kl_div_loss
+   l1_loss
+   mse_loss
+   nll_loss
+   smooth_l1_loss
+   triplet_loss
+   hinge_loss
+   huber_loss
+   log_cosh_loss
+   cosine_similarity_loss

docs/src/python/nn/module.rst

@@ -1,7 +1,36 @@
-mlx.nn.Module
-=============
+Module
+======
 
 .. currentmodule:: mlx.nn
 
 .. autoclass:: Module
-   :members:
+
+   .. rubric:: Attributes
+
+   .. autosummary::
+      :toctree: _autosummary
+   
+      Module.training
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+      :toctree: _autosummary
+   
+      Module.apply
+      Module.apply_to_modules
+      Module.children
+      Module.eval
+      Module.filter_and_map
+      Module.freeze
+      Module.leaf_modules
+      Module.load_weights
+      Module.modules
+      Module.named_modules
+      Module.parameters
+      Module.save_weights
+      Module.train
+      Module.trainable_parameters
+      Module.unfreeze
+      Module.update
+      Module.update_modules

docs/src/python/ops.rst

@@ -26,23 +26,34 @@ Operations
    argsort
    array_equal
    broadcast_to
+   ceil
+   clip
    concatenate
    convolve
    conv1d
    conv2d
    cos
    cosh
+   dequantize
    divide
+   divmod
    equal
    erf
    erfinv
    exp
    expand_dims
+   eye
+   flatten
+   floor
+   floor_divide
    full
    greater
    greater_equal
+   identity
+   inner
    less
    less_equal
+   linspace
    load
    log
    log2
@@ -50,6 +61,8 @@ Operations
    log1p
    logaddexp
    logical_not
+   logical_and
+   logical_or
    logsumexp
    matmul
    max
@@ -57,19 +70,27 @@ Operations
    mean
    min
    minimum
+   moveaxis
    multiply
    negative
    ones
    ones_like
+   outer
    partition
    pad
    prod
+   quantize
+   quantized_matmul
    reciprocal
+   repeat
    reshape
+   round
    rsqrt
    save
    savez
    savez_compressed
+   save_gguf
+   save_safetensors
    sigmoid
    sign
    sin
@@ -80,14 +101,20 @@ Operations
    sqrt
    square
    squeeze
+   stack
    stop_gradient
    subtract
    sum
+   swapaxes
    take
    take_along_axis
    tan
    tanh
+   tensordot
    transpose
+   tri
+   tril
+   triu
    var
    where
    zeros

docs/src/python/optimizers.rst

@@ -38,4 +38,10 @@ model's parameters and the **optimizer state**.
    OptimizerState
    Optimizer
    SGD
+   RMSprop
+   Adagrad
+   AdaDelta
    Adam
+   AdamW
+   Adamax
+   Lion

docs/src/python/transforms.rst

@@ -14,3 +14,4 @@ Transforms
    jvp
    vjp
    vmap
+   simplify

docs/src/usage/indexing.rst

@@ -0,0 +1,123 @@
+.. _indexing:
+
+Indexing Arrays
+===============
+
+.. currentmodule:: mlx.core
+
+For the most part, indexing an MLX :obj:`array` works the same as indexing a
+NumPy :obj:`numpy.ndarray`. See the `NumPy documentation
+<https://numpy.org/doc/stable/user/basics.indexing.html>`_ for more details on
+how that works.
+
+For example, you can use regular integers and slices (:obj:`slice`) to index arrays:
+
+.. code-block:: shell
+
+  >>> arr = mx.arange(10)
+  >>> arr[3]
+  array(3, dtype=int32)
+  >>> arr[-2]  # negative indexing works
+  array(8, dtype=int32)
+  >>> arr[2:8:2] # start, stop, stride
+  array([2, 4, 6], dtype=int32)
+
+For multi-dimensional arrays, the ``...`` or :obj:`Ellipsis` syntax works as in NumPy:
+
+.. code-block:: shell
+
+  >>> arr = mx.arange(8).reshape(2, 2, 2)
+  >>> arr[:, :, 0]
+  array(3, dtype=int32)
+  array([[0, 2],
+         [4, 6]], dtype=int32
+  >>> arr[..., 0]
+  array([[0, 2],
+         [4, 6]], dtype=int32
+
+You can index with ``None`` to create a new axis:
+
+.. code-block:: shell
+
+  >>> arr = mx.arange(8)
+  >>> arr.shape
+  [8]
+  >>> arr[None].shape
+  [1, 8]
+
+
+You can also use an :obj:`array` to index another :obj:`array`:
+
+.. code-block:: shell
+
+  >>> arr = mx.arange(10)
+  >>> idx = mx.array([5, 7]) 
+  >>> arr[idx]
+  array([5, 7], dtype=int32)
+
+Mixing and matching integers, :obj:`slice`, ``...``, and :obj:`array` indices
+works just as in NumPy.
+
+Other functions which may be useful for indexing arrays are :func:`take` and
+:func:`take_along_axis`.
+
+Differences from NumPy
+----------------------
+
+.. Note::
+
+  MLX indexing is different from NumPy indexing in two important ways:
+
+  * Indexing does not perform bounds checking. Indexing out of bounds is
+    undefined behavior.
+  * Boolean mask based indexing is not yet supported.
+
+The reason for the lack of bounds checking is that exceptions cannot propagate
+from the GPU. Performing bounds checking for array indices before launching the
+kernel would be extremely inefficient.
+
+Indexing with boolean masks is something that MLX may support in the future. In
+general, MLX has limited support for operations for which outputs
+*shapes* are dependent on input *data*. Other examples of these types of
+operations which MLX does not yet support include :func:`numpy.nonzero` and the
+single input version of :func:`numpy.where`.
+
+In Place Updates 
+----------------
+
+In place updates to indexed arrays are possible in MLX. For example:
+
+.. code-block:: shell
+
+  >>> a = mx.array([1, 2, 3])
+  >>> a[2] = 0
+  >>> a
+  array([1, 2, 0], dtype=int32)
+
+Just as in NumPy, in place updates will be reflected in all references to the
+same array:
+
+.. code-block:: shell
+
+  >>> a = mx.array([1, 2, 3])
+  >>> b = a
+  >>> b[2] = 0
+  >>> b
+  array([1, 2, 0], dtype=int32)
+  >>> a
+  array([1, 2, 0], dtype=int32)
+
+Transformations of functions which use in-place updates are allowed and work as
+expected. For example:
+
+.. code-block:: python
+
+   def fun(x, idx):
+       x[idx] = 2.0
+       return x.sum()
+
+   dfdx = mx.grad(fun)(mx.array([1.0, 2.0, 3.0]), mx.array([1]))
+   print(dfdx)  # Prints: array([1, 0, 1], dtype=float32)
+
+In the above ``dfdx`` will have the correct gradient, namely zeros at ``idx``
+and ones elsewhere.

docs/src/usage/lazy_evaluation.rst

@@ -0,0 +1,144 @@
+.. _lazy eval:
+
+Lazy Evaluation
+===============
+
+.. currentmodule:: mlx.core
+
+Why Lazy Evaluation
+-------------------
+
+When you perform operations in MLX, no computation actually happens. Instead a
+compute graph is recorded. The actual computation only happens if an
+:func:`eval` is performed.
+
+MLX uses lazy evaluation because it has some nice features, some of which we
+describe below. 
+
+Transforming Compute Graphs
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Lazy evaluation let's us record a compute graph without actually doing any
+computations. This is useful for function transformations like :func:`grad` and
+:func:`vmap` and graph optimizations like :func:`simplify`.
+
+Currently, MLX does not compile and rerun compute graphs. They are all
+generated dynamically. However, lazy evaluation makes it much easier to
+integrate compilation for future performance enhancements.
+
+Only Compute What You Use
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In MLX you do not need to worry as much about computing outputs that are never
+used. For example:
+
+.. code-block:: python
+
+  def fun(x):
+      a = fun1(x)
+      b = expensive_fun(a)
+      return a, b
+
+  y, _ = fun(x)
+
+Here, we never actually compute the output of ``expensive_fun``. Use this
+pattern with care though, as the graph of ``expensive_fun`` is still built, and
+that has some cost associated to it.
+
+Similarly, lazy evaluation can be beneficial for saving memory while keeping
+code simple. Say you have a very large model ``Model`` derived from
+:obj:`mlx.nn.Module`. You can instantiate this model with ``model = Model()``.
+Typically, this will initialize all of the weights as ``float32``, but the
+initialization does not actually compute anything until you perform an
+:func:`eval`. If you update the model with ``float16`` weights, your maximum
+consumed memory will be half that required if eager computation was used
+instead.
+
+This pattern is simple to do in MLX thanks to lazy computation:
+
+.. code-block:: python
+
+  model = Model() # no memory used yet
+  model.load_weights("weights_fp16.safetensors")
+
+When to Evaluate
+----------------
+
+A common question is when to use :func:`eval`. The trade-off is between
+letting graphs get too large and not batching enough useful work.
+
+For example:
+
+.. code-block:: python
+
+  for _ in range(100):
+       a = a + b
+       mx.eval(a)
+       b = b * 2
+       mx.eval(b)
+
+This is a bad idea because there is some fixed overhead with each graph
+evaluation. On the other hand, there is some slight overhead which grows with
+the compute graph size, so extremely large graphs (while computationally
+correct) can be costly.
+
+Luckily, a wide range of compute graph sizes work pretty well with MLX:
+anything from a few tens of operations to many thousands of operations per
+evaluation should be okay.
+
+Most numerical computations have an iterative outer loop (e.g. the iteration in
+stochastic gradient descent). A natural and usually efficient place to use
+:func:`eval` is at each iteration of this outer loop.
+
+Here is a concrete example:
+
+.. code-block:: python
+
+   for batch in dataset:
+
+       # Nothing has been evaluated yet
+       loss, grad = value_and_grad_fn(model, batch)
+
+       # Still nothing has been evaluated
+       optimizer.update(model, grad)
+
+       # Evaluate the loss and the new parameters which will
+       # run the full gradient computation and optimizer update
+       mx.eval(loss, model.parameters())
+
+
+An important behavior to be aware of is when the graph will be implicitly
+evaluated. Anytime you ``print`` an array, convert it to an
+:obj:`numpy.ndarray`, or otherwise access it's memory via :obj:`memoryview`,
+the graph will be evaluated. Saving arrays via :func:`save` (or any other MLX
+saving functions) will also evaluate the array.
+
+
+Calling :func:`array.item` on a scalar array will also evaluate it. In the
+example above, printing the loss (``print(loss)``) or adding the loss scalar to
+a list (``losses.append(loss.item())``) would cause a graph evaluation. If 
+these lines are before ``mx.eval(loss, model.parameters())`` then this
+will be a partial evaluation, computing only the forward pass.
+
+Also, calling :func:`eval` on an array or set of arrays multiple times is
+perfectly fine. This is effectively a no-op.
+
+.. warning::
+
+  Using scalar arrays for control-flow will cause an evaluation.
+
+Here is an example:
+
+.. code-block:: python
+
+   def fun(x):
+       h, y = first_layer(x)
+       if y > 0:  # An evaluation is done here!
+           z  = second_layer_a(h)
+       else:
+           z  = second_layer_b(h)
+       return z
+
+Using arrays for control flow should be done with care. The above example works
+and can even be used with gradient transformations. However, this can be very
+inefficient if evaluations are done too frequently.

docs/src/usage/numpy.rst

@@ -0,0 +1,108 @@
+.. _numpy:
+
+Conversion to NumPy and Other Frameworks
+========================================
+
+MLX array implements the `Python Buffer Protocol <https://docs.python.org/3/c-api/buffer.html>`_.
+Let's convert an array to NumPy and back.
+
+.. code-block:: python
+
+  import mlx.core as mx
+  import numpy as np
+
+  a = mx.arange(3)
+  b = np.array(a) # copy of a
+  c = mx.array(b) # copy of b
+
+.. note::
+
+    Since NumPy does not support ``bfloat16`` arrays, you will need to convert to ``float16`` or ``float32`` first:
+    ``np.array(a.astype(mx.float32))``.
+    Otherwise, you will receive an error like: ``Item size 2 for PEP 3118 buffer format string does not match the dtype V item size 0.``
+
+By default, NumPy copies data to a new array. This can be prevented by creating an array view:
+
+.. code-block:: python
+
+  a = mx.arange(3)
+  a_view = np.array(a, copy=False)
+  print(a_view.flags.owndata) # False
+  a_view[0] = 1
+  print(a[0].item()) # 1
+
+A NumPy array view is a normal NumPy array, except that it does not own its memory.
+This means writing to the view is reflected in the original array.
+
+While this is quite powerful to prevent copying arrays, it should be noted that external changes to the memory of arrays cannot be reflected in gradients.
+
+Let's demonstrate this in an example:
+
+.. code-block:: python
+
+  def f(x):
+      x_view = np.array(x, copy=False)
+      x_view[:] *= x_view # modify memory without telling mx
+      return x.sum()
+
+  x = mx.array([3.0])
+  y, df = mx.value_and_grad(f)(x)
+  print("f(x) = x² =", y.item()) # 9.0
+  print("f'(x) = 2x !=", df.item()) # 1.0
+
+
+The function ``f`` indirectly modifies the array ``x`` through a memory view.
+However, this modification is not reflected in the gradient, as seen in the last line outputting ``1.0``,
+representing the gradient of the sum operation alone.
+The squaring of ``x`` occurs externally to MLX, meaning that no gradient is incorporated.
+It's important to note that a similar issue arises during array conversion and copying.
+For instance, a function defined as ``mx.array(np.array(x)**2).sum()`` would also result in an incorrect gradient,
+even though no in-place operations on MLX memory are executed.
+
+PyTorch
+-------
+
+.. warning:: 
+
+   PyTorch Support for :obj:`memoryview` is experimental and can break for
+   multi-dimensional arrays. Casting to NumPy first is advised for now.
+
+PyTorch supports the buffer protocol, but it requires an explicit :obj:`memoryview`.
+
+.. code-block:: python
+
+  import mlx.core as mx
+  import torch
+
+  a = mx.arange(3)
+  b = torch.tensor(memoryview(a))
+  c = mx.array(b.numpy())
+
+Conversion from PyTorch tensors back to arrays must be done via intermediate NumPy arrays with ``numpy()``.
+
+JAX
+---
+JAX fully supports the buffer protocol.
+
+.. code-block:: python
+
+  import mlx.core as mx
+  import jax.numpy as jnp
+
+  a = mx.arange(3)
+  b = jnp.array(a)
+  c = mx.array(b)
+
+TensorFlow
+----------
+
+TensorFlow supports the buffer protocol, but it requires an explicit :obj:`memoryview`.
+
+.. code-block:: python
+
+  import mlx.core as mx
+  import tensorflow as tf
+
+  a = mx.arange(3)
+  b = tf.constant(memoryview(a))
+  c = mx.array(b)

docs/src/usage/quick_start.rst

@@ -40,6 +40,9 @@ automatically evaluate the array.
   >> np.array(c)   # Also evaluates c
   array([2., 4., 6., 8.], dtype=float32)
 
+
+See the page on :ref:`Lazy Evaluation <lazy eval>` for more details.
+
 Function and Graph Transformations
 ----------------------------------
 
@@ -62,10 +65,3 @@ and :func:`jvp` for Jacobian-vector products.
 
 Use :func:`value_and_grad` to efficiently compute both a function's output and
 gradient with respect to the function's input. 
-
-
-Devices and Streams 
--------------------
-
-
-

docs/src/usage/saving_and_loading.rst

@@ -0,0 +1,81 @@
+.. _saving_and_loading:
+
+Saving and Loading Arrays
+=========================
+
+.. currentmodule:: mlx.core
+
+MLX supports multiple array serialization formats.
+
+.. list-table:: Serialization Formats
+   :widths: 20 8 25 25 
+   :header-rows: 1
+
+   * - Format 
+     - Extension 
+     - Function
+     - Notes 
+   * - NumPy 
+     - ``.npy`` 
+     - :func:`save`
+     - Single arrays only
+   * - NumPy archive 
+     - ``.npz`` 
+     - :func:`savez` and :func:`savez_compressed`
+     - Multiple arrays 
+   * - Safetensors
+     - ``.safetensors`` 
+     - :func:`save_safetensors`
+     - Multiple arrays 
+   * - GGUF 
+     - ``.gguf`` 
+     - :func:`save_gguf`
+     - Multiple arrays
+
+The :func:`load` function will load any of the supported serialization
+formats. It determines the format from the extensions. The output of
+:func:`load` depends on the format. 
+
+Here's an example of saving a single array to a file:
+
+.. code-block:: shell
+
+   >>> a = mx.array([1.0])
+   >>> mx.save("array", a)
+
+The array ``a`` will be saved in the file ``array.npy`` (notice the extension
+is automatically added). Including the extension is optional; if it is missing
+it will be added. You can load the array with:
+
+.. code-block:: shell
+
+   >>> mx.load("array.npy", a)
+   array([1], dtype=float32)
+
+Here's an example of saving several arrays to a single file:
+
+.. code-block:: shell
+
+   >>> a = mx.array([1.0])
+   >>> b = mx.array([2.0])
+   >>> mx.savez("arrays", a, b=b)
+
+For compatibility with :func:`numpy.savez` the MLX :func:`savez` takes arrays
+as arguments. If the keywords are missing, then default names will be
+provided. This can be loaded with:
+
+.. code-block:: shell
+
+   >>> mx.load("arrays.npz")
+   {'b': array([2], dtype=float32), 'arr_0': array([1], dtype=float32)}
+
+In this case :func:`load` returns a dictionary of names to arrays.
+
+The functions :func:`save_safetensors` and :func:`save_gguf` are similar to
+:func:`savez`, but they take as input a :obj:`dict` of string names to arrays:
+
+.. code-block:: shell
+
+   >>> a = mx.array([1.0])
+   >>> b = mx.array([2.0])
+   >>> mx.save_safetensors("arrays", {"a": a, "b": b})

docs/src/usage/unified_memory.rst

@@ -0,0 +1,78 @@
+.. _unified_memory:
+
+Unified Memory
+==============
+
+.. currentmodule:: mlx.core
+
+Apple silicon has a unified memory architecture. The CPU and GPU have direct
+access to the same memory pool. MLX is designed to take advantage of that.
+
+Concretely, when you make an array in MLX you don't have to specify its location:
+
+
+.. code-block:: python
+
+  a = mx.random.normal((100,))
+  b = mx.random.normal((100,))
+
+Both ``a`` and ``b`` live in unified memory.
+
+In MLX, rather than moving arrays to devices, you specify the device when you
+run the operation. Any device can perform any operation on ``a`` and ``b``
+without needing to move them from one memory location to another. For example: 
+
+.. code-block:: python
+
+  mx.add(a, b, stream=mx.cpu)
+  mx.add(a, b, stream=mx.gpu)
+
+In the above, both the CPU and the GPU will perform the same add
+operation. The operations can (and likely will) be run in parallel since
+there are no dependencies between them. See :ref:`using_streams` for more
+information the semantics of streams in MLX.
+
+In the above ``add`` example, there are no dependencies between operations, so
+there is no possibility for race conditions. If there are dependencies, the
+MLX scheduler will automatically manage them. For example:
+
+.. code-block:: python
+
+  c = mx.add(a, b, stream=mx.cpu)
+  d = mx.add(a, c, stream=mx.gpu)
+
+In the above case, the second ``add`` runs on the GPU but it depends on the
+output of the first ``add`` which is running on the CPU. MLX will
+automatically insert a dependency between the two streams so that the second
+``add`` only starts executing after the first is complete and ``c`` is
+available.
+
+A Simple Example
+~~~~~~~~~~~~~~~~
+
+Here is a more interesting (albeit slightly contrived example) of how unified
+memory can be helpful. Suppose we have the following computation:
+
+.. code-block:: python
+
+  def fun(a, b, d1, d2):
+    x = mx.matmul(a, b, stream=d1)
+    for _ in range(500):
+        b = mx.exp(b, stream=d2)
+    return x, b
+
+which we want to run with the following arguments:
+
+.. code-block:: python
+
+  a = mx.random.uniform(shape=(4096, 512))
+  b = mx.random.uniform(shape=(512, 4))
+
+The first ``matmul`` operation is a good fit for the GPU since it's more
+compute dense. The second sequence of operations are a better fit for the CPU,
+since they are very small and would probably be overhead bound on the GPU.
+
+If we time the computation fully on the GPU, we get 2.8 milliseconds. But if we
+run the computation with ``d1=mx.gpu`` and ``d2=mx.cpu``, then the time is only
+about 1.4 milliseconds, about twice as fast. These times were measured on an M1
+Max.

docs/src/usage/using_streams.rst

@@ -1,3 +1,5 @@
+.. _using_streams:
+
 Using Streams
 =============
 

examples/cpp/tutorial.cpp

@@ -57,7 +57,7 @@ void array_basics() {
   assert(z.shape(0) == 2);
   assert(z.shape(1) == 2);
 
-  // To actually run the compuation you must evaluate `z`.
+  // To actually run the computation you must evaluate `z`.
   // Under the hood, mlx records operations in a graph.
   // The variable `z` is a node in the graph which points to its operation
   // and inputs. When `eval` is called on an array (or arrays), the array and

examples/extensions/axpby/axpby.cpp

@@ -26,7 +26,7 @@ namespace mlx::core {
 ///////////////////////////////////////////////////////////////////////////////
 
 /**
- *  Scale and sum two vectors elementwise
+ *  Scale and sum two vectors element-wise
  *  z = alpha * x + beta * y
  *
  *  Follow numpy style broadcasting between x and y
@@ -91,21 +91,21 @@ void axpby_impl(
   T alpha = static_cast<T>(alpha_);
   T beta = static_cast<T>(beta_);
 
-  // Do the elementwise operation for each output
+  // Do the element-wise operation for each output
   for (size_t out_idx = 0; out_idx < out.size(); out_idx++) {
     // Map linear indices to offsets in x and y
     auto x_offset = elem_to_loc(out_idx, x.shape(), x.strides());
     auto y_offset = elem_to_loc(out_idx, y.shape(), y.strides());
 
     // We allocate the output to be contiguous and regularly strided
-    // (defaults to row major) and hence it doesn't need additonal mapping
+    // (defaults to row major) and hence it doesn't need additional mapping
     out_ptr[out_idx] = alpha * x_ptr[x_offset] + beta * y_ptr[y_offset];
   }
 }
 
 /** Fall back implementation for evaluation on CPU */
 void Axpby::eval(const std::vector<array>& inputs, array& out) {
-  // Check the inputs (registered in the op while contructing the out array)
+  // Check the inputs (registered in the op while constructing the out array)
   assert(inputs.size() == 2);
   auto& x = inputs[0];
   auto& y = inputs[1];
@@ -192,7 +192,7 @@ void Axpby::eval_cpu(const std::vector<array>& inputs, array& out) {
   eval(inputs, out);
 }
 
-#else // Accelerate not avaliable
+#else // Accelerate not available
 
 /** Evaluate primitive on CPU falling back to common backend */
 void Axpby::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -254,7 +254,7 @@ void Axpby::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder->setComputePipelineState(kernel);
 
   // Kernel parameters are registered with buffer indices corresponding to
-  // those in the kernel decelaration at axpby.metal
+  // those in the kernel declaration at axpby.metal
   int ndim = out.ndim();
   size_t nelem = out.size();
 
@@ -287,7 +287,7 @@ void Axpby::eval_gpu(const std::vector<array>& inputs, array& out) {
   // Fix the 3D size of the launch grid (in terms of threads)
   MTL::Size grid_dims = MTL::Size(nelem, 1, 1);
 
-  // Launch the grid with the given number of threads divded among
+  // Launch the grid with the given number of threads divided among
   // the given threadgroups
   compute_encoder->dispatchThreads(grid_dims, group_dims);
 }
@@ -311,8 +311,8 @@ array Axpby::jvp(
     const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   // Forward mode diff that pushes along the tangents
-  // The jvp transform on the the primitive can built with ops
-  // that are scheduled on the same stream as the primtive
+  // The jvp transform on the primitive can built with ops
+  // that are scheduled on the same stream as the primitive
 
   // If argnums = {0}, we only push along x in which case the
   // jvp is just the tangent scaled by alpha
@@ -345,7 +345,7 @@ std::vector<array> Axpby::vjp(
   return vjps;
 }
 
-/** Vectorize primitve along given axis */
+/** Vectorize primitive along given axis */
 std::pair<array, int> Axpby::vmap(
     const std::vector<array>& inputs,
     const std::vector<int>& axes) {

examples/extensions/axpby/axpby.h

@@ -12,7 +12,7 @@ namespace mlx::core {
 ///////////////////////////////////////////////////////////////////////////////
 
 /**
- *  Scale and sum two vectors elementwise
+ *  Scale and sum two vectors element-wise
  *  z = alpha * x + beta * y
  *
  *  Follow numpy style broadcasting between x and y
@@ -39,7 +39,7 @@ class Axpby : public Primitive {
    * A primitive must know how to evaluate itself on the CPU/GPU
    * for the given inputs and populate the output array.
    *
-   * To avoid unecessary allocations, the evaluation function
+   * To avoid unnecessary allocations, the evaluation function
    * is responsible for allocating space for the array.
    */
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
@@ -58,7 +58,7 @@ class Axpby : public Primitive {
       const std::vector<int>& argnums) override;
 
   /**
-   * The primitive must know how to vectorize itself accross
+   * The primitive must know how to vectorize itself across
    * the given axes. The output is a pair containing the array
    * representing the vectorized computation and the axis which
    * corresponds to the output vectorized dimension.

examples/extensions/axpby/axpby.metal

@@ -59,5 +59,5 @@ template <typename T>
 
 instantiate_axpby(float32, float);
 instantiate_axpby(float16, half);
-instantiate_axpby(bflot16, bfloat16_t);
+instantiate_axpby(bfloat16, bfloat16_t);
 instantiate_axpby(complex64, complex64_t);

examples/extensions/bindings.cpp

@@ -23,7 +23,7 @@ PYBIND11_MODULE(mlx_sample_extensions, m) {
       py::kw_only(),
       "stream"_a = py::none(),
       R"pbdoc(
-        Scale and sum two vectors elementwise
+        Scale and sum two vectors element-wise
         ``z = alpha * x + beta * y``
         
         Follows numpy style broadcasting between ``x`` and ``y``

examples/extensions/mlx_sample_extensions/__init__.py

@@ -1,4 +1,5 @@
 # Copyright © 2023 Apple Inc.
 
 import mlx.core as mx
+
 from .mlx_sample_extensions import *

examples/extensions/setup.py

@@ -1,8 +1,9 @@
 # Copyright © 2023 Apple Inc.
 
-from mlx import extension
 from setuptools import setup
 
+from mlx import extension
+
 if __name__ == "__main__":
     setup(
         name="mlx_sample_extensions",
@@ -14,5 +15,5 @@ if __name__ == "__main__":
         package_dir={"": "."},
         package_data={"mlx_sample_extensions": ["*.so", "*.dylib", "*.metallib"]},
         zip_safe=False,
-        python_requires=">=3.7",
+        python_requires=">=3.8",
     )

examples/python/linear_regression.py

@@ -1,8 +1,9 @@
 # Copyright © 2023 Apple Inc.
 
-import mlx.core as mx
 import time
 
+import mlx.core as mx
+
 num_features = 100
 num_examples = 1_000
 num_iters = 10_000

examples/python/logistic_regression.py

@@ -1,8 +1,9 @@
 # Copyright © 2023 Apple Inc.
 
-import mlx.core as mx
 import time
 
+import mlx.core as mx
+
 num_features = 100
 num_examples = 1_000
 num_iters = 10_000

mlx/CMakeLists.txt

@@ -8,17 +8,17 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/ops.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/graph_utils.cpp
-  ${CMAKE_CURRENT_SOURCE_DIR}/load.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/scheduler.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/transforms.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/linalg.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h
 )
 
 add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/common)
-
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/io)
 if (MLX_BUILD_ACCELERATE) 
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/accelerate)
 else()

mlx/allocator.cpp

@@ -9,7 +9,7 @@
 namespace mlx::core::allocator {
 
 Buffer malloc(size_t size) {
-  auto buffer = allocator().malloc(size);
+  auto buffer = allocator().malloc(size, /* allow_swap */ true);
   if (size && !buffer.ptr()) {
     std::ostringstream msg;
     msg << "[malloc] Unable to allocate " << size << " bytes.";
@@ -22,7 +22,7 @@ void free(Buffer buffer) {
   return allocator().free(buffer);
 }
 
-Buffer CommonAllocator::malloc(size_t size) {
+Buffer CommonAllocator::malloc(size_t size, bool) {
   return Buffer{std::malloc(size)};
 }
 
@@ -38,6 +38,11 @@ Buffer malloc_or_wait(size_t size) {
     buffer = allocator().malloc(size);
   }
 
+  // Try swapping if needed
+  if (size && !buffer.ptr()) {
+    buffer = allocator().malloc(size, /* allow_swap = */ true);
+  }
+
   if (size && !buffer.ptr()) {
     std::ostringstream msg;
     msg << "[malloc_or_wait] Unable to allocate " << size << " bytes.";

mlx/allocator.h

@@ -37,9 +37,9 @@ void free(Buffer buffer);
 Buffer malloc_or_wait(size_t size);
 
 class Allocator {
-  /** Abstract base clase for a memory allocator. */
+  /** Abstract base class for a memory allocator. */
  public:
-  virtual Buffer malloc(size_t size) = 0;
+  virtual Buffer malloc(size_t size, bool allow_swap = false) = 0;
   virtual void free(Buffer buffer) = 0;
 
   Allocator() = default;
@@ -55,7 +55,7 @@ Allocator& allocator();
 class CommonAllocator : public Allocator {
   /** A general CPU allocator. */
  public:
-  virtual Buffer malloc(size_t size) override;
+  virtual Buffer malloc(size_t size, bool allow_swap = false) override;
   virtual void free(Buffer buffer) override;
 
  private:

mlx/array.cpp

@@ -6,6 +6,7 @@
 #include "mlx/ops.h"
 #include "mlx/primitives.h"
 #include "mlx/transforms.h"
+#include "mlx/transforms_impl.h"
 
 namespace mlx::core {
 
@@ -21,6 +22,12 @@ std::pair<size_t, std::vector<size_t>> cum_prod(const std::vector<int>& shape) {
   return {cum_prod, strides};
 }
 
+/** Return true if we are currently performing a function transformation in
+ * order to keep the graph when evaluating tracer arrays. */
+bool in_tracing() {
+  return detail::InTracing::in_tracing();
+}
+
 } // namespace
 
 array::array(const std::complex<float>& val, Dtype dtype /* = complex64 */)
@@ -32,7 +39,7 @@ array::array(const std::complex<float>& val, Dtype dtype /* = complex64 */)
 array::array(
     const std::vector<int>& shape,
     Dtype dtype,
-    std::unique_ptr<Primitive> primitive,
+    std::shared_ptr<Primitive> primitive,
     const std::vector<array>& inputs)
     : array_desc_(std::make_shared<ArrayDesc>(
           shape,
@@ -40,6 +47,23 @@ array::array(
           std::move(primitive),
           inputs)) {}
 
+std::vector<array> array::make_arrays(
+    const std::vector<std::vector<int>>& shapes,
+    const std::vector<Dtype>& dtypes,
+    std::shared_ptr<Primitive> primitive,
+    const std::vector<array>& inputs) {
+  std::vector<array> outputs;
+  for (int i = 0; i < shapes.size(); ++i) {
+    outputs.push_back(array(shapes[i], dtypes[i], primitive, inputs));
+  }
+  for (int i = 0; i < outputs.size(); ++i) {
+    auto siblings = outputs;
+    siblings.erase(siblings.begin() + i);
+    outputs[i].set_siblings(std::move(siblings), i);
+  }
+  return outputs;
+}
+
 array::array(std::initializer_list<float> data)
     : array_desc_(std::make_shared<ArrayDesc>(
           std::vector<int>{static_cast<int>(data.size())},
@@ -59,11 +83,17 @@ array::array(
 
 void array::detach() {
   array_desc_->inputs.clear();
+  array_desc_->siblings.clear();
+  array_desc_->position = 0;
   array_desc_->primitive = nullptr;
 }
 
-void array::eval(bool retain_graph /* = false */) {
-  mlx::core::eval({*this}, retain_graph);
+void array::eval() {
+  mlx::core::eval({*this});
+}
+
+bool array::is_tracer() const {
+  return array_desc_->is_tracer && in_tracing();
 }
 
 void array::set_data(allocator::Buffer buffer, deleter_t d) {
@@ -116,7 +146,7 @@ array::ArrayDesc::ArrayDesc(const std::vector<int>& shape, Dtype dtype)
 array::ArrayDesc::ArrayDesc(
     const std::vector<int>& shape,
     Dtype dtype,
-    std::unique_ptr<Primitive> primitive,
+    std::shared_ptr<Primitive> primitive,
     const std::vector<array>& inputs)
     : shape(shape),
       dtype(dtype),
@@ -128,10 +158,6 @@ array::ArrayDesc::ArrayDesc(
   }
 }
 
-// Needed because the Primitive type used in array.h is incomplete and the
-// compiler needs to see the call to the desctructor after the type is complete.
-array::ArrayDesc::~ArrayDesc() = default;
-
 array::ArrayIterator::reference array::ArrayIterator::operator*() const {
   auto start = std::vector<int>(arr.ndim(), 0);
   auto end = arr.shape();

mlx/array.h

@@ -1,5 +1,4 @@
 // Copyright © 2023 Apple Inc.
-
 #pragma once
 #include <algorithm>
 #include <cstdint>
@@ -116,11 +115,11 @@ class array {
   };
 
   /** Evaluate the array. */
-  void eval(bool retain_graph = false);
+  void eval();
 
   /** Get the value from a scalar array. */
   template <typename T>
-  T item(bool retain_graph = false);
+  T item();
 
   struct ArrayIterator {
     using iterator_category = std::random_access_iterator_tag;
@@ -154,8 +153,8 @@ class array {
     };
 
    private:
-    int idx;
     const array& arr;
+    int idx;
   };
 
   ArrayIterator begin() const {
@@ -174,7 +173,13 @@ class array {
   array(
       const std::vector<int>& shape,
       Dtype dtype,
-      std::unique_ptr<Primitive> primitive,
+      std::shared_ptr<Primitive> primitive,
+      const std::vector<array>& inputs);
+
+  static std::vector<array> make_arrays(
+      const std::vector<std::vector<int>>& shapes,
+      const std::vector<Dtype>& dtypes,
+      std::shared_ptr<Primitive> primitive,
       const std::vector<array>& inputs);
 
   /** A unique identifier for an array. */
@@ -182,6 +187,11 @@ class array {
     return reinterpret_cast<std::uintptr_t>(array_desc_.get());
   }
 
+  /** A unique identifier for an arrays primitive. */
+  std::uintptr_t primitive_id() const {
+    return reinterpret_cast<std::uintptr_t>(array_desc_->primitive.get());
+  }
+
   struct Data {
     allocator::Buffer buffer;
     deleter_t d;
@@ -219,12 +229,32 @@ class array {
     return array_desc_->inputs;
   };
 
-  /** A non-const reference to the array's inputs so that they can be used to
-   * edit the graph. */
-  std::vector<array>& editable_inputs() {
+  std::vector<array>& inputs() {
     return array_desc_->inputs;
   }
 
+  /** The array's siblings. */
+  const std::vector<array>& siblings() const {
+    return array_desc_->siblings;
+  };
+
+  void set_siblings(std::vector<array> siblings, uint16_t position) {
+    array_desc_->siblings = std::move(siblings);
+    array_desc_->position = position;
+  }
+
+  /** The outputs of the array's primitive (i.e. this array and
+   * its siblings) in the order the primitive expects. */
+  std::vector<array> outputs() const {
+    auto idx = array_desc_->position;
+    std::vector<array> outputs;
+    outputs.reserve(siblings().size() + 1);
+    outputs.insert(outputs.end(), siblings().begin(), siblings().begin() + idx);
+    outputs.push_back(*this);
+    outputs.insert(outputs.end(), siblings().begin() + idx, siblings().end());
+    return outputs;
+  };
+
   /** Detach the array from the graph. */
   void detach();
 
@@ -265,9 +295,7 @@ class array {
     array_desc_->is_tracer = is_tracer;
   }
   // Check if the array is a tracer array
-  bool is_tracer() const {
-    return array_desc_->is_tracer;
-  }
+  bool is_tracer() const;
 
   void set_data(allocator::Buffer buffer, deleter_t d = allocator::free);
 
@@ -301,7 +329,7 @@ class array {
     std::vector<size_t> strides;
     size_t size;
     Dtype dtype;
-    std::unique_ptr<Primitive> primitive{nullptr};
+    std::shared_ptr<Primitive> primitive{nullptr};
 
     // Indicates an array is being used in a graph transform
     // and should not be detached from the graph
@@ -323,16 +351,19 @@ class array {
     Flags flags;
 
     std::vector<array> inputs;
+    // An array to keep track of the siblings from a multi-output
+    // primitive.
+    std::vector<array> siblings;
+    // The arrays position in the output list
+    uint32_t position{0};
 
     explicit ArrayDesc(const std::vector<int>& shape, Dtype dtype);
 
     explicit ArrayDesc(
         const std::vector<int>& shape,
         Dtype dtype,
-        std::unique_ptr<Primitive> primitive,
+        std::shared_ptr<Primitive> primitive,
         const std::vector<array>& inputs);
-
-    ~ArrayDesc();
   };
 
   // The ArrayDesc contains the details of the materialized array including the
@@ -381,11 +412,11 @@ array::array(
 }
 
 template <typename T>
-T array::item(bool retain_graph /* = false */) {
+T array::item() {
   if (size() != 1) {
     throw std::invalid_argument("item can only be called on arrays of size 1.");
   }
-  eval(retain_graph);
+  eval();
   return *data<T>();
 }
 

mlx/backend/accelerate/CMakeLists.txt

@@ -4,6 +4,7 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
 )

mlx/backend/accelerate/primitives.cpp

@@ -17,6 +17,12 @@
     primitive::eval(inputs, out);                                          \
   }
 
+#define DEFAULT_MULTI(primitive)                                       \
+  void primitive::eval_cpu(                                            \
+      const std::vector<array>& inputs, std::vector<array>& outputs) { \
+    primitive::eval(inputs, outputs);                                  \
+  }
+
 namespace mlx::core {
 
 // Use the default implementation for the following primitives
@@ -26,12 +32,14 @@ DEFAULT(ArgReduce)
 DEFAULT(ArgSort)
 DEFAULT(AsStrided)
 DEFAULT(Broadcast)
+DEFAULT(Ceil)
 DEFAULT(Concatenate)
 DEFAULT(Copy)
 DEFAULT(Equal)
 DEFAULT(Erf)
 DEFAULT(ErfInv)
 DEFAULT(FFT)
+DEFAULT(Floor)
 DEFAULT(Gather)
 DEFAULT(Greater)
 DEFAULT(GreaterEqual)
@@ -39,12 +47,15 @@ DEFAULT(Less)
 DEFAULT(LessEqual)
 DEFAULT(Load)
 DEFAULT(LogicalNot)
+DEFAULT(LogicalAnd)
+DEFAULT(LogicalOr)
 DEFAULT(LogAddExp)
 DEFAULT(NotEqual)
 DEFAULT(Pad)
 DEFAULT(Partition)
 DEFAULT(RandomBits)
 DEFAULT(Reshape)
+DEFAULT(Round)
 DEFAULT(Scatter)
 DEFAULT(Sigmoid)
 DEFAULT(Sign)
@@ -52,6 +63,7 @@ DEFAULT(Slice)
 DEFAULT(Sort)
 DEFAULT(StopGradient)
 DEFAULT(Transpose)
+DEFAULT_MULTI(DivMod)
 
 void Abs::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
@@ -322,6 +334,45 @@ void Divide::eval_cpu(const std::vector<array>& inputs, array& out) {
   }
 }
 
+// TODO: Avoid code duplication with the common backend.
+struct RemainderFn {
+  template <typename T>
+  std::enable_if_t<!std::is_integral_v<T>, T> operator()(
+      T numerator,
+      T denominator) {
+    return std::fmod(numerator, denominator);
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_integral_v<T>, T> operator()(
+      T numerator,
+      T denominator) {
+    return numerator % denominator;
+  }
+};
+
+void Remainder::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+
+  if (a.dtype() == float32) {
+    binary(
+        a,
+        b,
+        out,
+        RemainderFn{},
+        UseDefaultBinaryOp(),
+        UseDefaultBinaryOp(),
+        [](const auto* a, const auto* b, auto* o, auto n) {
+          int num_el = n;
+          vvremainderf((float*)o, (const float*)a, (const float*)b, &num_el);
+        });
+  } else {
+    binary(a, b, out, RemainderFn{});
+  }
+}
+
 void Exp::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
@@ -494,7 +545,7 @@ void Power::eval_cpu(const std::vector<array>& inputs, array& out) {
       b.flags().row_contiguous) {
     int size = a.size();
     out.set_data(allocator::malloc_or_wait(out.nbytes()));
-    vvpowf(out.data<float>(), a.data<float>(), b.data<float>(), &size);
+    vvpowf(out.data<float>(), b.data<float>(), a.data<float>(), &size);
   } else {
     eval(inputs, out);
   }

mlx/backend/accelerate/quantized.cpp

@@ -0,0 +1,103 @@
+// Copyright © 2023 Apple Inc.
+
+#include <cassert>
+
+#include <simd/vector.h>
+
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+namespace {
+
+void _qmm_t_4_64(
+    float* result,
+    const float* x,
+    const uint32_t* w,
+    const float* scales,
+    const float* biases,
+    int M,
+    int N,
+    int K) {
+  constexpr int bits = 4;
+  constexpr int group_size = 64;
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int pack_factor = 32 / bits;
+  constexpr int packs_in_group = group_size / pack_factor;
+  const int Kg = K / group_size;
+  const int Kw = K / pack_factor;
+
+  for (int m = 0; m < M; m++) {
+    const uint32_t* w_local = w;
+    const float* scales_local = scales;
+    const float* biases_local = biases;
+
+    for (int n = 0; n < N; n++) {
+      const simd_float16* x_local = (simd_float16*)x;
+      simd_float16 sum = 0;
+      for (int k = 0; k < K; k += group_size) {
+        float scale = *scales_local++;
+        float bias = *biases_local++;
+
+        for (int kw = 0; kw < packs_in_group; kw += 2) {
+          // TODO: vectorize this properly
+          simd_uint16 wi;
+          for (int e = 0; e < 2; e++) {
+            uint32_t wii = *w_local++;
+            for (int p = 0; p < 8; p++) {
+              wi[e * 8 + p] = wii & bitmask;
+              wii >>= bits;
+            }
+          }
+          simd_float16 wf = simd_float(wi);
+          wf *= scale;
+          wf += bias;
+
+          sum += (*x_local) * wf;
+          x_local++;
+        }
+      }
+
+      *result = simd_reduce_add(sum);
+      result++;
+    }
+
+    x += K;
+  }
+}
+
+} // namespace
+
+void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 4);
+
+  auto& x = inputs[0];
+  auto& w = inputs[1];
+  auto& scales = inputs[2];
+  auto& biases = inputs[3];
+
+  bool condition =
+      (transpose_ && x.flags().row_contiguous && w.flags().row_contiguous &&
+       scales.flags().row_contiguous && biases.flags().row_contiguous &&
+       x.dtype() == float32 && bits_ == 4 && group_size_ == 64);
+
+  if (condition) {
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    int K = x.shape(-1);
+    int M = x.size() / K;
+    int N = out.shape(-1);
+    _qmm_t_4_64(
+        out.data<float>(),
+        x.data<float>(),
+        w.data<uint32_t>(),
+        scales.data<float>(),
+        biases.data<float>(),
+        M,
+        N,
+        K);
+  } else {
+    eval(inputs, out);
+  }
+}
+
+} // namespace mlx::core

mlx/backend/common/CMakeLists.txt

@@ -8,6 +8,7 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/erf.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp

mlx/backend/common/binary.cpp

@@ -6,6 +6,7 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/binary.h"
+#include "mlx/backend/common/binary_two.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
@@ -75,6 +76,61 @@ void Add::eval(const std::vector<array>& inputs, array& out) {
   binary(a, b, out, [](auto x, auto y) { return x + y; });
 }
 
+void DivMod::eval(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  auto integral_op = [](auto x, auto y) {
+    return std::make_pair(x / y, x % y);
+  };
+  auto float_op = [](auto x, auto y) {
+    return std::make_pair(std::trunc(x / y), std::fmod(x, y));
+  };
+  switch (outputs[0].dtype()) {
+    case bool_:
+      binary_op<bool>(a, b, outputs, integral_op);
+    case uint8:
+      binary_op<uint8_t>(a, b, outputs, integral_op);
+      break;
+    case uint16:
+      binary_op<uint16_t>(a, b, outputs, integral_op);
+      break;
+    case uint32:
+      binary_op<uint32_t>(a, b, outputs, integral_op);
+      break;
+    case uint64:
+      binary_op<uint64_t>(a, b, outputs, integral_op);
+      break;
+    case int8:
+      binary_op<int8_t>(a, b, outputs, integral_op);
+      break;
+    case int16:
+      binary_op<int16_t>(a, b, outputs, integral_op);
+      break;
+    case int32:
+      binary_op<int32_t>(a, b, outputs, integral_op);
+      break;
+    case int64:
+      binary_op<int64_t>(a, b, outputs, integral_op);
+      break;
+    case float16:
+      binary_op<float16_t>(a, b, outputs, float_op);
+      break;
+    case float32:
+      binary_op<float>(a, b, outputs, float_op);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t>(a, b, outputs, float_op);
+      break;
+    case complex64:
+      // Should never get here
+      throw std::runtime_error("[DivMod] Complex type not supported");
+      break;
+  }
+}
+
 void Divide::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
@@ -82,6 +138,29 @@ void Divide::eval(const std::vector<array>& inputs, array& out) {
   binary(a, b, out, [](auto x, auto y) { return x / y; });
 }
 
+struct RemainderFn {
+  template <typename T>
+  std::enable_if_t<!std::is_integral_v<T>, T> operator()(
+      T numerator,
+      T denominator) {
+    return std::fmod(numerator, denominator);
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_integral_v<T>, T> operator()(
+      T numerator,
+      T denominator) {
+    return numerator % denominator;
+  }
+};
+
+void Remainder::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, RemainderFn{});
+}
+
 void Equal::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   if (equal_nan_) {

mlx/backend/common/binary.h

@@ -73,6 +73,12 @@ struct UseDefaultBinaryOp {
     // Should we throw? This should normally never be called.
     assert(false);
   }
+
+  template <typename T, typename U>
+  void operator()(const T* a, const T* b, U* dst_a, U* dst_b, int size) {
+    // Should we throw? This should normally never be called.
+    assert(false);
+  }
 };
 
 template <typename T, typename U, typename Op>
@@ -89,6 +95,18 @@ struct DefaultVectorScalar {
       a++;
     }
   }
+
+  void operator()(const T* a, const T* b, U* dst_a, U* dst_b, int size) {
+    T scalar = *b;
+    while (size-- > 0) {
+      auto dst = op(*a, scalar);
+      *dst_a = dst.first;
+      *dst_b = dst.second;
+      dst_a++;
+      dst_b++;
+      a++;
+    }
+  }
 };
 
 template <typename T, typename U, typename Op>
@@ -105,6 +123,18 @@ struct DefaultScalarVector {
       b++;
     }
   }
+
+  void operator()(const T* a, const T* b, U* dst_a, U* dst_b, int size) {
+    T scalar = *a;
+    while (size-- > 0) {
+      auto dst = op(scalar, *b);
+      *dst_a = dst.first;
+      *dst_b = dst.second;
+      dst_a++;
+      dst_b++;
+      b++;
+    }
+  }
 };
 
 template <typename T, typename U, typename Op>
@@ -121,6 +151,18 @@ struct DefaultVectorVector {
       b++;
     }
   }
+
+  void operator()(const T* a, const T* b, U* dst_a, U* dst_b, int size) {
+    while (size-- > 0) {
+      auto dst = op(*a, *b);
+      *dst_a = dst.first;
+      *dst_b = dst.second;
+      dst_a++;
+      dst_b++;
+      a++;
+      b++;
+    }
+  }
 };
 
 template <typename T, typename U, typename Op>

mlx/backend/common/binary_two.h

@@ -0,0 +1,536 @@
+// Copyright © 2023 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/common/binary.h"
+#include "mlx/backend/common/utils.h"
+
+namespace mlx::core {
+
+namespace {
+
+template <typename T, typename U, typename Op>
+void binary_op_dims1(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  for (size_t i = 0; i < out_a.size(); ++i) {
+    auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
+    dst_a[i] = dst.first;
+    dst_b[i] = dst.second;
+    a_idx += a.strides()[0];
+    b_idx += b.strides()[0];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims1(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op,
+    int stride) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; i++) {
+    op(a_ptr + a_idx, b_ptr + b_idx, dst_a, dst_b, stride);
+    a_idx += a.strides()[0];
+    b_idx += b.strides()[0];
+    dst_a += stride;
+    dst_b += stride;
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims2(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  size_t out_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
+      dst_a[out_idx] = dst.first;
+      dst_b[out_idx++] = dst.second;
+      a_idx += a.strides()[1];
+      b_idx += b.strides()[1];
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims2(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op,
+    int stride) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      op(a_ptr + a_idx, b_ptr + b_idx, dst_a, dst_b, stride);
+      a_idx += a.strides()[1];
+      b_idx += b.strides()[1];
+      dst_a += stride;
+      dst_b += stride;
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims3(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  size_t out_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      for (size_t k = 0; k < a.shape()[2]; ++k) {
+        auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
+        dst_a[out_idx] = dst.first;
+        dst_b[out_idx++] = dst.second;
+        a_idx += a.strides()[2];
+        b_idx += b.strides()[2];
+      }
+      a_idx += a.strides()[1] - a.strides()[2] * a.shape()[2];
+      b_idx += b.strides()[1] - b.strides()[2] * b.shape()[2];
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims4(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  size_t out_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      for (size_t k = 0; k < a.shape()[2]; ++k) {
+        for (size_t ii = 0; ii < a.shape()[3]; ++ii) {
+          auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
+          dst_a[out_idx] = dst.first;
+          dst_b[out_idx++] = dst.second;
+          a_idx += a.strides()[3];
+          b_idx += b.strides()[3];
+        }
+        a_idx += a.strides()[2] - a.strides()[3] * a.shape()[3];
+        b_idx += b.strides()[2] - b.strides()[3] * b.shape()[3];
+      }
+      a_idx += a.strides()[1] - a.strides()[2] * a.shape()[2];
+      b_idx += b.strides()[1] - b.strides()[2] * b.shape()[2];
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dispatch_dims(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op) {
+  switch (out_a.ndim()) {
+    case 1:
+      binary_op_dims1<T, U, Op>(a, b, out_a, out_b, op);
+      return;
+    case 2:
+      binary_op_dims2<T, U, Op>(a, b, out_a, out_b, op);
+      return;
+    case 3:
+      binary_op_dims3<T, U, Op>(a, b, out_a, out_b, op);
+      return;
+    case 4:
+      binary_op_dims4<T, U, Op>(a, b, out_a, out_b, op);
+      return;
+  }
+
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  for (size_t i = 0; i < out_a.size(); i++) {
+    int a_idx = elem_to_loc(i, a.shape(), a.strides());
+    int b_idx = elem_to_loc(i, b.shape(), b.strides());
+    std::tie(dst_a[i], dst_b[i]) = op(a_ptr[a_idx], b_ptr[b_idx]);
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dispatch_dims(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op,
+    int dim,
+    int stride) {
+  // Number of dimensions to loop over for vectorized ops
+  switch (dim) {
+    case 1:
+      binary_op_dims1<T, U, Op>(a, b, out_a, out_b, op, stride);
+      return;
+    case 2:
+      binary_op_dims2<T, U, Op>(a, b, out_a, out_b, op, stride);
+      return;
+  }
+
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst_a = out_a.data<U>();
+  U* dst_b = out_b.data<U>();
+  for (size_t i = 0; i < out_a.size(); i += stride) {
+    int a_idx = elem_to_loc(i, a.shape(), a.strides());
+    int b_idx = elem_to_loc(i, b.shape(), b.strides());
+    op(a_ptr + a_idx, b_ptr + b_idx, dst_a, dst_b, stride);
+    dst_a += stride;
+    dst_b += stride;
+  }
+}
+
+template <
+    typename T,
+    typename U,
+    typename Op,
+    typename OpSV,
+    typename OpVS,
+    typename OpVV>
+void binary_op(
+    const array& a,
+    const array& b,
+    array& out_a,
+    array& out_b,
+    Op op,
+    OpSV opsv,
+    OpVS opvs,
+    OpVV opvv) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out_a, bopt);
+  set_binary_op_output_data(a, b, out_b, bopt);
+
+  // The full computation is scalar scalar so call the base op once
+  if (bopt == ScalarScalar) {
+    std::tie(*(out_a.data<U>()), *(out_b.data<U>())) =
+        op(*a.data<T>(), *b.data<T>());
+    return;
+  }
+
+  // The full computation is scalar vector so delegate to the op
+  if (bopt == ScalarVector) {
+    opsv(
+        a.data<T>(),
+        b.data<T>(),
+        out_a.data<U>(),
+        out_b.data<U>(),
+        b.data_size());
+    return;
+  }
+
+  // The full computation is vector scalar so delegate to the op
+  if (bopt == VectorScalar) {
+    opvs(
+        a.data<T>(),
+        b.data<T>(),
+        out_a.data<U>(),
+        out_b.data<U>(),
+        a.data_size());
+    return;
+  }
+
+  // The full computation is vector vector so delegate to the op
+  if (bopt == VectorVector) {
+    opvv(
+        a.data<T>(),
+        b.data<T>(),
+        out_a.data<U>(),
+        out_b.data<U>(),
+        out_a.size());
+    return;
+  }
+
+  // General computation so let's try to optimize
+
+  // Get the left-most dim such that the array is row contiguous after
+  auto& strides = out_a.strides();
+  auto leftmost_rc_dim = [&strides](const array& arr) {
+    int d = arr.ndim() - 1;
+    for (; d >= 0 && arr.strides()[d] == strides[d]; d--) {
+    }
+    return d + 1;
+  };
+  auto a_rc_dim = leftmost_rc_dim(a);
+  auto b_rc_dim = leftmost_rc_dim(b);
+
+  // Get the left-most dim such that the array is a broadcasted "scalar" after
+  auto leftmost_s_dim = [](const array& arr) {
+    int d = arr.ndim() - 1;
+    for (; d >= 0 && arr.strides()[d] == 0; d--) {
+    }
+    return d + 1;
+  };
+  auto a_s_dim = leftmost_s_dim(a);
+  auto b_s_dim = leftmost_s_dim(b);
+
+  auto ndim = out_a.ndim();
+
+  // Case 1: LxM and FxM where L and F are broadcastable and M is row contiguous
+  int dim = ndim;
+  if (int d = std::max(a_rc_dim, b_rc_dim); d < ndim) {
+    bopt = VectorVector;
+    dim = d;
+    // Case 2: LxM and Fx1 where L and F are broadcastable and M is row
+    // contiguous
+  } else if (int d = std::max(a_rc_dim, b_s_dim); d < ndim) {
+    bopt = VectorScalar;
+    dim = d;
+    // Case 3: Lx1 and FxM where L and F are broadcastable and M is row
+    // contiguous
+  } else if (int d = std::max(a_s_dim, b_rc_dim); d < ndim) {
+    bopt = ScalarVector;
+    dim = d;
+  }
+
+  // Can be sure dim > 0 since otherwise we would have used one of the fully
+  // contiguous methods above. Except for the case that the flags do not
+  // correspond to the underlying contiguity.
+  size_t stride;
+  if (dim == 0 || strides[dim - 1] < 16) {
+    stride = 1;
+    bopt = General;
+    dim = ndim;
+  } else {
+    stride = strides[dim - 1];
+  }
+
+  switch (bopt) {
+    case VectorVector:
+      binary_op_dispatch_dims<T, U>(a, b, out_a, out_b, opvv, dim, stride);
+      break;
+    case VectorScalar:
+      binary_op_dispatch_dims<T, U>(a, b, out_a, out_b, opvs, dim, stride);
+      break;
+    case ScalarVector:
+      binary_op_dispatch_dims<T, U>(a, b, out_a, out_b, opsv, dim, stride);
+      break;
+    default:
+      binary_op_dispatch_dims<T, U>(a, b, out_a, out_b, op);
+      break;
+  }
+}
+
+template <typename T, typename Op, typename OpSV, typename OpVS, typename OpVV>
+void binary_op(
+    const array& a,
+    const array& b,
+    std::vector<array>& outputs,
+    Op op,
+    OpSV opsv,
+    OpVS opvs,
+    OpVV opvv) {
+  // TODO: The following mess of constexpr evaluations can probably be achieved
+  //       with template specializations and overloading. Would it be simpler?
+
+  if (std::is_same<decltype(opsv), UseDefaultBinaryOp>::value) {
+    if (std::is_same<decltype(opvs), UseDefaultBinaryOp>::value) {
+      if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+        // All ops are UseDefaultBinaryOp (why oh why would someone call that?)
+        binary_op<T, T>(
+            a,
+            b,
+            outputs[0],
+            outputs[1],
+            op,
+            DefaultScalarVector<T, T, Op>(op),
+            DefaultVectorScalar<T, T, Op>(op),
+            DefaultVectorVector<T, T, Op>(op));
+      } else {
+        // opsv and opvs were UseDefaultBinaryOp
+        binary_op<T, T>(
+            a,
+            b,
+            outputs[0],
+            outputs[1],
+            op,
+            DefaultScalarVector<T, T, Op>(op),
+            DefaultVectorScalar<T, T, Op>(op),
+            opvv);
+      }
+    } else if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+      // opsv and opvv were UseDefaultBinaryOp
+      binary_op<T, T>(
+          a,
+          b,
+          outputs[0],
+          outputs[1],
+          op,
+          DefaultScalarVector<T, T, Op>(op),
+          opvs,
+          DefaultVectorVector<T, T, Op>(op));
+    } else {
+      // opsv was UseDefaultBinaryOp
+      binary_op<T, T>(
+          a,
+          b,
+          outputs[0],
+          outputs[1],
+          op,
+          DefaultScalarVector<T, T, Op>(op),
+          opvs,
+          opvv);
+    }
+  } else if (std::is_same<decltype(opvs), UseDefaultBinaryOp>::value) {
+    if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+      // opvs and opvv were UseDefaultBinaryOp
+      binary_op<T, T>(
+          a,
+          b,
+          outputs[0],
+          outputs[1],
+          op,
+          opsv,
+          DefaultVectorScalar<T, T, Op>(op),
+          DefaultVectorVector<T, T, Op>(op));
+    } else {
+      // opvs was UseDefaultBinaryOp
+      binary_op<T, T>(
+          a,
+          b,
+          outputs[0],
+          outputs[1],
+          op,
+          opsv,
+          DefaultVectorScalar<T, T, Op>(op),
+          opvv);
+    }
+  } else if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+    // opvv was UseDefaultBinaryOp
+    binary_op<T, T>(
+        a,
+        b,
+        outputs[0],
+        outputs[1],
+        op,
+        opsv,
+        opvs,
+        DefaultVectorVector<T, T, Op>(op));
+  } else {
+    // All ops provided
+    binary_op<T, T>(a, b, outputs[0], outputs[1], op, opsv, opvs, opvv);
+  }
+}
+
+template <typename T, typename Op>
+void binary_op(
+    const array& a,
+    const array& b,
+    std::vector<array>& outputs,
+    Op op) {
+  DefaultScalarVector<T, T, Op> opsv(op);
+  DefaultVectorScalar<T, T, Op> opvs(op);
+  DefaultVectorVector<T, T, Op> opvv(op);
+  binary_op<T, T>(a, b, outputs[0], outputs[1], op, opsv, opvs, opvv);
+}
+
+template <typename... Ops>
+void binary(
+    const array& a,
+    const array& b,
+    std::vector<array>& outputs,
+    Ops... ops) {
+  switch (outputs[0].dtype()) {
+    case bool_:
+      binary_op<bool>(a, b, outputs, ops...);
+      break;
+    case uint8:
+      binary_op<uint8_t>(a, b, outputs, ops...);
+      break;
+    case uint16:
+      binary_op<uint16_t>(a, b, outputs, ops...);
+      break;
+    case uint32:
+      binary_op<uint32_t>(a, b, outputs, ops...);
+      break;
+    case uint64:
+      binary_op<uint64_t>(a, b, outputs, ops...);
+      break;
+    case int8:
+      binary_op<int8_t>(a, b, outputs, ops...);
+      break;
+    case int16:
+      binary_op<int16_t>(a, b, outputs, ops...);
+      break;
+    case int32:
+      binary_op<int32_t>(a, b, outputs, ops...);
+      break;
+    case int64:
+      binary_op<int64_t>(a, b, outputs, ops...);
+      break;
+    case float16:
+      binary_op<float16_t>(a, b, outputs, ops...);
+      break;
+    case float32:
+      binary_op<float>(a, b, outputs, ops...);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t>(a, b, outputs, ops...);
+      break;
+    case complex64:
+      binary_op<complex64_t>(a, b, outputs, ops...);
+      break;
+  }
+}
+
+} // namespace
+
+} // namespace mlx::core

mlx/backend/common/conv.cpp

@@ -357,7 +357,7 @@ void explicit_gemm_conv_1D_cpu(
     gemm_out.set_data(allocator::malloc_or_wait(gemm_out.nbytes()));
   }
 
-  // Peform gemm
+  // Perform gemm
   cblas_sgemm(
       CblasRowMajor,
       CblasNoTrans, // no trans A
@@ -459,7 +459,7 @@ void explicit_gemm_conv_2D_cpu(
     gemm_out.set_data(allocator::malloc_or_wait(gemm_out.nbytes()));
   }
 
-  // Peform gemm
+  // Perform gemm
   cblas_sgemm(
       CblasRowMajor,
       CblasNoTrans, // no trans A

mlx/backend/common/default_primitives.cpp

@@ -1,6 +1,10 @@
 // Copyright © 2023 Apple Inc.
 
+#ifdef ACCELERATE_NEW_LAPACK
+#include <vecLib/cblas_new.h>
+#else
 #include <cblas.h>
+#endif
 
 #include "mlx/array.h"
 #include "mlx/backend/common/copy.h"
@@ -12,6 +16,12 @@
     primitive::eval(inputs, out);                                          \
   }
 
+#define DEFAULT_MULTI(primitive)                                       \
+  void primitive::eval_cpu(                                            \
+      const std::vector<array>& inputs, std::vector<array>& outputs) { \
+    primitive::eval(inputs, outputs);                                  \
+  }
+
 namespace mlx::core {
 
 DEFAULT(Abs)
@@ -29,17 +39,20 @@ DEFAULT(ArgSort)
 DEFAULT(AsType)
 DEFAULT(AsStrided)
 DEFAULT(Broadcast)
+DEFAULT(Ceil)
 DEFAULT(Concatenate)
 DEFAULT(Convolution)
 DEFAULT(Copy)
 DEFAULT(Cos)
 DEFAULT(Cosh)
 DEFAULT(Divide)
+DEFAULT(Remainder)
 DEFAULT(Equal)
 DEFAULT(Erf)
 DEFAULT(ErfInv)
 DEFAULT(Exp)
 DEFAULT(FFT)
+DEFAULT(Floor)
 DEFAULT(Full)
 DEFAULT(Gather)
 DEFAULT(Greater)
@@ -50,6 +63,8 @@ DEFAULT(Load)
 DEFAULT(Log)
 DEFAULT(Log1p)
 DEFAULT(LogicalNot)
+DEFAULT(LogicalAnd)
+DEFAULT(LogicalOr)
 DEFAULT(LogAddExp)
 DEFAULT(Maximum)
 DEFAULT(Minimum)
@@ -59,9 +74,11 @@ DEFAULT(NotEqual)
 DEFAULT(Pad)
 DEFAULT(Partition)
 DEFAULT(Power)
+DEFAULT(QuantizedMatmul)
 DEFAULT(RandomBits)
 DEFAULT(Reduce)
 DEFAULT(Reshape)
+DEFAULT(Round)
 DEFAULT(Scan)
 DEFAULT(Scatter)
 DEFAULT(Sigmoid)
@@ -78,6 +95,7 @@ DEFAULT(Subtract)
 DEFAULT(Tan)
 DEFAULT(Tanh)
 DEFAULT(Transpose)
+DEFAULT_MULTI(DivMod)
 
 void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
   if (out.dtype() != float32) {

mlx/backend/common/load.cpp

@@ -5,7 +5,7 @@
 #include <utility>
 
 #include "mlx/allocator.h"
-#include "mlx/load.h"
+#include "mlx/io/load.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
@@ -13,7 +13,7 @@ namespace mlx::core {
 namespace {
 
 template <const uint8_t scalar_size>
-void swap_endianess(uint8_t* data_bytes, size_t N) {
+void swap_endianness(uint8_t* data_bytes, size_t N) {
   struct Elem {
     uint8_t bytes[scalar_size];
   };
@@ -39,13 +39,13 @@ void Load::eval(const std::vector<array>& inputs, array& out) {
   if (swap_endianness_) {
     switch (out.itemsize()) {
       case 2:
-        swap_endianess<2>(out.data<uint8_t>(), out.data_size());
+        swap_endianness<2>(out.data<uint8_t>(), out.data_size());
         break;
       case 4:
-        swap_endianess<4>(out.data<uint8_t>(), out.data_size());
+        swap_endianness<4>(out.data<uint8_t>(), out.data_size());
         break;
       case 8:
-        swap_endianess<8>(out.data<uint8_t>(), out.data_size());
+        swap_endianness<8>(out.data<uint8_t>(), out.data_size());
         break;
     }
   }

mlx/backend/common/primitives.cpp

@@ -8,6 +8,7 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/arange.h"
+#include "mlx/backend/common/binary.h"
 #include "mlx/backend/common/copy.h"
 #include "mlx/backend/common/erf.h"
 #include "mlx/backend/common/threefry.h"
@@ -167,6 +168,17 @@ void Broadcast::eval(const std::vector<array>& inputs, array& out) {
   out.copy_shared_buffer(in, strides, flags, in.data_size());
 }
 
+void Ceil::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  auto& in = inputs[0];
+  if (not is_integral(in.dtype())) {
+    unary_fp(in, out, [](auto x) { return std::ceil(x); });
+  } else {
+    // No-op integer types
+    out.copy_shared_buffer(in);
+  }
+}
+
 void Concatenate::eval(const std::vector<array>& inputs, array& out) {
   std::vector<int> sizes;
   sizes.push_back(0);
@@ -287,6 +299,17 @@ void Exp::eval(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void Floor::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  auto& in = inputs[0];
+  if (not is_integral(in.dtype())) {
+    unary_fp(in, out, [](auto x) { return std::floor(x); });
+  } else {
+    // No-op integer types
+    out.copy_shared_buffer(in);
+  }
+}
+
 void Full::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
@@ -342,6 +365,20 @@ void LogicalNot::eval(const std::vector<array>& inputs, array& out) {
   unary(in, out, [](auto x) { return !x; });
 }
 
+void LogicalAnd::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2); // LogicalAnd requires two input arrays
+  auto& in1 = inputs[0];
+  auto& in2 = inputs[1];
+  binary(in1, in2, out, [](auto x, auto y) { return x && y; });
+}
+
+void LogicalOr::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2); // LogicalOr requires two input arrays
+  auto& in1 = inputs[0];
+  auto& in2 = inputs[1];
+  binary(in1, in2, out, [](auto x, auto y) { return x || y; });
+}
+
 void Negative::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
@@ -444,6 +481,17 @@ void Reshape::eval(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void Round::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  auto& in = inputs[0];
+  if (not is_integral(in.dtype())) {
+    unary_fp(in, out, RoundOp());
+  } else {
+    // No-op integer types
+    out.copy_shared_buffer(in);
+  }
+}
+
 void Sigmoid::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];

mlx/backend/common/quantized.cpp

@@ -0,0 +1,268 @@
+// Copyright © 2023 Apple Inc.
+
+#include <cassert>
+#include <iostream>
+
+#include "mlx/backend/metal/copy.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+namespace {
+
+template <typename T, int bits, int group_size>
+void _qmm(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const T* scales,
+    const T* biases,
+    int M,
+    int N,
+    int K) {
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int pack_factor = 32 / bits;
+  constexpr int packs_in_group = group_size / pack_factor;
+  const int Ng = N / group_size;
+  const int Nw = N / pack_factor;
+
+  for (int m = 0; m < M; m++) {
+    const uint32_t* w_local = w;
+    const T* scales_local = scales;
+    const T* biases_local = biases;
+
+    std::fill(result, result + N, 0);
+
+    for (int k = 0; k < K; k++) {
+      T* result_local = result;
+      T xi = *x++;
+
+      for (int n = 0; n < N; n += group_size) {
+        T scale = *scales_local++;
+        T bias = *biases_local++;
+        for (int ng = 0; ng < packs_in_group; ng++) {
+          uint32_t wi = *w_local++;
+
+#pragma clang loop unroll(full)
+          for (int p = 0; p < pack_factor; p++) {
+            (*result_local++) +=
+                xi * (scale * static_cast<T>(wi & bitmask) + bias);
+            wi >>= bits;
+          }
+        }
+      }
+    }
+
+    result += N;
+  }
+}
+
+template <typename T, int bits, int group_size>
+void _qmm_t(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const T* scales,
+    const T* biases,
+    int M,
+    int N,
+    int K) {
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int pack_factor = 32 / bits;
+  constexpr int packs_in_group = group_size / pack_factor;
+  const int Kg = K / group_size;
+  const int Kw = K / pack_factor;
+
+  for (int m = 0; m < M; m++) {
+    const uint32_t* w_local = w;
+    const T* scales_local = scales;
+    const T* biases_local = biases;
+
+    for (int n = 0; n < N; n++) {
+      const T* x_local = x;
+      T sum = 0;
+      for (int k = 0; k < K; k += group_size) {
+        T scale = *scales_local++;
+        T bias = *biases_local++;
+
+        for (int kw = 0; kw < packs_in_group; kw++) {
+          uint32_t wi = *w_local++;
+
+#pragma clang loop unroll(full)
+          for (int p = 0; p < pack_factor; p++) {
+            sum += (*x_local++) * (scale * static_cast<T>(wi & bitmask) + bias);
+            wi >>= bits;
+          }
+        }
+      }
+      *result = sum;
+      result++;
+    }
+
+    x += K;
+  }
+}
+
+template <typename T>
+void _qmm_dispatch_typed(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const T* scales,
+    const T* biases,
+    int M,
+    int N,
+    int K,
+    int group_size,
+    int bits,
+    bool transposed_w) {
+  switch (bits) {
+    case 2: {
+      switch (group_size) {
+        case 64:
+          if (transposed_w) {
+            return _qmm_t<T, 2, 64>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 2, 64>(result, x, w, scales, biases, M, N, K);
+          }
+        case 128:
+          if (transposed_w) {
+            return _qmm_t<T, 2, 128>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 2, 128>(result, x, w, scales, biases, M, N, K);
+          }
+      }
+    }
+    case 4: {
+      switch (group_size) {
+        case 64:
+          if (transposed_w) {
+            return _qmm_t<T, 4, 64>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 4, 64>(result, x, w, scales, biases, M, N, K);
+          }
+        case 128:
+          if (transposed_w) {
+            return _qmm_t<T, 4, 128>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 4, 128>(result, x, w, scales, biases, M, N, K);
+          }
+      }
+    }
+    case 8: {
+      switch (group_size) {
+        case 64:
+          if (transposed_w) {
+            return _qmm_t<T, 8, 64>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 8, 64>(result, x, w, scales, biases, M, N, K);
+          }
+        case 128:
+          if (transposed_w) {
+            return _qmm_t<T, 8, 128>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 8, 128>(result, x, w, scales, biases, M, N, K);
+          }
+      }
+    }
+  }
+  std::ostringstream msg;
+  msg << "Quantization type not supported. Provided bits=" << bits
+      << " and group_size=" << group_size
+      << ". The supported options are bits in "
+      << "{2, 4, 8} and group_size in {64, 128}.";
+  throw std::invalid_argument(msg.str());
+}
+
+void _qmm_dispatch(
+    array out,
+    const array& x,
+    const array& w,
+    const array& scales,
+    const array& biases,
+    int bits,
+    int group_size,
+    bool transposed_w) {
+  int K = x.shape(-1);
+  int M = x.size() / K;
+  int N = out.shape(-1);
+
+  switch (x.dtype()) {
+    case float32:
+      _qmm_dispatch_typed<float>(
+          out.data<float>(),
+          x.data<float>(),
+          w.data<uint32_t>(),
+          scales.data<float>(),
+          biases.data<float>(),
+          M,
+          N,
+          K,
+          bits,
+          group_size,
+          transposed_w);
+      break;
+    case float16:
+      _qmm_dispatch_typed<float16_t>(
+          out.data<float16_t>(),
+          x.data<float16_t>(),
+          w.data<uint32_t>(),
+          scales.data<float16_t>(),
+          biases.data<float16_t>(),
+          M,
+          N,
+          K,
+          bits,
+          group_size,
+          transposed_w);
+      break;
+    case bfloat16:
+      _qmm_dispatch_typed<bfloat16_t>(
+          out.data<bfloat16_t>(),
+          x.data<bfloat16_t>(),
+          w.data<uint32_t>(),
+          scales.data<bfloat16_t>(),
+          biases.data<bfloat16_t>(),
+          M,
+          N,
+          K,
+          bits,
+          group_size,
+          transposed_w);
+      break;
+    default:
+      throw std::invalid_argument(
+          "[quantized_matmul] only floating types are supported");
+  }
+}
+
+} // namespace
+
+void QuantizedMatmul::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 4);
+
+  auto& x_pre = inputs[0];
+  auto& w_pre = inputs[1];
+  auto& scales_pre = inputs[2];
+  auto& biases_pre = inputs[3];
+
+  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 x = ensure_row_contiguous(x_pre);
+  auto w = ensure_row_contiguous(w_pre);
+  auto scales = ensure_row_contiguous(scales_pre);
+  auto biases = ensure_row_contiguous(biases_pre);
+
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
+}
+
+} // namespace mlx::core

mlx/backend/common/reduce.h

@@ -126,7 +126,7 @@ struct ReductionPlan {
 ReductionPlan get_reduction_plan(const array& x, const std::vector<int> axes) {
   // The data is all there and we are reducing over everything
   if (x.size() == x.data_size() && axes.size() == x.ndim() &&
-      (x.flags().row_contiguous || x.flags().col_contiguous)) {
+      x.flags().contiguous) {
     return ContiguousAllReduce;
   }
 

mlx/backend/common/unary.h

@@ -53,6 +53,17 @@ struct SignOp {
   }
 };
 
+struct RoundOp {
+  template <typename T>
+  T operator()(T x) {
+    return std::round(x);
+  }
+
+  complex64_t operator()(complex64_t x) {
+    return {std::round(x.real()), std::round(x.imag())};
+  }
+};
+
 template <typename T, typename Op>
 void unary_op(const array& a, array& out, Op op) {
   const T* a_ptr = a.data<T>();

mlx/backend/metal/CMakeLists.txt

@@ -10,6 +10,7 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/metal.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp

mlx/backend/metal/allocator.cpp

@@ -26,13 +26,10 @@ namespace metal {
 namespace {
 
 BufferCache::BufferCache(MTL::Device* device)
-    : device_(device),
-      head_(nullptr),
-      tail_(nullptr),
-      pool_size_(0),
-      gc_limit_(0.95 * device_->recommendedMaxWorkingSetSize()) {}
+    : device_(device), head_(nullptr), tail_(nullptr), pool_size_(0) {}
 
 BufferCache::~BufferCache() {
+  auto thread_pool = metal::new_scoped_memory_pool();
   clear();
 }
 
@@ -54,12 +51,16 @@ MTL::Buffer* BufferCache::reuse_from_cache(size_t size) {
 
   // Find the closest buffer in pool
   MTL::Buffer* pbuf = nullptr;
+
+  // Make sure we use most of the available memory
   auto it = buffer_pool_.lower_bound(size);
 
-  // Make sure we use > 50% of the available memory
-  while (!pbuf && it != buffer_pool_.end() && it->first < 2 * size) {
+  // Make sure we use most of the available memory
+  while (!pbuf && it != buffer_pool_.end() &&
+         it->first < std::min(2 * size, size + 2 * vm_page_size)) {
     // Collect from the cache
     pbuf = it->second->buf;
+
     // Remove from cache
     remove_from_list(it->second);
     delete it->second;
@@ -85,13 +86,9 @@ void BufferCache::recycle_to_cache(MTL::Buffer* buf) {
   }
 }
 
-size_t BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
-  min_bytes_to_free += device_->currentAllocatedSize() - gc_limit_;
-
+void BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
   if (min_bytes_to_free >= 0.9 * pool_size_) {
-    size_t old_pool_size = pool_size_;
     clear();
-    return old_pool_size;
   } else {
     std::lock_guard<std::mutex> lk(cache_mutex_);
     size_t total_bytes_freed = 0;
@@ -104,9 +101,7 @@ size_t BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
       }
       remove_from_list(tail_);
     }
-
     pool_size_ -= total_bytes_freed;
-    return total_bytes_freed;
   }
 }
 
@@ -125,8 +120,9 @@ void BufferCache::add_at_head(BufferCache::BufferHolder* to_add) {
 }
 
 void BufferCache::remove_from_list(BufferCache::BufferHolder* to_remove) {
-  if (!to_remove)
+  if (!to_remove) {
     return;
+  }
 
   // If in the middle
   if (to_remove->prev && to_remove->next) {
@@ -153,26 +149,37 @@ MetalAllocator::MetalAllocator()
     : device_(device(mlx::core::Device::gpu).mtl_device()),
       buffer_cache_(device_),
       peak_allocated_size_(0),
-      block_limit_(1.5 * device_->recommendedMaxWorkingSetSize()) {}
+      block_limit_(1.5 * device_->recommendedMaxWorkingSetSize()),
+      gc_limit_(0.95 * device_->recommendedMaxWorkingSetSize()) {}
+
+Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
+  // Metal doesn't like empty buffers
+  if (size == 0) {
+    return Buffer{nullptr};
+  }
 
-Buffer MetalAllocator::malloc(size_t size) {
   // Align up memory
   if (size > vm_page_size) {
     size = vm_page_size * ((size + vm_page_size - 1) / vm_page_size);
   }
 
+  // Try the cache
   MTL::Buffer* buf = buffer_cache_.reuse_from_cache(size);
 
-  // Prepare to allocate new memory as needed
   if (!buf) {
-    // If we are under very high memoory pressure, we don't allocate further
-    if (device_->currentAllocatedSize() >= block_limit_) {
+    // If there is too much memory pressure, fail (likely causes a wait).
+    if (!allow_swap && device_->currentAllocatedSize() + size >= block_limit_) {
       return Buffer{nullptr};
     }
 
-    // If we are still under memory pressure, try cleaning cache
-    if (buffer_cache_.can_garbage_collect()) {
-      buffer_cache_.release_cached_buffers(size);
+    auto thread_pool = metal::new_scoped_memory_pool();
+
+    // If we have a lot of memory pressure, check if we can reclaim some memory
+    // from the cache
+    if (device_->currentAllocatedSize() + size >= gc_limit_) {
+      size_t min_bytes_to_free =
+          size + device_->currentAllocatedSize() - gc_limit_;
+      buffer_cache_.release_cached_buffers(min_bytes_to_free);
     }
 
     // Allocate new buffer if needed

mlx/backend/metal/allocator.h

@@ -23,11 +23,7 @@ class BufferCache {
 
   MTL::Buffer* reuse_from_cache(size_t size);
   void recycle_to_cache(MTL::Buffer* buf);
-  size_t release_cached_buffers(size_t min_bytes_to_free);
-
-  bool can_garbage_collect() {
-    return pool_size_ > 0 && device_->currentAllocatedSize() > gc_limit_;
-  }
+  void release_cached_buffers(size_t min_bytes_to_free);
 
  private:
   struct BufferHolder {
@@ -49,7 +45,6 @@ class BufferCache {
   BufferHolder* head_;
   BufferHolder* tail_;
   size_t pool_size_;
-  size_t gc_limit_;
 };
 
 } // namespace
@@ -57,7 +52,7 @@ class BufferCache {
 class MetalAllocator : public allocator::Allocator {
   /** Allocator for Metal GPUs. */
  public:
-  virtual Buffer malloc(size_t size) override;
+  virtual Buffer malloc(size_t size, bool allow_swap = false) override;
   virtual void free(Buffer buffer) override;
 
  private:
@@ -71,6 +66,7 @@ class MetalAllocator : public allocator::Allocator {
   // Allocation stats
   size_t peak_allocated_size_;
   size_t block_limit_;
+  size_t gc_limit_;
 };
 
 MetalAllocator& allocator();

mlx/backend/metal/conv.cpp

@@ -68,7 +68,7 @@ void explicit_gemm_conv_1D_gpu(
   array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
   copy_gpu(in_strided_view, in_strided, CopyType::General, s);
 
-  // Peform gemm
+  // Perform gemm
   std::vector<array> copies = {in_padded, in_strided};
   mlx_matmul(
       s,
@@ -260,7 +260,7 @@ void explicit_gemm_conv_2D_gpu(
   array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
   copy_gpu(in_strided_view, in_strided, CopyType::General, s);
 
-  // Peform gemm
+  // Perform gemm
   std::vector<array> copies = {in_padded, in_strided};
   mlx_matmul(
       s,

mlx/backend/metal/copy.cpp

@@ -20,6 +20,9 @@ void copy_gpu(const array& in, array& out, CopyType ctype, const Stream& s) {
   } else {
     out.set_data(allocator::malloc_or_wait(out.nbytes()));
   }
+  if (out.size() == 0) {
+    return;
+  }
   if (ctype == CopyType::GeneralGeneral) {
     ctype = CopyType::General;
   }

mlx/backend/metal/device.cpp

@@ -17,8 +17,6 @@ namespace fs = std::filesystem;
 
 namespace mlx::core::metal {
 
-static Device metal_device_;
-
 namespace {
 
 // TODO nicer way to set this or possibly expose as an environment variable
@@ -27,7 +25,8 @@ static constexpr int MAX_BUFFERS_PER_QUEUE = 12;
 static constexpr const char* default_mtllib_path = METAL_PATH;
 
 auto load_device() {
-  MTL::Device* device = MTL::CreateSystemDefaultDevice();
+  auto devices = MTL::CopyAllDevices();
+  auto device = static_cast<MTL::Device*>(devices->object(0));
   if (!device) {
     throw std::runtime_error("Failed to load device");
   }
@@ -44,6 +43,25 @@ std::pair<MTL::Library*, NS::Error*> load_library_from_path(
   return std::make_pair(lib, error);
 }
 
+#ifdef SWIFTPM_BUNDLE
+MTL::Library* try_load_bundle(MTL::Device* device, NS::URL* url) {
+  std::string bundle_path = std::string(url->fileSystemRepresentation()) + "/" +
+      SWIFTPM_BUNDLE + ".bundle";
+  auto bundle = NS::Bundle::alloc()->init(
+      NS::String::string(bundle_path.c_str(), NS::UTF8StringEncoding));
+  if (bundle != nullptr) {
+    std::string resource_path =
+        std::string(bundle->resourceURL()->fileSystemRepresentation()) + "/" +
+        "default.metallib";
+    auto [lib, error] = load_library_from_path(device, resource_path.c_str());
+    if (lib) {
+      return lib;
+    }
+  }
+  return nullptr;
+}
+#endif
+
 MTL::Library* load_library(
     MTL::Device* device,
     const std::string& lib_name = "mlx",
@@ -57,6 +75,26 @@ MTL::Library* load_library(
     }
   }
 
+#ifdef SWIFTPM_BUNDLE
+  // try to load from a swiftpm resource bundle -- scan the available bundles to
+  // find one that contains the named bundle
+  {
+    MTL::Library* library =
+        try_load_bundle(device, NS::Bundle::mainBundle()->bundleURL());
+    if (library != nullptr) {
+      return library;
+    }
+    auto bundles = NS::Bundle::allBundles();
+    for (int i = 0, c = (int)bundles->count(); i < c; i++) {
+      auto bundle = reinterpret_cast<NS::Bundle*>(bundles->object(i));
+      library = try_load_bundle(device, bundle->resourceURL());
+      if (library != nullptr) {
+        return library;
+      }
+    }
+  }
+#endif
+
   // Couldn't find it so let's load it from default_mtllib_path
   {
     auto [lib, error] = load_library_from_path(device, lib_path);
@@ -73,15 +111,23 @@ MTL::Library* load_library(
 
 } // namespace
 
-Device::Device()
-    : pool_(NS::AutoreleasePool::alloc()->init()),
-      device_(load_device()),
-      library_map_({{"mlx", load_library(device_)}}) {}
+Device::Device() {
+  auto pool = new_scoped_memory_pool();
+  device_ = load_device();
+  library_map_ = {{"mlx", load_library(device_)}};
+}
 
 Device::~Device() {
+  auto pool = new_scoped_memory_pool();
   for (auto& q : queue_map_) {
     q.second->release();
   }
+  for (auto& b : buffer_map_) {
+    b.second.second->release();
+  }
+  for (auto& e : encoder_map_) {
+    e.second->release();
+  }
   for (auto& k : kernel_map_) {
     k.second->release();
   }
@@ -89,10 +135,11 @@ Device::~Device() {
     l.second->release();
   }
   device_->release();
-  pool_->release();
 }
 
 void Device::new_queue(int index) {
+  auto thread_pool = metal::new_scoped_memory_pool();
+
   // Multiple threads can ask the device for queues
   // We lock this as a critical section for safety
   const std::lock_guard<std::mutex> lock(mtx_);
@@ -198,6 +245,7 @@ void Device::register_library(
 MTL::ComputePipelineState* Device::get_kernel(
     const std::string& name,
     const std::string& lib_name /* = "mlx" */) {
+  auto pool = new_scoped_memory_pool();
   // Look for cached kernel
   if (auto it = kernel_map_.find(name); it != kernel_map_.end()) {
     return it->second;
@@ -240,17 +288,18 @@ MTL::ComputePipelineState* Device::get_kernel(
 }
 
 Device& device(mlx::core::Device) {
-  return metal_device_;
+  static Device metal_device;
+  return metal_device;
 }
 
-NS::AutoreleasePool*& thread_autorelease_pool() {
-  static thread_local NS::AutoreleasePool* p =
-      NS::AutoreleasePool::alloc()->init();
-  return p;
+std::shared_ptr<void> new_scoped_memory_pool() {
+  auto dtor = [](void* ptr) {
+    static_cast<NS::AutoreleasePool*>(ptr)->release();
+  };
+  return std::shared_ptr<void>(NS::AutoreleasePool::alloc()->init(), dtor);
 }
 
 void new_stream(Stream stream) {
-  thread_autorelease_pool();
   if (stream.device == mlx::core::Device::gpu) {
     device(stream.device).new_queue(stream.index);
   }

mlx/backend/metal/device.h

@@ -67,7 +67,6 @@ class Device {
       const std::vector<MTL::ArgumentDescriptor*>& arg_descs) const;
 
  private:
-  NS::AutoreleasePool* pool_;
   MTL::Device* device_;
   std::unordered_map<int32_t, MTL::CommandQueue*> queue_map_;
   std::unordered_map<int32_t, std::pair<int, MTL::CommandBuffer*>> buffer_map_;
@@ -78,6 +77,5 @@ class Device {
 };
 
 Device& device(mlx::core::Device);
-NS::AutoreleasePool*& thread_autorelease_pool();
 
 } // namespace mlx::core::metal

mlx/backend/metal/indexing.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2023 Apple Inc.
-
 #include <algorithm>
 #include <cassert>
 #include <numeric>
@@ -33,6 +32,9 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 
   out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  if (out.size() == 0) {
+    return;
+  }
 
   auto& s = stream();
   auto& d = metal::device(s.device);
@@ -102,7 +104,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
         static_cast<size_t*>(idx_strides_buf.raw_ptr()) + i * idx_ndim);
   }
 
-  // Allocate the argument bufer
+  // Allocate the argument buffer
   auto arg_buf = allocator::malloc_or_wait(arg_enc->encodedLength());
 
   // Register data with the encoder
@@ -110,14 +112,18 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   for (int i = 0; i < nidx; ++i) {
     set_array_buffer(compute_encoder, arg_enc, inputs[i + 1], i);
   }
-  arg_enc->setBuffer(
-      static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()), 0, nidx + 1);
-  compute_encoder->useResource(
-      static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()), MTL::ResourceUsageRead);
-  arg_enc->setBuffer(
-      static_cast<MTL::Buffer*>(idx_strides_buf.ptr()), 0, nidx + 2);
-  compute_encoder->useResource(
-      static_cast<MTL::Buffer*>(idx_strides_buf.ptr()), MTL::ResourceUsageRead);
+  if (idx_ndim > 0) {
+    arg_enc->setBuffer(
+        static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()), 0, nidx + 1);
+    compute_encoder->useResource(
+        static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()),
+        MTL::ResourceUsageRead);
+    arg_enc->setBuffer(
+        static_cast<MTL::Buffer*>(idx_strides_buf.ptr()), 0, nidx + 2);
+    compute_encoder->useResource(
+        static_cast<MTL::Buffer*>(idx_strides_buf.ptr()),
+        MTL::ResourceUsageRead);
+  }
   *static_cast<int*>(arg_enc->constantData(nidx + 3)) = idx_ndim;
 
   // Set all the buffers
@@ -163,6 +169,11 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
       inputs[0].data_size() == 1 ? CopyType::Scalar : CopyType::General;
   copy_gpu(inputs[0], out, copy_type);
 
+  // Empty update
+  if (inputs.back().size() == 0) {
+    return;
+  }
+
   // Get stream
   auto& s = stream();
   auto& d = metal::device(s.device);
@@ -246,7 +257,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
         static_cast<size_t*>(idx_strides_buf.raw_ptr()) + i * idx_ndim);
   }
 
-  // Allocate the argument bufer
+  // Allocate the argument buffer
   auto arg_buf = allocator::malloc_or_wait(arg_enc->encodedLength());
 
   // Register data with the encoder
@@ -254,14 +265,18 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   for (int i = 0; i < nidx; ++i) {
     set_array_buffer(compute_encoder, arg_enc, inputs[i + 1], i);
   }
-  arg_enc->setBuffer(
-      static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()), 0, nidx + 1);
-  compute_encoder->useResource(
-      static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()), MTL::ResourceUsageRead);
-  arg_enc->setBuffer(
-      static_cast<MTL::Buffer*>(idx_strides_buf.ptr()), 0, nidx + 2);
-  compute_encoder->useResource(
-      static_cast<MTL::Buffer*>(idx_strides_buf.ptr()), MTL::ResourceUsageRead);
+  if (idx_ndim > 0) {
+    arg_enc->setBuffer(
+        static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()), 0, nidx + 1);
+    compute_encoder->useResource(
+        static_cast<MTL::Buffer*>(idx_shapes_buf.ptr()),
+        MTL::ResourceUsageRead);
+    arg_enc->setBuffer(
+        static_cast<MTL::Buffer*>(idx_strides_buf.ptr()), 0, nidx + 2);
+    compute_encoder->useResource(
+        static_cast<MTL::Buffer*>(idx_strides_buf.ptr()),
+        MTL::ResourceUsageRead);
+  }
   *static_cast<int*>(arg_enc->constantData(nidx + 3)) = idx_ndim;
 
   compute_encoder->setBuffer(static_cast<MTL::Buffer*>(arg_buf.ptr()), 0, 0);
@@ -272,14 +287,32 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   set_array_buffer(compute_encoder, upd, 1);
   set_array_buffer(compute_encoder, out, 2);
-  compute_encoder->setBytes(upd.shape().data(), upd_ndim * sizeof(int), 3);
-  compute_encoder->setBytes(upd.strides().data(), upd_ndim * sizeof(size_t), 4);
+  if (upd_ndim == 0) {
+    // Need placeholders so Metal doesn't compalain
+    int shape_ = 0;
+    size_t stride_ = 0;
+    compute_encoder->setBytes(&shape_, sizeof(int), 3);
+    compute_encoder->setBytes(&stride_, sizeof(size_t), 4);
+  } else {
+    compute_encoder->setBytes(upd.shape().data(), upd_ndim * sizeof(int), 3);
+    compute_encoder->setBytes(
+        upd.strides().data(), upd_ndim * sizeof(size_t), 4);
+  }
   compute_encoder->setBytes(&upd_ndim, sizeof(size_t), 5);
   compute_encoder->setBytes(&upd_size, sizeof(size_t), 6);
 
   size_t out_ndim = out.ndim();
-  compute_encoder->setBytes(out.shape().data(), out_ndim * sizeof(int), 7);
-  compute_encoder->setBytes(out.strides().data(), out_ndim * sizeof(size_t), 8);
+  if (out_ndim == 0) {
+    // Need placeholders so Metal doesn't compalain
+    int shape_ = 0;
+    size_t stride_ = 0;
+    compute_encoder->setBytes(&shape_, sizeof(int), 7);
+    compute_encoder->setBytes(&stride_, sizeof(size_t), 8);
+  } else {
+    compute_encoder->setBytes(out.shape().data(), out_ndim * sizeof(int), 7);
+    compute_encoder->setBytes(
+        out.strides().data(), out_ndim * sizeof(size_t), 8);
+  }
   compute_encoder->setBytes(&out_ndim, sizeof(size_t), 9);
   compute_encoder->setBytes(axes_.data(), axes_.size() * sizeof(int), 10);
 

mlx/backend/metal/kernels/CMakeLists.txt

@@ -14,10 +14,12 @@ set(
   "arange"
   "arg_reduce"
   "binary"
+  "binary_two"
   "conv"
   "copy"
   "gemm"
   "gemv"
+  "quantized"
   "random"
   "reduce"
   "scan"

mlx/backend/metal/kernels/arg_reduce.metal

@@ -114,7 +114,7 @@ template <typename T, typename Op, int N_READS>
   //    4. Reduce among them and go to 3
   //    4. Reduce in each simd_group
   //    6. Write in the thread local memory
-  //    6. Reduce them accross thread group
+  //    6. Reduce them across thread group
   //    7. Write the output without need for atomic
   Op op;
 

mlx/backend/metal/kernels/binary.metal

@@ -14,6 +14,13 @@ struct Divide {
   template <typename T> T operator()(T x, T y) { return x / y; }
 };
 
+struct Remainder {
+  template <typename T> T operator()(T x, T y) { return x % y; }
+  template <> float operator()(float x, float y) { return fmod(x, y); }
+  template <> half operator()(half x, half y) { return fmod(x, y); }
+  template <> bfloat16_t operator()(bfloat16_t x, bfloat16_t y) { return fmod(x, y); }
+};
+
 struct Equal {
   template <typename T> bool operator()(T x, T y) { return x == y; }
 };
@@ -124,6 +131,16 @@ struct Subtract {
   template <typename T> T operator()(T x, T y) { return x - y; }
 };
 
+struct LogicalAnd {
+    template <typename T>
+    T operator()(T x, T y) { return x && y; };
+};
+
+struct LogicalOr {
+    template <typename T>
+    T operator()(T x, T y) { return x || y; };
+};
+
 template <typename T, typename U, typename Op>
 [[kernel]] void binary_op_s2s(
     device const T* a,
@@ -350,7 +367,7 @@ template <typename T, typename U, typename Op>
   instantiate_binary_all(name, complex64, complex64_t, bool, op)
 
 instantiate_binary_types(add, Add)
-instantiate_binary_float(div, Divide)
+instantiate_binary_types(div, Divide)
 instantiate_binary_types_bool(eq, Equal)
 instantiate_binary_types_bool(ge, Greater)
 instantiate_binary_types_bool(geq, GreaterEqual)
@@ -363,9 +380,13 @@ instantiate_binary_types(min, Minimum)
 instantiate_binary_types(mul, Multiply)
 instantiate_binary_types(sub, Subtract)
 instantiate_binary_types(pow, Power)
+instantiate_binary_types(rem, Remainder)
 
 // NaNEqual only needed for floating point types with boolean output
 instantiate_binary_all(naneq, float16, half, bool, NaNEqual)
 instantiate_binary_all(naneq, float32, float, bool, NaNEqual)
 instantiate_binary_all(naneq, bfloat16, bfloat16_t, bool, NaNEqual)
 instantiate_binary_all(naneq, complex64, complex64_t, bool, NaNEqual)
+
+instantiate_binary_all(lor, bool_, bool, bool, LogicalOr)
+instantiate_binary_all(land, bool_, bool, bool, LogicalAnd)

mlx/backend/metal/kernels/binary_two.metal

@@ -0,0 +1,259 @@
+// Copyright © 2023 Apple Inc.
+
+#include <metal_integer>
+#include <metal_math>
+
+#include "mlx/backend/metal/kernels/utils.h"
+#include "mlx/backend/metal/kernels/bf16.h"
+
+struct FloorDivide {
+  template <typename T> T operator()(T x, T y) { return x / y; }
+  template <> float operator()(float x, float y) { return trunc(x / y); }
+  template <> half operator()(half x, half y) { return trunc(x / y); }
+  template <> bfloat16_t operator()(bfloat16_t x, bfloat16_t y) { return trunc(x / y); }
+};
+
+struct Remainder {
+  template <typename T> T operator()(T x, T y) { return x % y; }
+  template <> float operator()(float x, float y) { return fmod(x, y); }
+  template <> half operator()(half x, half y) { return fmod(x, y); }
+  template <> bfloat16_t operator()(bfloat16_t x, bfloat16_t y) { return fmod(x, y); }
+};
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_s2s(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    uint index [[thread_position_in_grid]]) {
+  c[index] = Op1()(a[0], b[0]);
+  d[index] = Op2()(a[0], b[0]);
+}
+
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_ss(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    uint index [[thread_position_in_grid]]) {
+  c[index] = Op1()(a[0], b[0]);
+  d[index] = Op2()(a[0], b[0]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_sv(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    uint index [[thread_position_in_grid]]) {
+  c[index] = Op1()(a[0], b[index]);
+  d[index] = Op2()(a[0], b[index]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_vs(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    uint index [[thread_position_in_grid]]) {
+  c[index] = Op1()(a[index], b[0]);
+  d[index] = Op2()(a[index], b[0]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_vv(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    uint index [[thread_position_in_grid]]) {
+  c[index] = Op1()(a[index], b[index]);
+  d[index] = Op2()(a[index], b[index]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_g_nd1(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    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);
+  c[index] = Op1()(a[a_idx], b[b_idx]);
+  d[index] = Op2()(a[a_idx], b[b_idx]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_g_nd2(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    constant const size_t a_strides[2],
+    constant const size_t b_strides[2],
+    uint2 index [[thread_position_in_grid]],
+    uint2 grid_dim [[threads_per_grid]]) {
+  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;
+  c[out_idx] = Op1()(a[a_idx], b[b_idx]);
+  d[out_idx] = Op2()(a[a_idx], b[b_idx]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_g_nd3(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    constant const size_t a_strides[3],
+    constant const size_t b_strides[3],
+    uint3 index [[thread_position_in_grid]],
+    uint3 grid_dim [[threads_per_grid]]) {
+  auto a_idx = elem_to_loc_3(index, a_strides);
+  auto b_idx = elem_to_loc_3(index, b_strides);
+  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  c[out_idx] = Op1()(a[a_idx], b[b_idx]);
+  d[out_idx] = Op2()(a[a_idx], b[b_idx]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2, int DIM>
+[[kernel]] void binary_op_g_nd(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    constant const int shape[DIM],
+    constant const size_t a_strides[DIM],
+    constant const size_t b_strides[DIM],
+    uint3 index [[thread_position_in_grid]],
+    uint3 grid_dim [[threads_per_grid]]) {
+  auto idx = elem_to_loc_2_nd<DIM>(index, shape, a_strides, b_strides);
+  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  c[out_idx] = Op1()(a[idx.x], b[idx.y]);
+  d[out_idx] = Op2()(a[idx.x], b[idx.y]);
+}
+
+template <typename T, typename U, typename Op1, typename Op2>
+[[kernel]] void binary_op_g(
+    device const T* a,
+    device const T* b,
+    device U* c,
+    device U* d,
+    constant const int* shape,
+    constant const size_t* a_strides,
+    constant const size_t* b_strides,
+    constant const int& ndim,
+    uint3 index [[thread_position_in_grid]],
+    uint3 grid_dim [[threads_per_grid]]) {
+  auto idx = elem_to_loc_2_nd(index, shape, a_strides, b_strides, ndim);
+  size_t out_idx = index.x + grid_dim.x * (index.y + grid_dim.y * index.z);
+  c[out_idx] = Op1()(a[idx.x], b[idx.y]);
+  d[out_idx] = Op2()(a[idx.x], b[idx.y]);
+}
+
+#define instantiate_binary(name, itype, otype, op1, op2, bopt) \
+  template [[host_name(name)]] \
+  [[kernel]] void binary_op_##bopt<itype, otype, op1, op2>( \
+      device const itype* a, \
+      device const itype* b, \
+      device otype* c, \
+      device otype* d, \
+      uint index [[thread_position_in_grid]]);
+
+#define instantiate_binary_g_dim(name, itype, otype, op1, op2, dims) \
+  template [[host_name(name "_" #dims)]] \
+  [[kernel]] void binary_op_g_nd<itype, otype, op1, op2, dims>( \
+      device const itype* a, \
+      device const itype* b, \
+      device otype* c, \
+      device otype* d, \
+      constant const int shape[dims], \
+      constant const size_t a_strides[dims], \
+      constant const size_t b_strides[dims], \
+      uint3 index [[thread_position_in_grid]], \
+      uint3 grid_dim [[threads_per_grid]]);
+
+#define instantiate_binary_g_nd(name, itype, otype, op1, op2) \
+  template [[host_name(name "_1")]] \
+  [[kernel]] void binary_op_g_nd1<itype, otype, op1, op2>( \
+      device const itype* a, \
+      device const itype* b, \
+      device otype* c, \
+      device otype* d, \
+      constant const size_t& a_stride, \
+      constant const size_t& b_stride, \
+      uint index [[thread_position_in_grid]]); \
+  template [[host_name(name "_2")]] \
+  [[kernel]] void binary_op_g_nd2<itype, otype, op1, op2>( \
+      device const itype* a, \
+      device const itype* b, \
+      device otype* c, \
+      device otype* d, \
+      constant const size_t a_strides[2], \
+      constant const size_t b_strides[2], \
+      uint2 index [[thread_position_in_grid]], \
+      uint2 grid_dim [[threads_per_grid]]); \
+  template [[host_name(name "_3")]] \
+  [[kernel]] void binary_op_g_nd3<itype, otype, op1, op2>( \
+      device const itype* a, \
+      device const itype* b, \
+      device otype* c, \
+      device otype* d, \
+      constant const size_t a_strides[3], \
+      constant const size_t b_strides[3], \
+      uint3 index [[thread_position_in_grid]], \
+      uint3 grid_dim [[threads_per_grid]]); \
+  instantiate_binary_g_dim(name, itype, otype, op1, op2, 4) \
+  instantiate_binary_g_dim(name, itype, otype, op1, op2, 5)
+
+
+#define instantiate_binary_g(name, itype, otype, op1, op2) \
+  template [[host_name(name)]] \
+  [[kernel]] void binary_op_g<itype, otype, op2, op2>( \
+      device const itype* a, \
+      device const itype* b, \
+      device otype* c, \
+      device otype* d, \
+      constant const int* shape, \
+      constant const size_t* a_strides, \
+      constant const size_t* b_strides, \
+      constant const int& ndim, \
+      uint3 index [[thread_position_in_grid]], \
+      uint3 grid_dim [[threads_per_grid]]);
+
+#define instantiate_binary_all(name, tname, itype, otype, op1, op2) \
+  instantiate_binary("ss" #name #tname, itype, otype, op1, op2, ss) \
+  instantiate_binary("sv" #name #tname, itype, otype, op1, op2, sv) \
+  instantiate_binary("vs" #name #tname, itype, otype, op1, op2, vs) \
+  instantiate_binary("vv" #name #tname, itype, otype, op1, op2, vv) \
+  instantiate_binary_g("g" #name #tname, itype, otype, op1, op2) \
+  instantiate_binary_g_nd("g" #name #tname, itype, otype, op1, op2)
+
+#define instantiate_binary_float(name, op1, op2) \
+  instantiate_binary_all(name, float16, half, half, op1, op2) \
+  instantiate_binary_all(name, float32, float, float, op1, op2) \
+  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op1, op2)
+
+#define instantiate_binary_types(name, op1, op2) \
+  instantiate_binary_all(name, bool_, bool, bool, op1, op2) \
+  instantiate_binary_all(name, uint8, uint8_t, uint8_t, op1, op2) \
+  instantiate_binary_all(name, uint16, uint16_t, uint16_t, op1, op2) \
+  instantiate_binary_all(name, uint32, uint32_t, uint32_t, op1, op2) \
+  instantiate_binary_all(name, uint64, uint64_t, uint64_t, op1, op2) \
+  instantiate_binary_all(name, int8, int8_t, int8_t, op1, op2) \
+  instantiate_binary_all(name, int16, int16_t, int16_t, op1, op2) \
+  instantiate_binary_all(name, int32, int32_t, int32_t, op1, op2) \
+  instantiate_binary_all(name, int64, int64_t, int64_t, op1, op2) \
+  instantiate_binary_all(name, complex64, complex64_t, complex64_t, op1, op2) \
+  instantiate_binary_float(name, op1, op2)
+
+instantiate_binary_types(divmod, FloorDivide, Remainder)

mlx/backend/metal/kernels/complex.h

@@ -45,7 +45,7 @@ struct complex64_t {
       typename = typename enable_if<can_convert_to_complex64<T>>::type>
   constexpr complex64_t(T x) constant : real(x), imag(0) {}
 
-  // Converstions from complex64_t
+  // Conversions from complex64_t
   template <
       typename T,
       typename = typename enable_if<can_convert_from_complex64<T>>::type>
@@ -110,3 +110,16 @@ constexpr complex64_t operator-(complex64_t a, complex64_t b) {
 constexpr complex64_t operator*(complex64_t a, complex64_t b) {
   return {a.real * b.real - a.imag * b.imag, a.real * b.imag + a.imag * b.real};
 }
+
+constexpr complex64_t operator/(complex64_t a, complex64_t b) {
+  auto denom = b.real * b.real + b.imag * b.imag;
+  auto x = a.real * b.real + a.imag * b.imag;
+  auto y = a.imag * b.real - a.real * b.imag;
+  return {x / denom, y / denom};
+}
+
+constexpr complex64_t operator%(complex64_t a, complex64_t b) {
+  auto real = a.real - (b.real * static_cast<int64_t>(a.real / b.real));
+  auto imag = a.imag - (b.imag * static_cast<int64_t>(a.imag / b.imag));
+  return {real, imag};
+}

mlx/backend/metal/kernels/gemm/conv.h

@@ -105,7 +105,7 @@ struct Conv2DInputBlockLoader {
         }
       }
 
-      // Zero pad otherwize
+      // Zero pad otherwise
       else {
 #pragma clang loop unroll(full)
         for (short j = 0; j < vec_size; ++j) {
@@ -334,7 +334,7 @@ struct Conv2DBlockMMA {
       }
 
       simdgroup_barrier(mem_flags::mem_none);
-// Multiply and accumulate into resulr simdgroup matrices
+// Multiply and accumulate into result simdgroup matrices
 #pragma clang loop unroll(full)
       for (short i = 0; i < TM; i++) {
 #pragma clang loop unroll(full)

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

@@ -93,13 +93,13 @@ struct BlockLoader {
           tmp_idx[j] = bj + j < src_tile_dim.x ? j : 0;
         }
 
-        // Read all valid indcies into tmp_val
+        // Read all valid indices into tmp_val
 #pragma clang loop unroll(full)
         for (short j = 0; j < vec_size; j++) {
           tmp_val[j] = src[i * src_ld + tmp_idx[j]];
         }
 
-        // Zero out uneeded values
+        // Zero out unneeded values
 #pragma clang loop unroll(full)
         for (short j = 0; j < vec_size; j++) {
           tmp_val[j] = bj + j < src_tile_dim.x ? tmp_val[j] : T(0);
@@ -241,7 +241,7 @@ struct BlockMMA {
       }
 
       simdgroup_barrier(mem_flags::mem_none);
-// Multiply and accumulate into resulr simdgroup matrices
+// Multiply and accumulate into result simdgroup matrices
 #pragma clang loop unroll(full)
       for (short i = 0; i < TM; i++) {
 #pragma clang loop unroll(full)

mlx/backend/metal/kernels/gemv.metal

@@ -28,7 +28,7 @@ struct GEMVKernel {
   static_assert(BN == SIMD_SIZE, "gemv block must have a width of SIMD_SIZE");
 
   // - The matrix of size (M = out_vec_size, N = in_vec_size) is divided up 
-  //   into blocks of (BM * TM, BN * TN) divided amoung threadgroups
+  //   into blocks of (BM * TM, BN * TN) divided among threadgroups
   // - Every thread works on a block of (TM, TN)
   // - We assume each thead group is launched with (BN, BM, 1) threads
   //
@@ -42,7 +42,7 @@ struct GEMVKernel {
   // Edge case handling:
   // - The threadgroup with the largest tid will have blocks that exceed the matrix
   //   * The blocks that start outside the matrix are never read (thread results remain zero)
-  //   * The last thread that partialy overlaps with the matrix is shifted inwards 
+  //   * The last thread that partially overlaps with the matrix is shifted inwards 
   //     such that the thread block fits exactly in the matrix
 
   MLX_MTL_CONST short tgp_mem_size = BN * TN * 2;
@@ -166,7 +166,7 @@ template <
 struct GEMVTKernel {
 
   // - The matrix of size (M = in_vec_size, N = out_vec_size) is divided up 
-  //   into blocks of (BM * TM, BN * TN) divided amoung threadgroups
+  //   into blocks of (BM * TM, BN * TN) divided among threadgroups
   // - Every thread works on a block of (TM, TN)
   // - We assume each thead group is launched with (BN, BM, 1) threads
   //
@@ -180,7 +180,7 @@ struct GEMVTKernel {
   // Edge case handling:
   // - The threadgroup with the largest tid will have blocks that exceed the matrix
   //   * The blocks that start outside the matrix are never read (thread results remain zero)
-  //   * The last thread that partialy overlaps with the matrix is shifted inwards 
+  //   * The last thread that partially overlaps with the matrix is shifted inwards 
   //     such that the thread block fits exactly in the matrix
 
 

mlx/backend/metal/kernels/quantized.metal

@@ -0,0 +1,568 @@
+// Copyright © 2023 Apple Inc.
+
+#include <metal_stdlib>
+#include <metal_simdgroup>
+
+#include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/defines.h"
+#include "mlx/backend/metal/kernels/gemm/gemm.h"
+#include "mlx/backend/metal/kernels/utils.h"
+
+using namespace metal;
+
+#define MLX_MTL_CONST static constant constexpr const
+
+MLX_MTL_CONST int SIMD_SIZE = 32;
+
+template <typename T, const int BM, const int BN, const int group_size, const int bits>
+[[kernel]] void qmv(
+    const device uint32_t* w [[buffer(0)]],
+    const device T* scales [[buffer(1)]],
+    const device T* biases [[buffer(2)]],
+    const device T* x [[buffer(3)]],
+    device T* y [[buffer(4)]],
+    const constant int& in_vec_size [[buffer(5)]],
+    const constant int& out_vec_size [[buffer(6)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint lid [[thread_index_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+
+  static_assert(BN == SIMD_SIZE, "qmv expects BN to be equal to SIMD_SIZE");
+
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int el_per_thread = 32 / bits;
+  constexpr int colgroup = BN * el_per_thread;
+  constexpr int groups_per_block = colgroup / group_size;
+  constexpr int simdgroups_fetching_vec = colgroup / SIMD_SIZE;
+
+  threadgroup T scales_block[BM * groups_per_block];
+  threadgroup T biases_block[BM * groups_per_block];
+  threadgroup T x_block[colgroup];
+
+  thread uint32_t w_local;
+  thread T result = 0;
+  thread T scale = 1;
+  thread T bias = 0;
+  thread T x_thread[el_per_thread];
+
+  // Adjust positions
+  const int in_vec_size_w = in_vec_size / el_per_thread;
+  const int in_vec_size_g = in_vec_size / group_size;
+  int out_row = tid.y * BM + simd_gid;
+  w += out_row * in_vec_size_w;
+  scales += out_row * in_vec_size_g;
+  biases += out_row * in_vec_size_g;
+  x += tid.z * in_vec_size;
+  y += tid.z * out_vec_size;
+
+  // Loop over in_vec in blocks of colgroup
+  for (int i=0; i<in_vec_size; i+=colgroup) {
+    // Load the vec to shared memory
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    if (simd_gid < simdgroups_fetching_vec) {
+      x_block[lid] = x[lid + i];
+    }
+    if (simd_lid == 0) {
+      #pragma clang loop unroll(full)
+      for (int j=0; j<groups_per_block; j++) {
+        scales_block[simd_gid * groups_per_block + j] = scales[i / group_size + j];
+      }
+      #pragma clang loop unroll(full)
+      for (int j=0; j<groups_per_block; j++) {
+        biases_block[simd_gid * groups_per_block + j] = biases[i / group_size + j];
+      }
+    }
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Load in_vec, scale, bias to registers
+    #pragma clang loop unroll(full)
+    for (int j=0; j<el_per_thread; j++) {
+      x_thread[j] = x_block[simd_lid*el_per_thread + j];
+    }
+    scale = scales_block[simd_gid * groups_per_block + simd_lid * el_per_thread / group_size];
+    bias = biases_block[simd_gid * groups_per_block + simd_lid * el_per_thread / group_size];
+
+    // Load the matrix elements
+    w_local = w[i / el_per_thread + simd_lid];
+
+    // Do all the work.
+    #pragma clang loop unroll(full)
+    for (int k=0; k<el_per_thread; k++) {
+      result += (scale * static_cast<T>(w_local & bitmask) + bias) * x_thread[k];
+      w_local >>= bits;
+    }
+  }
+
+  // Accumulate in the simdgroup
+  result = simd_sum(result);
+
+  // Store the result
+  if (simd_lid == 0) {
+    y[out_row] = result;
+  }
+}
+
+
+template <typename T, const int BM, const int BN, const int group_size, const int bits>
+[[kernel]] void qvm(
+    const device T* x [[buffer(0)]],
+    const device uint32_t* w [[buffer(1)]],
+    const device T* scales [[buffer(2)]],
+    const device T* biases [[buffer(3)]],
+    device T* y [[buffer(4)]],
+    const constant int& in_vec_size [[buffer(5)]],
+    const constant int& out_vec_size [[buffer(6)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint lid [[thread_index_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+
+  static_assert(BM == SIMD_SIZE, "qvm expects BM to be equal to SIMD_SIZE");
+  static_assert(BN == BM, "qvm expects a block size of 32x32");
+
+  (void)lid;
+
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int el_per_int = 32 / bits;
+  constexpr int colgroup = BN * el_per_int;
+  constexpr int groups_per_block = colgroup / group_size;
+
+  threadgroup T scales_block[BM * groups_per_block];
+  threadgroup T biases_block[BM * groups_per_block];
+  threadgroup T x_block[BM];
+
+  thread uint32_t w_local;
+  thread T result[el_per_int] = {0};
+  thread T scale = 1;
+  thread T bias = 0;
+  thread T x_local = 0;
+
+  // Adjust positions
+  const int out_vec_size_w = out_vec_size / el_per_int;
+  const int out_vec_size_g = out_vec_size / group_size;
+  int out_col = (tid.y * BN + simd_gid) * el_per_int;
+  w += out_col / el_per_int;
+  scales += out_col / group_size;
+  biases += out_col / group_size;
+  x += tid.z * in_vec_size;
+  y += tid.z * out_vec_size + out_col;
+
+  if (out_col >= out_vec_size) {
+    return;
+  }
+
+  // Loop over in_vec in blocks of colgroup
+  for (int i=0; i<in_vec_size; i+=BM) {
+    // Load the vec to shared memory
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    if (simd_gid == 0) {
+      x_block[simd_lid] = x[simd_lid + i];
+    }
+
+    // Load the scales and biases to shared memory
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    if (simd_gid == 0) {
+      #pragma clang loop unroll(full)
+      for (int j=0; j<groups_per_block; j++) {
+        scales_block[simd_lid * groups_per_block + j] = scales[(i + simd_lid) * out_vec_size_g + j];
+      }
+      #pragma clang loop unroll(full)
+      for (int j=0; j<groups_per_block; j++) {
+        biases_block[simd_lid * groups_per_block + j] = biases[(i + simd_lid) * out_vec_size_g + j];
+      }
+    }
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Load in_vec, scale, bias to registers
+    x_local = x_block[simd_lid];
+    scale = scales_block[simd_lid * groups_per_block + (simd_gid * el_per_int) / group_size];
+    bias = biases_block[simd_lid * groups_per_block + (simd_gid * el_per_int) / group_size];
+
+    // Load the matrix elements
+    w_local = w[(i + simd_lid) * out_vec_size_w];
+
+    // Do all the work.
+    #pragma clang loop unroll(full)
+    for (int k=0; k<el_per_int; k++) {
+      result[k] += (scale * static_cast<T>(w_local & bitmask) + bias) * x_local;
+      w_local >>= bits;
+    }
+  }
+
+  // Accumulate in the simdgroup
+  #pragma clang loop unroll(full)
+  for (int k=0; k<el_per_int; k++) {
+    result[k] = simd_sum(result[k]);
+  }
+
+  // Store the result
+  if (simd_lid == 0) {
+    #pragma clang loop unroll(full)
+    for (int k=0; k<el_per_int; k++) {
+      y[k] = result[k];
+    }
+  }
+}
+
+
+template <typename T, const int BM, const int BK, const int BN, const int group_size, const int bits>
+[[kernel]] void qmm_t(
+    const device T* x [[buffer(0)]],
+    const device uint32_t* w [[buffer(1)]],
+    const device T* scales [[buffer(2)]],
+    const device T* biases [[buffer(3)]],
+    device T* y [[buffer(4)]],
+    const constant int& M [[buffer(5)]],
+    const constant int& N [[buffer(6)]],
+    const constant int& K [[buffer(7)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint lid [[thread_index_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+
+  static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
+  static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
+
+  const uint lidy = lid / SIMD_SIZE;
+
+  constexpr int WM = 2;
+  constexpr int WN = 2;
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int el_per_int = 32 / bits;
+  constexpr int ints_per_block = BK / el_per_int;
+  constexpr int groups_per_block = (BK / group_size > 0) ? (BK / group_size) : 1;
+  constexpr int groups_per_simd = BN / (WM * WN);
+  constexpr int w_els_per_thread = (BN * BK / el_per_int) / (SIMD_SIZE * WM * WN);
+
+  // Instantiate the appropriate BlockMMA and Loader
+  using mma_t = BlockMMA<T, BM, BN, BK, WM, WN, false, true>;
+  using loader_x_t = BlockLoader<T, BM, BK, BK, 4, WM * WN * SIMD_SIZE, false, true, 0>;
+
+  threadgroup T scales_block[BN * groups_per_block];
+  threadgroup T biases_block[BN * groups_per_block];
+  threadgroup T Xs[BM * BK];
+  threadgroup T Ws[BN * BK];
+
+  // Set the block
+  const int K_w = K / el_per_int;
+  const int K_g = K / group_size;
+  const int y_row = tid.y * BM;
+  const int y_col = tid.x * BN;
+  x += y_row * K;
+  w += y_col * K_w;
+  scales += y_col * K_g;
+  biases += y_col * K_g;
+  y += y_row * N + y_col;
+
+  // Make the x loader and mma operation
+  const short num_els = min(BM, M - y_row);
+  loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);
+  mma_t mma_op(simd_gid, simd_lid);
+
+  for (int k=0; k<K; k += BK) {
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Load the x tile
+    if (num_els < BM) {
+        loader_x.load_safe(short2(BK, num_els));
+    } else {
+        loader_x.load_unsafe();
+    }
+
+    // Load the scale and bias
+    if (simd_lid == 0) {
+      threadgroup T *scales_block_local = scales_block + lidy * groups_per_block * groups_per_simd;
+      threadgroup T *biases_block_local = biases_block + lidy * groups_per_block * groups_per_simd;
+      const device T *scales_local = scales + lidy * groups_per_simd * K_g + k / group_size;
+      const device T *biases_local = biases + lidy * groups_per_simd * K_g + k / group_size;
+      #pragma clang loop unroll(full)
+      for (int gs=0; gs<groups_per_simd; gs++) {
+        #pragma clang loop unroll(full)
+        for (int gc=0; gc<groups_per_block; gc++) {
+          scales_block_local[gc] = scales_local[gc];
+          biases_block_local[gc] = biases_local[gc];
+        }
+        scales_block_local += groups_per_block;
+        scales_local += K_g;
+        biases_block_local += groups_per_block;
+        biases_local += K_g;
+      }
+    }
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Load the w tile
+    {
+      for (int wo=0; wo<w_els_per_thread; wo++) {
+        int offset = lid * w_els_per_thread + wo;
+        int offset_row = offset / (BK / el_per_int);
+        int offset_col = offset % (BK / el_per_int);
+        const device uint32_t * w_local = w + offset_row * K_w + offset_col;
+        threadgroup T * Ws_local = Ws + offset_row * BK + offset_col * el_per_int;
+
+        uint32_t wi = *w_local;
+        T scale = scales_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];
+        T bias = biases_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];
+
+        #pragma clang loop unroll(full)
+        for (int t=0; t<el_per_int; t++) {
+          Ws_local[t] = scale * static_cast<T>(wi & bitmask) + bias;
+          wi >>= bits;
+        }
+      }
+    }
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Multiply and accumulate threadgroup elements
+    mma_op.mma(Xs, Ws);
+
+    // Prepare for next iteration
+    loader_x.next();
+    w += ints_per_block;
+    // scales and biases cannot be advanced because they would have to be
+    // advanced every other iteration or sth.
+  }
+
+  // Store results to device memory
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  if (num_els < BM) {
+    mma_op.store_result_safe(y, N, short2(BN, num_els));
+  } else {
+    mma_op.store_result(y, N);
+  }
+}
+
+
+template <typename T, const int BM, const int BK, const int BN, const int group_size, const int bits>
+[[kernel]] void qmm_n(
+    const device T* x [[buffer(0)]],
+    const device uint32_t* w [[buffer(1)]],
+    const device T* scales [[buffer(2)]],
+    const device T* biases [[buffer(3)]],
+    device T* y [[buffer(4)]],
+    const constant int& M [[buffer(5)]],
+    const constant int& N [[buffer(6)]],
+    const constant int& K [[buffer(7)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint lid [[thread_index_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+
+  static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
+  static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
+
+  const uint lidy = lid / SIMD_SIZE;
+
+  constexpr int WM = 2;
+  constexpr int WN = 2;
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int el_per_int = 32 / bits;
+  constexpr int groups_per_block = (BN / group_size > 0) ? (BN / group_size) : 1;
+  constexpr int groups_per_simd = BK / (WM * WN);
+  constexpr int w_els_per_thread = (BK * BN / el_per_int) / (SIMD_SIZE * WM * WN);
+
+  // Instantiate the appropriate BlockMMA and Loader
+  using mma_t = BlockMMA<T, BM, BN, BK, WM, WN, false, false>;
+  using loader_x_t = BlockLoader<T, BM, BK, BK, 4, WM * WN * SIMD_SIZE, false, true, 0>;
+
+  threadgroup T scales_block[BK * groups_per_block];
+  threadgroup T biases_block[BK * groups_per_block];
+  threadgroup T Xs[BM * BK];
+  threadgroup T Ws[BK * BN];
+
+  // Set the block
+  const int N_w = N / el_per_int;
+  const int N_g = N / group_size;
+  const int y_row = tid.y * BM;
+  const int y_col = tid.x * BN;
+  x += y_row * K;
+  w += y_col / el_per_int;
+  scales += y_col / group_size;
+  biases += y_col / group_size;
+  y += y_row * N + y_col;
+
+  // Make the x loader and mma operation
+  const short num_els = min(BM, M - y_row);
+  loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);
+  mma_t mma_op(simd_gid, simd_lid);
+
+  for (int k=0; k<K; k += BK) {
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Load the x tile
+    if (num_els < BM) {
+        loader_x.load_safe(short2(BK, num_els));
+    } else {
+        loader_x.load_unsafe();
+    }
+
+    // Load the scale and bias
+    if (simd_lid == 0) {
+      threadgroup T *scales_block_local = scales_block + lidy * groups_per_block * groups_per_simd;
+      threadgroup T *biases_block_local = biases_block + lidy * groups_per_block * groups_per_simd;
+      const device T *scales_local = scales + lidy * groups_per_simd * N_g;
+      const device T *biases_local = biases + lidy * groups_per_simd * N_g;
+      #pragma clang loop unroll(full)
+      for (int gs=0; gs<groups_per_simd; gs++) {
+        #pragma clang loop unroll(full)
+        for (int gc=0; gc<groups_per_block; gc++) {
+          scales_block_local[gc] = scales_local[gc];
+          biases_block_local[gc] = biases_local[gc];
+        }
+        scales_block_local += groups_per_block;
+        scales_local += N_g;
+        biases_block_local += groups_per_block;
+        biases_local += N_g;
+      }
+    }
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Load the w tile
+    {
+      for (int wo=0; wo<w_els_per_thread; wo++) {
+        int offset = lid * w_els_per_thread + wo;
+        int offset_row = offset / (BN / el_per_int);
+        int offset_col = offset % (BN / el_per_int);
+        const device uint32_t * w_local = w + offset_row * N_w + offset_col;
+        threadgroup T * Ws_local = Ws + offset_row * BN + offset_col * el_per_int;
+
+        uint32_t wi = *w_local;
+        T scale = scales_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];
+        T bias = biases_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];
+
+        #pragma clang loop unroll(full)
+        for (int t=0; t<el_per_int; t++) {
+          Ws_local[t] = scale * static_cast<T>(wi & bitmask) + bias;
+          wi >>= bits;
+        }
+      }
+    }
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // Multiply and accumulate threadgroup elements
+    mma_op.mma(Xs, Ws);
+
+    // Prepare for next iteration
+    loader_x.next();
+    w += BK * N_w;
+    scales += BK * N_g;
+    biases += BK * N_g;
+  }
+
+  // Store results to device memory
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  if (num_els < BM) {
+    mma_op.store_result_safe(y, N, short2(BN, num_els));
+  } else {
+    mma_op.store_result(y, N);
+  }
+}
+
+
+#define instantiate_qmv(name, itype, group_size, bits) \
+  template [[host_name("qmv_" #name "_gs_" #group_size "_b_" #bits)]] \
+  [[kernel]] void qmv<itype, 32, 32, group_size, bits>( \
+    const device uint32_t* w [[buffer(0)]], \
+    const device itype* scales [[buffer(1)]], \
+    const device itype* biases [[buffer(2)]], \
+    const device itype* x [[buffer(3)]], \
+    device itype* y [[buffer(4)]], \
+    const constant int& in_vec_size [[buffer(5)]], \
+    const constant int& out_vec_size [[buffer(6)]], \
+    uint3 tid [[threadgroup_position_in_grid]], \
+    uint lid [[thread_index_in_threadgroup]], \
+    uint simd_gid [[simdgroup_index_in_threadgroup]], \
+    uint simd_lid [[thread_index_in_simdgroup]]);
+
+#define instantiate_qmv_types(group_size, bits) \
+  instantiate_qmv(float32, float, group_size, bits) \
+  instantiate_qmv(float16, half, group_size, bits) \
+  instantiate_qmv(bfloat16, bfloat16_t, group_size, bits)
+
+instantiate_qmv_types(128, 2)
+instantiate_qmv_types(128, 4)
+instantiate_qmv_types(128, 8)
+instantiate_qmv_types( 64, 2)
+instantiate_qmv_types( 64, 4)
+instantiate_qmv_types( 64, 8)
+
+#define instantiate_qvm(name, itype, group_size, bits) \
+  template [[host_name("qvm_" #name "_gs_" #group_size "_b_" #bits)]] \
+  [[kernel]] void qvm<itype, 32, 32, group_size, bits>( \
+    const device itype* x [[buffer(0)]], \
+    const device uint32_t* w [[buffer(1)]], \
+    const device itype* scales [[buffer(2)]], \
+    const device itype* biases [[buffer(3)]], \
+    device itype* y [[buffer(4)]], \
+    const constant int& in_vec_size [[buffer(5)]], \
+    const constant int& out_vec_size [[buffer(6)]], \
+    uint3 tid [[threadgroup_position_in_grid]], \
+    uint lid [[thread_index_in_threadgroup]], \
+    uint simd_gid [[simdgroup_index_in_threadgroup]], \
+    uint simd_lid [[thread_index_in_simdgroup]]);
+
+#define instantiate_qvm_types(group_size, bits) \
+  instantiate_qvm(float32, float, group_size, bits) \
+  instantiate_qvm(float16, half, group_size, bits) \
+  instantiate_qvm(bfloat16, bfloat16_t, group_size, bits)
+
+instantiate_qvm_types(128, 2)
+instantiate_qvm_types(128, 4)
+instantiate_qvm_types(128, 8)
+instantiate_qvm_types( 64, 2)
+instantiate_qvm_types( 64, 4)
+instantiate_qvm_types( 64, 8)
+
+#define instantiate_qmm_t(name, itype, group_size, bits) \
+  template [[host_name("qmm_t_" #name "_gs_" #group_size "_b_" #bits)]] \
+  [[kernel]] void qmm_t<itype, 32, 64, 32, group_size, bits>( \
+      const device itype* x [[buffer(0)]], \
+      const device uint32_t* w [[buffer(1)]], \
+      const device itype* scales [[buffer(2)]], \
+      const device itype* biases [[buffer(3)]], \
+      device itype* y [[buffer(4)]], \
+      const constant int& M [[buffer(5)]], \
+      const constant int& N [[buffer(6)]], \
+      const constant int& K [[buffer(7)]], \
+      uint3 tid [[threadgroup_position_in_grid]], \
+      uint lid [[thread_index_in_threadgroup]], \
+      uint simd_gid [[simdgroup_index_in_threadgroup]], \
+      uint simd_lid [[thread_index_in_simdgroup]]);
+
+#define instantiate_qmm_t_types(group_size, bits) \
+  instantiate_qmm_t(float32, float, group_size, bits) \
+  instantiate_qmm_t(float16, half, group_size, bits) \
+  instantiate_qmm_t(bfloat16, bfloat16_t, group_size, bits)
+
+instantiate_qmm_t_types(128, 2)
+instantiate_qmm_t_types(128, 4)
+instantiate_qmm_t_types(128, 8)
+instantiate_qmm_t_types( 64, 2)
+instantiate_qmm_t_types( 64, 4)
+instantiate_qmm_t_types( 64, 8)
+
+#define instantiate_qmm_n(name, itype, group_size, bits) \
+  template [[host_name("qmm_n_" #name "_gs_" #group_size "_b_" #bits)]] \
+  [[kernel]] void qmm_n<itype, 32, 32, 64, group_size, bits>( \
+      const device itype* x [[buffer(0)]], \
+      const device uint32_t* w [[buffer(1)]], \
+      const device itype* scales [[buffer(2)]], \
+      const device itype* biases [[buffer(3)]], \
+      device itype* y [[buffer(4)]], \
+      const constant int& M [[buffer(5)]], \
+      const constant int& N [[buffer(6)]], \
+      const constant int& K [[buffer(7)]], \
+      uint3 tid [[threadgroup_position_in_grid]], \
+      uint lid [[thread_index_in_threadgroup]], \
+      uint simd_gid [[simdgroup_index_in_threadgroup]], \
+      uint simd_lid [[thread_index_in_simdgroup]]);
+
+#define instantiate_qmm_n_types(group_size, bits) \
+  instantiate_qmm_n(float32, float, group_size, bits) \
+  instantiate_qmm_n(float16, half, group_size, bits) \
+  instantiate_qmm_n(bfloat16, bfloat16_t, group_size, bits)
+
+instantiate_qmm_n_types(128, 2)
+instantiate_qmm_n_types(128, 4)
+instantiate_qmm_n_types(128, 8)
+instantiate_qmm_n_types( 64, 2)
+instantiate_qmm_n_types( 64, 4)
+instantiate_qmm_n_types( 64, 8)

mlx/backend/metal/kernels/reduce.metal

@@ -65,7 +65,7 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
     in += grid_size * N_READS;
   }
 
-  // Sepate case for the last set as we close the reduction size 
+  // Separate case for the last set as we close the reduction size 
   size_t curr_idx = (gid + r * (size_t)grid_size) * N_READS;
   if (curr_idx < in_size) {
     int max_reads = in_size - curr_idx;
@@ -112,88 +112,33 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
 
-///////////////////////////////////////////////////////////////////////////////
-// General reduce
-///////////////////////////////////////////////////////////////////////////////
-
-template <typename T, typename U, typename Op>
-[[kernel]] void general_reduce(
-    const device T *in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
-    const device int *in_shape [[buffer(2)]],
-    const device size_t *in_strides [[buffer(3)]],
-    const device size_t *out_strides [[buffer(4)]],
-    const device size_t& ndim [[buffer(5)]],
-    uint gid [[thread_position_in_grid]]) {
-  Op op;
-  auto in_idx = elem_to_loc(gid, in_shape, in_strides, ndim);
-  auto out_idx = elem_to_loc(gid, in_shape, out_strides, ndim);
-  op.atomic_update(out, static_cast<U>(in[in_idx]), out_idx);
-}
-
-template <typename T, typename U, typename Op, int NDIM>
-[[kernel]] void general_reduce(
-    const device T *in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
-    const device int *in_shape [[buffer(2)]],
-    const device size_t *in_strides [[buffer(3)]],
-    const device size_t *out_strides [[buffer(4)]],
-    uint gid [[thread_position_in_grid]]) {
-  Op op;
-  auto in_idx = elem_to_loc_nd<NDIM>(gid, in_shape, in_strides);
-  auto out_idx = elem_to_loc_nd<NDIM>(gid, in_shape, out_strides);
-  op.atomic_update(out, static_cast<U>(in[in_idx]), out_idx);
-}
-
-#define instantiate_general_reduce_helper(name, itype, otype, op) \
-  template [[host_name("general_reduce_" #name)]] \
-  [[kernel]] void general_reduce<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
-      const device int *in_shape [[buffer(2)]], \
-      const device size_t *in_strides [[buffer(3)]], \
-      const device size_t *out_strides [[buffer(4)]], \
-      const device size_t& ndim [[buffer(5)]], \
-      uint gid [[thread_position_in_grid]]);
-
-#define instantiate_general_reduce_helper_nd(name, itype, otype, op, n) \
-  template [[host_name("general_reduce_" #name "_dim_" #n)]] \
-  [[kernel]] void general_reduce<itype, otype, op, n>( \
-      const device itype *in [[buffer(0)]], \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
-      const device int *in_shape [[buffer(2)]], \
-      const device size_t *in_strides [[buffer(3)]], \
-      const device size_t *out_strides [[buffer(4)]], \
-      uint gid [[thread_position_in_grid]]);
-
-#define instantiate_general_reduce(name, itype, otype, op) \
-  instantiate_general_reduce_helper(name, itype, otype, op) \
-  instantiate_general_reduce_helper_nd(name, itype, otype, op, 1) \
-  instantiate_general_reduce_helper_nd(name, itype, otype, op, 2) \
-  instantiate_general_reduce_helper_nd(name, itype, otype, op, 3) \
-  instantiate_general_reduce_helper_nd(name, itype, otype, op, 4)
-
-
 ///////////////////////////////////////////////////////////////////////////////
 // Row atomics
 ///////////////////////////////////////////////////////////////////////////////
 
 template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
-[[kernel]] void row_reduce(
+[[kernel]] void row_reduce_general(
     const device T *in [[buffer(0)]],
-    device U *out [[buffer(1)]],
-    const device size_t& reduction_size [[buffer(2)]],
-    uint lid [[thread_position_in_threadgroup]],
-    uint lsize [[threads_per_threadgroup]],
-    uint tid [[threadgroup_position_in_grid]],
+    device mlx_atomic<U> *out [[buffer(1)]],
+    const constant size_t& reduction_size [[buffer(2)]],
+    const constant size_t& out_size [[buffer(3)]],
+    const constant int* shape [[buffer(4)]],
+    const constant size_t* strides [[buffer(5)]],
+    const constant int& ndim [[buffer(6)]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint3 lsize [[threads_per_threadgroup]],
+    uint3 tid [[threadgroup_position_in_grid]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_per_group [[simdgroups_per_threadgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
 
   Op op;
 
-  // Each threadgroup handles 1 reduction 
-  in += tid * reduction_size + lid * N_READS;
+  // Each threadgroup handles 1 reduction
+  // TODO: Specializing elem_to_loc would be slightly faster
+  int idx = tid.y * out_size + tid.x;
+  int extra_offset = elem_to_loc(idx, shape, strides, ndim);
+  in += extra_offset + lid.x * N_READS;
   
   // The reduction is accumulated here
   U total_val = Op::init;
@@ -201,7 +146,7 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 
   // Loop over the reduction size within thread group
   int r = 0;
-  for (; r < (int)ceildiv(reduction_size, N_READS*lsize) - 1; r++) {
+  for (; r < (int)ceildiv(reduction_size, N_READS*lsize.x) - 1; r++) {
     T vals[N_READS]; 
     for(int i = 0; i < N_READS; i++) {
       vals[i] = in[i];
@@ -210,11 +155,11 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
       total_val = op(static_cast<U>(vals[i]), total_val);
     }
 
-    in += lsize * N_READS;
+    in += lsize.x * N_READS;
   }
 
-  // Sepate case for the last set as we close the reduction size   
-  size_t reduction_index = (lid + (size_t)lsize * r) * N_READS;
+  // Separate case for the last set as we close the reduction size   
+  size_t reduction_index = (lid.x + (size_t)lsize.x * r) * N_READS;
   if(reduction_index < reduction_size) {
     int max_reads = reduction_size - reduction_index;
 
@@ -240,26 +185,30 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
   // Reduction within thread group
   //    Only needed if multiple simd groups
   if(reduction_size > simd_size) {
-    total_val = lid < simd_per_group ? local_vals[lid] : op.init;
+    total_val = lid.x < simd_per_group ? local_vals[lid.x] : op.init;
     total_val = op.simd_reduce(total_val);
   }
   // Update output
-  if (lid == 0) {
-    out[tid] = total_val;
+  if (lid.x == 0) {
+    op.atomic_update(out, total_val, tid.x);
   }
 }
 
-#define instantiate_row_reduce(name, itype, otype, op) \
-  template [[host_name("row_reduce_" #name)]] \
-  [[kernel]] void row_reduce<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device otype *out [[buffer(1)]], \
-      const device size_t& reduction_size [[buffer(2)]], \
-      uint lid [[thread_position_in_threadgroup]], \
-      uint lsize [[threads_per_threadgroup]], \
-      uint tid [[threadgroup_position_in_grid]], \
-      uint simd_lane_id [[thread_index_in_simdgroup]], \
-      uint simd_per_group [[simdgroups_per_threadgroup]], \
+#define instantiate_row_reduce_general(name, itype, otype, op) \
+  template [[host_name("row_reduce_general_" #name)]] \
+  [[kernel]] void row_reduce_general<itype, otype, op>( \
+      const device itype *in [[buffer(0)]],  \
+      device mlx_atomic<otype> *out [[buffer(1)]],  \
+      const constant size_t& reduction_size [[buffer(2)]],  \
+      const constant size_t& out_size [[buffer(3)]],  \
+      const constant int* shape [[buffer(4)]],  \
+      const constant size_t* strides [[buffer(5)]],  \
+      const constant int& ndim [[buffer(6)]],  \
+      uint3 lid [[thread_position_in_threadgroup]],  \
+      uint3 lsize [[threads_per_threadgroup]],  \
+      uint3 tid [[threadgroup_position_in_grid]],  \
+      uint simd_lane_id [[thread_index_in_simdgroup]],  \
+      uint simd_per_group [[simdgroups_per_threadgroup]],  \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
 
@@ -311,148 +260,57 @@ inline void _contiguous_strided_reduce(
 }
 
 template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
-[[kernel]] void col_reduce(
+[[kernel]] void col_reduce_general(
     const device T *in [[buffer(0)]],
     device mlx_atomic<U> *out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& reduction_stride [[buffer(3)]],
     const constant size_t& out_size [[buffer(4)]], 
+    const constant int* shape [[buffer(5)]],
+    const constant size_t* strides [[buffer(6)]],
+    const constant int& ndim [[buffer(7)]],
     threadgroup U *local_data [[threadgroup(0)]],
-    uint2 tid [[threadgroup_position_in_grid]], 
-    uint2 lid [[thread_position_in_threadgroup]], 
-    uint2 lsize [[threads_per_threadgroup]]) {
-  auto out_idx = tid.x * lsize.x + lid.x;  
-  
+    uint3 tid [[threadgroup_position_in_grid]], 
+    uint3 lid [[thread_position_in_threadgroup]], 
+    uint3 lsize [[threads_per_threadgroup]]) {
+  auto out_idx = tid.x * lsize.x + lid.x;
+  auto in_idx = elem_to_loc(
+    out_idx + tid.z * out_size,
+    shape,
+    strides,
+    ndim
+  );
+
   if(out_idx < out_size) {
     _contiguous_strided_reduce<T, U, Op, N_READS>(
       in, 
       out, 
       local_data, 
-      out_idx, 
+      in_idx,
       out_idx, 
       reduction_size, 
       reduction_stride, 
-      tid, 
-      lid, 
-      lsize); 
+      tid.xy, 
+      lid.xy, 
+      lsize.xy); 
   }
 }
 
-#define instantiate_col_reduce(name, itype, otype, op) \
-  template [[host_name("col_reduce_" #name)]] \
-  [[kernel]] void col_reduce<itype, otype, op>( \
+#define instantiate_col_reduce_general(name, itype, otype, op) \
+  template [[host_name("col_reduce_general_" #name)]] \
+  [[kernel]] void col_reduce_general<itype, otype, op>( \
       const device itype *in [[buffer(0)]], \
       device mlx_atomic<otype> *out [[buffer(1)]], \
       const constant size_t& reduction_size [[buffer(2)]], \
       const constant size_t& reduction_stride [[buffer(3)]], \
       const constant size_t& out_size [[buffer(4)]], \
+      const constant int* shape [[buffer(5)]],  \
+      const constant size_t* strides [[buffer(6)]],  \
+      const constant int& ndim [[buffer(7)]],  \
       threadgroup otype *local_data [[threadgroup(0)]], \
-      uint2 tid [[threadgroup_position_in_grid]], \
-      uint2 lid [[thread_position_in_threadgroup]], \
-      uint2 lsize [[threads_per_threadgroup]]);
-
-template <typename T, typename U, typename Op, int NDIM, int N_READS = 16>
-[[kernel]] void contiguous_strided_reduce(
-    const device T *in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
-    const constant size_t& reduction_size [[buffer(2)]],
-    const constant size_t& reduction_stride [[buffer(3)]],
-    const constant size_t& out_size [[buffer(4)]], 
-    const device int* in_shape [[buffer(5)]],
-    const device size_t* in_strides [[buffer(6)]],
-    threadgroup U *local_data [[threadgroup(0)]],
-    uint2 tid [[threadgroup_position_in_grid]], 
-    uint2 lid [[thread_position_in_threadgroup]], 
-    uint2 lsize [[threads_per_threadgroup]]) {
-  
-  auto out_idx = tid.x * lsize.x + lid.x; 
-  auto in_idx = elem_to_loc_nd<NDIM>(out_idx, in_shape, in_strides);
-  
-  if(out_idx < out_size) {
-    _contiguous_strided_reduce<T, U, Op, N_READS>(
-      in, 
-      out, 
-      local_data, 
-      in_idx, 
-      out_idx, 
-      reduction_size, 
-      reduction_stride, 
-      tid, 
-      lid, 
-      lsize); 
-  }
-}
-
-template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
-[[kernel]] void contiguous_strided_reduce(
-    const device T *in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
-    const constant size_t& reduction_size [[buffer(2)]],
-    const constant size_t& reduction_stride [[buffer(3)]],
-    const constant size_t& out_size [[buffer(4)]], 
-    const device int* in_shape [[buffer(5)]],
-    const device size_t* in_strides [[buffer(6)]],
-    const device size_t& in_dim [[buffer(7)]], 
-    threadgroup U *local_data [[threadgroup(0)]],
-    uint2 tid [[threadgroup_position_in_grid]], 
-    uint2 lid [[thread_position_in_threadgroup]], 
-    uint2 lsize [[threads_per_threadgroup]]) {
-  
-  auto out_idx = tid.x * lsize.x + lid.x; 
-  auto in_idx = elem_to_loc(out_idx, in_shape, in_strides, in_dim);
-  
-  if(out_idx < out_size) {
-    _contiguous_strided_reduce<T, U, Op, N_READS>(
-      in, 
-      out, 
-      local_data, 
-      in_idx, 
-      out_idx, 
-      reduction_size, 
-      reduction_stride, 
-      tid, 
-      lid, 
-      lsize); 
-  }
-}
-
-#define instantiate_contiguous_strided_helper(name, itype, otype, op) \
-  template [[host_name("contiguous_strided_reduce_" #name)]] \
-  [[kernel]] void contiguous_strided_reduce<itype, otype, op>( \
-      const device itype *in [[buffer(0)]], \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
-      const constant size_t& reduction_size [[buffer(2)]], \
-      const constant size_t& reduction_stride [[buffer(3)]], \
-      const constant size_t& out_size [[buffer(4)]], \
-      const device int* in_shape [[buffer(5)]], \
-      const device size_t* in_strides [[buffer(6)]], \
-      const device size_t& in_dim [[buffer(7)]], \
-      threadgroup otype *local_data [[threadgroup(0)]], \
-      uint2 tid [[threadgroup_position_in_grid]], \
-      uint2 lid [[thread_position_in_threadgroup]], \
-      uint2 lsize [[threads_per_threadgroup]]);
-
-#define instantiate_contiguous_strided_helper_nd(name, itype, otype, op, n) \
-  template [[host_name("contiguous_strided_reduce_" #name "_dim_" #n)]] \
-  [[kernel]] void contiguous_strided_reduce<itype, otype, op, n>( \
-      const device itype *in [[buffer(0)]], \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
-      const constant size_t& reduction_size [[buffer(2)]], \
-      const constant size_t& reduction_stride [[buffer(3)]], \
-      const constant size_t& out_size [[buffer(4)]], \
-      const device int* in_shape [[buffer(5)]], \
-      const device size_t* in_strides [[buffer(6)]], \
-      threadgroup otype *local_data [[threadgroup(0)]], \
-      uint2 tid [[threadgroup_position_in_grid]],  \
-      uint2 lid [[thread_position_in_threadgroup]],  \
-      uint2 lsize [[threads_per_threadgroup]]);
-
-#define instantiate_contiguous_strided(name, itype, otype, op) \
-  instantiate_contiguous_strided_helper(name, itype, otype, op) \
-  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 1) \
-  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 2) \
-  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 3) \
-  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 4)
+      uint3 tid [[threadgroup_position_in_grid]], \
+      uint3 lid [[thread_position_in_threadgroup]], \
+      uint3 lsize [[threads_per_threadgroup]]);
 
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -461,10 +319,8 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 
 #define instantiate_reduce(name, itype, otype, op) \
   instantiate_all_reduce(name, itype, otype, op) \
-  instantiate_row_reduce(name, itype, otype, op) \
-  instantiate_col_reduce(name, itype, otype, op) \
-  instantiate_contiguous_strided(name, itype, otype, op) \
-  instantiate_general_reduce(name, itype, otype, op)
+  instantiate_row_reduce_general(name, itype, otype, op) \
+  instantiate_col_reduce_general(name, itype, otype, op)
 
 #define instantiate_same_reduce(name, tname, type, op) \
   instantiate_init_reduce(name ##tname, type, op<type>) \
@@ -535,4 +391,4 @@ instantiate_same_reduce(max_, float16, half, Max)
 instantiate_same_reduce(max_, float32, float, Max)
 
 instantiate_same_reduce(min_, bfloat16, bfloat16_t, Min)
-instantiate_same_reduce(max_, bfloat16, bfloat16_t, Max)
+instantiate_same_reduce(max_, bfloat16, bfloat16_t, Max)

mlx/backend/metal/kernels/sort.metal

@@ -592,7 +592,7 @@ template <
     bool ARG_SORT,
     short BLOCK_THREADS,
     short N_PER_THREAD>
-[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void mb_block_partiton(
+[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void mb_block_partition(
     device idx_t* block_partitions [[buffer(0)]],
     const device val_t* dev_vals [[buffer(1)]],
     const device idx_t* dev_idxs [[buffer(2)]],
@@ -777,8 +777,8 @@ template <
       const device size_t* nc_strides [[buffer(7)]], \
       uint3 tid [[threadgroup_position_in_grid]], \
       uint3 lid [[thread_position_in_threadgroup]]); \
-  template [[host_name("mb_block_partiton_" #vtname "_" #itname "_bn" #bn "_tn" #tn)]] \
-  [[kernel]] void mb_block_partiton<vtype, itype, arg_sort, bn, tn>( \
+  template [[host_name("mb_block_partition_" #vtname "_" #itname "_bn" #bn "_tn" #tn)]] \
+  [[kernel]] void mb_block_partition<vtype, itype, arg_sort, bn, tn>( \
     device itype* block_partitions [[buffer(0)]], \
     const device vtype* dev_vals [[buffer(1)]], \
     const device itype* dev_idxs [[buffer(2)]], \

mlx/backend/metal/kernels/unary.metal

@@ -43,6 +43,19 @@ struct ArcTanh {
   template <typename T> T operator()(T x) { return metal::precise::atanh(x); };
 };
 
+struct Ceil {
+  template <typename T> T operator()(T x) { return metal::ceil(x); };
+  template <> int8_t operator()(int8_t x) { return x; };
+  template <> int16_t operator()(int16_t x) { return x; };
+  template <> int32_t operator()(int32_t x) { return x; };
+  template <> int64_t operator()(int64_t x) { return x; };
+  template <> uint8_t operator()(uint8_t x) { return x; };
+  template <> uint16_t operator()(uint16_t x) { return x; };
+  template <> uint32_t operator()(uint32_t x) { return x; };
+  template <> uint64_t operator()(uint64_t x) { return x; };
+  template <> bool operator()(bool x) { return x; };
+};
+
 struct Cos {
   template <typename T> T operator()(T x) { return metal::precise::cos(x); };
 
@@ -83,6 +96,19 @@ struct Exp {
   }
 };
 
+struct Floor {
+  template <typename T> T operator()(T x) { return metal::floor(x); };
+  template <> int8_t operator()(int8_t x) { return x; };
+  template <> int16_t operator()(int16_t x) { return x; };
+  template <> int32_t operator()(int32_t x) { return x; };
+  template <> int64_t operator()(int64_t x) { return x; };
+  template <> uint8_t operator()(uint8_t x) { return x; };
+  template <> uint16_t operator()(uint16_t x) { return x; };
+  template <> uint32_t operator()(uint32_t x) { return x; };
+  template <> uint64_t operator()(uint64_t x) { return x; };
+  template <> bool operator()(bool x) { return x; };
+};
+
 struct Log {
   template <typename T> T operator()(T x) { return metal::precise::log(x); };
 };
@@ -107,6 +133,11 @@ struct Negative {
   template <typename T> T operator()(T x) { return -x; };
 };
 
+struct Round {
+  template <typename T> T operator()(T x) { return metal::round(x); };
+  template <> complex64_t operator()(complex64_t x) { return {metal::round(x.real), metal::round(x.imag)}; };
+};
+
 struct Sigmoid {
   template <typename T>
   T operator()(T x) {
@@ -253,9 +284,11 @@ instantiate_unary_float(arcsin, ArcSin)
 instantiate_unary_float(arcsinh, ArcSinh)
 instantiate_unary_float(arctan, ArcTan)
 instantiate_unary_float(arctanh, ArcTanh)
+instantiate_unary_types(ceil, Ceil)
 instantiate_unary_float(cos, Cos)
 instantiate_unary_float(cosh, Cosh)
 instantiate_unary_float(exp, Exp)
+instantiate_unary_types(floor, Floor)
 instantiate_unary_float(log, Log)
 instantiate_unary_float(log2, Log2)
 instantiate_unary_float(log10, Log10)
@@ -272,6 +305,7 @@ instantiate_unary_float(sqrt, Sqrt)
 instantiate_unary_float(rsqrt, Rsqrt)
 instantiate_unary_float(tan, Tan)
 instantiate_unary_float(tanh, Tanh)
+instantiate_unary_float(round, Round)
 
 instantiate_unary_all(abs, complex64, complex64_t, Abs)
 instantiate_unary_all(cos, complex64, complex64_t, Cos)
@@ -282,5 +316,6 @@ instantiate_unary_all(sin, complex64, complex64_t, Sin)
 instantiate_unary_all(sinh, complex64, complex64_t, Sinh)
 instantiate_unary_all(tan, complex64, complex64_t, Tan)
 instantiate_unary_all(tanh, complex64, complex64_t, Tanh)
+instantiate_unary_all(round, complex64, complex64_t, Round)
 
 instantiate_unary_all(lnot, bool_, bool, LogicalNot)

mlx/backend/metal/matmul.cpp

@@ -61,7 +61,7 @@ inline void mps_matmul(
   //  2. Only one of a or b has batch_size_out matrices worth of data and
   //     the other has matrix worth of data
 
-  // The matrix dimsenisons of a and b are sure to be regularly strided
+  // The matrix dimensions of a and b are sure to be regularly strided
   if (batch_size_out > 1) {
     // No broadcasting defaults
     auto batch_size_a = a.data_size() / (M * K);

mlx/backend/metal/metal.cpp

@@ -4,7 +4,6 @@
 #include <future>
 #include <memory>
 
-#include "mlx/array.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/primitives.h"
 #include "mlx/scheduler.h"
@@ -46,44 +45,40 @@ MTL::CommandBuffer* increment_command_buffer(Stream s) {
 std::function<void()> make_task(
     array& arr,
     std::vector<std::shared_future<void>> deps,
-    std::shared_ptr<std::promise<void>> p,
-    bool retain_graph) {
-  auto task =
-      [retain_graph, arr, deps = std::move(deps), p = std::move(p)]() mutable {
-        for (auto& d : deps) {
-          d.wait();
-        }
-        auto s = arr.primitive().stream();
-        auto command_buffer = increment_command_buffer(s);
-        arr.primitive().eval_gpu(arr.inputs(), arr);
-        if (p) {
-          metal::device(s.device).end_encoding(s.index);
-          scheduler::notify_new_task(s);
-          command_buffer->addCompletedHandler(
-              [retain_graph, s, arr, p = std::move(p)](
-                  MTL::CommandBuffer*) mutable {
-                if (!retain_graph) {
-                  arr.detach();
-                }
-                p->set_value();
-                // Signal this thread to clear the pool on a synchroniztion.
-                scheduler::enqueue(s, []() {
-                  thread_autorelease_pool()->release();
-                  thread_autorelease_pool() =
-                      NS::AutoreleasePool::alloc()->init();
-                });
-                scheduler::notify_task_completion(s);
-              });
-          metal::device(s.device).commit_command_buffer(s.index);
-        } else {
-          command_buffer->addCompletedHandler(
-              [retain_graph, s, arr](MTL::CommandBuffer*) mutable {
-                if (!retain_graph) {
-                  arr.detach();
-                }
-              });
-        }
-      };
+    std::shared_ptr<std::promise<void>> p) {
+  auto task = [arr, deps = std::move(deps), p = std::move(p)]() mutable {
+    auto pool = new_scoped_memory_pool();
+    for (auto& d : deps) {
+      d.wait();
+    }
+    auto s = arr.primitive().stream();
+    auto command_buffer = increment_command_buffer(s);
+    auto outputs = arr.outputs();
+    arr.primitive().eval_gpu(arr.inputs(), outputs);
+    if (p) {
+      metal::device(s.device).end_encoding(s.index);
+      scheduler::notify_new_task(s);
+      command_buffer->addCompletedHandler(
+          [s, arr, p = std::move(p)](MTL::CommandBuffer*) mutable {
+            if (!arr.is_tracer()) {
+              arr.detach();
+              for (auto s : arr.siblings()) {
+                s.detach();
+              }
+            }
+            p->set_value();
+            scheduler::notify_task_completion(s);
+          });
+      metal::device(s.device).commit_command_buffer(s.index);
+    } else {
+      command_buffer->addCompletedHandler(
+          [s, arr](MTL::CommandBuffer*) mutable {
+            if (!arr.is_tracer()) {
+              arr.detach();
+            }
+          });
+    }
+  };
   return task;
 }
 

mlx/backend/metal/metal.h

@@ -20,11 +20,11 @@ constexpr bool is_available() {
 }
 
 void new_stream(Stream stream);
+std::shared_ptr<void> new_scoped_memory_pool();
 
 std::function<void()> make_task(
     array& arr,
     std::vector<std::shared_future<void>> deps,
-    std::shared_ptr<std::promise<void>> p,
-    bool retain_graph);
+    std::shared_ptr<std::promise<void>> p);
 
 } // namespace mlx::core::metal

mlx/backend/metal/primitives.cpp

@@ -19,6 +19,101 @@ namespace {
 
 static constexpr int METAL_MAX_INDEX_ARRAYS = 10;
 
+void binary_op(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs,
+    const std::string op) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, outputs[0], bopt);
+  set_binary_op_output_data(a, b, outputs[1], bopt);
+
+  auto& out = outputs[0];
+  if (out.size() == 0) {
+    return;
+  }
+
+  // Try to collapse contiguous dims
+  auto [shape, strides] = collapse_contiguous_dims(a, b, out);
+  auto& strides_a = strides[0];
+  auto& strides_b = strides[1];
+  auto& strides_out = strides[2];
+
+  std::ostringstream kname;
+  switch (bopt) {
+    case ScalarScalar:
+      kname << "ss";
+      break;
+    case ScalarVector:
+      kname << "sv";
+      break;
+    case VectorScalar:
+      kname << "vs";
+      break;
+    case VectorVector:
+      kname << "vv";
+      break;
+    case General:
+      kname << "g";
+      break;
+  }
+  kname << op << type_to_name(a);
+  if (bopt == General && out.ndim() <= MAX_BINARY_SPECIALIZED_DIMS) {
+    kname << "_" << shape.size();
+  }
+
+  auto& s = out.primitive().stream();
+  auto& d = metal::device(s.device);
+  auto kernel = d.get_kernel(kname.str());
+  auto compute_encoder = d.get_command_encoder(s.index);
+  compute_encoder->setComputePipelineState(kernel);
+  set_array_buffer(compute_encoder, a, 0);
+  set_array_buffer(compute_encoder, b, 1);
+  set_array_buffer(compute_encoder, outputs[0], 2);
+  set_array_buffer(compute_encoder, outputs[1], 3);
+
+  if (bopt == General) {
+    auto ndim = shape.size();
+    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);
+    } 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);
+    }
+
+    if (ndim > MAX_BINARY_SPECIALIZED_DIMS) {
+      compute_encoder->setBytes(&ndim, sizeof(int), 7);
+    }
+
+    // Launch up to 3D grid of threads
+    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);
+    NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
+    if (thread_group_size != 1024) {
+      throw std::runtime_error("[Metal::binary] Must use 1024 sized block");
+    }
+    auto group_dims = get_block_dims(dim0, dim1, rest);
+    MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
+    compute_encoder->dispatchThreads(grid_dims, group_dims);
+  } else {
+    // Launch a 1D grid of threads
+    size_t nthreads = out.data_size();
+    MTL::Size grid_dims = MTL::Size(nthreads, 1, 1);
+    NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
+    if (thread_group_size > nthreads) {
+      thread_group_size = nthreads;
+    }
+    MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
+    compute_encoder->dispatchThreads(grid_dims, group_dims);
+  }
+}
+
 void binary_op(
     const std::vector<array>& inputs,
     array& out,
@@ -28,6 +123,9 @@ void binary_op(
   auto& b = inputs[1];
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
+  if (out.size() == 0) {
+    return;
+  }
 
   // Try to collapse contiguous dims
   auto [shape, strides] = collapse_contiguous_dims(a, b, out);
@@ -84,9 +182,9 @@ void binary_op(
     }
 
     // Launch up to 3D grid of threads
-    int dim0 = ndim > 0 ? shape[ndim - 1] : 1;
-    int dim1 = ndim > 1 ? shape[ndim - 2] : 1;
-    int rest = out.size() / (dim0 * dim1);
+    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);
     NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
     if (thread_group_size != 1024) {
       throw std::runtime_error("[Metal::binary] Must use 1024 sized block");
@@ -122,6 +220,9 @@ void unary_op(
   } else {
     out.set_data(allocator::malloc_or_wait(out.nbytes()));
   }
+  if (in.size() == 0) {
+    return;
+  }
 
   auto& s = out.primitive().stream();
   auto& d = metal::device(s.device);
@@ -171,6 +272,9 @@ void arange_set_scalars(T start, T next, MTL::ComputeCommandEncoder* enc) {
 void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 0);
   out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  if (out.size() == 0) {
+    return;
+  }
   auto& s = stream();
   auto& d = metal::device(s.device);
   auto kernel = d.get_kernel("arange" + type_to_name(out));
@@ -215,7 +319,8 @@ void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
       arange_set_scalars<float>(start_, start_ + step_, compute_encoder);
       break;
     case bfloat16:
-      throw std::runtime_error("[Arange::eval_gpu] Does not support bfloat16");
+      arange_set_scalars<bfloat16_t>(start_, start_ + step_, compute_encoder);
+      break;
     case complex64:
       throw std::runtime_error("[Arange::eval_gpu] Does not support complex64");
   }
@@ -297,9 +402,18 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
     compute_encoder->setComputePipelineState(kernel);
     set_array_buffer(compute_encoder, in, 0);
     set_array_buffer(compute_encoder, out, 1);
-    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);
+    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);
+    } 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->setBytes(&ndim, sizeof(size_t), 5);
     compute_encoder->setBytes(&axis_stride, sizeof(size_t), 6);
     compute_encoder->setBytes(&axis_size, sizeof(size_t), 7);
@@ -363,6 +477,16 @@ void Divide::eval_gpu(const std::vector<array>& inputs, array& out) {
   binary_op(inputs, out, "div");
 }
 
+void DivMod::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  binary_op(inputs, outputs, "divmod");
+}
+
+void Remainder::eval_gpu(const std::vector<array>& inputs, array& out) {
+  binary_op(inputs, out, "rem");
+}
+
 void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
   binary_op(inputs, out, equal_nan_ ? "naneq" : "eq");
 }
@@ -434,6 +558,20 @@ void LogicalNot::eval_gpu(const std::vector<array>& inputs, array& out) {
   unary_op(inputs, out, "lnot");
 }
 
+void LogicalAnd::eval_gpu(const std::vector<array>& inputs, array& out) {
+  binary_op(
+      inputs,
+      out,
+      "land"); // Assume "land" is the operation identifier for logical AND
+}
+
+void LogicalOr::eval_gpu(const std::vector<array>& inputs, array& out) {
+  binary_op(
+      inputs,
+      out,
+      "lor"); // Assume "lor" is the operation identifier for logical OR
+}
+
 void LogAddExp::eval_gpu(const std::vector<array>& inputs, array& out) {
   binary_op(inputs, out, "lae");
 }
@@ -446,6 +584,14 @@ void Minimum::eval_gpu(const std::vector<array>& inputs, array& out) {
   binary_op(inputs, out, "min");
 }
 
+void Floor::eval_gpu(const std::vector<array>& inputs, array& out) {
+  unary_op(inputs, out, "floor");
+}
+
+void Ceil::eval_gpu(const std::vector<array>& inputs, array& out) {
+  unary_op(inputs, out, "ceil");
+}
+
 void Multiply::eval_gpu(const std::vector<array>& inputs, array& out) {
   binary_op(inputs, out, "mul");
 }
@@ -504,6 +650,9 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
   size_t elems_per_key = out.size() / num_keys;
   size_t bytes_per_key = out.itemsize() * elems_per_key;
   out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  if (out.size() == 0) {
+    return;
+  }
 
   size_t out_per_key = (bytes_per_key + 4 - 1) / 4;
   size_t half_size = out_per_key / 2;
@@ -551,6 +700,17 @@ void Reshape::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void Round::eval_gpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  const auto& in = inputs[0];
+  if (not is_integral(in.dtype())) {
+    unary_op(inputs, out, "round");
+  } else {
+    // No-op integer types
+    out.copy_shared_buffer(in);
+  }
+}
+
 void Sigmoid::eval_gpu(const std::vector<array>& inputs, array& out) {
   unary_op(inputs, out, "sigmoid");
 }

mlx/backend/metal/quantized.cpp

@@ -0,0 +1,172 @@
+// Copyright © 2023 Apple Inc.
+
+#include <cassert>
+
+#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/metal/device.h"
+#include "mlx/backend/metal/utils.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+void QuantizedMatmul::eval_gpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 4);
+
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  auto& s = stream();
+  auto& d = metal::device(s.device);
+
+  auto& x_pre = inputs[0];
+  auto& w_pre = inputs[1];
+  auto& scales_pre = inputs[2];
+  auto& biases_pre = inputs[3];
+
+  std::vector<array> copies;
+  auto ensure_row_contiguous = [&copies, &s](const array& arr) {
+    if (arr.flags().row_contiguous) {
+      return arr;
+    } else {
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy_gpu(arr, arr_copy, CopyType::General, s);
+      copies.push_back(arr_copy);
+      return arr_copy;
+    }
+  };
+  auto x = ensure_row_contiguous(x_pre);
+  auto w = ensure_row_contiguous(w_pre);
+  auto scales = ensure_row_contiguous(scales_pre);
+  auto biases = ensure_row_contiguous(biases_pre);
+
+  int D = x.shape(-1);
+  int B = x.size() / D;
+  int O = out.shape(-1);
+  if (transpose_) {
+    // Route to the qmv kernel
+    if (B < 6) {
+      std::ostringstream kname;
+      kname << "qmv_" << type_to_name(out) << "_gs_" << group_size_ << "_b_"
+            << bits_;
+
+      // Encode and dispatch kernel
+      auto compute_encoder = d.get_command_encoder(s.index);
+      auto kernel = d.get_kernel(kname.str());
+      compute_encoder->setComputePipelineState(kernel);
+
+      int bo = 32;
+      int bd = 32;
+      MTL::Size group_dims = MTL::Size(bd, bo, 1);
+      MTL::Size grid_dims = MTL::Size(1, O / bo, B);
+
+      set_array_buffer(compute_encoder, w, 0);
+      set_array_buffer(compute_encoder, scales, 1);
+      set_array_buffer(compute_encoder, biases, 2);
+      set_array_buffer(compute_encoder, x, 3);
+      set_array_buffer(compute_encoder, out, 4);
+      compute_encoder->setBytes(&D, sizeof(int), 5);
+      compute_encoder->setBytes(&O, sizeof(int), 6);
+
+      compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+    }
+
+    // Route to the qmm_t kernel
+    else {
+      std::ostringstream kname;
+      kname << "qmm_t_" << type_to_name(out) << "_gs_" << group_size_ << "_b_"
+            << bits_;
+
+      // Encode and dispatch kernel
+      auto compute_encoder = d.get_command_encoder(s.index);
+      auto kernel = d.get_kernel(kname.str());
+      compute_encoder->setComputePipelineState(kernel);
+
+      int wn = 2;
+      int wm = 2;
+      int bm = 32;
+      int bn = 32;
+      int bk = 64;
+      MTL::Size group_dims = MTL::Size(32, wn, wm);
+      MTL::Size grid_dims = MTL::Size(O / bn, (B + bm - 1) / bm, 1);
+
+      set_array_buffer(compute_encoder, x, 0);
+      set_array_buffer(compute_encoder, w, 1);
+      set_array_buffer(compute_encoder, scales, 2);
+      set_array_buffer(compute_encoder, biases, 3);
+      set_array_buffer(compute_encoder, out, 4);
+      compute_encoder->setBytes(&B, sizeof(int), 5);
+      compute_encoder->setBytes(&O, sizeof(int), 6);
+      compute_encoder->setBytes(&D, sizeof(int), 7);
+
+      compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+    }
+  } else {
+    // Route to the qvm kernel
+    if (B < 4) {
+      std::ostringstream kname;
+      kname << "qvm_" << type_to_name(out) << "_gs_" << group_size_ << "_b_"
+            << bits_;
+
+      // Encode and dispatch kernel
+      auto compute_encoder = d.get_command_encoder(s.index);
+      auto kernel = d.get_kernel(kname.str());
+      compute_encoder->setComputePipelineState(kernel);
+
+      int bo = 32;
+      int bd = 32;
+      MTL::Size group_dims = MTL::Size(bd, bo, 1);
+      MTL::Size grid_dims = MTL::Size(1, (w.shape(1) + bo - 1) / bo, B);
+
+      set_array_buffer(compute_encoder, x, 0);
+      set_array_buffer(compute_encoder, w, 1);
+      set_array_buffer(compute_encoder, scales, 2);
+      set_array_buffer(compute_encoder, biases, 3);
+      set_array_buffer(compute_encoder, out, 4);
+      compute_encoder->setBytes(&D, sizeof(int), 5);
+      compute_encoder->setBytes(&O, sizeof(int), 6);
+
+      compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+    }
+
+    // Route to the qmm_n kernel
+    else {
+      std::ostringstream kname;
+      kname << "qmm_n_" << type_to_name(out) << "_gs_" << group_size_ << "_b_"
+            << bits_;
+
+      // Encode and dispatch kernel
+      auto compute_encoder = d.get_command_encoder(s.index);
+      auto kernel = d.get_kernel(kname.str());
+      compute_encoder->setComputePipelineState(kernel);
+
+      int wn = 2;
+      int wm = 2;
+      int bm = 32;
+      int bn = 64;
+      int bk = 32;
+      MTL::Size group_dims = MTL::Size(32, wn, wm);
+      MTL::Size grid_dims = MTL::Size(O / bn, (B + bm - 1) / bm, 1);
+
+      if ((O % bn) != 0) {
+        std::ostringstream msg;
+        msg << "[quantized_matmul] The output size should be divisible by "
+            << bn << " but received " << O << ".";
+        throw std::runtime_error(msg.str());
+      }
+
+      set_array_buffer(compute_encoder, x, 0);
+      set_array_buffer(compute_encoder, w, 1);
+      set_array_buffer(compute_encoder, scales, 2);
+      set_array_buffer(compute_encoder, biases, 3);
+      set_array_buffer(compute_encoder, out, 4);
+      compute_encoder->setBytes(&B, sizeof(int), 5);
+      compute_encoder->setBytes(&O, sizeof(int), 6);
+      compute_encoder->setBytes(&D, sizeof(int), 7);
+
+      compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+    }
+  }
+
+  d.get_command_buffer(s.index)->addCompletedHandler(
+      [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
+}
+
+} // namespace mlx::core