update install docs and requirements (#2419)

README.md

@@ -11,10 +11,10 @@ brought to you by Apple machine learning research.
 
 Some key features of MLX include:
 
- - **Familiar APIs**: MLX has a Python API that closely follows NumPy.  MLX
+ - **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
    also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
    [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
-   the Python API.  MLX has higher-level packages like `mlx.nn` and
+   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.
 
@@ -68,18 +68,23 @@ 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 MLX on
+macOS, run:
 
-**With `pip`**:
-
-```
+```bash
 pip install mlx
 ```
 
-**With `conda`**:
+To install the CUDA backend on Linux, run:
 
+```bash
+pip install "mlx[cuda]"
 ```
-conda install -c conda-forge mlx
+
+To install a CPU-only Linux package, run:
+
+```bash
+pip install "mlx[cpu]"
 ```
 
 Checkout the

docs/src/install.rst

@@ -13,7 +13,7 @@ silicon computer is
 
     pip install mlx
 
-To install from PyPI you must meet the following requirements:
+To install from PyPI your system must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
 - Using a native Python >= 3.9
@@ -26,13 +26,22 @@ To install from PyPI you must meet the following requirements:
 CUDA
 ^^^^
 
-MLX has a CUDA backend which you can use on any Linux platform with CUDA 12
-and SM 7.0 (Volta) and up. To install MLX with CUDA support, run:
+MLX has a CUDA backend which you can install with:
 
 .. code-block:: shell
 
     pip install "mlx[cuda]"
 
+To install the CUDA package from PyPi your system must meet the following
+requirements:
+
+- Nvidia architecture >= SM 7.0 (Volta)
+- Nvidia driver >= 550.54.14
+- CUDA toolkit >= 12.0
+- Linux distribution with glibc >= 2.35
+- Python >= 3.9
+
+
 CPU-only (Linux)
 ^^^^^^^^^^^^^^^^
 
@@ -42,6 +51,13 @@ For a CPU-only version of MLX that runs on Linux use:
 
     pip install "mlx[cpu]"
 
+To install the CPU-only package from PyPi your system must meet the following
+requirements:
+
+- Linux distribution with glibc >= 2.35
+- Python >= 3.9
+
+
 Troubleshooting
 ^^^^^^^^^^^^^^^
 

mlx/backend/metal/quantized.cpp

@@ -265,9 +265,15 @@ void qvm_split_k(
   MTL::Size group_dims = MTL::Size(bk, 2, 1);
   MTL::Size grid_dims = MTL::Size(M, N / bn, B);
 
-  int x_batch_ndims = x.ndim() - 2;
   auto x_shape = x.shape();
   auto x_strides = x.strides();
+  if (x_shape.size() == 1) {
+    x_shape.insert(x_shape.begin(), 1);
+    x_strides.insert(x_strides.begin(), 0);
+  }
+
+  int x_ndim = x_shape.size();
+  int x_batch_ndims = x_ndim - 2;
   int w_batch_ndims = w.ndim() - 2;
   auto w_shape = w.shape();
   auto w_strides = w.strides();
@@ -278,7 +284,7 @@ void qvm_split_k(
   x_shape.insert(x_shape.end() - 2, split_k);
   x_shape.back() /= split_k;
   x_strides.insert(x_strides.end() - 2, split_D);
-  x_strides[x.ndim() - 1] = split_D;
+  x_strides[x_ndim - 1] = split_D;
   x_batch_ndims += 1;
 
   w_shape.insert(w_shape.end() - 2, split_k);
@@ -291,6 +297,9 @@ void qvm_split_k(
   int final_block_size = K - (split_k - 1) * split_D;
 
   auto temp_shape = out.shape();
+  if (temp_shape.size() == 1) {
+    temp_shape.insert(temp_shape.begin(), 1);
+  }
   temp_shape.insert(temp_shape.end() - 2, split_k);
   array intermediate(temp_shape, x.dtype(), nullptr, {});
   intermediate.set_data(allocator::malloc(intermediate.nbytes()));

python/scripts/repair_linux.sh

@@ -2,6 +2,7 @@
 
 auditwheel repair dist/* \
   --plat manylinux_2_35_x86_64 \
+  --only-plat \
   --exclude libmlx* \
   -w wheel_tmp
 

python/tests/test_quantized.py

@@ -220,6 +220,19 @@ class TestQuantized(mlx_tests.MLXTestCase):
                 self.assertEqual(y_q.shape, y_hat.shape)
                 self.assertLess((y_q - y_hat).abs().max(), 2e-3)
 
+        # Test with 1D vector
+        group_size = 32
+        bits = 8
+        N = 2048
+        x = 1e-1 * mx.random.normal(shape=(N,), key=k1)
+        w = 1e-1 * mx.random.normal(shape=(N, N), key=k2)
+        w_q, scales, biases = mx.quantize(w, group_size, bits)
+        w_hat = mx.dequantize(w_q, scales, biases, group_size, bits)
+        y_q = mx.quantized_matmul(x, w_q, scales, biases, False, group_size, bits)
+        y_hat = x @ w_hat
+        self.assertEqual(y_q.shape, y_hat.shape)
+        self.assertLess((y_q - y_hat).abs().max(), 2e-3)
+
     def test_throw(self):
         x = mx.random.normal(shape=(10, 512))
         w = mx.random.normal(shape=(32, 512))