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docs/build/html/.buildinfo
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docs/build/html/.buildinfo
vendored
@ -1,4 +1,4 @@
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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: f0a8f1723eac189223b8c3a08df4cb42
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config: 617e63568890a453837209bb7514fddd
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tags: 645f666f9bcd5a90fca523b33c5a78b7
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@ -8,23 +8,26 @@ MLX supports writing custom Metal kernels through the Python and C++ APIs.
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Simple Example
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--------------
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.. currentmodule:: mlx.core
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Let's write a custom kernel that computes ``exp`` elementwise:
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.. code-block:: python
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def exp_elementwise(a: mx.array):
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source = """
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uint elem = thread_position_in_grid.x;
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T tmp = inp[elem];
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out[elem] = metal::exp(tmp);
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"""
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source = """
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uint elem = thread_position_in_grid.x;
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T tmp = inp[elem];
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out[elem] = metal::exp(tmp);
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"""
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kernel = mx.fast.metal_kernel(
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name="myexp",
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input_names=["inp"],
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output_names=["out"],
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source=source,
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)
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kernel = mx.fast.metal_kernel(
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name="myexp",
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input_names=["inp"],
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output_names=["out"],
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source=source,
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)
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def exp_elementwise(a: mx.array):
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outputs = kernel(
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inputs=[a],
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template=[("T", mx.float32)],
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@ -39,8 +42,13 @@ Let's write a custom kernel that computes ``exp`` elementwise:
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b = exp_elementwise(a)
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assert mx.allclose(b, mx.exp(a))
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Every time you make a kernel, a new Metal library is created and possibly
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JIT compiled. To reduce the overhead from that, build the kernel once with
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:func:`fast.metal_kernel` and then use it many times.
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.. note::
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We are only required to pass the body of the Metal kernel in ``source``.
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Only pass the body of the Metal kernel in ``source``. The function
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signature is generated automatically.
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The full function signature will be generated using:
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@ -78,44 +86,51 @@ Putting this all together, the generated function signature for ``myexp`` is as
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template [[host_name("custom_kernel_myexp_float")]] [[kernel]] decltype(custom_kernel_myexp_float<float>) custom_kernel_myexp_float<float>;
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Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads <https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_ function.
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This means we will launch ``mx.prod(grid)`` threads, subdivided into ``threadgroup`` size threadgroups.
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For optimal performance, each thread group dimension should be less than or equal to the corresponding grid dimension.
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Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads
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<https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_
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function. This means we will launch ``mx.prod(grid)`` threads, subdivided into
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``threadgroup`` size threadgroups. For optimal performance, each thread group
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dimension should be less than or equal to the corresponding grid dimension.
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Passing ``verbose=True`` to ``mx.fast.metal_kernel.__call__`` will print the generated code for debugging purposes.
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Passing ``verbose=True`` to :func:`ast.metal_kernel.__call__` will print the
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generated code for debugging purposes.
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Using Shape/Strides
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-------------------
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``mx.fast.metal_kernel`` supports an argument ``ensure_row_contiguous`` which is ``True`` by default.
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This will copy the ``mx.array`` inputs if needed before the kernel is launched to ensure that the memory layout is row contiguous.
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Generally this makes writing the kernel easier, since we don't have to worry about gaps or the ordering of the dims
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when indexing.
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:func:`fast.metal_kernel` supports an argument ``ensure_row_contiguous`` which
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is ``True`` by default. This will copy the array inputs if needed
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before the kernel is launched to ensure that the memory layout is row
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contiguous. Generally this makes writing the kernel easier, since we don't
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have to worry about gaps or the ordering of the dims when indexing.
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If we want to avoid this copy, ``metal_kernel`` automatically passes ``a_shape``, ``a_strides`` and ``a_ndim`` for each
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input array ``a`` if any are present in ``source``.
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We can then use MLX's built in indexing utils to fetch the right elements for each thread.
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If we want to avoid this copy, :func:`fast.metal_kernel` automatically passes
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``a_shape``, ``a_strides`` and ``a_ndim`` for each input array ``a`` if any are
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present in ``source``. We can then use MLX's built in indexing utils to fetch
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the right elements for each thread.
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Let's convert ``myexp`` above to support arbitrarily strided arrays without relying on a copy from ``ensure_row_contiguous``:
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Let's convert ``myexp`` above to support arbitrarily strided arrays without
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relying on a copy from ``ensure_row_contiguous``:
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.. code-block:: python
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source = """
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uint elem = thread_position_in_grid.x;
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// Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
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uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
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T tmp = inp[loc];
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// Output arrays are always row contiguous
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out[elem] = metal::exp(tmp);
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"""
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kernel = mx.fast.metal_kernel(
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name="myexp_strided",
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input_names=["inp"],
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output_names=["out"],
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source=source
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)
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def exp_elementwise(a: mx.array):
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source = """
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uint elem = thread_position_in_grid.x;
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// Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
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uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
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T tmp = inp[loc];
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// Output arrays are always row contiguous
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out[elem] = metal::exp(tmp);
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"""
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kernel = mx.fast.metal_kernel(
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name="myexp_strided",
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input_names=["inp"],
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output_names=["out"],
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source=source
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)
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outputs = kernel(
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inputs=[a],
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template=[("T", mx.float32)],
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@ -142,137 +157,139 @@ We'll start with the following MLX implementation using standard ops:
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.. code-block:: python
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def grid_sample_ref(x, grid):
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N, H_in, W_in, _ = x.shape
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ix = ((grid[..., 0] + 1) * W_in - 1) / 2
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iy = ((grid[..., 1] + 1) * H_in - 1) / 2
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def grid_sample_ref(x, grid):
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N, H_in, W_in, _ = x.shape
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ix = ((grid[..., 0] + 1) * W_in - 1) / 2
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iy = ((grid[..., 1] + 1) * H_in - 1) / 2
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ix_nw = mx.floor(ix).astype(mx.int32)
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iy_nw = mx.floor(iy).astype(mx.int32)
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ix_nw = mx.floor(ix).astype(mx.int32)
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iy_nw = mx.floor(iy).astype(mx.int32)
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ix_ne = ix_nw + 1
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iy_ne = iy_nw
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ix_ne = ix_nw + 1
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iy_ne = iy_nw
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ix_sw = ix_nw
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iy_sw = iy_nw + 1
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ix_sw = ix_nw
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iy_sw = iy_nw + 1
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ix_se = ix_nw + 1
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iy_se = iy_nw + 1
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ix_se = ix_nw + 1
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iy_se = iy_nw + 1
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nw = (ix_se - ix) * (iy_se - iy)
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ne = (ix - ix_sw) * (iy_sw - iy)
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sw = (ix_ne - ix) * (iy - iy_ne)
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se = (ix - ix_nw) * (iy - iy_nw)
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nw = (ix_se - ix) * (iy_se - iy)
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ne = (ix - ix_sw) * (iy_sw - iy)
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sw = (ix_ne - ix) * (iy - iy_ne)
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se = (ix - ix_nw) * (iy - iy_nw)
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I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
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I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
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I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
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I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
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I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
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I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
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I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
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I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
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mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
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mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
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mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
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mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
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mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
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mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
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mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
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mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
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I_nw *= mask_nw[..., None]
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I_ne *= mask_ne[..., None]
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I_sw *= mask_sw[..., None]
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I_se *= mask_se[..., None]
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I_nw *= mask_nw[..., None]
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I_ne *= mask_ne[..., None]
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I_sw *= mask_sw[..., None]
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I_se *= mask_se[..., None]
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output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
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output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
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return output
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return output
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Now let's use ``mx.custom_function`` together with ``mx.fast.metal_kernel``
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Now let's use :func:`custom_function` together with :func:`fast.metal_kernel`
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to write a fast GPU kernel for both the forward and backward passes.
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First we'll implement the forward pass as a fused kernel:
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.. code-block:: python
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@mx.custom_function
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def grid_sample(x, grid):
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source = """
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uint elem = thread_position_in_grid.x;
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int H = x_shape[1];
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int W = x_shape[2];
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int C = x_shape[3];
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int gH = grid_shape[1];
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int gW = grid_shape[2];
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assert x.ndim == 4, "`x` must be 4D."
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assert grid.ndim == 4, "`grid` must be 4D."
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int w_stride = C;
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int h_stride = W * w_stride;
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int b_stride = H * h_stride;
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B, _, _, C = x.shape
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_, gN, gM, D = grid.shape
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out_shape = (B, gN, gM, C)
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uint grid_idx = elem / C * 2;
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float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
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float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
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assert D == 2, "Last dim of `grid` must be size 2."
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int ix_nw = floor(ix);
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int iy_nw = floor(iy);
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source = """
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uint elem = thread_position_in_grid.x;
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int H = x_shape[1];
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int W = x_shape[2];
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int C = x_shape[3];
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int gH = grid_shape[1];
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int gW = grid_shape[2];
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int ix_ne = ix_nw + 1;
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int iy_ne = iy_nw;
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int w_stride = C;
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int h_stride = W * w_stride;
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int b_stride = H * h_stride;
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int ix_sw = ix_nw;
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int iy_sw = iy_nw + 1;
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uint grid_idx = elem / C * 2;
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float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
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float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
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int ix_se = ix_nw + 1;
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int iy_se = iy_nw + 1;
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int ix_nw = floor(ix);
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int iy_nw = floor(iy);
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T nw = (ix_se - ix) * (iy_se - iy);
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T ne = (ix - ix_sw) * (iy_sw - iy);
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T sw = (ix_ne - ix) * (iy - iy_ne);
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T se = (ix - ix_nw) * (iy - iy_nw);
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int ix_ne = ix_nw + 1;
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int iy_ne = iy_nw;
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int batch_idx = elem / C / gH / gW * b_stride;
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int channel_idx = elem % C;
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int base_idx = batch_idx + channel_idx;
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int ix_sw = ix_nw;
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int iy_sw = iy_nw + 1;
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T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
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T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
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T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
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T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
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int ix_se = ix_nw + 1;
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int iy_se = iy_nw + 1;
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I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
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I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
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I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
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I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
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T nw = (ix_se - ix) * (iy_se - iy);
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T ne = (ix - ix_sw) * (iy_sw - iy);
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T sw = (ix_ne - ix) * (iy - iy_ne);
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T se = (ix - ix_nw) * (iy - iy_nw);
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out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
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"""
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int batch_idx = elem / C / gH / gW * b_stride;
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int channel_idx = elem % C;
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int base_idx = batch_idx + channel_idx;
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kernel = mx.fast.metal_kernel(
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name="grid_sample",
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input_names=["x", "grid"],
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output_names=["out"],
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source=source,
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)
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T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
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T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
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T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
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T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
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@mx.custom_function
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def grid_sample(x, grid):
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I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
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I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
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I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
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I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
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assert x.ndim == 4, "`x` must be 4D."
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assert grid.ndim == 4, "`grid` must be 4D."
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out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
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"""
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kernel = mx.fast.metal_kernel(
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name="grid_sample",
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input_names=["x", "grid"],
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output_names=["out"],
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source=source,
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)
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outputs = kernel(
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inputs=[x, grid],
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template=[("T", x.dtype)],
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output_shapes=[out_shape],
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output_dtypes=[x.dtype],
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grid=(np.prod(out_shape), 1, 1),
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threadgroup=(256, 1, 1),
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||||
)
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return outputs[0]
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B, _, _, C = x.shape
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||||
_, gN, gM, D = grid.shape
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out_shape = (B, gN, gM, C)
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||||
assert D == 2, "Last dim of `grid` must be size 2."
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||||
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||||
outputs = kernel(
|
||||
inputs=[x, grid],
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||||
template=[("T", x.dtype)],
|
||||
output_shapes=[out_shape],
|
||||
output_dtypes=[x.dtype],
|
||||
grid=(np.prod(out_shape), 1, 1),
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||||
threadgroup=(256, 1, 1),
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||||
)
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return outputs[0]
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||||
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||||
For a reasonably sized input such as:
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||||
|
||||
.. code-block:: python
|
||||
|
||||
x.shape = (8, 1024, 1024, 64)
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||||
grid.shape = (8, 256, 256, 2)
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||||
x.shape = (8, 1024, 1024, 64)
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||||
grid.shape = (8, 256, 256, 2)
|
||||
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||||
On an M1 Max, we see a big performance improvement:
|
||||
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||||
@ -281,11 +298,11 @@ On an M1 Max, we see a big performance improvement:
|
||||
Grid Sample VJP
|
||||
---------------
|
||||
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||||
Since we decorated ``grid_sample`` with ``mx.custom_function``, we can now define
|
||||
its custom vjp transform so MLX can differentiate it.
|
||||
Since we decorated ``grid_sample`` with :func:`custom_function`, we can now
|
||||
define its custom vjp transform so MLX can differentiate it.
|
||||
|
||||
The backwards pass requires atomically updating ``x_grad``/``grid_grad`` and so
|
||||
requires a few extra ``mx.fast.metal_kernel`` features:
|
||||
requires a few extra :func:`fast.metal_kernel` features:
|
||||
|
||||
* ``init_value=0``
|
||||
Initialize all of the kernel's outputs to this value before it runs. This allows us to update only part of the output arrays with the kernel.
|
||||
@ -299,128 +316,129 @@ We can then implement the backwards pass as follows:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
@grid_sample.vjp
|
||||
def grid_sample_vjp(primals, cotangent, _):
|
||||
x, grid = primals
|
||||
B, _, _, C = x.shape
|
||||
_, gN, gM, D = grid.shape
|
||||
source = """
|
||||
uint elem = thread_position_in_grid.x;
|
||||
int H = x_shape[1];
|
||||
int W = x_shape[2];
|
||||
int C = x_shape[3];
|
||||
// Pad C to the nearest larger simdgroup size multiple
|
||||
int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
|
||||
|
||||
assert D == 2, "Last dim of `grid` must be size 2."
|
||||
int gH = grid_shape[1];
|
||||
int gW = grid_shape[2];
|
||||
|
||||
source = """
|
||||
uint elem = thread_position_in_grid.x;
|
||||
int H = x_shape[1];
|
||||
int W = x_shape[2];
|
||||
int C = x_shape[3];
|
||||
// Pad C to the nearest larger simdgroup size multiple
|
||||
int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
|
||||
int w_stride = C;
|
||||
int h_stride = W * w_stride;
|
||||
int b_stride = H * h_stride;
|
||||
|
||||
int gH = grid_shape[1];
|
||||
int gW = grid_shape[2];
|
||||
uint grid_idx = elem / C_padded * 2;
|
||||
float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
|
||||
float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
|
||||
|
||||
int w_stride = C;
|
||||
int h_stride = W * w_stride;
|
||||
int b_stride = H * h_stride;
|
||||
int ix_nw = floor(ix);
|
||||
int iy_nw = floor(iy);
|
||||
|
||||
uint grid_idx = elem / C_padded * 2;
|
||||
float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
|
||||
float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
|
||||
int ix_ne = ix_nw + 1;
|
||||
int iy_ne = iy_nw;
|
||||
|
||||
int ix_nw = floor(ix);
|
||||
int iy_nw = floor(iy);
|
||||
int ix_sw = ix_nw;
|
||||
int iy_sw = iy_nw + 1;
|
||||
|
||||
int ix_ne = ix_nw + 1;
|
||||
int iy_ne = iy_nw;
|
||||
int ix_se = ix_nw + 1;
|
||||
int iy_se = iy_nw + 1;
|
||||
|
||||
int ix_sw = ix_nw;
|
||||
int iy_sw = iy_nw + 1;
|
||||
T nw = (ix_se - ix) * (iy_se - iy);
|
||||
T ne = (ix - ix_sw) * (iy_sw - iy);
|
||||
T sw = (ix_ne - ix) * (iy - iy_ne);
|
||||
T se = (ix - ix_nw) * (iy - iy_nw);
|
||||
|
||||
int ix_se = ix_nw + 1;
|
||||
int iy_se = iy_nw + 1;
|
||||
int batch_idx = elem / C_padded / gH / gW * b_stride;
|
||||
int channel_idx = elem % C_padded;
|
||||
int base_idx = batch_idx + channel_idx;
|
||||
|
||||
T nw = (ix_se - ix) * (iy_se - iy);
|
||||
T ne = (ix - ix_sw) * (iy_sw - iy);
|
||||
T sw = (ix_ne - ix) * (iy - iy_ne);
|
||||
T se = (ix - ix_nw) * (iy - iy_nw);
|
||||
T gix = T(0);
|
||||
T giy = T(0);
|
||||
if (channel_idx < C) {
|
||||
int cot_index = elem / C_padded * C + channel_idx;
|
||||
T cot = cotangent[cot_index];
|
||||
if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
|
||||
int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
|
||||
|
||||
int batch_idx = elem / C_padded / gH / gW * b_stride;
|
||||
int channel_idx = elem % C_padded;
|
||||
int base_idx = batch_idx + channel_idx;
|
||||
T I_nw = x[offset];
|
||||
gix -= I_nw * (iy_se - iy) * cot;
|
||||
giy -= I_nw * (ix_se - ix) * cot;
|
||||
}
|
||||
if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
|
||||
int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
|
||||
|
||||
T gix = T(0);
|
||||
T giy = T(0);
|
||||
if (channel_idx < C) {
|
||||
int cot_index = elem / C_padded * C + channel_idx;
|
||||
T cot = cotangent[cot_index];
|
||||
if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
|
||||
int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
|
||||
T I_ne = x[offset];
|
||||
gix += I_ne * (iy_sw - iy) * cot;
|
||||
giy -= I_ne * (ix - ix_sw) * cot;
|
||||
}
|
||||
if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
|
||||
int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
|
||||
|
||||
T I_nw = x[offset];
|
||||
gix -= I_nw * (iy_se - iy) * cot;
|
||||
giy -= I_nw * (ix_se - ix) * cot;
|
||||
}
|
||||
if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
|
||||
int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
|
||||
T I_sw = x[offset];
|
||||
gix -= I_sw * (iy - iy_ne) * cot;
|
||||
giy += I_sw * (ix_ne - ix) * cot;
|
||||
}
|
||||
if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
|
||||
int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
|
||||
|
||||
T I_ne = x[offset];
|
||||
gix += I_ne * (iy_sw - iy) * cot;
|
||||
giy -= I_ne * (ix - ix_sw) * cot;
|
||||
}
|
||||
if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
|
||||
int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
|
||||
T I_se = x[offset];
|
||||
gix += I_se * (iy - iy_nw) * cot;
|
||||
giy += I_se * (ix - ix_nw) * cot;
|
||||
}
|
||||
}
|
||||
|
||||
T I_sw = x[offset];
|
||||
gix -= I_sw * (iy - iy_ne) * cot;
|
||||
giy += I_sw * (ix_ne - ix) * cot;
|
||||
}
|
||||
if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
|
||||
int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
|
||||
atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
|
||||
T gix_mult = W / 2;
|
||||
T giy_mult = H / 2;
|
||||
|
||||
T I_se = x[offset];
|
||||
gix += I_se * (iy - iy_nw) * cot;
|
||||
giy += I_se * (ix - ix_nw) * cot;
|
||||
}
|
||||
}
|
||||
// Reduce across each simdgroup first.
|
||||
// This is much faster than relying purely on atomics.
|
||||
gix = simd_sum(gix);
|
||||
giy = simd_sum(giy);
|
||||
|
||||
T gix_mult = W / 2;
|
||||
T giy_mult = H / 2;
|
||||
if (thread_index_in_simdgroup == 0) {
|
||||
atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
|
||||
atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
|
||||
}
|
||||
"""
|
||||
kernel = mx.fast.metal_kernel(
|
||||
name="grid_sample_grad",
|
||||
input_names=["x", "grid", "cotangent"],
|
||||
output_names=["x_grad", "grid_grad"],
|
||||
source=source,
|
||||
atomic_outputs=True,
|
||||
)
|
||||
|
||||
// Reduce across each simdgroup first.
|
||||
// This is much faster than relying purely on atomics.
|
||||
gix = simd_sum(gix);
|
||||
giy = simd_sum(giy);
|
||||
@grid_sample.vjp
|
||||
def grid_sample_vjp(primals, cotangent, _):
|
||||
x, grid = primals
|
||||
B, _, _, C = x.shape
|
||||
_, gN, gM, D = grid.shape
|
||||
|
||||
if (thread_index_in_simdgroup == 0) {
|
||||
atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
|
||||
atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
|
||||
}
|
||||
"""
|
||||
kernel = mx.fast.metal_kernel(
|
||||
name="grid_sample_grad",
|
||||
input_names=["x", "grid", "cotangent"],
|
||||
output_names=["x_grad", "grid_grad"],
|
||||
source=source,
|
||||
atomic_outputs=True,
|
||||
)
|
||||
# pad the output channels to simd group size
|
||||
# so that our `simd_sum`s don't overlap.
|
||||
simdgroup_size = 32
|
||||
C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
|
||||
grid_size = B * gN * gM * C_padded
|
||||
outputs = kernel(
|
||||
inputs=[x, grid, cotangent],
|
||||
template=[("T", x.dtype)],
|
||||
output_shapes=[x.shape, grid.shape],
|
||||
output_dtypes=[x.dtype, x.dtype],
|
||||
grid=(grid_size, 1, 1),
|
||||
threadgroup=(256, 1, 1),
|
||||
init_value=0,
|
||||
)
|
||||
return outputs[0], outputs[1]
|
||||
assert D == 2, "Last dim of `grid` must be size 2."
|
||||
|
||||
# pad the output channels to simd group size
|
||||
# so that our `simd_sum`s don't overlap.
|
||||
simdgroup_size = 32
|
||||
C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
|
||||
grid_size = B * gN * gM * C_padded
|
||||
outputs = kernel(
|
||||
inputs=[x, grid, cotangent],
|
||||
template=[("T", x.dtype)],
|
||||
output_shapes=[x.shape, grid.shape],
|
||||
output_dtypes=[x.dtype, x.dtype],
|
||||
grid=(grid_size, 1, 1),
|
||||
threadgroup=(256, 1, 1),
|
||||
init_value=0,
|
||||
)
|
||||
return outputs[0], outputs[1]
|
||||
|
||||
There's an even larger speed up for the vjp:
|
||||
|
||||
|
||||
6
docs/build/html/_sources/dev/extensions.rst
vendored
6
docs/build/html/_sources/dev/extensions.rst
vendored
@ -397,11 +397,11 @@ below.
|
||||
std::ostringstream kname;
|
||||
kname << "axpby_" << "general_" << type_to_name(out);
|
||||
|
||||
// Make sure the metal library is available
|
||||
d.register_library("mlx_ext");
|
||||
// Load the metal library
|
||||
auto lib = d.get_library("mlx_ext");
|
||||
|
||||
// Make a kernel from this metal library
|
||||
auto kernel = d.get_kernel(kname.str(), "mlx_ext");
|
||||
auto kernel = d.get_kernel(kname.str(), lib);
|
||||
|
||||
// Prepare to encode kernel
|
||||
auto& compute_encoder = d.get_command_encoder(s.index);
|
||||
|
||||
59
docs/build/html/_sources/install.rst
vendored
59
docs/build/html/_sources/install.rst
vendored
@ -30,6 +30,16 @@ MLX is also available on conda-forge. To install MLX with conda do:
|
||||
|
||||
conda install conda-forge::mlx
|
||||
|
||||
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:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
pip install mlx-cuda
|
||||
|
||||
|
||||
Troubleshooting
|
||||
^^^^^^^^^^^^^^^
|
||||
@ -65,6 +75,8 @@ Build Requirements
|
||||
Python API
|
||||
^^^^^^^^^^
|
||||
|
||||
.. _python install:
|
||||
|
||||
To build and install the MLX python library from source, first, clone MLX from
|
||||
`its GitHub repo <https://github.com/ml-explore/mlx>`_:
|
||||
|
||||
@ -107,6 +119,8 @@ IDE:
|
||||
C++ API
|
||||
^^^^^^^
|
||||
|
||||
.. _cpp install:
|
||||
|
||||
Currently, MLX must be built and installed from source.
|
||||
|
||||
Similarly to the python library, to build and install the MLX C++ library start
|
||||
@ -185,6 +199,7 @@ should point to the path to the built metal library.
|
||||
|
||||
xcrun -sdk macosx --show-sdk-version
|
||||
|
||||
|
||||
Binary Size Minimization
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@ -213,6 +228,50 @@ be anwywhere from a few hundred millisecond to a few seconds depending on the
|
||||
application. Once a kernel is compiled, it will be cached by the system. The
|
||||
Metal kernel cache persists across reboots.
|
||||
|
||||
Linux
|
||||
^^^^^
|
||||
|
||||
To build from source on Linux (CPU only), install the BLAS and LAPACK headers.
|
||||
For example on Ubuntu, run the following:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
apt-get update -y
|
||||
apt-get install libblas-dev liblapack-dev liblapacke-dev -y
|
||||
|
||||
From here follow the instructions to install either the :ref:`Python <python
|
||||
install>` or :ref:`C++ <cpp install>` APIs.
|
||||
|
||||
CUDA
|
||||
^^^^
|
||||
|
||||
To build from source on Linux with CUDA, install the BLAS and LAPACK headers
|
||||
and the CUDA toolkit. For example on Ubuntu, run the following:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
|
||||
dpkg -i cuda-keyring_1.1-1_all.deb
|
||||
apt-get update -y
|
||||
apt-get -y install cuda-toolkit-12-9
|
||||
apt-get install libblas-dev liblapack-dev liblapacke-dev -y
|
||||
|
||||
|
||||
When building either the Python or C++ APIs make sure to pass the cmake flag
|
||||
``MLX_BUILD_CUDA=ON``. For example, to build the Python API run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
CMAKE_BUILD_PARALLEL_LEVEL=8 CMAKE_ARGS="-DMLX_BUILD_CUDA=ON" pip install -e ".[dev]"
|
||||
|
||||
To build the C++ package run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
mkdir -p build && cd build
|
||||
cmake .. -DMLX_BUILD_CUDA=ON && make -j
|
||||
|
||||
|
||||
Troubleshooting
|
||||
^^^^^^^^^^^^^^^
|
||||
|
||||
|
||||
10
docs/build/html/_sources/usage/indexing.rst
vendored
10
docs/build/html/_sources/usage/indexing.rst
vendored
@ -107,6 +107,16 @@ same array:
|
||||
>>> a
|
||||
array([1, 2, 0], dtype=int32)
|
||||
|
||||
|
||||
Note, unlike NumPy, updates to the same location are nondeterministic:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
>>> a = mx.array([1, 2, 3])
|
||||
>>> a[[0, 0]] = mx.array([4, 5])
|
||||
|
||||
The first element of ``a`` could be ``4`` or ``5``.
|
||||
|
||||
Transformations of functions which use in-place updates are allowed and work as
|
||||
expected. For example:
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
const DOCUMENTATION_OPTIONS = {
|
||||
VERSION: '0.26.1',
|
||||
VERSION: '0.26.2',
|
||||
LANGUAGE: 'en',
|
||||
COLLAPSE_INDEX: false,
|
||||
BUILDER: 'html',
|
||||
|
||||
8
docs/build/html/cpp/ops.html
vendored
8
docs/build/html/cpp/ops.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>Operations — MLX 0.26.1 documentation</title>
|
||||
<title>Operations — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
422
docs/build/html/dev/custom_metal_kernels.html
vendored
422
docs/build/html/dev/custom_metal_kernels.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
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<title>Custom Metal Kernels — MLX 0.26.1 documentation</title>
|
||||
<title>Custom Metal Kernels — MLX 0.26.2 documentation</title>
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|
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@ -36,7 +36,7 @@
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@ -926,19 +926,20 @@ document.write(`
|
||||
<section id="simple-example">
|
||||
<h2>Simple Example<a class="headerlink" href="#simple-example" title="Link to this heading">#</a></h2>
|
||||
<p>Let’s write a custom kernel that computes <code class="docutils literal notranslate"><span class="pre">exp</span></code> elementwise:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">exp_elementwise</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
|
||||
<span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> T tmp = inp[elem];</span>
|
||||
<span class="s2"> out[elem] = metal::exp(tmp);</span>
|
||||
<span class="s2"> """</span>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> T tmp = inp[elem];</span>
|
||||
<span class="s2"> out[elem] = metal::exp(tmp);</span>
|
||||
<span class="s2">"""</span>
|
||||
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"myexp"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"inp"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"out"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"myexp"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"inp"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"out"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">exp_elementwise</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
|
||||
<span class="n">outputs</span> <span class="o">=</span> <span class="n">kernel</span><span class="p">(</span>
|
||||
<span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">a</span><span class="p">],</span>
|
||||
<span class="n">template</span><span class="o">=</span><span class="p">[(</span><span class="s2">"T"</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">float32</span><span class="p">)],</span>
|
||||
@ -954,9 +955,13 @@ document.write(`
|
||||
<span class="k">assert</span> <span class="n">mx</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">a</span><span class="p">))</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Every time you make a kernel, a new Metal library is created and possibly
|
||||
JIT compiled. To reduce the overhead from that, build the kernel once with
|
||||
<a class="reference internal" href="../python/_autosummary/mlx.core.fast.metal_kernel.html#mlx.core.fast.metal_kernel" title="mlx.core.fast.metal_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">fast.metal_kernel()</span></code></a> and then use it many times.</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>We are only required to pass the body of the Metal kernel in <code class="docutils literal notranslate"><span class="pre">source</span></code>.</p>
|
||||
<p>Only pass the body of the Metal kernel in <code class="docutils literal notranslate"><span class="pre">source</span></code>. The function
|
||||
signature is generated automatically.</p>
|
||||
</div>
|
||||
<p>The full function signature will be generated using:</p>
|
||||
<ul class="simple">
|
||||
@ -1004,37 +1009,43 @@ All the attributes defined in Table 5.8 of the <a class="reference external" hre
|
||||
<span class="k">template</span><span class="w"> </span><span class="p">[[</span><span class="n">host_name</span><span class="p">(</span><span class="s">"custom_kernel_myexp_float"</span><span class="p">)]]</span><span class="w"> </span><span class="p">[[</span><span class="n">kernel</span><span class="p">]]</span><span class="w"> </span><span class="k">decltype</span><span class="p">(</span><span class="n">custom_kernel_myexp_float</span><span class="o"><</span><span class="kt">float</span><span class="o">></span><span class="p">)</span><span class="w"> </span><span class="n">custom_kernel_myexp_float</span><span class="o"><</span><span class="kt">float</span><span class="o">></span><span class="p">;</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Note: <code class="docutils literal notranslate"><span class="pre">grid</span></code> and <code class="docutils literal notranslate"><span class="pre">threadgroup</span></code> are parameters to the Metal <a class="reference external" href="https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads">dispatchThreads</a> function.
|
||||
This means we will launch <code class="docutils literal notranslate"><span class="pre">mx.prod(grid)</span></code> threads, subdivided into <code class="docutils literal notranslate"><span class="pre">threadgroup</span></code> size threadgroups.
|
||||
For optimal performance, each thread group dimension should be less than or equal to the corresponding grid dimension.</p>
|
||||
<p>Passing <code class="docutils literal notranslate"><span class="pre">verbose=True</span></code> to <code class="docutils literal notranslate"><span class="pre">mx.fast.metal_kernel.__call__</span></code> will print the generated code for debugging purposes.</p>
|
||||
<p>Note: <code class="docutils literal notranslate"><span class="pre">grid</span></code> and <code class="docutils literal notranslate"><span class="pre">threadgroup</span></code> are parameters to the Metal <a class="reference external" href="https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads">dispatchThreads</a>
|
||||
function. This means we will launch <code class="docutils literal notranslate"><span class="pre">mx.prod(grid)</span></code> threads, subdivided into
|
||||
<code class="docutils literal notranslate"><span class="pre">threadgroup</span></code> size threadgroups. For optimal performance, each thread group
|
||||
dimension should be less than or equal to the corresponding grid dimension.</p>
|
||||
<p>Passing <code class="docutils literal notranslate"><span class="pre">verbose=True</span></code> to <code class="xref py py-func docutils literal notranslate"><span class="pre">ast.metal_kernel.__call__()</span></code> will print the
|
||||
generated code for debugging purposes.</p>
|
||||
</section>
|
||||
<section id="using-shape-strides">
|
||||
<h2>Using Shape/Strides<a class="headerlink" href="#using-shape-strides" title="Link to this heading">#</a></h2>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">mx.fast.metal_kernel</span></code> supports an argument <code class="docutils literal notranslate"><span class="pre">ensure_row_contiguous</span></code> which is <code class="docutils literal notranslate"><span class="pre">True</span></code> by default.
|
||||
This will copy the <code class="docutils literal notranslate"><span class="pre">mx.array</span></code> inputs if needed before the kernel is launched to ensure that the memory layout is row contiguous.
|
||||
Generally this makes writing the kernel easier, since we don’t have to worry about gaps or the ordering of the dims
|
||||
when indexing.</p>
|
||||
<p>If we want to avoid this copy, <code class="docutils literal notranslate"><span class="pre">metal_kernel</span></code> automatically passes <code class="docutils literal notranslate"><span class="pre">a_shape</span></code>, <code class="docutils literal notranslate"><span class="pre">a_strides</span></code> and <code class="docutils literal notranslate"><span class="pre">a_ndim</span></code> for each
|
||||
input array <code class="docutils literal notranslate"><span class="pre">a</span></code> if any are present in <code class="docutils literal notranslate"><span class="pre">source</span></code>.
|
||||
We can then use MLX’s built in indexing utils to fetch the right elements for each thread.</p>
|
||||
<p>Let’s convert <code class="docutils literal notranslate"><span class="pre">myexp</span></code> above to support arbitrarily strided arrays without relying on a copy from <code class="docutils literal notranslate"><span class="pre">ensure_row_contiguous</span></code>:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">exp_elementwise</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
|
||||
<span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included</span>
|
||||
<span class="s2"> uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);</span>
|
||||
<span class="s2"> T tmp = inp[loc];</span>
|
||||
<span class="s2"> // Output arrays are always row contiguous</span>
|
||||
<span class="s2"> out[elem] = metal::exp(tmp);</span>
|
||||
<span class="s2"> """</span>
|
||||
<p><a class="reference internal" href="../python/_autosummary/mlx.core.fast.metal_kernel.html#mlx.core.fast.metal_kernel" title="mlx.core.fast.metal_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">fast.metal_kernel()</span></code></a> supports an argument <code class="docutils literal notranslate"><span class="pre">ensure_row_contiguous</span></code> which
|
||||
is <code class="docutils literal notranslate"><span class="pre">True</span></code> by default. This will copy the array inputs if needed
|
||||
before the kernel is launched to ensure that the memory layout is row
|
||||
contiguous. Generally this makes writing the kernel easier, since we don’t
|
||||
have to worry about gaps or the ordering of the dims when indexing.</p>
|
||||
<p>If we want to avoid this copy, <a class="reference internal" href="../python/_autosummary/mlx.core.fast.metal_kernel.html#mlx.core.fast.metal_kernel" title="mlx.core.fast.metal_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">fast.metal_kernel()</span></code></a> automatically passes
|
||||
<code class="docutils literal notranslate"><span class="pre">a_shape</span></code>, <code class="docutils literal notranslate"><span class="pre">a_strides</span></code> and <code class="docutils literal notranslate"><span class="pre">a_ndim</span></code> for each input array <code class="docutils literal notranslate"><span class="pre">a</span></code> if any are
|
||||
present in <code class="docutils literal notranslate"><span class="pre">source</span></code>. We can then use MLX’s built in indexing utils to fetch
|
||||
the right elements for each thread.</p>
|
||||
<p>Let’s convert <code class="docutils literal notranslate"><span class="pre">myexp</span></code> above to support arbitrarily strided arrays without
|
||||
relying on a copy from <code class="docutils literal notranslate"><span class="pre">ensure_row_contiguous</span></code>:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included</span>
|
||||
<span class="s2"> uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);</span>
|
||||
<span class="s2"> T tmp = inp[loc];</span>
|
||||
<span class="s2"> // Output arrays are always row contiguous</span>
|
||||
<span class="s2"> out[elem] = metal::exp(tmp);</span>
|
||||
<span class="s2">"""</span>
|
||||
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"myexp_strided"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"inp"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"out"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"myexp_strided"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"inp"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"out"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">exp_elementwise</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
|
||||
<span class="n">outputs</span> <span class="o">=</span> <span class="n">kernel</span><span class="p">(</span>
|
||||
<span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">a</span><span class="p">],</span>
|
||||
<span class="n">template</span><span class="o">=</span><span class="p">[(</span><span class="s2">"T"</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">float32</span><span class="p">)],</span>
|
||||
@ -1100,10 +1111,67 @@ We can then use MLX’s built in indexing utils to fetch the right elements for
|
||||
<span class="k">return</span> <span class="n">output</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Now let’s use <code class="docutils literal notranslate"><span class="pre">mx.custom_function</span></code> together with <code class="docutils literal notranslate"><span class="pre">mx.fast.metal_kernel</span></code>
|
||||
<p>Now let’s use <a class="reference internal" href="../python/_autosummary/mlx.core.custom_function.html#mlx.core.custom_function" title="mlx.core.custom_function"><code class="xref py py-func docutils literal notranslate"><span class="pre">custom_function()</span></code></a> together with <a class="reference internal" href="../python/_autosummary/mlx.core.fast.metal_kernel.html#mlx.core.fast.metal_kernel" title="mlx.core.fast.metal_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">fast.metal_kernel()</span></code></a>
|
||||
to write a fast GPU kernel for both the forward and backward passes.</p>
|
||||
<p>First we’ll implement the forward pass as a fused kernel:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@mx</span><span class="o">.</span><span class="n">custom_function</span>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> int H = x_shape[1];</span>
|
||||
<span class="s2"> int W = x_shape[2];</span>
|
||||
<span class="s2"> int C = x_shape[3];</span>
|
||||
<span class="s2"> int gH = grid_shape[1];</span>
|
||||
<span class="s2"> int gW = grid_shape[2];</span>
|
||||
|
||||
<span class="s2"> int w_stride = C;</span>
|
||||
<span class="s2"> int h_stride = W * w_stride;</span>
|
||||
<span class="s2"> int b_stride = H * h_stride;</span>
|
||||
|
||||
<span class="s2"> uint grid_idx = elem / C * 2;</span>
|
||||
<span class="s2"> float ix = ((grid[grid_idx] + 1) * W - 1) / 2;</span>
|
||||
<span class="s2"> float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;</span>
|
||||
|
||||
<span class="s2"> int ix_nw = floor(ix);</span>
|
||||
<span class="s2"> int iy_nw = floor(iy);</span>
|
||||
|
||||
<span class="s2"> int ix_ne = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_ne = iy_nw;</span>
|
||||
|
||||
<span class="s2"> int ix_sw = ix_nw;</span>
|
||||
<span class="s2"> int iy_sw = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> int ix_se = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_se = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> T nw = (ix_se - ix) * (iy_se - iy);</span>
|
||||
<span class="s2"> T ne = (ix - ix_sw) * (iy_sw - iy);</span>
|
||||
<span class="s2"> T sw = (ix_ne - ix) * (iy - iy_ne);</span>
|
||||
<span class="s2"> T se = (ix - ix_nw) * (iy - iy_nw);</span>
|
||||
|
||||
<span class="s2"> int batch_idx = elem / C / gH / gW * b_stride;</span>
|
||||
<span class="s2"> int channel_idx = elem % C;</span>
|
||||
<span class="s2"> int base_idx = batch_idx + channel_idx;</span>
|
||||
|
||||
<span class="s2"> T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];</span>
|
||||
<span class="s2"> T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];</span>
|
||||
<span class="s2"> T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];</span>
|
||||
<span class="s2"> T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];</span>
|
||||
|
||||
<span class="s2"> I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;</span>
|
||||
<span class="s2"> I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;</span>
|
||||
<span class="s2"> I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;</span>
|
||||
<span class="s2"> I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;</span>
|
||||
|
||||
<span class="s2"> out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;</span>
|
||||
<span class="s2">"""</span>
|
||||
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"grid_sample"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"grid"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"out"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="nd">@mx</span><span class="o">.</span><span class="n">custom_function</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">grid_sample</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">grid</span><span class="p">):</span>
|
||||
|
||||
<span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">,</span> <span class="s2">"`x` must be 4D."</span>
|
||||
@ -1115,61 +1183,6 @@ to write a fast GPU kernel for both the forward and backward passes.</p>
|
||||
|
||||
<span class="k">assert</span> <span class="n">D</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"Last dim of `grid` must be size 2."</span>
|
||||
|
||||
<span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> int H = x_shape[1];</span>
|
||||
<span class="s2"> int W = x_shape[2];</span>
|
||||
<span class="s2"> int C = x_shape[3];</span>
|
||||
<span class="s2"> int gH = grid_shape[1];</span>
|
||||
<span class="s2"> int gW = grid_shape[2];</span>
|
||||
|
||||
<span class="s2"> int w_stride = C;</span>
|
||||
<span class="s2"> int h_stride = W * w_stride;</span>
|
||||
<span class="s2"> int b_stride = H * h_stride;</span>
|
||||
|
||||
<span class="s2"> uint grid_idx = elem / C * 2;</span>
|
||||
<span class="s2"> float ix = ((grid[grid_idx] + 1) * W - 1) / 2;</span>
|
||||
<span class="s2"> float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;</span>
|
||||
|
||||
<span class="s2"> int ix_nw = floor(ix);</span>
|
||||
<span class="s2"> int iy_nw = floor(iy);</span>
|
||||
|
||||
<span class="s2"> int ix_ne = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_ne = iy_nw;</span>
|
||||
|
||||
<span class="s2"> int ix_sw = ix_nw;</span>
|
||||
<span class="s2"> int iy_sw = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> int ix_se = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_se = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> T nw = (ix_se - ix) * (iy_se - iy);</span>
|
||||
<span class="s2"> T ne = (ix - ix_sw) * (iy_sw - iy);</span>
|
||||
<span class="s2"> T sw = (ix_ne - ix) * (iy - iy_ne);</span>
|
||||
<span class="s2"> T se = (ix - ix_nw) * (iy - iy_nw);</span>
|
||||
|
||||
<span class="s2"> int batch_idx = elem / C / gH / gW * b_stride;</span>
|
||||
<span class="s2"> int channel_idx = elem % C;</span>
|
||||
<span class="s2"> int base_idx = batch_idx + channel_idx;</span>
|
||||
|
||||
<span class="s2"> T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];</span>
|
||||
<span class="s2"> T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];</span>
|
||||
<span class="s2"> T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];</span>
|
||||
<span class="s2"> T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];</span>
|
||||
|
||||
<span class="s2"> I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;</span>
|
||||
<span class="s2"> I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;</span>
|
||||
<span class="s2"> I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;</span>
|
||||
<span class="s2"> I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;</span>
|
||||
|
||||
<span class="s2"> out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;</span>
|
||||
<span class="s2"> """</span>
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"grid_sample"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"grid"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"out"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">outputs</span> <span class="o">=</span> <span class="n">kernel</span><span class="p">(</span>
|
||||
<span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">grid</span><span class="p">],</span>
|
||||
<span class="n">template</span><span class="o">=</span><span class="p">[(</span><span class="s2">"T"</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)],</span>
|
||||
@ -1191,10 +1204,10 @@ to write a fast GPU kernel for both the forward and backward passes.</p>
|
||||
</section>
|
||||
<section id="grid-sample-vjp">
|
||||
<h2>Grid Sample VJP<a class="headerlink" href="#grid-sample-vjp" title="Link to this heading">#</a></h2>
|
||||
<p>Since we decorated <code class="docutils literal notranslate"><span class="pre">grid_sample</span></code> with <code class="docutils literal notranslate"><span class="pre">mx.custom_function</span></code>, we can now define
|
||||
its custom vjp transform so MLX can differentiate it.</p>
|
||||
<p>Since we decorated <code class="docutils literal notranslate"><span class="pre">grid_sample</span></code> with <a class="reference internal" href="../python/_autosummary/mlx.core.custom_function.html#mlx.core.custom_function" title="mlx.core.custom_function"><code class="xref py py-func docutils literal notranslate"><span class="pre">custom_function()</span></code></a>, we can now
|
||||
define its custom vjp transform so MLX can differentiate it.</p>
|
||||
<p>The backwards pass requires atomically updating <code class="docutils literal notranslate"><span class="pre">x_grad</span></code>/<code class="docutils literal notranslate"><span class="pre">grid_grad</span></code> and so
|
||||
requires a few extra <code class="docutils literal notranslate"><span class="pre">mx.fast.metal_kernel</span></code> features:</p>
|
||||
requires a few extra <a class="reference internal" href="../python/_autosummary/mlx.core.fast.metal_kernel.html#mlx.core.fast.metal_kernel" title="mlx.core.fast.metal_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">fast.metal_kernel()</span></code></a> features:</p>
|
||||
<ul class="simple">
|
||||
<li><dl class="simple">
|
||||
<dt><code class="docutils literal notranslate"><span class="pre">init_value=0</span></code></dt><dd><p>Initialize all of the kernel’s outputs to this value before it runs. This allows us to update only part of the output arrays with the kernel.</p>
|
||||
@ -1210,7 +1223,107 @@ See section 6.15 of the <a class="reference external" href="https://developer.ap
|
||||
</li>
|
||||
</ul>
|
||||
<p>We can then implement the backwards pass as follows:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@grid_sample</span><span class="o">.</span><span class="n">vjp</span>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> int H = x_shape[1];</span>
|
||||
<span class="s2"> int W = x_shape[2];</span>
|
||||
<span class="s2"> int C = x_shape[3];</span>
|
||||
<span class="s2"> // Pad C to the nearest larger simdgroup size multiple</span>
|
||||
<span class="s2"> int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;</span>
|
||||
|
||||
<span class="s2"> int gH = grid_shape[1];</span>
|
||||
<span class="s2"> int gW = grid_shape[2];</span>
|
||||
|
||||
<span class="s2"> int w_stride = C;</span>
|
||||
<span class="s2"> int h_stride = W * w_stride;</span>
|
||||
<span class="s2"> int b_stride = H * h_stride;</span>
|
||||
|
||||
<span class="s2"> uint grid_idx = elem / C_padded * 2;</span>
|
||||
<span class="s2"> float ix = ((grid[grid_idx] + 1) * W - 1) / 2;</span>
|
||||
<span class="s2"> float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;</span>
|
||||
|
||||
<span class="s2"> int ix_nw = floor(ix);</span>
|
||||
<span class="s2"> int iy_nw = floor(iy);</span>
|
||||
|
||||
<span class="s2"> int ix_ne = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_ne = iy_nw;</span>
|
||||
|
||||
<span class="s2"> int ix_sw = ix_nw;</span>
|
||||
<span class="s2"> int iy_sw = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> int ix_se = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_se = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> T nw = (ix_se - ix) * (iy_se - iy);</span>
|
||||
<span class="s2"> T ne = (ix - ix_sw) * (iy_sw - iy);</span>
|
||||
<span class="s2"> T sw = (ix_ne - ix) * (iy - iy_ne);</span>
|
||||
<span class="s2"> T se = (ix - ix_nw) * (iy - iy_nw);</span>
|
||||
|
||||
<span class="s2"> int batch_idx = elem / C_padded / gH / gW * b_stride;</span>
|
||||
<span class="s2"> int channel_idx = elem % C_padded;</span>
|
||||
<span class="s2"> int base_idx = batch_idx + channel_idx;</span>
|
||||
|
||||
<span class="s2"> T gix = T(0);</span>
|
||||
<span class="s2"> T giy = T(0);</span>
|
||||
<span class="s2"> if (channel_idx < C) {</span>
|
||||
<span class="s2"> int cot_index = elem / C_padded * C + channel_idx;</span>
|
||||
<span class="s2"> T cot = cotangent[cot_index];</span>
|
||||
<span class="s2"> if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_nw = x[offset];</span>
|
||||
<span class="s2"> gix -= I_nw * (iy_se - iy) * cot;</span>
|
||||
<span class="s2"> giy -= I_nw * (ix_se - ix) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_ne = x[offset];</span>
|
||||
<span class="s2"> gix += I_ne * (iy_sw - iy) * cot;</span>
|
||||
<span class="s2"> giy -= I_ne * (ix - ix_sw) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_sw = x[offset];</span>
|
||||
<span class="s2"> gix -= I_sw * (iy - iy_ne) * cot;</span>
|
||||
<span class="s2"> giy += I_sw * (ix_ne - ix) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_se * h_stride + ix_se * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_se = x[offset];</span>
|
||||
<span class="s2"> gix += I_se * (iy - iy_nw) * cot;</span>
|
||||
<span class="s2"> giy += I_se * (ix - ix_nw) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> }</span>
|
||||
|
||||
<span class="s2"> T gix_mult = W / 2;</span>
|
||||
<span class="s2"> T giy_mult = H / 2;</span>
|
||||
|
||||
<span class="s2"> // Reduce across each simdgroup first.</span>
|
||||
<span class="s2"> // This is much faster than relying purely on atomics.</span>
|
||||
<span class="s2"> gix = simd_sum(gix);</span>
|
||||
<span class="s2"> giy = simd_sum(giy);</span>
|
||||
|
||||
<span class="s2"> if (thread_index_in_simdgroup == 0) {</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2">"""</span>
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"grid_sample_grad"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"grid"</span><span class="p">,</span> <span class="s2">"cotangent"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"x_grad"</span><span class="p">,</span> <span class="s2">"grid_grad"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span><span class="p">,</span>
|
||||
<span class="n">atomic_outputs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="nd">@grid_sample</span><span class="o">.</span><span class="n">vjp</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">grid_sample_vjp</span><span class="p">(</span><span class="n">primals</span><span class="p">,</span> <span class="n">cotangent</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>
|
||||
<span class="n">x</span><span class="p">,</span> <span class="n">grid</span> <span class="o">=</span> <span class="n">primals</span>
|
||||
<span class="n">B</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">C</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span>
|
||||
@ -1218,105 +1331,6 @@ See section 6.15 of the <a class="reference external" href="https://developer.ap
|
||||
|
||||
<span class="k">assert</span> <span class="n">D</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"Last dim of `grid` must be size 2."</span>
|
||||
|
||||
<span class="n">source</span> <span class="o">=</span> <span class="s2">"""</span>
|
||||
<span class="s2"> uint elem = thread_position_in_grid.x;</span>
|
||||
<span class="s2"> int H = x_shape[1];</span>
|
||||
<span class="s2"> int W = x_shape[2];</span>
|
||||
<span class="s2"> int C = x_shape[3];</span>
|
||||
<span class="s2"> // Pad C to the nearest larger simdgroup size multiple</span>
|
||||
<span class="s2"> int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;</span>
|
||||
|
||||
<span class="s2"> int gH = grid_shape[1];</span>
|
||||
<span class="s2"> int gW = grid_shape[2];</span>
|
||||
|
||||
<span class="s2"> int w_stride = C;</span>
|
||||
<span class="s2"> int h_stride = W * w_stride;</span>
|
||||
<span class="s2"> int b_stride = H * h_stride;</span>
|
||||
|
||||
<span class="s2"> uint grid_idx = elem / C_padded * 2;</span>
|
||||
<span class="s2"> float ix = ((grid[grid_idx] + 1) * W - 1) / 2;</span>
|
||||
<span class="s2"> float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;</span>
|
||||
|
||||
<span class="s2"> int ix_nw = floor(ix);</span>
|
||||
<span class="s2"> int iy_nw = floor(iy);</span>
|
||||
|
||||
<span class="s2"> int ix_ne = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_ne = iy_nw;</span>
|
||||
|
||||
<span class="s2"> int ix_sw = ix_nw;</span>
|
||||
<span class="s2"> int iy_sw = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> int ix_se = ix_nw + 1;</span>
|
||||
<span class="s2"> int iy_se = iy_nw + 1;</span>
|
||||
|
||||
<span class="s2"> T nw = (ix_se - ix) * (iy_se - iy);</span>
|
||||
<span class="s2"> T ne = (ix - ix_sw) * (iy_sw - iy);</span>
|
||||
<span class="s2"> T sw = (ix_ne - ix) * (iy - iy_ne);</span>
|
||||
<span class="s2"> T se = (ix - ix_nw) * (iy - iy_nw);</span>
|
||||
|
||||
<span class="s2"> int batch_idx = elem / C_padded / gH / gW * b_stride;</span>
|
||||
<span class="s2"> int channel_idx = elem % C_padded;</span>
|
||||
<span class="s2"> int base_idx = batch_idx + channel_idx;</span>
|
||||
|
||||
<span class="s2"> T gix = T(0);</span>
|
||||
<span class="s2"> T giy = T(0);</span>
|
||||
<span class="s2"> if (channel_idx < C) {</span>
|
||||
<span class="s2"> int cot_index = elem / C_padded * C + channel_idx;</span>
|
||||
<span class="s2"> T cot = cotangent[cot_index];</span>
|
||||
<span class="s2"> if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_nw = x[offset];</span>
|
||||
<span class="s2"> gix -= I_nw * (iy_se - iy) * cot;</span>
|
||||
<span class="s2"> giy -= I_nw * (ix_se - ix) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_ne = x[offset];</span>
|
||||
<span class="s2"> gix += I_ne * (iy_sw - iy) * cot;</span>
|
||||
<span class="s2"> giy -= I_ne * (ix - ix_sw) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_sw = x[offset];</span>
|
||||
<span class="s2"> gix -= I_sw * (iy - iy_ne) * cot;</span>
|
||||
<span class="s2"> giy += I_sw * (ix_ne - ix) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {</span>
|
||||
<span class="s2"> int offset = base_idx + iy_se * h_stride + ix_se * w_stride;</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);</span>
|
||||
|
||||
<span class="s2"> T I_se = x[offset];</span>
|
||||
<span class="s2"> gix += I_se * (iy - iy_nw) * cot;</span>
|
||||
<span class="s2"> giy += I_se * (ix - ix_nw) * cot;</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> }</span>
|
||||
|
||||
<span class="s2"> T gix_mult = W / 2;</span>
|
||||
<span class="s2"> T giy_mult = H / 2;</span>
|
||||
|
||||
<span class="s2"> // Reduce across each simdgroup first.</span>
|
||||
<span class="s2"> // This is much faster than relying purely on atomics.</span>
|
||||
<span class="s2"> gix = simd_sum(gix);</span>
|
||||
<span class="s2"> giy = simd_sum(giy);</span>
|
||||
|
||||
<span class="s2"> if (thread_index_in_simdgroup == 0) {</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);</span>
|
||||
<span class="s2"> atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);</span>
|
||||
<span class="s2"> }</span>
|
||||
<span class="s2"> """</span>
|
||||
<span class="n">kernel</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">fast</span><span class="o">.</span><span class="n">metal_kernel</span><span class="p">(</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">"grid_sample_grad"</span><span class="p">,</span>
|
||||
<span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"grid"</span><span class="p">,</span> <span class="s2">"cotangent"</span><span class="p">],</span>
|
||||
<span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"x_grad"</span><span class="p">,</span> <span class="s2">"grid_grad"</span><span class="p">],</span>
|
||||
<span class="n">source</span><span class="o">=</span><span class="n">source</span><span class="p">,</span>
|
||||
<span class="n">atomic_outputs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
<span class="c1"># pad the output channels to simd group size</span>
|
||||
<span class="c1"># so that our `simd_sum`s don't overlap.</span>
|
||||
<span class="n">simdgroup_size</span> <span class="o">=</span> <span class="mi">32</span>
|
||||
|
||||
14
docs/build/html/dev/extensions.html
vendored
14
docs/build/html/dev/extensions.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>Custom Extensions in MLX — MLX 0.26.1 documentation</title>
|
||||
<title>Custom Extensions in MLX — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
@ -1305,11 +1305,11 @@ below.</p>
|
||||
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ostringstream</span><span class="w"> </span><span class="n">kname</span><span class="p">;</span>
|
||||
<span class="w"> </span><span class="n">kname</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">"axpby_"</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">"general_"</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">type_to_name</span><span class="p">(</span><span class="n">out</span><span class="p">);</span>
|
||||
|
||||
<span class="w"> </span><span class="c1">// Make sure the metal library is available</span>
|
||||
<span class="w"> </span><span class="n">d</span><span class="p">.</span><span class="n">register_library</span><span class="p">(</span><span class="s">"mlx_ext"</span><span class="p">);</span>
|
||||
<span class="w"> </span><span class="c1">// Load the metal library</span>
|
||||
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">lib</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d</span><span class="p">.</span><span class="n">get_library</span><span class="p">(</span><span class="s">"mlx_ext"</span><span class="p">);</span>
|
||||
|
||||
<span class="w"> </span><span class="c1">// Make a kernel from this metal library</span>
|
||||
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d</span><span class="p">.</span><span class="n">get_kernel</span><span class="p">(</span><span class="n">kname</span><span class="p">.</span><span class="n">str</span><span class="p">(),</span><span class="w"> </span><span class="s">"mlx_ext"</span><span class="p">);</span>
|
||||
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">kernel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d</span><span class="p">.</span><span class="n">get_kernel</span><span class="p">(</span><span class="n">kname</span><span class="p">.</span><span class="n">str</span><span class="p">(),</span><span class="w"> </span><span class="n">lib</span><span class="p">);</span>
|
||||
|
||||
<span class="w"> </span><span class="c1">// Prepare to encode kernel</span>
|
||||
<span class="w"> </span><span class="k">auto</span><span class="o">&</span><span class="w"> </span><span class="n">compute_encoder</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">d</span><span class="p">.</span><span class="n">get_command_encoder</span><span class="p">(</span><span class="n">s</span><span class="p">.</span><span class="n">index</span><span class="p">);</span>
|
||||
|
||||
8
docs/build/html/dev/metal_debugger.html
vendored
8
docs/build/html/dev/metal_debugger.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>Metal Debugger — MLX 0.26.1 documentation</title>
|
||||
<title>Metal Debugger — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
8
docs/build/html/dev/mlx_in_cpp.html
vendored
8
docs/build/html/dev/mlx_in_cpp.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>Using MLX in C++ — MLX 0.26.1 documentation</title>
|
||||
<title>Using MLX in C++ — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -136,8 +136,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>Linear Regression — MLX 0.26.1 documentation</title>
|
||||
<title>Linear Regression — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>LLM inference — MLX 0.26.1 documentation</title>
|
||||
<title>LLM inference — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
8
docs/build/html/examples/mlp.html
vendored
8
docs/build/html/examples/mlp.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>Multi-Layer Perceptron — MLX 0.26.1 documentation</title>
|
||||
<title>Multi-Layer Perceptron — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
8
docs/build/html/genindex.html
vendored
8
docs/build/html/genindex.html
vendored
@ -7,7 +7,7 @@
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Index — MLX 0.26.1 documentation</title>
|
||||
<title>Index — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -35,7 +35,7 @@
|
||||
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -136,8 +136,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
8
docs/build/html/index.html
vendored
8
docs/build/html/index.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>MLX — MLX 0.26.1 documentation</title>
|
||||
<title>MLX — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -139,8 +139,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
68
docs/build/html/install.html
vendored
68
docs/build/html/install.html
vendored
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>Build and Install — MLX 0.26.1 documentation</title>
|
||||
<title>Build and Install — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
@ -906,6 +906,7 @@ document.write(`
|
||||
<nav aria-label="Page">
|
||||
<ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#python-installation">Python Installation</a><ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#cuda">CUDA</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#troubleshooting">Troubleshooting</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
@ -916,7 +917,9 @@ document.write(`
|
||||
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#binary-size-minimization">Binary Size Minimization</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">Troubleshooting</a><ul class="nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#linux">Linux</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">CUDA</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id4">Troubleshooting</a><ul class="nav section-nav flex-column">
|
||||
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#metal-not-found">Metal not found</a></li>
|
||||
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#x86-shell">x86 Shell</a></li>
|
||||
</ul>
|
||||
@ -958,6 +961,14 @@ It is highly recommended to use macOS 14 (Sonoma)</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>install<span class="w"> </span>conda-forge::mlx
|
||||
</pre></div>
|
||||
</div>
|
||||
<section id="cuda">
|
||||
<h3>CUDA<a class="headerlink" href="#cuda" title="Link to this heading">#</a></h3>
|
||||
<p>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:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>mlx-cuda
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="troubleshooting">
|
||||
<h3>Troubleshooting<a class="headerlink" href="#troubleshooting" title="Link to this heading">#</a></h3>
|
||||
<p><em>My OS and Python versions are in the required range but pip still does not find
|
||||
@ -988,7 +999,7 @@ the output of <code class="docutils literal notranslate"><span class="pre">uname
|
||||
</section>
|
||||
<section id="python-api">
|
||||
<h3>Python API<a class="headerlink" href="#python-api" title="Link to this heading">#</a></h3>
|
||||
<p>To build and install the MLX python library from source, first, clone MLX from
|
||||
<p id="python-install">To build and install the MLX python library from source, first, clone MLX from
|
||||
<a class="reference external" href="https://github.com/ml-explore/mlx">its GitHub repo</a>:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>git@github.com:ml-explore/mlx.git<span class="w"> </span>mlx<span class="w"> </span><span class="o">&&</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>mlx
|
||||
</pre></div>
|
||||
@ -1018,7 +1029,7 @@ IDE:</p>
|
||||
</section>
|
||||
<section id="c-api">
|
||||
<h3>C++ API<a class="headerlink" href="#c-api" title="Link to this heading">#</a></h3>
|
||||
<p>Currently, MLX must be built and installed from source.</p>
|
||||
<p id="cpp-install">Currently, MLX must be built and installed from source.</p>
|
||||
<p>Similarly to the python library, to build and install the MLX C++ library start
|
||||
by cloning MLX from <a class="reference external" href="https://github.com/ml-explore/mlx">its GitHub repo</a>:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>git@github.com:ml-explore/mlx.git<span class="w"> </span>mlx<span class="w"> </span><span class="o">&&</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>mlx
|
||||
@ -1041,8 +1052,8 @@ cmake<span class="w"> </span>..<span class="w"> </span><span class="o">&&
|
||||
directory as the executable statically linked to <code class="docutils literal notranslate"><span class="pre">libmlx.a</span></code> or the
|
||||
preprocessor constant <code class="docutils literal notranslate"><span class="pre">METAL_PATH</span></code> should be defined at build time and it
|
||||
should point to the path to the built metal library.</p>
|
||||
<div class="pst-scrollable-table-container"><table class="table" id="id4">
|
||||
<caption><span class="caption-text">Build Options</span><a class="headerlink" href="#id4" title="Link to this table">#</a></caption>
|
||||
<div class="pst-scrollable-table-container"><table class="table" id="id5">
|
||||
<caption><span class="caption-text">Build Options</span><a class="headerlink" href="#id5" title="Link to this table">#</a></caption>
|
||||
<colgroup>
|
||||
<col style="width: 75.8%" />
|
||||
<col style="width: 24.2%" />
|
||||
@ -1125,8 +1136,40 @@ application. Once a kernel is compiled, it will be cached by the system. The
|
||||
Metal kernel cache persists across reboots.</p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="linux">
|
||||
<h3>Linux<a class="headerlink" href="#linux" title="Link to this heading">#</a></h3>
|
||||
<p>To build from source on Linux (CPU only), install the BLAS and LAPACK headers.
|
||||
For example on Ubuntu, run the following:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>apt-get<span class="w"> </span>update<span class="w"> </span>-y
|
||||
apt-get<span class="w"> </span>install<span class="w"> </span>libblas-dev<span class="w"> </span>liblapack-dev<span class="w"> </span>liblapacke-dev<span class="w"> </span>-y
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>From here follow the instructions to install either the <a class="reference internal" href="#python-install"><span class="std std-ref">Python</span></a> or <a class="reference internal" href="#cpp-install"><span class="std std-ref">C++</span></a> APIs.</p>
|
||||
</section>
|
||||
<section id="id3">
|
||||
<h3>Troubleshooting<a class="headerlink" href="#id3" title="Link to this heading">#</a></h3>
|
||||
<h3>CUDA<a class="headerlink" href="#id3" title="Link to this heading">#</a></h3>
|
||||
<p>To build from source on Linux with CUDA, install the BLAS and LAPACK headers
|
||||
and the CUDA toolkit. For example on Ubuntu, run the following:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>wget<span class="w"> </span>https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
|
||||
dpkg<span class="w"> </span>-i<span class="w"> </span>cuda-keyring_1.1-1_all.deb
|
||||
apt-get<span class="w"> </span>update<span class="w"> </span>-y
|
||||
apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>cuda-toolkit-12-9
|
||||
apt-get<span class="w"> </span>install<span class="w"> </span>libblas-dev<span class="w"> </span>liblapack-dev<span class="w"> </span>liblapacke-dev<span class="w"> </span>-y
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>When building either the Python or C++ APIs make sure to pass the cmake flag
|
||||
<code class="docutils literal notranslate"><span class="pre">MLX_BUILD_CUDA=ON</span></code>. For example, to build the Python API run:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">CMAKE_BUILD_PARALLEL_LEVEL</span><span class="o">=</span><span class="m">8</span><span class="w"> </span><span class="nv">CMAKE_ARGS</span><span class="o">=</span><span class="s2">"-DMLX_BUILD_CUDA=ON"</span><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span><span class="s2">".[dev]"</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>To build the C++ package run:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>mkdir<span class="w"> </span>-p<span class="w"> </span>build<span class="w"> </span><span class="o">&&</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>build
|
||||
cmake<span class="w"> </span>..<span class="w"> </span>-DMLX_BUILD_CUDA<span class="o">=</span>ON<span class="w"> </span><span class="o">&&</span><span class="w"> </span>make<span class="w"> </span>-j
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="id4">
|
||||
<h3>Troubleshooting<a class="headerlink" href="#id4" title="Link to this heading">#</a></h3>
|
||||
<section id="metal-not-found">
|
||||
<h4>Metal not found<a class="headerlink" href="#metal-not-found" title="Link to this heading">#</a></h4>
|
||||
<p>You see the following error when you try to build:</p>
|
||||
@ -1214,6 +1257,7 @@ wipe your build cache with <code class="docutils literal notranslate"><span clas
|
||||
<nav class="bd-toc-nav page-toc">
|
||||
<ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#python-installation">Python Installation</a><ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#cuda">CUDA</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#troubleshooting">Troubleshooting</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
@ -1224,7 +1268,9 @@ wipe your build cache with <code class="docutils literal notranslate"><span clas
|
||||
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#binary-size-minimization">Binary Size Minimization</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">Troubleshooting</a><ul class="nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#linux">Linux</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">CUDA</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id4">Troubleshooting</a><ul class="nav section-nav flex-column">
|
||||
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#metal-not-found">Metal not found</a></li>
|
||||
<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#x86-shell">x86 Shell</a></li>
|
||||
</ul>
|
||||
|
||||
BIN
docs/build/html/objects.inv
vendored
BIN
docs/build/html/objects.inv
vendored
Binary file not shown.
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>mlx.core.Device — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.Device — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
|
||||
<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
|
||||
<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.2 documentation - Home"/>
|
||||
<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>mlx.core.Dtype — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.Dtype — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
|
||||
<script src="../../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
||||
|
||||
<script src="../../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
|
||||
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||||
|
||||
<title>mlx.core.DtypeCategory — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.DtypeCategory — MLX 0.26.2 documentation</title>
|
||||
|
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|
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<title>mlx.core.abs — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.abs — MLX 0.26.2 documentation</title>
|
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@ -36,7 +36,7 @@
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||||
<title>mlx.core.add — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.add — MLX 0.26.2 documentation</title>
|
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|
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@ -36,7 +36,7 @@
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@ -137,8 +137,8 @@
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@ -8,7 +8,7 @@
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||||
<title>mlx.core.addmm — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.addmm — MLX 0.26.2 documentation</title>
|
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@ -36,7 +36,7 @@
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@ -8,7 +8,7 @@
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||||
<title>mlx.core.all — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.all — MLX 0.26.2 documentation</title>
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@ -36,7 +36,7 @@
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@ -137,8 +137,8 @@
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
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</a></div>
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@ -8,7 +8,7 @@
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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||||
<title>mlx.core.allclose — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.allclose — MLX 0.26.2 documentation</title>
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|
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||||
@ -36,7 +36,7 @@
|
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@ -137,8 +137,8 @@
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<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
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</a></div>
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@ -8,7 +8,7 @@
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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<title>mlx.core.any — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.any — MLX 0.26.2 documentation</title>
|
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|
||||
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@ -36,7 +36,7 @@
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@ -137,8 +137,8 @@
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</a></div>
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<title>mlx.core.arange — MLX 0.26.1 documentation</title>
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||||
<title>mlx.core.arange — MLX 0.26.2 documentation</title>
|
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@ -36,7 +36,7 @@
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<title>mlx.core.arccos — MLX 0.26.1 documentation</title>
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<title>mlx.core.arccos — MLX 0.26.2 documentation</title>
|
||||
|
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@ -36,7 +36,7 @@
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@ -137,8 +137,8 @@
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<title>mlx.core.arccosh — MLX 0.26.1 documentation</title>
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<title>mlx.core.arccosh — MLX 0.26.2 documentation</title>
|
||||
|
||||
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@ -36,7 +36,7 @@
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<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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<title>mlx.core.arcsin — MLX 0.26.1 documentation</title>
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<title>mlx.core.arcsin — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
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<title>mlx.core.arcsinh — MLX 0.26.2 documentation</title>
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<title>mlx.core.arctan — MLX 0.26.2 documentation</title>
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||||
<title>mlx.core.arctan2 — MLX 0.26.2 documentation</title>
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<title>mlx.core.arctanh — MLX 0.26.2 documentation</title>
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<title>mlx.core.argmax — MLX 0.26.2 documentation</title>
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@ -36,7 +36,7 @@
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<title>mlx.core.argmin — MLX 0.26.2 documentation</title>
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<title>mlx.core.argpartition — MLX 0.26.1 documentation</title>
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<title>mlx.core.argpartition — MLX 0.26.2 documentation</title>
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<title>mlx.core.argsort — MLX 0.26.2 documentation</title>
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<title>mlx.core.array.T — MLX 0.26.2 documentation</title>
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||||
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||||
</a></div>
|
||||
@ -928,7 +928,7 @@ document.write(`
|
||||
<dd><p>An N-dimensional array object.</p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="mlx.core.array.__init__">
|
||||
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#mlx.core.array" title="mlx.core.array"><span class="pre">array</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">scalar</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#mlx.core.array" title="mlx.core.array"><span class="pre">array</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="mlx.core.Dtype.html#mlx.core.Dtype" title="mlx.core.Dtype"><span class="pre">Dtype</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.core.array.__init__" title="Link to this definition">#</a></dt>
|
||||
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#mlx.core.array" title="mlx.core.array"><span class="pre">array</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">scalar</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#mlx.core.array" title="mlx.core.array"><span class="pre">array</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="mlx.core.Dtype.html#mlx.core.Dtype" title="mlx.core.Dtype"><span class="pre">Dtype</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.core.array.__init__" title="Link to this definition">#</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<p class="rubric">Methods</p>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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||||
|
||||
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|
||||
<title>mlx.core.array.imag — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
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<script src="../../_static/documentation_options.js?v=20507f52"></script>
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||||
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@ -137,8 +137,8 @@
|
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|
||||
|
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|
||||
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||||
<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
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||||
|
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</a></div>
|
||||
|
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@ -8,7 +8,7 @@
|
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<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>mlx.core.array.var — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.array.var — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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||||
<script src="../../_static/documentation_options.js?v=3724ff34"></script>
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||||
<script src="../../_static/documentation_options.js?v=20507f52"></script>
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@ -137,8 +137,8 @@
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||||
|
||||
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||||
<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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|
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</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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||||
|
||||
<title>mlx.core.array.view — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.array.view — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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||||
<script src="../../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../../_static/documentation_options.js?v=20507f52"></script>
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@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
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||||
|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<title>mlx.core.array_equal — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.array_equal — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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<script src="../../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
|
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|
||||
<script src="../../_static/documentation_options.js?v=3724ff34"></script>
|
||||
<script src="../../_static/documentation_options.js?v=20507f52"></script>
|
||||
<script src="../../_static/doctools.js?v=9a2dae69"></script>
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<script src="../../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
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@ -137,8 +137,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
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||||
|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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||||
|
||||
<title>mlx.core.as_strided — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.as_strided — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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<script src="../../_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
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|
||||
<script src="../../_static/documentation_options.js?v=3724ff34"></script>
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<script src="../../_static/documentation_options.js?v=20507f52"></script>
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<script src="../../_static/doctools.js?v=9a2dae69"></script>
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<script src="../../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
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@ -137,8 +137,8 @@
|
||||
|
||||
|
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|
||||
<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
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||||
|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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||||
|
||||
<title>mlx.core.async_eval — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.async_eval — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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<script src="../../_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
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@ -137,8 +137,8 @@
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||||
|
||||
|
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<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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|
||||
<title>mlx.core.atleast_1d — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.atleast_1d — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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@ -137,8 +137,8 @@
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|
||||
|
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<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.1 documentation - Home"/>`);</script>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.26.2 documentation - Home"/>`);</script>
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|
||||
|
||||
</a></div>
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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|
||||
<title>mlx.core.atleast_2d — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.atleast_2d — MLX 0.26.2 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@
|
||||
<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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@ -137,8 +137,8 @@
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<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.26.1 documentation - Home"/>
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</a></div>
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|
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@ -8,7 +8,7 @@
|
||||
<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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<title>mlx.core.atleast_3d — MLX 0.26.1 documentation</title>
|
||||
<title>mlx.core.atleast_3d — MLX 0.26.2 documentation</title>
|
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@ -36,7 +36,7 @@
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<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
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</a></div>
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|
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