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github.com/ml-explore/mlx
/ functions
Functions
4,946 in github.com/ml-explore/mlx
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Functions
4,946
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Types & classes
770
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Endpoints
5
↓ 2 callers
Function
ensure_row_contiguous
mlx/backend/cpu/distributed.cpp:12
↓ 2 callers
Function
eval
mlx/transforms.cpp:330
↓ 2 callers
Function
eval_cpu
mlx/primitives.h:137
↓ 2 callers
Function
eval_gpu
mlx/primitives.h:142
↓ 2 callers
Function
eval_impl
mlx/transforms.cpp:74
↓ 2 callers
Function
expand_dims_impl
mlx/ops.cpp:589
↓ 2 callers
Function
expm1f
Compute exponential base e minus 1. max ulp err = 0.99746 */
mlx/backend/metal/kernels/expm1f.h:80
↓ 2 callers
Function
export_to_dot
mlx/graph_utils.h:48
↓ 2 callers
Function
flop_count
mlx/einsum.cpp:125
↓ 2 callers
Function
fmt_bytes
mlx/distributed/jaccl/lib/examples/allreduce_bench.cpp:70
↓ 2 callers
Function
fmt_row
(row)
benchmarks/python/segmented_mm_bench.py:88
↓ 2 callers
Function
fmt_row
(row)
benchmarks/python/block_masked_mm_bench.py:74
↓ 2 callers
Function
fp_qmm_dispatch
mlx/backend/cpu/quantized.cpp:673
↓ 2 callers
Function
fp_quantize
mlx/backend/metal/kernels/fp_quantized.h:1805
↓ 2 callers
Method
free
mlx/backend/cuda/allocator.cpp:147
↓ 2 callers
Method
from_embedding
Create a :obj:`QuantizedEmbedding` layer from an :obj:`Embedding` layer.
python/mlx/nn/layers/quantized.py:180
↓ 2 callers
Method
from_file
Parse the json hostfile that contains both the hostnames to ssh into and the ips to communicate over when using the ring backend. It can also
python/mlx/_distributed_utils/common.py:37
↓ 2 callers
Method
from_linear
Create a :obj:`QQLinear` layer from a :obj:`Linear` layer.
python/mlx/nn/layers/quantized.py:411
↓ 2 callers
Method
from_list
(cls, hostlist, repeats=1)
python/mlx/_distributed_utils/common.py:91
↓ 2 callers
Method
from_quantized_linear
( cls, quantized_linear_layer: Module, *, segments: Union[int, list] = 1,
python/mlx/nn/layers/distributed.py:459
↓ 2 callers
Function
full_impl
mlx/ops.cpp:297
↓ 2 callers
Function
full_like
mlx/ops.cpp:317
↓ 2 callers
Function
gather_mm
Compute matrix product with matrix-level gather */
mlx/ops.cpp:5785
↓ 2 callers
Function
gather_mm_grad
Calculate the gradient wrt to the weights of the following calculation y = gather_mm(x, w.T, lhs_indices, rhs_indices, sorted) Note the transpose ab
mlx/primitives.cpp:121
↓ 2 callers
Function
gather_qmm
mlx/ops.cpp:5266
↓ 2 callers
Function
gather_qmv
mlx/backend/metal/quantized.cpp:960
↓ 2 callers
Function
gather_sort
(x, indices)
benchmarks/python/gather_qmm_bench.py:13
↓ 2 callers
Function
gelu_1
tests/compile_tests.cpp:409
↓ 2 callers
Function
gemm_conv
mlx/backend/cuda/conv/conv.h:77
↓ 2 callers
Function
gemv
mlx/backend/metal/matmul.cpp:1182
↓ 2 callers
Function
get_2d_grid_dims_common
mlx/backend/common/utils.cpp:148
↓ 2 callers
Function
get_block_dims_common
mlx/backend/common/utils.cpp:117
↓ 2 callers
Function
get_copy_kernel
mlx/backend/metal/jit_kernels.cpp:194
↓ 2 callers
Function
get_cpu_architecture
Get CPU architecture string at runtime
mlx/backend/cpu/device_info.cpp:20
↓ 2 callers
Function
get_gbyte_size
(in_vec_len, out_vec_len, np_dtype)
benchmarks/python/blas/bench_gemv.py:129
↓ 2 callers
Function
get_gflop_count
(in_vec_len, out_vec_len)
benchmarks/python/blas/bench_gemv.py:123
↓ 2 callers
Function
get_memory_limit
mlx/backend/cuda/allocator.cpp:433
↓ 2 callers
Function
get_padded_scale_dims
Compute padded dimensions for tiled layout Tiles are 128 rows × 4 columns, must allocate full tiles
mlx/backend/cuda/quantized/qqmm_utils.h:12
↓ 2 callers
Function
get_peak_memory
mlx/backend/cuda/allocator.cpp:424
↓ 2 callers
Function
get_ptx_path
mlx/backend/cuda/jit_module.cpp:124
↓ 2 callers
Function
get_qmv_batch_limit
mlx/backend/metal/quantized.cpp:84
↓ 2 callers
Method
get_ring_connectivity
mlx/distributed/jaccl/lib/jaccl/jaccl.cpp:158
↓ 2 callers
Function
get_shape
mlx/io/gguf.cpp:50
↓ 2 callers
Method
get_size
mlx/distributed/jaccl/lib/jaccl/jaccl.h:29
↓ 2 callers
Function
get_steel_gemm_gather_kernel
mlx/backend/metal/jit_kernels.cpp:626
↓ 2 callers
Function
get_steel_gemm_splitk_accum_kernel
mlx/backend/metal/jit_kernels.cpp:559
↓ 2 callers
Function
get_twiddle
Compute an FFT twiddle factor
mlx/backend/metal/kernels/fft/radix.h:29
↓ 2 callers
Function
get_version
()
setup.py:26
↓ 2 callers
Function
gumbel
mlx/random.cpp:361
↓ 2 callers
Function
hadamard_mn_contiguous
mlx/backend/metal/hadamard.cpp:60
↓ 2 callers
Function
implicit_gemm_conv_2D_gpu
mlx/backend/metal/conv.cpp:191
↓ 2 callers
Function
implicit_gemm_conv_3D_gpu
mlx/backend/metal/conv.cpp:503
↓ 2 callers
Function
in_tracing
Return true if we are currently performing a function transformation in * order to keep the graph when evaluating tracer arrays. */
mlx/transforms_impl.h:69
↓ 2 callers
Function
init
mlx/distributed/mpi/mpi.cpp:490
↓ 2 callers
Function
inv_impl
mlx/linalg.cpp:296
↓ 2 callers
Function
is_available
mlx/backend/metal/metal.cpp:10
↓ 2 callers
Function
is_available
mlx/distributed/mpi/mpi.cpp:486
↓ 2 callers
Function
is_big_endian
mlx/export.cpp:23
↓ 2 callers
Function
is_empty_dim
mlx/backend/cuda/device.cpp:33
↓ 2 callers
Method
is_equivalent
mlx/fast.cpp:180
↓ 2 callers
Function
is_none_slice
python/src/indexing.cpp:12
↓ 2 callers
Function
is_ostream_object
python/src/load.cpp:39
↓ 2 callers
Method
is_signaled
mlx/backend/metal/event.cpp:57
↓ 2 callers
Function
is_valid_index_type
python/src/indexing.cpp:81
↓ 2 callers
Method
is_valid_ring
mlx/distributed/jaccl/lib/jaccl/jaccl.cpp:125
↓ 2 callers
Function
itemsize
The size of the array's datatype in bytes. */
mlx/array.h:95
↓ 2 callers
Function
layer_norm
(x, w, b, eps)
benchmarks/python/layer_norm_bench.py:10
↓ 2 callers
Method
listen
mlx/distributed/jaccl/lib/jaccl/tcp.cpp:90
↓ 2 callers
Method
load
mlx/backend/metal/kernels/steel/gemm/nax.h:666
↓ 2 callers
Function
load_nvrtc
mlx/backend/cuda/delayload.cpp:26
↓ 2 callers
Method
load_safe
mlx/backend/metal/kernels/steel/conv/loaders/loader_general.h:140
↓ 2 callers
Function
load_swiftpm_library
mlx/backend/metal/device.cpp:129
↓ 2 callers
Method
log_debug
mlx/backend/metal/kernels/logging.h:19
↓ 2 callers
Function
loss_fn
(params, X, y)
examples/export/train_mlp.py:51
↓ 2 callers
Function
lu_helper
mlx/linalg.cpp:586
↓ 2 callers
Function
make_connections
* The counterpoint of `accept_connections`. Basically connect to each of the * provided addresses. */
mlx/distributed/ring/ring.cpp:349
↓ 2 callers
Function
make_index_args
mlx/backend/metal/indexing.cpp:24
↓ 2 callers
Function
make_regex_search
(regexes)
benchmarks/python/comparative/compare.py:38
↓ 2 callers
Function
make_string
mlx/backend/metal/utils.h:22
↓ 2 callers
Function
make_tracer
Create a tracer copy of a primal for use in vjp/jvp. If the primal is a stale Copy from a previous transform call (not an active tracer), peel it off
mlx/transforms.cpp:32
↓ 2 callers
Method
malloc
mlx/backend/cuda/allocator.cpp:134
↓ 2 callers
Function
mask_matrix
mlx/backend/cpu/masked_mm.cpp:18
↓ 2 callers
Function
matmul_bnns
mlx/backend/cpu/gemms/bnns.cpp:29
↓ 2 callers
Function
matmul_general
mlx/backend/cpu/matmul.cpp:69
↓ 2 callers
Function
matrix_norm
mlx/linalg.cpp:80
↓ 2 callers
Function
max_mb_per_buffer
mlx/utils.h:160
↓ 2 callers
Function
max_ops_per_buffer
mlx/utils.h:154
↓ 2 callers
Function
measure
(fn)
benchmarks/python/masked_scatter.py:99
↓ 2 callers
Function
merge
Helper that merges two arrays in the graph by setting the parents of the source to point to the destination. The arrays are assumed to be coming from
mlx/compile.cpp:257
↓ 2 callers
Function
mlx_get_item_array
python/src/indexing.cpp:108
↓ 2 callers
Function
mlx_naive_block_masked_mm
MLX naive: expand masks and use regular matmul.
benchmarks/python/block_masked_mm_bench.py:38
↓ 2 callers
Function
mlx_savez_helper
python/src/load.cpp:423
↓ 2 callers
Function
mlx_to_np_array
python/src/convert.cpp:153
↓ 2 callers
Method
move_to_unified_memory
mlx/backend/cuda/allocator.cpp:301
↓ 2 callers
Method
mtl_residency_set
mlx/backend/metal/resident.h:19
↓ 2 callers
Function
ndim
The number of dimensions of the array. */
mlx/array.h:110
↓ 2 callers
Function
normalize_dynamic_slice_inputs
mlx/ops.cpp:701
↓ 2 callers
Function
normalized_strides
In MLX a singleton dim (shape[dim] == 1) can have any stride, but in cuDNN whether a tensor is contiguous is determined with: shape[dim] == shape[dim
mlx/backend/cuda/cudnn_utils.cpp:21
↓ 2 callers
Function
np_logaddexp
(x1: np.ndarray, x2: np.ndarray)
python/tests/test_ops.py:25
↓ 2 callers
Method
open
python/src/load.cpp:76
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