code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def _update_clocks(low_global_core_id, high_global_core_id):
"""Synchronizes the vector clocks for the cores with ids in the range between the two arguments."""
shared_memory = _get_shared_memory()
# Despite only updating the vector clocks for some cores, we still need to
# hold the global lock to ensure that n... | Synchronizes the vector clocks for the cores with ids in the range between the two arguments. | _update_clocks | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _update_clocks_for_device_barrier(device_id):
"""Synchronizes the vector clocks for the cores on the given device."""
shared_memory = _get_shared_memory()
low_core_id = device_id * shared_memory.num_cores_per_device
high_core_id = (device_id + 1) * shared_memory.num_cores_per_device
_update_clocks(low_cor... | Synchronizes the vector clocks for the cores on the given device. | _update_clocks_for_device_barrier | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _allocate_buffer(
device_id: Array,
local_core_id: Array | None,
memory_space: Array,
val: Array,
):
"""Allocates a memory buffer on the device with id `device_id` and core with id `local_core_id`.
Args:
device_id: Singleton array holding the device id where the buffer will be
allocat... | Allocates a memory buffer on the device with id `device_id` and core with id `local_core_id`.
Args:
device_id: Singleton array holding the device id where the buffer will be
allocated.
local_core_id: None or singleton array holding the core id where the buffer
will be allocated. If None, a buffer... | _allocate_buffer | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _allocate_semaphores(
device_id: Array, local_core_id: Array | None, shape: Array
):
"""Allocates semaphores on the device with id `device_id` and core with id `local_core_id`.
The number of semaphores allocated is given by the product of the entries in
`shape`.
Since for each semaphore id there is re... | Allocates semaphores on the device with id `device_id` and core with id `local_core_id`.
The number of semaphores allocated is given by the product of the entries in
`shape`.
Since for each semaphore id there is really only one global `Semaphore`
object, 'allocation' of semaphores per device and core here mea... | _allocate_semaphores | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _to_int(x : int | Array | None) -> int | None:
"""Converts a value to an integer, or returns None if the value is None."""
if x is None:
return None
return int(x) | Converts a value to an integer, or returns None if the value is None. | _to_int | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _get_parallel_dim_semantics(
compiler_params: dict[str, Any], num_dimensions_in_grid: int,
) -> tuple[bool, ...]:
"""Returns a tuple indicating which grid dimensions have parallel semantics.
Args:
compiler_params: Representation of a `mosaic_core.CompilerParams` object
as a dictionary.
num_di... | Returns a tuple indicating which grid dimensions have parallel semantics.
Args:
compiler_params: Representation of a `mosaic_core.CompilerParams` object
as a dictionary.
num_dimensions_in_grid: The number of dimensions in the grid.
Returns:
A tuple of booleans where the entry at index `i` is `Tr... | _get_parallel_dim_semantics | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _get_parallel_subgrid_size(
parallel_semantics_per_dim: tuple[bool, ...], grid: tuple[int, ...]
) -> int:
"""Returns the size of the subgrid along the parallel dimensions."""
return functools.reduce(
lambda x, y: x * y,
(
dim_size if parallel_dim else 1
for dim_size, parallel... | Returns the size of the subgrid along the parallel dimensions. | _get_parallel_subgrid_size | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _get_randomized_grid_coordinates(
grid: tuple[int, ...],
compiler_params: dict[str, Any],
random_seed: int | None,
) -> _GridPointCoordinatesPerDim:
"""Returns a tuple of randomized coordinates for each 'parallel' dimension in `grid`.
For a dimension with 'parallel' semantics at position `d` in the... | Returns a tuple of randomized coordinates for each 'parallel' dimension in `grid`.
For a dimension with 'parallel' semantics at position `d` in the grid, the
returned tuple contains a random permutation of the sequence `[0,...,
grid[d] - 1]` at index `d`. For each dimension with 'arbitrary' semantics,
the resu... | _get_randomized_grid_coordinates | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _get_grid_point(
loop_indices: tuple[Array, ...],
grid_point_coordinates: _GridPointCoordinatesPerDim,
) -> Array:
"""Indexes each entry in `grid_point_coordinates` with the corresponding entry in `loop_indices`.
If an entry in `grid_point_coordinates` is an empty array, the corresponding
entry in th... | Indexes each entry in `grid_point_coordinates` with the corresponding entry in `loop_indices`.
If an entry in `grid_point_coordinates` is an empty array, the corresponding
entry in the returned array is the corresponding entry in `loop_indices`.
Otherwise, the returned array contains the entry in `grid_point_coo... | _get_grid_point | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _pad_to_block_dimension(value, block_shape, interpret_params):
"""Pads values so the shape evenly divides into block dimensions.
For example, if values has a shape of (33, 2, 5) with a block_shape of
(32, 2, 4), this function will pad the value of shape to (64, 2, 8).
Args:
value: Array to be padded.
... | Pads values so the shape evenly divides into block dimensions.
For example, if values has a shape of (33, 2, 5) with a block_shape of
(32, 2, 4), this function will pad the value of shape to (64, 2, 8).
Args:
value: Array to be padded.
block_shape: Block shapes to use for padding. If None, no padding wi... | _pad_to_block_dimension | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _body(
carry: tuple[
jnp.int32,
tuple[jnp.int32, ...],
jnp.ndarray,
list[jnp.ndarray],
list[jnp.ndarray],
],
) -> tuple[
jnp.int32,
tuple[jnp.int32, ...],
jnp.ndarray,
list[jnp.ndarray],
list[jnp.... | Performs one execution of the kernel body.
Execution of `jaxpr` is preceded by reading kernel input buffers and
followed by writing kernel output buffers.
Args:
carry: (iteration_idx, loop_idx, grid_point, prev_start_indices,
cur_start_indices).
- iteration_idx: the... | _body | python | jax-ml/jax | jax/_src/pallas/mosaic/interpret.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/interpret.py | Apache-2.0 |
def _compute_name_stack_updates(
old_name_stack: list[str],
new_name_stack: list[str]
) -> tuple[list[str], list[str]]:
"""Computes the popped/pushed items to the name stack after an update.
Args:
old_name_stack: The name stack prior to the update.
new_name_stack: The name stack after the update.
... | Computes the popped/pushed items to the name stack after an update.
Args:
old_name_stack: The name stack prior to the update.
new_name_stack: The name stack after the update.
Returns:
popped: A list of names popped from the name stack as part of the update.
pushed: A list of names pushed to the na... | _compute_name_stack_updates | python | jax-ml/jax | jax/_src/pallas/mosaic/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/lowering.py | Apache-2.0 |
def _prng_key_load_lowering_rule(ctx: LoweringRuleContext, *args_flat, args_tree) -> KeyScalarBundle:
"""Lowering rule for loading PRNG keys from SMEM.
PRNG key loads are currently lowered as a list of scalar loads from SMEM,
rather than a single vector load.
We store these scalars in a bundle type called KeyS... | Lowering rule for loading PRNG keys from SMEM.
PRNG key loads are currently lowered as a list of scalar loads from SMEM,
rather than a single vector load.
We store these scalars in a bundle type called KeyScalarBundle, which has
special case handling for functions that consume the key such as set_seed.
| _prng_key_load_lowering_rule | python | jax-ml/jax | jax/_src/pallas/mosaic/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/lowering.py | Apache-2.0 |
def _maybe_cast_load_to_bool(
ctx, out_aval, val: ir.Value
) -> tuple[ir.Value, jnp.dtype]:
"""Casts a memref load value to bool if the requested value is a bool.
Mosaic does not support boolean-type memrefs, since booleans
typically live in mask registers. We instead load booleans as integers from
memrefs... | Casts a memref load value to bool if the requested value is a bool.
Mosaic does not support boolean-type memrefs, since booleans
typically live in mask registers. We instead load booleans as integers from
memrefs and move them to mask registers on load using this function.
Args:
out_aval: The output aval ... | _maybe_cast_load_to_bool | python | jax-ml/jax | jax/_src/pallas/mosaic/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/lowering.py | Apache-2.0 |
def _maybe_cast_store_to_memref_type(
ctx: LoweringRuleContext, expected_aval, val: ir.Value
) -> ir.Value:
"""Casts a boolean value back to an integer for storing in a memref."""
if expected_aval.dtype != jnp.bool_:
return val
int_out_type = aval_to_ir_type(
ctx.lowering_context.dynamic_shape_repla... | Casts a boolean value back to an integer for storing in a memref. | _maybe_cast_store_to_memref_type | python | jax-ml/jax | jax/_src/pallas/mosaic/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/lowering.py | Apache-2.0 |
def jax_dot_dims_to_tpu_dot_dot_dims(dimension_numbers, lhs_shape, rhs_shape):
"""Converts a jax dot dimension numbers to a tpu dot dimension numbers.
Jax dot dimension numbers are given as a tuple of tuples of sequences of ints
of the form ((lhs_contracting_dims, rhs_contracting_dims), (lhs_batch_dims,
rhs_ba... | Converts a jax dot dimension numbers to a tpu dot dimension numbers.
Jax dot dimension numbers are given as a tuple of tuples of sequences of ints
of the form ((lhs_contracting_dims, rhs_contracting_dims), (lhs_batch_dims,
rhs_batch_dims)).
TPU dot dimension numbers are given as an MLIR definition of the form... | jax_dot_dims_to_tpu_dot_dot_dims | python | jax-ml/jax | jax/_src/pallas/mosaic/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/lowering.py | Apache-2.0 |
def _maybe_cast_to_int(x: jax.Array | jax_core.AbstractValue):
"""Casts boolean values to integers.
We perform this cast because Mosaic does not directly support bool values
for Memrefs. Instead, we load bools as integers and cast them to bools
after loading from a memref inside of the kernel.
"""
assert i... | Casts boolean values to integers.
We perform this cast because Mosaic does not directly support bool values
for Memrefs. Instead, we load bools as integers and cast them to bools
after loading from a memref inside of the kernel.
| _maybe_cast_to_int | python | jax-ml/jax | jax/_src/pallas/mosaic/pallas_call_registration.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pallas_call_registration.py | Apache-2.0 |
def pallas_call_tpu_lowering_rule(
ctx: mlir.LoweringRuleContext,
*in_nodes,
jaxpr: jax_core.Jaxpr,
grid_mapping: pallas_core.GridMapping,
mesh: pallas_core.Mesh | None,
input_output_aliases: tuple[tuple[int, int], ...],
debug: bool,
interpret: bool,
compiler_params: dict[str, pallas... | Lowers a pallas_call to a Mosaic TPU custom call. | pallas_call_tpu_lowering_rule | python | jax-ml/jax | jax/_src/pallas/mosaic/pallas_call_registration.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pallas_call_registration.py | Apache-2.0 |
def _broadcast_pytree_to(from_pytree, to_pytree):
"""Broadcast a prefix pytree to a given full tree."""
proxy = object()
treedef = tree_util.tree_structure(to_pytree)
broadcast_leaves = []
def add_leaves(i, x):
broadcast_leaves.extend(
[i] * tree_util.tree_structure(x).num_leaves)
try:
tree_... | Broadcast a prefix pytree to a given full tree. | _broadcast_pytree_to | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def _make_block_ds(
idx: jax.Array | int, size: jax.Array | int
) -> pl.Slice:
"""Make a DMA slice with mosaic size hints."""
out = pl.ds(idx * size, size)
assert isinstance(out, pl.Slice)
return out | Make a DMA slice with mosaic size hints. | _make_block_ds | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def _get_block_shape(spec: pl.BlockSpec) -> tuple[int, ...]:
"""Get the block shape for a given block spec."""
def _get_dim_size(bd):
match bd:
case pl.Blocked(block_size):
return block_size
case pl.Element(block_size):
return block_size
case pl.BoundedSlice(block_size):
... | Get the block shape for a given block spec. | _get_block_shape | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def create(cls, spec: pl.BlockSpec, dtype, buffer_type, needs_swap_ref=True
) -> BufferedRef:
"""Create a BufferedRef.
Args:
spec: pallas blockspec.
dtype: dtype for buffers.
buffer_type: enum indicating whether this is an input, output, or in/out
accumulator buffered refe... | Create a BufferedRef.
Args:
spec: pallas blockspec.
dtype: dtype for buffers.
buffer_type: enum indicating whether this is an input, output, or in/out
accumulator buffered reference.
needs_swap_ref: whether a swap slots tracker needs to be allocated.
Returns:
Initialized ... | create | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def with_slot_index(
self, slot_index: int | jax.Array | None
) -> "BufferedRef":
"""Returns a new BufferedRef with the given slot index."""
return dataclasses.replace(self, _current_slot_reg=slot_index) | Returns a new BufferedRef with the given slot index. | with_slot_index | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def bind_existing_ref(self, window_ref, indices):
"""For handling VMEM references, the pipeline aliases the existing ref."""
if self.memory_space == VMEM:
return dataclasses.replace(
self, window_ref=window_ref.at[self.compute_slice(indices)]
)
return self | For handling VMEM references, the pipeline aliases the existing ref. | bind_existing_ref | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def compute_slice(self, grid_indices):
"""Compute DMA slice from grid indices."""
indices = self.compute_index(*grid_indices)
assert len(self.block_shape) == len(indices)
indexer = []
for bd, idx in zip(self.block_shape, indices, strict=True):
match bd:
case None | pl.Squeezed():
... | Compute DMA slice from grid indices. | compute_slice | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def copy_in(self, src_ref, grid_indices):
"""Starts copy of HBM dma slice into the current slot."""
assert self.is_input
if self.memory_space == VMEM: return
assert not (self.window_ref is None or isinstance(self.window_ref, REF))
assert self.sem_recvs is not None
if self.swap is not None:
... | Starts copy of HBM dma slice into the current slot. | copy_in | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def copy_out(self, dst_ref, grid_indices):
"""Starts copy of HBM dma slice from the current slot."""
assert self.is_output
if self.memory_space == VMEM: return
assert not (self.window_ref is None or isinstance(self.window_ref, REF))
assert self.sem_sends is not None
if self.swap is not None:
... | Starts copy of HBM dma slice from the current slot. | copy_out | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def wait_in(self, src_ref, grid_indices):
"""Waits for input copy to finish."""
assert self.is_input
if self.memory_space == VMEM: return
assert not (self.window_ref is None or isinstance(self.window_ref, REF))
assert self.sem_recvs is not None
src_slice = self.get_dma_slice(src_ref.shape, src_r... | Waits for input copy to finish. | wait_in | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def wait_out(self, dst_ref, grid_indices):
"""Waits for output copy to finish."""
assert self.is_output
if self.memory_space == VMEM: return
assert not (self.window_ref is None or isinstance(self.window_ref, REF))
assert self.sem_sends is not None
# In a double buffer, previous slot is the same ... | Waits for output copy to finish. | wait_out | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def set_accumulator(self, init=False):
"""Set accumulator or zero it out to initialize."""
assert self.is_accumulator
if self.accum_ref is not None:
accum_dtype = self.accum_ref.dtype
def _init():
self.accum_ref[...] = jnp.zeros_like(self.accum_ref[...])
def _set():
self.ac... | Set accumulator or zero it out to initialize. | set_accumulator | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def __init__(
self,
step: jax.Array,
indices: tuple[int | jax.Array, ...],
grid: tuple[int | jax.Array, ...],
grid_offsets: tuple[int | jax.Array, ...],
first_cycle=None,
last_cycle=None,
init_accumulators=None,
trace_scopes=True,
):
"""Initializes scheduler.
... | Initializes scheduler.
Args:
step: inner step number.
indices: current grid indices.
grid: pallas grid for BufferedRefs.
grid_offsets: offsets for grid indices (used for megacore).
first_cycle: whether this is the first invocation of the pipeline.
last_cycle: whether this is the... | __init__ | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def skip_input_copies_when_init_accumulators(schedule) -> Any:
"""Skip input copies in schedule when init_accumulators is True."""
new_schedule = {**schedule}
for k in ["prologue_copy_in", "wait_in", "copy_in"]:
def new_pred(original_pred_fn, *a):
pred = original_pred_fn(*a)
if a[1].is_accumulato... | Skip input copies in schedule when init_accumulators is True. | skip_input_copies_when_init_accumulators | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def get_pipeline_schedule(schedule) -> Any:
"""Retrieve a named pipeline schedule or pass through fully specified one."""
predefined_schedules = {
'default': _default_schedule,
'fixed': _fixed_schedule
}
if isinstance(schedule, str):
return predefined_schedules[schedule].copy()
return schedule | Retrieve a named pipeline schedule or pass through fully specified one. | get_pipeline_schedule | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def make_pipeline_allocations(
*refs,
in_specs=None,
out_specs=None,
should_accumulate_out=False,
needs_swap_ref=True,
):
"""Create BufferedRefs for the pipeline.
This function creates buffered refs for an inner pipeline that can be
created at the top-level of a pallas call such that they may... | Create BufferedRefs for the pipeline.
This function creates buffered refs for an inner pipeline that can be
created at the top-level of a pallas call such that they may be reused across
multiple invocations of the inner pipeline.
Args:
in_specs: input pallas block specs
out_specs: output pallas block ... | make_pipeline_allocations | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def pipeline(
*refs: Any,
scratches=None,
allocations=None,
first_cycle: CondVal = True,
last_cycle: CondVal = True,
init_accumulators: CondVal = False,
prefetch=None,
postyeet=None,
schedule=None,
body_prologue=None,
):
"""
Run the pipeline.
Args:
*ref_args:... |
Run the pipeline.
Args:
*ref_args: a list of pallas refs (or more generally a list of pytrees of
pallas refs)
scratches: scratch buffers for the inner kernel
allocations: a list of BufferedRefs, one corresponding to each ref
first_cycle: boolean indicating if this is the first ... | pipeline | python | jax-ml/jax | jax/_src/pallas/mosaic/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/pipeline.py | Apache-2.0 |
def make_async_remote_copy(src_ref, dst_ref, send_sem, recv_sem, device_id,
device_id_type: primitives.DeviceIdType = primitives.DeviceIdType.MESH):
"""Creates a description of a remote copy operation.
Copies data from src_ref on the current device to dst_ref on the device
specified by... | Creates a description of a remote copy operation.
Copies data from src_ref on the current device to dst_ref on the device
specified by device_id. Both semaphores should be waited on using the
descriptor on both source and target devices.
Note that device_id can also refer to the current device.
Args:
s... | make_async_remote_copy | python | jax-ml/jax | jax/_src/pallas/mosaic/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/primitives.py | Apache-2.0 |
def wrap_pallas_seed(*seeds, impl):
"""Joins scalar into a single PRNG key."""
impl = jax_random.resolve_prng_impl(impl)
return join_key_p.bind(*seeds, impl=impl) | Joins scalar into a single PRNG key. | wrap_pallas_seed | python | jax-ml/jax | jax/_src/pallas/mosaic/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/primitives.py | Apache-2.0 |
def to_pallas_key(key: jax.Array) -> jax.Array:
"""Helper function for converting non-Pallas PRNG keys into Pallas keys."""
# Handle new-style typed PRNG keys.
generate_key = functools.partial(
jax.random.bits, shape=tpu_key_impl.key_shape, dtype=jnp.uint32
)
vmapped_key = False
if jnp.issubdtype(key.... | Helper function for converting non-Pallas PRNG keys into Pallas keys. | to_pallas_key | python | jax-ml/jax | jax/_src/pallas/mosaic/random.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/random.py | Apache-2.0 |
def _make_stateful_sampler(sampler: SampleFnType) -> KeylessSampleFnType:
"""Converts a jax.random sampling function to a stateful version.
Args:
sampler: A sampling function that consumes a key and returns
random samples.
Returns:
A stateful sampling function with the key argument removed.
"""
... | Converts a jax.random sampling function to a stateful version.
Args:
sampler: A sampling function that consumes a key and returns
random samples.
Returns:
A stateful sampling function with the key argument removed.
| _make_stateful_sampler | python | jax-ml/jax | jax/_src/pallas/mosaic/random.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/random.py | Apache-2.0 |
def sample_block(sampler_fn: SampleFnType,
global_key: jax.Array,
block_size: Shape,
tile_size: Shape,
total_size: Shape,
block_index: tuple[typing.ArrayLike, ...] | None = None,
**kwargs) -> jax.Array:
"""Samples a ... | Samples a block of random values with invariance guarantees.
`sample_block` allows the sampling of identical blocks of random values
across kernels with different block shapes and iteration orders. Each call
to `sample_block` returns a `block_size`-shaped array of random samples
corresponding to the `block_ind... | sample_block | python | jax-ml/jax | jax/_src/pallas/mosaic/random.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/random.py | Apache-2.0 |
def skip(f):
"""Skips the verification of the given function."""
def wrapper(*args, **kwargs):
is_not_verifying = assume(normally=1, when_verifying=0)
lax.cond(is_not_verifying)(lambda: f(*args, **kwargs))
return wrapper | Skips the verification of the given function. | skip | python | jax-ml/jax | jax/_src/pallas/mosaic/verification.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/verification.py | Apache-2.0 |
def define_model(model):
"""Replaces a function with its simplified model during verification."""
def decorator(f):
def wrapper(*args, **kwargs):
lax.cond(
assume(normally=1, when_verifying=0),
lambda: f(*args, **kwargs),
lambda: model(*args, **kwargs),
)
return wra... | Replaces a function with its simplified model during verification. | define_model | python | jax-ml/jax | jax/_src/pallas/mosaic/verification.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic/verification.py | Apache-2.0 |
def is_trivial_index(idx, shape) -> bool:
"""Checks if the index selects the entire shape."""
# Slices that select the entire dimension.
def _slices(d):
slices = [slice(b, e, s) for b, e, s in it.product([0, None], [d, None], [1, None])]
return [indexing.Slice(0, d, 1), *slices]
if isinstance(idx, tup... | Checks if the index selects the entire shape. | is_trivial_index | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/core.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/core.py | Apache-2.0 |
def _is_known_divisible(value, divisor, fuel=10) -> bool:
"""Returns True if the value is statically known to be divisible by the divisor."""
if divisor == 1:
return True
if fuel < 0:
return False
if not isinstance(value.owner, ir.Operation):
return False
def_op = value.owner.opview
match def_op... | Returns True if the value is statically known to be divisible by the divisor. | _is_known_divisible | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/core.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/core.py | Apache-2.0 |
def flatten_ref_union(ref_union: AbstractRefUnion) -> tuple[_Ref, ...]:
"""Flattens a union of trees of references into a tuple of references.
This is the moral equivalent of `jax.tree.leaves` for aliased references.
"""
flat_refs = []
union_bytes = 0
for ref_group in ref_union.refs:
byte_offset = 0
... | Flattens a union of trees of references into a tuple of references.
This is the moral equivalent of `jax.tree.leaves` for aliased references.
| flatten_ref_union | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/core.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/core.py | Apache-2.0 |
def remote_ref(
ref: _Ref,
device_id: jax.typing.ArrayLike,
device_id_type: pallas_primitives.DeviceIdType = pallas_primitives.DeviceIdType.MESH,
) -> pallas_core.TransformedRef:
"""Translate memref to a symmetric memref on a peer device."""
if not isinstance(ref, pallas_core.TransformedRef):
if not... | Translate memref to a symmetric memref on a peer device. | remote_ref | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/core.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/core.py | Apache-2.0 |
def nd_loop(
grid: Sequence[int],
*,
collective_axes: Sequence[Hashable] | Hashable,
) -> Callable[[Callable[[Sequence[jax.Array]], None]], None]:
"""A loop over a multi-dimensional grid partitioned along the given axes.
For example, if ``collective_axes`` is ``"x"`` with :func:`lax.axis_size`
equal ... | A loop over a multi-dimensional grid partitioned along the given axes.
For example, if ``collective_axes`` is ``"x"`` with :func:`lax.axis_size`
equal to 4 and the grid is (2, 3), the implementation would produce the
following iteration order
loop step index axis index
0 (0, 0) ... | nd_loop | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/helpers.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/helpers.py | Apache-2.0 |
def _estimate_resources(
ctx: ResourceEstimatorContext, jaxpr: jax_core.Jaxpr
) -> Resources:
"""Estimates the resources required by the kernel."""
rs = Resources(smem_scratch_bytes=0)
for eqn in jaxpr.eqns:
# TODO(slebedev): Add support for other primitives, notably control flow.
if rule := _resource... | Estimates the resources required by the kernel. | _estimate_resources | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def single_lane_predicate(self) -> ir.Value:
"""Returns a predicate that is True for a single lane within the current
thread semantics.
"""
assert self.lowering_semantics == mgpu.LoweringSemantics.Lane
match self.primitive_semantics:
case gpu_core.PrimitiveSemantics.Warpgroup:
return s... | Returns a predicate that is True for a single lane within the current
thread semantics.
| single_lane_predicate | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def reserve_barrier(
self, barrier: mgpu.Barrier
) -> mgpu.BarrierRef | mgpu.DialectBarrierRef | mgpu.CollectiveBarrierRef:
"""Reserves a barrier.
Raises:
RuntimeError: If the barrier is already reserved.
"""
available = self.runtime_barriers.get(barrier, [])
if not available:
r... | Reserves a barrier.
Raises:
RuntimeError: If the barrier is already reserved.
| reserve_barrier | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def scratch_view(
self, structs: Sequence[jax.ShapeDtypeStruct]
) -> Sequence[ir.Value]:
"""Creates a view into the runtime scratch buffer for each struct.
This is a low-level API. Use it only if you know what you are doing.
The function allocates bytes at the top of a stack, which need to be
... | Creates a view into the runtime scratch buffer for each struct.
This is a low-level API. Use it only if you know what you are doing.
The function allocates bytes at the top of a stack, which need to be
deallocated in a FIFO fashion with :meth:`ModuleContext.stack_free_smem`.
After deallocation, the vi... | scratch_view | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def _unravel_program_id(
block_id: ir.Value,
axis: int,
dimensions: tuple[int, ...],
row_major: bool = False
) -> ir.Value:
"""Computes the program ID for axes compressed into one block dimension."""
if row_major:
div_value = math.prod(dimensions[axis+1:])
else:
div_value = math.prod(dimen... | Computes the program ID for axes compressed into one block dimension. | _unravel_program_id | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def _handle_dtype_bitcast(
ref: ir.Value, src_dtype: ir.Type, dst_dtype: ir.Type
) -> ir.Value:
"""Allows bitcasting a SMEM ref from one element type to another.
Args:
ref: the reference to bitcast.
src_dtype: the source element type.
dst_dtype: the destination element type.
Returns:
A bitca... | Allows bitcasting a SMEM ref from one element type to another.
Args:
ref: the reference to bitcast.
src_dtype: the source element type.
dst_dtype: the destination element type.
Returns:
A bitcasted version of `ref` with element type `dst_dtype`.
Raises:
ValueError: if the source ref is not ... | _handle_dtype_bitcast | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def _transform_dtype(
dtype: dtypes.DType,
transforms: Sequence[state_types.Transform],
) -> dtypes.DType:
"""Applies `t.transform_dtype` for `t` in `transforms` sequentially on `dtype`."""
for transform in transforms:
dtype = transform.transform_dtype(dtype)
return dtype | Applies `t.transform_dtype` for `t` in `transforms` sequentially on `dtype`. | _transform_dtype | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def _bcast_wg(
x: object,
y: object,
x_aval: jax_core.ShapedArray,
y_aval: jax_core.ShapedArray,
out_aval: jax_core.ShapedArray,
) -> tuple[ir.Value, ir.Value]:
"""Ensures that ``x`` and ``y`` have the expected shapes and dtypes.
More specifically, the inputs are converted to vectors of the sam... | Ensures that ``x`` and ``y`` have the expected shapes and dtypes.
More specifically, the inputs are converted to vectors of the same dtype
as ``x_aval`` and ``y_aval``, and broadcasted to the output shape
if necessary.
| _bcast_wg | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def merge_indexers(
indexers: Sequence[indexing.NDIndexer]) -> indexing.NDIndexer:
"""Merges multiple indexers into a single indexer.
This function computes a new indexer such that applying the
new indexer produces the same result as applying the sequence
of input indexers in order from first-to-last.
""... | Merges multiple indexers into a single indexer.
This function computes a new indexer such that applying the
new indexer produces the same result as applying the sequence
of input indexers in order from first-to-last.
| merge_indexers | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/lowering.py | Apache-2.0 |
def _get_slot(step, has_seq_dim):
"""Returns the buffer slot given the pipeline step."""
if has_seq_dim:
return step
else:
return 0 | Returns the buffer slot given the pipeline step. | _get_slot | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/pipeline.py | Apache-2.0 |
def _compute_registers(
memory_registers: int,
num_compute_wgs: int,
) -> int:
"""Returns the max number of registers to use in compute threads.
We start with the theoretical max registers per thread if one wargroup
(128 threads) used the entire SM's 64k register file (64k / 128 = 512).
Then reserve `m... | Returns the max number of registers to use in compute threads.
We start with the theoretical max registers per thread if one wargroup
(128 threads) used the entire SM's 64k register file (64k / 128 = 512).
Then reserve `memory_registers` for the producer warpgroup and distribute
the remaining registers evenly ... | _compute_registers | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/pipeline.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/pipeline.py | Apache-2.0 |
def load(
src: _Ref,
idx,
*,
layout: Layout | ParameterizedLayout | None = None,
optimized: bool = True,
) -> jax.Array:
"""Loads from a reference into an array with the specified layout.
Args:
src: The reference to load from. Can be either in SMEM or GMEM.
idx: The index to load from.
... | Loads from a reference into an array with the specified layout.
Args:
src: The reference to load from. Can be either in SMEM or GMEM.
idx: The index to load from.
layout: The optional layout to use for the resulting array.
optimized: If True, a compilation error will be raised if no optimized
i... | load | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/primitives.py | Apache-2.0 |
def copy_gmem_to_smem(
src: _Ref,
dst: _Ref,
barrier: _Ref,
*,
collective_axes: str | tuple[str, ...] | None = None,
partitioned_axis: int | None = None,
) -> None:
"""Asynchronously copies a GMEM reference to a SMEM reference.
If collective_axes is specified, this performs a multicast copy... | Asynchronously copies a GMEM reference to a SMEM reference.
If collective_axes is specified, this performs a multicast copy where
all CUDA blocks that share the same index along the collective axis
receive a copy of the same block of data loaded from `dst` to `src`.
If both collective_axes and partitioned_axi... | copy_gmem_to_smem | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/primitives.py | Apache-2.0 |
def wgmma(acc: gpu_core.WGMMAAbstractAccumulatorRef, a, b) -> None:
"""Performs an asynchronous warp group matmul-accumulate on the given references.
Conceptually, this is equivalent to doing ``acc[...] += a[...] @ b[...]``,
except that the computation is performed asynchronously.
Args:
acc: The accumulat... | Performs an asynchronous warp group matmul-accumulate on the given references.
Conceptually, this is equivalent to doing ``acc[...] += a[...] @ b[...]``,
except that the computation is performed asynchronously.
Args:
acc: The accumulator reference. Needs to be allocated via
:func:`jax.experimental.pal... | wgmma | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/primitives.py | Apache-2.0 |
def tcgen05_mma(acc: _Ref,
a: _Ref,
b: _Ref,
barrier: _Ref,
accumulate: bool | jax.Array = True,
collective_axis: str | None = None):
"""Asynchronous matrix-multiply accumulate for TensorCore gen 5 (Blackwell).
If run in collective mod... | Asynchronous matrix-multiply accumulate for TensorCore gen 5 (Blackwell).
If run in collective mode, `acc`, `a` (LHS), and `b` (RHS) should correspond
to half of the total inputs to the MMA, where `acc` and `a` (LHS) are split
in half along the rows and `b` (RHS) is split along the columns like so:
---------... | tcgen05_mma | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/primitives.py | Apache-2.0 |
def _undo_transforms(
raw_ref: pallas_core.AbstractMemoryRef,
memory_transforms: Sequence[gpu_core.MemoryRefTransform],
):
"""Extract the `Transform`s that reverse the `MemoryRefTransform`s"""
tmp_ref = state_types.TransformedRef(raw_ref, transforms=())
tmp_ref = functools.reduce(lambda r, t: t.undo(r), r... | Extract the `Transform`s that reverse the `MemoryRefTransform`s | _undo_transforms | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/primitives.py | Apache-2.0 |
def inline_mgpu(*, arg_types=(), return_type=None):
r"""Returns a decorator that inlines Mosaic GPU code.
This allows using lower-level Mosaic GPU abstractions and operations, which
are otherwise not directly exposed in Pallas.
Example::
layout = plgpu.Layout.WG_STRIDED(x_ref.shape, vec_size=4)
... | Returns a decorator that inlines Mosaic GPU code.
This allows using lower-level Mosaic GPU abstractions and operations, which
are otherwise not directly exposed in Pallas.
Example::
layout = plgpu.Layout.WG_STRIDED(x_ref.shape, vec_size=4)
@plgpu.inline_mgpu(
arg_types=(plgpu.RefType(),)... | inline_mgpu | python | jax-ml/jax | jax/_src/pallas/mosaic_gpu/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/mosaic_gpu/primitives.py | Apache-2.0 |
def _is_contiguous_int4(block_info: BlockInfo, nd_indexer: NDIndexer) -> bool:
"""Returns True if the block is contiguous in the last dimension."""
# In order to loaded as `uint8` the index must be an aligned slice.
return (
block_info.full_shape_dtype.dtype in (jnp.int4, jnp.uint4)
and block_info.sta... | Returns True if the block is contiguous in the last dimension. | _is_contiguous_int4 | python | jax-ml/jax | jax/_src/pallas/triton/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/triton/lowering.py | Apache-2.0 |
def _reinterpret_int4_as_uint8(
block_info: BlockInfo, nd_indexer: NDIndexer
) -> tuple[BlockInfo, NDIndexer]:
"""Returns a new block info and indexer that reads `int4` as `uint8`."""
last_idx = nd_indexer.indices[-1]
new_last_idx = indexing.Slice(last_idx.start // 2, last_idx.size // 2)
new_indices = (*nd_... | Returns a new block info and indexer that reads `int4` as `uint8`. | _reinterpret_int4_as_uint8 | python | jax-ml/jax | jax/_src/pallas/triton/lowering.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/triton/lowering.py | Apache-2.0 |
def approx_tanh(x: jax.Array) -> jax.Array:
r"""Elementwise approximate hyperbolic tangent: :math:`\mathrm{tanh}(x)`.
See
https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#floating-point-instructions-tanh.
"""
if x.dtype == jnp.float16:
asm = "tanh.approx.f16 $0, $1;"
constraint = "h"... | Elementwise approximate hyperbolic tangent: :math:`\mathrm{tanh}(x)`.
See
https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#floating-point-instructions-tanh.
| approx_tanh | python | jax-ml/jax | jax/_src/pallas/triton/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/triton/primitives.py | Apache-2.0 |
def elementwise_inline_asm(
asm: str,
*,
args: Sequence[jax.Array],
constraints: str,
pack: int,
result_shape_dtypes: Sequence[jax.ShapeDtypeStruct],
) -> Sequence[jax.Array]:
"""Inline assembly applying an elementwise operation.
Args:
asm: The assembly code to run.
args: The argume... | Inline assembly applying an elementwise operation.
Args:
asm: The assembly code to run.
args: The arguments to pass to the assembly code.
constraints: LLVM inline assembly `constraints
<https://llvm.org/docs/LangRef.html#inline-asm-constraint-string>`_.
pack: The number of elements from each ar... | elementwise_inline_asm | python | jax-ml/jax | jax/_src/pallas/triton/primitives.py | https://github.com/jax-ml/jax/blob/master/jax/_src/pallas/triton/primitives.py | Apache-2.0 |
def dct(x: Array, type: int = 2, n: int | None = None,
axis: int = -1, norm: str | None = None) -> Array:
"""Computes the discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.dct`.
Args:
x: array
type: integer, default = 2. Currently only type 2 is supported.
n: inte... | Computes the discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.dct`.
Args:
x: array
type: integer, default = 2. Currently only type 2 is supported.
n: integer, default = x.shape[axis]. The length of the transform.
If larger than ``x.shape[axis]``, the input will be ze... | dct | python | jax-ml/jax | jax/_src/scipy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/fft.py | Apache-2.0 |
def dctn(x: Array, type: int = 2,
s: Sequence[int] | None=None,
axes: Sequence[int] | None = None,
norm: str | None = None) -> Array:
"""Computes the multidimensional discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.dctn`.
Args:
x: array
type: inte... | Computes the multidimensional discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.dctn`.
Args:
x: array
type: integer, default = 2. Currently only type 2 is supported.
s: integer or sequence of integers. Specifies the shape of the result. If not
specified, it will defau... | dctn | python | jax-ml/jax | jax/_src/scipy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/fft.py | Apache-2.0 |
def idct(x: Array, type: int = 2, n: int | None = None,
axis: int = -1, norm: str | None = None) -> Array:
"""Computes the inverse discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.idct`.
Args:
x: array
type: integer, default = 2. Currently only type 2 is supported.
... | Computes the inverse discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.idct`.
Args:
x: array
type: integer, default = 2. Currently only type 2 is supported.
n: integer, default = x.shape[axis]. The length of the transform.
If larger than ``x.shape[axis]``, the input w... | idct | python | jax-ml/jax | jax/_src/scipy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/fft.py | Apache-2.0 |
def idctn(x: Array, type: int = 2,
s: Sequence[int] | None=None,
axes: Sequence[int] | None = None,
norm: str | None = None) -> Array:
"""Computes the multidimensional inverse discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.idctn`.
Args:
x: array
... | Computes the multidimensional inverse discrete cosine transform of the input
JAX implementation of :func:`scipy.fft.idctn`.
Args:
x: array
type: integer, default = 2. Currently only type 2 is supported.
s: integer or sequence of integers. Specifies the shape of the result. If not
specified, it w... | idctn | python | jax-ml/jax | jax/_src/scipy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/fft.py | Apache-2.0 |
def trapezoid(y: ArrayLike, x: ArrayLike | None = None, dx: ArrayLike = 1.0,
axis: int = -1) -> Array:
r"""
Integrate along the given axis using the composite trapezoidal rule.
JAX implementation of :func:`scipy.integrate.trapezoid`
The trapezoidal rule approximates the integral under a curve by... |
Integrate along the given axis using the composite trapezoidal rule.
JAX implementation of :func:`scipy.integrate.trapezoid`
The trapezoidal rule approximates the integral under a curve by summing the
areas of trapezoids formed between adjacent data points.
Args:
y: array of data to integrate.
x: ... | trapezoid | python | jax-ml/jax | jax/_src/scipy/integrate.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/integrate.py | Apache-2.0 |
def svd(a: ArrayLike, full_matrices: bool = True, compute_uv: bool = True,
overwrite_a: bool = False, check_finite: bool = True,
lapack_driver: str = 'gesdd') -> Array | tuple[Array, Array, Array]:
r"""Compute the singular value decomposition.
JAX implementation of :func:`scipy.linalg.svd`.
The ... | Compute the singular value decomposition.
JAX implementation of :func:`scipy.linalg.svd`.
The SVD of a matrix `A` is given by
.. math::
A = U\Sigma V^H
- :math:`U` contains the left singular vectors and satisfies :math:`U^HU=I`
- :math:`V` contains the right singular vectors and satisfies :math:`V^H... | svd | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def schur(a: ArrayLike, output: str = 'real') -> tuple[Array, Array]:
"""Compute the Schur decomposition
Only implemented on CPU.
JAX implementation of :func:`scipy.linalg.schur`.
The Schur form `T` of a matrix `A` satisfies:
.. math::
A = Z T Z^H
where `Z` is unitary, and `T` is upper-triangular... | Compute the Schur decomposition
Only implemented on CPU.
JAX implementation of :func:`scipy.linalg.schur`.
The Schur form `T` of a matrix `A` satisfies:
.. math::
A = Z T Z^H
where `Z` is unitary, and `T` is upper-triangular for the complex-valued Schur
decomposition (i.e. ``output="complex"``) a... | schur | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def lu_factor(a: ArrayLike, overwrite_a: bool = False, check_finite: bool = True) -> tuple[Array, Array]:
"""Factorization for LU-based linear solves
JAX implementation of :func:`scipy.linalg.lu_factor`.
This function returns a result suitable for use with :func:`jax.scipy.linalg.lu_solve`.
For direct LU deco... | Factorization for LU-based linear solves
JAX implementation of :func:`scipy.linalg.lu_factor`.
This function returns a result suitable for use with :func:`jax.scipy.linalg.lu_solve`.
For direct LU decompositions, prefer :func:`jax.scipy.linalg.lu`.
Args:
a: input array of shape ``(..., M, N)``.
overw... | lu_factor | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def lu_solve(lu_and_piv: tuple[Array, ArrayLike], b: ArrayLike, trans: int = 0,
overwrite_b: bool = False, check_finite: bool = True) -> Array:
"""Solve a linear system using an LU factorization
JAX implementation of :func:`scipy.linalg.lu_solve`. Uses the output
of :func:`jax.scipy.linalg.lu_factor... | Solve a linear system using an LU factorization
JAX implementation of :func:`scipy.linalg.lu_solve`. Uses the output
of :func:`jax.scipy.linalg.lu_factor`.
Args:
lu_and_piv: ``(lu, piv)``, output of :func:`~jax.scipy.linalg.lu_factor`.
``lu`` is an array of shape ``(..., M, N)``, containing ``L`` in i... | lu_solve | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def lu(a: ArrayLike, permute_l: bool = False, overwrite_a: bool = False,
check_finite: bool = True) -> tuple[Array, Array] | tuple[Array, Array, Array]:
"""Compute the LU decomposition
JAX implementation of :func:`scipy.linalg.lu`.
The LU decomposition of a matrix `A` is:
.. math::
A = P L U
... | Compute the LU decomposition
JAX implementation of :func:`scipy.linalg.lu`.
The LU decomposition of a matrix `A` is:
.. math::
A = P L U
where `P` is a permutation matrix, `L` is lower-triangular and `U` is upper-triangular.
Args:
a: array of shape ``(..., M, N)`` to decompose.
permute_l: i... | lu | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def qr(a: ArrayLike, overwrite_a: bool = False, lwork: Any = None, mode: str = "full",
pivoting: bool = False, check_finite: bool = True
) -> tuple[Array] | tuple[Array, Array] | tuple[Array, Array, Array]:
"""Compute the QR decomposition of an array
JAX implementation of :func:`scipy.linalg.qr`.
T... | Compute the QR decomposition of an array
JAX implementation of :func:`scipy.linalg.qr`.
The QR decomposition of a matrix `A` is given by
.. math::
A = QR
Where `Q` is a unitary matrix (i.e. :math:`Q^HQ=I`) and `R` is an upper-triangular
matrix.
Args:
a: array of shape (..., M, N)
mode: Co... | qr | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def solve_triangular(a: ArrayLike, b: ArrayLike, trans: int | str = 0, lower: bool = False,
unit_diagonal: bool = False, overwrite_b: bool = False,
debug: Any = None, check_finite: bool = True) -> Array:
"""Solve a triangular linear system of equations
JAX implementation o... | Solve a triangular linear system of equations
JAX implementation of :func:`scipy.linalg.solve_triangular`.
This solves a (batched) linear system of equations ``a @ x = b`` for ``x``
given a triangular matrix ``a`` and a vector or matrix ``b``.
Args:
a: array of shape ``(..., N, N)``. Only part of the arr... | solve_triangular | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def expm(A: ArrayLike, *, upper_triangular: bool = False, max_squarings: int = 16) -> Array:
"""Compute the matrix exponential
JAX implementation of :func:`scipy.linalg.expm`.
Args:
A: array of shape ``(..., N, N)``
upper_triangular: if True, then assume that ``A`` is upper-triangular. Default=False.
... | Compute the matrix exponential
JAX implementation of :func:`scipy.linalg.expm`.
Args:
A: array of shape ``(..., N, N)``
upper_triangular: if True, then assume that ``A`` is upper-triangular. Default=False.
max_squarings: The number of squarings in the scaling-and-squaring approximation method
(de... | expm | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def expm_frechet(A: ArrayLike, E: ArrayLike, *, method: str | None = None,
compute_expm: bool = True) -> Array | tuple[Array, Array]:
"""Compute the Frechet derivative of the matrix exponential.
JAX implementation of :func:`scipy.linalg.expm_frechet`
Args:
A: array of shape ``(..., N, N)``
... | Compute the Frechet derivative of the matrix exponential.
JAX implementation of :func:`scipy.linalg.expm_frechet`
Args:
A: array of shape ``(..., N, N)``
E: array of shape ``(..., N, N)``; specifies the direction of the derivative.
compute_expm: if True (default) then compute and return ``expm(A)``.
... | expm_frechet | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def block_diag(*arrs: ArrayLike) -> Array:
"""Create a block diagonal matrix from input arrays.
JAX implementation of :func:`scipy.linalg.block_diag`.
Args:
*arrs: arrays of at most two dimensions
Returns:
2D block-diagonal array constructed by placing the input arrays
along the diagonal.
Exam... | Create a block diagonal matrix from input arrays.
JAX implementation of :func:`scipy.linalg.block_diag`.
Args:
*arrs: arrays of at most two dimensions
Returns:
2D block-diagonal array constructed by placing the input arrays
along the diagonal.
Examples:
>>> A = jnp.ones((1, 1))
>>> B = j... | block_diag | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def rsf2csf(T: ArrayLike, Z: ArrayLike, check_finite: bool = True) -> tuple[Array, Array]:
"""Convert real Schur form to complex Schur form.
JAX implementation of :func:`scipy.linalg.rsf2csf`.
Args:
T: array of shape ``(..., N, N)`` containing the real Schur form of the input.
Z: array of shape ``(..., ... | Convert real Schur form to complex Schur form.
JAX implementation of :func:`scipy.linalg.rsf2csf`.
Args:
T: array of shape ``(..., N, N)`` containing the real Schur form of the input.
Z: array of shape ``(..., N, N)`` containing the corresponding Schur transformation
matrix.
check_finite: unused... | rsf2csf | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def hessenberg(a: ArrayLike, *, calc_q: bool = False, overwrite_a: bool = False,
check_finite: bool = True) -> Array | tuple[Array, Array]:
"""Compute the Hessenberg form of the matrix
JAX implementation of :func:`scipy.linalg.hessenberg`.
The Hessenberg form `H` of a matrix `A` satisfies:
.. ... | Compute the Hessenberg form of the matrix
JAX implementation of :func:`scipy.linalg.hessenberg`.
The Hessenberg form `H` of a matrix `A` satisfies:
.. math::
A = Q H Q^H
where `Q` is unitary and `H` is zero below the first subdiagonal.
Args:
a : array of shape ``(..., N, N)``
calc_q: if Tru... | hessenberg | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def hilbert(n: int) -> Array:
r"""Create a Hilbert matrix of order n.
JAX implementation of :func:`scipy.linalg.hilbert`.
The Hilbert matrix is defined by:
.. math::
H_{ij} = \frac{1}{i + j + 1}
for :math:`1 \le i \le n` and :math:`1 \le j \le n`.
Args:
n: the size of the matrix to create.
... | Create a Hilbert matrix of order n.
JAX implementation of :func:`scipy.linalg.hilbert`.
The Hilbert matrix is defined by:
.. math::
H_{ij} = \frac{1}{i + j + 1}
for :math:`1 \le i \le n` and :math:`1 \le j \le n`.
Args:
n: the size of the matrix to create.
Returns:
A Hilbert matrix of sh... | hilbert | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def pascal(n: int, kind: str | None = None) -> Array:
r"""Create a Pascal matrix approximation of order n.
JAX implementation of :func:`scipy.linalg.pascal`.
The elements of the Pascal matrix approximate the binomial coefficients. This
implementation is not exact as JAX does not support exact factorials.
A... | Create a Pascal matrix approximation of order n.
JAX implementation of :func:`scipy.linalg.pascal`.
The elements of the Pascal matrix approximate the binomial coefficients. This
implementation is not exact as JAX does not support exact factorials.
Args:
n: the size of the matrix to create.
kind: (opt... | pascal | python | jax-ml/jax | jax/_src/scipy/linalg.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/linalg.py | Apache-2.0 |
def map_coordinates(
input: ArrayLike, coordinates: Sequence[ArrayLike], order: int,
mode: str = 'constant', cval: ArrayLike = 0.0,
):
"""
Map the input array to new coordinates using interpolation.
JAX implementation of :func:`scipy.ndimage.map_coordinates`
Given an input array and a set of coordinat... |
Map the input array to new coordinates using interpolation.
JAX implementation of :func:`scipy.ndimage.map_coordinates`
Given an input array and a set of coordinates, this function returns the
interpolated values of the input array at those coordinates.
Args:
input: N-dimensional input array from whic... | map_coordinates | python | jax-ml/jax | jax/_src/scipy/ndimage.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/ndimage.py | Apache-2.0 |
def fftconvolve(in1: ArrayLike, in2: ArrayLike, mode: str = "full",
axes: Sequence[int] | None = None) -> Array:
"""
Convolve two N-dimensional arrays using Fast Fourier Transform (FFT).
JAX implementation of :func:`scipy.signal.fftconvolve`.
Args:
in1: left-hand input to the convolution.
... |
Convolve two N-dimensional arrays using Fast Fourier Transform (FFT).
JAX implementation of :func:`scipy.signal.fftconvolve`.
Args:
in1: left-hand input to the convolution.
in2: right-hand input to the convolution. Must have ``in1.ndim == in2.ndim``.
mode: controls the size of the output. Available... | fftconvolve | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def convolve(in1: Array, in2: Array, mode: str = 'full', method: str = 'auto',
precision: PrecisionLike = None) -> Array:
"""Convolution of two N-dimensional arrays.
JAX implementation of :func:`scipy.signal.convolve`.
Args:
in1: left-hand input to the convolution.
in2: right-hand input to ... | Convolution of two N-dimensional arrays.
JAX implementation of :func:`scipy.signal.convolve`.
Args:
in1: left-hand input to the convolution.
in2: right-hand input to the convolution. Must have ``in1.ndim == in2.ndim``.
mode: controls the size of the output. Available operations are:
* ``"full"`... | convolve | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def convolve2d(in1: Array, in2: Array, mode: str = 'full', boundary: str = 'fill',
fillvalue: float = 0, precision: PrecisionLike = None) -> Array:
"""Convolution of two 2-dimensional arrays.
JAX implementation of :func:`scipy.signal.convolve2d`.
Args:
in1: left-hand input to the convolution.... | Convolution of two 2-dimensional arrays.
JAX implementation of :func:`scipy.signal.convolve2d`.
Args:
in1: left-hand input to the convolution. Must have ``in1.ndim == 2``.
in2: right-hand input to the convolution. Must have ``in2.ndim == 2``.
mode: controls the size of the output. Available operations... | convolve2d | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def correlate(in1: Array, in2: Array, mode: str = 'full', method: str = 'auto',
precision: PrecisionLike = None) -> Array:
"""Cross-correlation of two N-dimensional arrays.
JAX implementation of :func:`scipy.signal.correlate`.
Args:
in1: left-hand input to the cross-correlation.
in2: right... | Cross-correlation of two N-dimensional arrays.
JAX implementation of :func:`scipy.signal.correlate`.
Args:
in1: left-hand input to the cross-correlation.
in2: right-hand input to the cross-correlation. Must have ``in1.ndim == in2.ndim``.
mode: controls the size of the output. Available operations are:... | correlate | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def correlate2d(in1: Array, in2: Array, mode: str = 'full', boundary: str = 'fill',
fillvalue: float = 0, precision: PrecisionLike = None) -> Array:
"""Cross-correlation of two 2-dimensional arrays.
JAX implementation of :func:`scipy.signal.correlate2d`.
Args:
in1: left-hand input to the cro... | Cross-correlation of two 2-dimensional arrays.
JAX implementation of :func:`scipy.signal.correlate2d`.
Args:
in1: left-hand input to the cross-correlation. Must have ``in1.ndim == 2``.
in2: right-hand input to the cross-correlation. Must have ``in2.ndim == 2``.
mode: controls the size of the output. A... | correlate2d | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def detrend(data: ArrayLike, axis: int = -1, type: str = 'linear', bp: int = 0,
overwrite_data: None = None) -> Array:
"""
Remove linear or piecewise linear trends from data.
JAX implementation of :func:`scipy.signal.detrend`.
Args:
data: The input array containing the data to detrend.
axi... |
Remove linear or piecewise linear trends from data.
JAX implementation of :func:`scipy.signal.detrend`.
Args:
data: The input array containing the data to detrend.
axis: The axis along which to detrend. Default is -1 (the last axis).
type: The type of detrending. Can be:
* ``'linear'``: Fit ... | detrend | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def _fft_helper(x: Array, win: Array, detrend_func: Callable[[Array], Array],
nperseg: int, noverlap: int, nfft: int | None, sides: str) -> Array:
"""Calculate windowed FFT in the same way the original SciPy does.
"""
if x.dtype.kind == 'i':
x = x.astype(win.dtype)
*batch_shape, signal_leng... | Calculate windowed FFT in the same way the original SciPy does.
| _fft_helper | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def odd_ext(x: Array, n: int, axis: int = -1) -> Array:
"""Extends `x` along with `axis` by odd-extension.
This function was previously a part of "scipy.signal.signaltools" but is no
longer exposed.
Args:
x : input array
n : the number of points to be added to the both end
axis: the axis to be ext... | Extends `x` along with `axis` by odd-extension.
This function was previously a part of "scipy.signal.signaltools" but is no
longer exposed.
Args:
x : input array
n : the number of points to be added to the both end
axis: the axis to be extended
| odd_ext | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def _spectral_helper(x: Array, y: ArrayLike | None, fs: ArrayLike = 1.0,
window: str = 'hann', nperseg: int | None = None,
noverlap: int | None = None, nfft: int | None = None,
detrend_type: bool | str | Callable[[Array], Array] = 'constant',
... | LAX-backend implementation of `scipy.signal._spectral_helper`.
Unlike the original helper function, `y` can be None for explicitly
indicating auto-spectral (non cross-spectral) computation. In addition to
this, `detrend` argument is renamed to `detrend_type` for avoiding internal
name overlap.
| _spectral_helper | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def stft(x: Array, fs: ArrayLike = 1.0, window: str = 'hann', nperseg: int = 256,
noverlap: int | None = None, nfft: int | None = None,
detrend: bool = False, return_onesided: bool = True, boundary: str | None = 'zeros',
padded: bool = True, axis: int = -1) -> tuple[Array, Array, Array]:
""... |
Compute the short-time Fourier transform (STFT).
JAX implementation of :func:`scipy.signal.stft`.
Args:
x: Array representing a time series of input values.
fs: Sampling frequency of the time series (default: 1.0).
window: Data tapering window to apply to each segment. Can be a window function name... | stft | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def csd(x: Array, y: ArrayLike | None, fs: ArrayLike = 1.0, window: str = 'hann',
nperseg: int | None = None, noverlap: int | None = None,
nfft: int | None = None, detrend: str = 'constant',
return_onesided: bool = True, scaling: str = 'density',
axis: int = -1, average: str = 'mean') ->... |
Estimate cross power spectral density (CSD) using Welch's method.
This is a JAX implementation of :func:`scipy.signal.csd`. It is similar to
:func:`jax.scipy.signal.welch`, but it operates on two input signals and
estimates their cross-spectral density instead of the power spectral density
(PSD).
Args:
... | csd | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
def welch(x: Array, fs: ArrayLike = 1.0, window: str = 'hann',
nperseg: int | None = None, noverlap: int | None = None,
nfft: int | None = None, detrend: str = 'constant',
return_onesided: bool = True, scaling: str = 'density',
axis: int = -1, average: str = 'mean') -> tuple[Arra... |
Estimate power spectral density (PSD) using Welch's method.
This is a JAX implementation of :func:`scipy.signal.welch`. It divides the
input signal into overlapping segments, computes the modified periodogram for
each segment, and averages the results to obtain a smoother estimate of the PSD.
Args:
x: ... | welch | python | jax-ml/jax | jax/_src/scipy/signal.py | https://github.com/jax-ml/jax/blob/master/jax/_src/scipy/signal.py | Apache-2.0 |
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