Skip to content

P
pytensor
提交 1d19c375 authored 作者: Ricardo Vieira's avatar Ricardo Vieira 提交者： Ricardo Vieira
浏览文件
操作

Allow non-shared untraced SIT-SOT

上级 d10c61ba
全部展开 显示空白字符变更
内嵌 并排
正在显示 包含 184 行增加 和 87 行删除
pytensor/link/jax/dispatch/scan.py
浏览文件 @ 1d19c375
......@@ -60,23 +60,23 @@ def jax_funcify_Scan(op: Scan, **kwargs):
mit_mot_init,
mit_sot_init,
sit_sot_init,
op.outer_shared(outer_inputs),
op.outer_untraced_sit_sot(outer_inputs),
op.outer_non_seqs(outer_inputs),
) # JAX `init`
def jax_args_to_inner_func_args(carry, x):
"""Convert JAX scan arguments into format expected by scan_inner_func.
scan(carry, x) -> scan_inner_func(seqs, MIT-SOT, SIT-SOT, shared, non_seqs)
scan(carry, x) -> scan_inner_func(seqs, MIT-SOT, SIT-SOT, untraced SIT-SOT, non_seqs)
"""
# `carry` contains all inner taps, shared terms, and non_seqs
# `carry` contains all inner taps and non_seqs
(
i,
inner_mit_mot,
inner_mit_sot,
inner_sit_sot,
inner_shared,
inner_untraced_sit_sot,
inner_non_seqs,
) = carry
......@@ -108,7 +108,7 @@ def jax_funcify_Scan(op: Scan, **kwargs):
*mit_mot_flatten,
*mit_sot_flatten,
*inner_sit_sot,
*inner_shared,
*inner_untraced_sit_sot,
*inner_non_seqs,
)
......@@ -118,14 +118,14 @@ def jax_funcify_Scan(op: Scan, **kwargs):
):
"""Convert inner_scan_func outputs into format expected by JAX scan.
old_carry + (MIT-SOT_outs, SIT-SOT_outs, NIT-SOT_outs, shared_outs) -> (new_carry, ys)
old_carry + (MIT-SOT_outs, SIT-SOT_outs, NIT-SOT_outs, untraced_SIT-SOT_outs) -> (new_carry, ys)
"""
(
i,
old_mit_mot,
old_mit_sot,
_old_sit_sot,
_old_shared,
_old_untraced_sit_sot,
inner_non_seqs,
) = old_carry
......@@ -133,7 +133,7 @@ def jax_funcify_Scan(op: Scan, **kwargs):
new_mit_sot_vals = op.inner_mitsot_outs(inner_scan_outs)
new_sit_sot = op.inner_sitsot_outs(inner_scan_outs)
new_nit_sot = op.inner_nitsot_outs(inner_scan_outs)
new_shared = op.inner_shared_outs(inner_scan_outs)
new_untraced_sit_sot = op.inner_untraced_sit_sot_outs(inner_scan_outs)
# New carry for next step
# Update MIT-MOT buffer at positions indicated by output taps
......@@ -150,14 +150,14 @@ def jax_funcify_Scan(op: Scan, **kwargs):
old_mit_sot, new_mit_sot_vals, strict=True
)
]
# For SIT-SOT, and shared just pass along the new value
# For SIT-SOT just pass along the new value
# Non-sequences remain unchanged
new_carry = (
i + 1,
new_mit_mot,
new_mit_sot,
new_sit_sot,
new_shared,
new_untraced_sit_sot,
inner_non_seqs,
)
......@@ -183,7 +183,7 @@ def jax_funcify_Scan(op: Scan, **kwargs):
final_mit_mot,
_final_mit_sot,
_final_sit_sot,
final_shared,
final_untraced_sit_sot,
_final_non_seqs,
),
traces,
......@@ -238,7 +238,7 @@ def jax_funcify_Scan(op: Scan, **kwargs):
scan_outs_final = [
*final_mit_mot,
*get_partial_traces(traces),
*final_shared,
*final_untraced_sit_sot,
]
if len(scan_outs_final) == 1:
......
pytensor/link/numba/dispatch/scan.py
浏览文件 @ 1d19c375
......@@ -108,19 +108,19 @@ def numba_funcify_Scan(op: Scan, node, **kwargs):
outer_in_mit_sot_names = op.outer_mitsot(outer_in_names)
outer_in_sit_sot_names = op.outer_sitsot(outer_in_names)
outer_in_nit_sot_names = op.outer_nitsot(outer_in_names)
outer_in_shared_names = op.outer_shared(outer_in_names)
outer_in_untraced_sit_sot_names = op.outer_untraced_sit_sot(outer_in_names)
outer_in_non_seqs_names = op.outer_non_seqs(outer_in_names)
# These are all the outer-input names that have produce outputs/have output
# taps (i.e. they have inner-outputs and corresponding outer-outputs).
# Outer-outputs are ordered as follows:
# mit-mot-outputs + mit-sot-outputs + sit-sot-outputs + nit-sots + shared-outputs
# mit-mot-outputs + mit-sot-outputs + sit-sot-outputs + nit-sots + untraced-sit-sot-outputs
outer_in_outtap_names = (
outer_in_mit_mot_names
+ outer_in_mit_sot_names
+ outer_in_sit_sot_names
+ outer_in_nit_sot_names
+ outer_in_shared_names
+ outer_in_untraced_sit_sot_names
)
# We create distinct variables for/references to the storage arrays for
......@@ -138,8 +138,10 @@ def numba_funcify_Scan(op: Scan, node, **kwargs):
for outer_in_name in outer_in_nit_sot_names:
outer_in_to_storage_name[outer_in_name] = f"{outer_in_name}_nitsot_storage"
for outer_in_name in outer_in_shared_names:
outer_in_to_storage_name[outer_in_name] = f"{outer_in_name}_shared_storage"
for outer_in_name in outer_in_untraced_sit_sot_names:
outer_in_to_storage_name[outer_in_name] = (
f"{outer_in_name}_untraced_sit_sot_storage"
)
outer_output_names = list(outer_in_to_storage_name.values())
assert len(outer_output_names) == len(node.outputs)
......@@ -147,7 +149,7 @@ def numba_funcify_Scan(op: Scan, node, **kwargs):
# Construct the inner-input expressions (e.g. indexed storage expressions)
# Inner-inputs are ordered as follows:
# sequences + mit-mot-inputs + mit-sot-inputs + sit-sot-inputs +
# shared-inputs + non-sequences.
# untraced-sit-sot-inputs + non-sequences.
temp_scalar_storage_alloc_stmts: list[str] = []
inner_in_exprs_scalar: list[str] = []
inner_in_exprs: list[str] = []
......@@ -204,11 +206,9 @@ def numba_funcify_Scan(op: Scan, node, **kwargs):
# Inner-outputs consist of:
# mit-mot-outputs + mit-sot-outputs + sit-sot-outputs + nit-sots +
# shared-outputs [+ while-condition]
# untraced-sit-sot-outputs [+ while-condition]
inner_output_names = [f"inner_out_{i}" for i in range(len(op.inner_outputs))]
# inner_out_shared_names = op.inner_shared_outs(inner_output_names)
# The assignment statements that copy inner-outputs into the outer-outputs
# storage
inner_out_to_outer_in_stmts: list[str] = []
......
pytensor/scan/basic.py
浏览文件 @ 1d19c375
import typing
import warnings
from itertools import chain
......@@ -11,6 +12,7 @@ from pytensor.graph.basic import Constant, Variable
from pytensor.graph.op import get_test_value
from pytensor.graph.replace import clone_replace
from pytensor.graph.traversal import explicit_graph_inputs
from pytensor.graph.type import HasShape
from pytensor.graph.utils import MissingInputError, TestValueError
from pytensor.scan.op import Scan, ScanInfo
from pytensor.scan.utils import expand_empty, safe_new, until
......@@ -22,6 +24,10 @@ from pytensor.tensor.type import TensorType, integer_dtypes
from pytensor.updates import OrderedUpdates
if typing.TYPE_CHECKING:
from pytensor.tensor.type import TensorVariable
def get_updates_and_outputs(ls):
"""Recognize and order the updates, outputs, and stopping condition for a `Scan`.
......@@ -469,7 +475,7 @@ def scan(
# Make sure we get rid of numpy arrays or ints or anything like that
# passed as inputs to scan
non_seqs = []
non_seqs: list[Variable] = []
for elem in wrap_into_list(non_sequences):
if not isinstance(elem, Variable):
non_seqs.append(pt.as_tensor_variable(elem))
......@@ -685,10 +691,10 @@ def scan(
# MIT_MOT -- not provided by the user only by the grad function
n_mit_mot = 0
mit_mot_scan_inputs = []
mit_mot_inner_inputs = []
mit_mot_inner_outputs = []
mit_mot_out_slices = []
mit_mot_scan_inputs: list[TensorVariable] = []
mit_mot_inner_inputs: list[TensorVariable] = []
mit_mot_inner_outputs: list[TensorVariable] = []
mit_mot_out_slices: list[TensorVariable] = []
# SIT_SOT -- provided by the user
n_mit_sot = 0
......@@ -706,6 +712,12 @@ def scan(
sit_sot_inner_outputs = []
sit_sot_rightOrder = []
n_untraced_sit_sot_outs = 0
untraced_sit_sot_scan_inputs = []
untraced_sit_sot_inner_inputs = []
untraced_sit_sot_inner_outputs = []
untraced_sit_sot_rightOrder = []
# go through outputs picking up time slices as needed
for i, init_out in enumerate(outs_info):
# Note that our convention dictates that if an output uses
......@@ -741,17 +753,35 @@ def scan(
# We need now to allocate space for storing the output and copy
# the initial state over. We do this using the expand function
# defined in scan utils
if isinstance(actual_arg.type, HasShape):
sit_sot_scan_inputs.append(
expand_empty(
shape_padleft(actual_arg),
actual_n_steps,
)
)
sit_sot_inner_slices.append(actual_arg)
sit_sot_inner_inputs.append(arg)
sit_sot_rightOrder.append(i)
n_sit_sot += 1
else:
# Assume variables without shape cannot be stacked (e.g., RNG variables)
# Because this is new, issue a warning to inform the user, except for RNG, which were the main reason for this feature
from pytensor.tensor.random.type import RandomType
if not isinstance(arg.type, RandomType):
warnings.warn(
(
f"Output {actual_arg} (index {i}) with type {actual_arg.type} will be treated as untraced variable in scan. "
"Only the last value will be returned, not the entire sequence."
),
UserWarning,
)
untraced_sit_sot_scan_inputs.append(actual_arg)
untraced_sit_sot_inner_inputs.append(arg)
n_untraced_sit_sot_outs += 1
untraced_sit_sot_rightOrder.append(i)
elif init_out.get("taps", None):
if np.any(np.array(init_out.get("taps", [])) > 0):
......@@ -802,10 +832,11 @@ def scan(
# a map); in that case we do not have to do anything ..
# Re-order args
max_mit_sot = np.max([-1, *mit_sot_rightOrder]) + 1
max_sit_sot = np.max([-1, *sit_sot_rightOrder]) + 1
n_elems = np.max([max_mit_sot, max_sit_sot])
_ordered_args = [[] for x in range(n_elems)]
max_mit_sot = max(mit_sot_rightOrder, default=-1) + 1
max_sit_sot = max(sit_sot_rightOrder, default=-1) + 1
max_untraced_sit_sot_outs = max(untraced_sit_sot_rightOrder, default=-1) + 1
n_elems = np.max((max_mit_sot, max_sit_sot, max_untraced_sit_sot_outs))
_ordered_args: list[list[Variable]] = [[] for x in range(n_elems)]
offset = 0
for idx in range(n_mit_sot):
n_inputs = len(mit_sot_tap_array[idx])
......@@ -825,6 +856,11 @@ def scan(
else:
_ordered_args[sit_sot_rightOrder[idx]] = [sit_sot_inner_inputs[idx]]
for idx in range(n_untraced_sit_sot_outs):
_ordered_args[untraced_sit_sot_rightOrder[idx]] = [
untraced_sit_sot_inner_inputs[idx]
]
ordered_args = list(chain.from_iterable(_ordered_args))
if single_step_requested:
args = inner_slices + ordered_args + non_seqs
......@@ -939,18 +975,19 @@ def scan(
if "taps" in out and out["taps"] != [-1]:
mit_sot_inner_outputs.append(outputs[i])
# Step 5.2 Outputs with tap equal to -1
# Step 5.2 Outputs with tap equal to -1 (traced and untraced)
for i, out in enumerate(outs_info):
if "taps" in out and out["taps"] == [-1]:
sit_sot_inner_outputs.append(outputs[i])
output = outputs[i]
if isinstance(output.type, HasShape):
sit_sot_inner_outputs.append(output)
else:
untraced_sit_sot_inner_outputs.append(output)
# Step 5.3 Outputs that correspond to update rules of shared variables
# This whole special logic for shared variables is deprecated
sit_sot_shared: list[Variable] = []
inner_replacements = {}
n_shared_outs = 0
shared_scan_inputs = []
shared_inner_inputs = []
shared_inner_outputs = []
sit_sot_shared = []
no_update_shared_inputs = []
for input in dummy_inputs:
if not isinstance(input.variable, SharedVariable):
......@@ -976,8 +1013,8 @@ def scan(
new_var = safe_new(input.variable)
if getattr(input.variable, "name", None) is not None:
new_var.name = input.variable.name + "_copy"
if input.variable.name is not None:
new_var.name = f"{input.variable.name}_copy"
inner_replacements[input.variable] = new_var
......@@ -1003,10 +1040,10 @@ def scan(
sit_sot_shared.append(input.variable)
else:
shared_inner_inputs.append(new_var)
shared_scan_inputs.append(input.variable)
shared_inner_outputs.append(input.update)
n_shared_outs += 1
untraced_sit_sot_inner_inputs.append(new_var)
untraced_sit_sot_scan_inputs.append(input.variable)
untraced_sit_sot_inner_outputs.append(input.update)
n_untraced_sit_sot_outs += 1
else:
no_update_shared_inputs.append(input)
......@@ -1071,7 +1108,7 @@ def scan(
+ mit_mot_inner_inputs
+ mit_sot_inner_inputs
+ sit_sot_inner_inputs
+ shared_inner_inputs
+ untraced_sit_sot_inner_inputs
+ other_shared_inner_args
+ other_inner_args
)
......@@ -1081,7 +1118,7 @@ def scan(
+ mit_sot_inner_outputs
+ sit_sot_inner_outputs
+ nit_sot_inner_outputs
+ shared_inner_outputs
+ untraced_sit_sot_inner_outputs
)
if condition is not None:
inner_outs.append(condition)
......@@ -1101,7 +1138,7 @@ def scan(
mit_mot_out_slices=tuple(tuple(v) for v in mit_mot_out_slices),
mit_sot_in_slices=tuple(tuple(v) for v in mit_sot_tap_array),
sit_sot_in_slices=tuple((-1,) for x in range(n_sit_sot)),
n_shared_outs=n_shared_outs,
n_untraced_sit_sot_outs=n_untraced_sit_sot_outs,
n_nit_sot=n_nit_sot,
n_non_seqs=len(other_shared_inner_args) + len(other_inner_args),
as_while=as_while,
......@@ -1127,7 +1164,7 @@ def scan(
+ mit_mot_scan_inputs
+ mit_sot_scan_inputs
+ sit_sot_scan_inputs
+ shared_scan_inputs
+ untraced_sit_sot_scan_inputs
+ [actual_n_steps for x in range(n_nit_sot)]
+ other_shared_scan_args
+ other_scan_args
......@@ -1173,13 +1210,26 @@ def scan(
nit_sot_outs = remove_dimensions(scan_outs[offset : offset + n_nit_sot])
offset += n_nit_sot
for idx, update_rule in enumerate(scan_outs[offset : offset + n_shared_outs]):
update_map[shared_scan_inputs[idx]] = update_rule
_scan_out_list = mit_sot_outs + sit_sot_outs + nit_sot_outs
# Support for explicit untraced sit_sot
n_explicit_untraced_sit_sot_outs = len(untraced_sit_sot_rightOrder)
untraced_sit_sot_outs = scan_outs[
offset : offset + n_explicit_untraced_sit_sot_outs
]
offset += n_explicit_untraced_sit_sot_outs
for idx, update_rule in enumerate(scan_outs[offset:]):
update_map[untraced_sit_sot_scan_inputs[idx]] = update_rule
_scan_out_list = mit_sot_outs + sit_sot_outs + nit_sot_outs + untraced_sit_sot_outs
# Step 10. I need to reorder the outputs to be in the order expected by
# the user
rightOrder = mit_sot_rightOrder + sit_sot_rightOrder + nit_sot_rightOrder
rightOrder = (
mit_sot_rightOrder
+ sit_sot_rightOrder
+ untraced_sit_sot_rightOrder
+ nit_sot_rightOrder
)
scan_out_list = [None] * len(rightOrder)
for idx, pos in enumerate(rightOrder):
if pos >= 0:
......
pytensor/scan/op.py
浏览文件 @ 1d19c375
差异被折叠。
pytensor/scan/rewriting.py
浏览文件 @ 1d19c375
......@@ -110,7 +110,7 @@ def remove_constants_and_unused_inputs_scan(fgraph, node):
sum(len(x) for x in chain(op_info.mit_mot_in_slices, op_info.mit_sot_in_slices))
)
st += op_info.n_sit_sot
st += op_info.n_shared_outs
st += op_info.n_untraced_sit_sot_outs
op_ins = op.inner_inputs
op_outs = op.inner_outputs
......@@ -126,7 +126,7 @@ def remove_constants_and_unused_inputs_scan(fgraph, node):
+ op_info.n_mit_sot
+ op_info.n_sit_sot
+ op_info.n_nit_sot
+ op_info.n_shared_outs
+ op_info.n_untraced_sit_sot_outs
+ 1
)
outer_non_seqs = node.inputs[st:]
......@@ -983,7 +983,7 @@ class ScanInplaceOptimizer(GraphRewriter):
ls = op.outer_mitmot(node.inputs)
ls += op.outer_mitsot(node.inputs)
ls += op.outer_sitsot(node.inputs)
ls_end = op.outer_shared(node.inputs)
ls_end = op.outer_untraced_sit_sot(node.inputs)
ls_end += op.outer_nitsot(node.inputs)
ls_end += op.outer_non_seqs(node.inputs)
......@@ -1628,7 +1628,7 @@ def scan_save_mem_rewrite(fgraph, node, backend_supports_output_pre_allocation:
+ idx
+ op_info.n_seqs
+ 1
+ op_info.n_shared_outs
+ op_info.n_untraced_sit_sot_outs
)
if nw_inputs[pos] == node.inputs[0]:
nw_inputs[pos] = 1 if required_orphan else val
......@@ -1662,7 +1662,7 @@ def scan_save_mem_rewrite(fgraph, node, backend_supports_output_pre_allocation:
elif (
idx < op_info.n_mit_sot + op_info.n_sit_sot + op_info.n_nit_sot
):
in_idx = offset + idx + op_info.n_shared_outs
in_idx = offset + idx + op_info.n_untraced_sit_sot_outs
if nw_inputs[in_idx] == node.inputs[0]:
nw_inputs[in_idx] = nw_steps
......@@ -1886,8 +1886,8 @@ class ScanMerge(GraphRewriter):
for idx, nd in enumerate(nodes):
# Shared
inner_ins[idx].append(nd.op.inner_shared(nd.op.inner_inputs))
outer_ins += nd.op.outer_shared(nd.inputs)
inner_ins[idx].append(nd.op.inner_untraced_sit_sot(nd.op.inner_inputs))
outer_ins += nd.op.outer_untraced_sit_sot(nd.inputs)
for idx, nd in enumerate(nodes):
# NitSot
......@@ -1897,8 +1897,10 @@ class ScanMerge(GraphRewriter):
for idx, nd in enumerate(nodes):
# Shared
outer_outs += nd.op.outer_shared_outs(nd.outputs)
inner_outs[idx].append(nd.op.inner_shared_outs(nd.op.inner_outputs))
outer_outs += nd.op.outer_untraced_sit_sot_outs(nd.outputs)
inner_outs[idx].append(
nd.op.inner_untraced_sit_sot_outs(nd.op.inner_outputs)
)
n_non_seqs = 0
for idx, nd in enumerate(nodes):
......@@ -1978,7 +1980,9 @@ class ScanMerge(GraphRewriter):
mit_sot_in_slices=mit_sot_in_slices,
sit_sot_in_slices=sit_sot_in_slices,
n_nit_sot=sum(nd.op.info.n_nit_sot for nd in nodes),
n_shared_outs=sum(nd.op.info.n_shared_outs for nd in nodes),
n_untraced_sit_sot_outs=sum(
nd.op.info.n_untraced_sit_sot_outs for nd in nodes
),
n_non_seqs=n_non_seqs,
as_while=as_while,
)
......@@ -2360,7 +2364,7 @@ def scan_push_out_dot1(fgraph, node):
# When seq[t] is a vector/matrix and `value` is a matrix
# Note that this works when only you need X[-1] in the end
# and assumes dimshuffle are applied to vectors before calling dot
op = node.op
op: Scan = node.op
sitsot_ins = op.inner_sitsot(op.inner_inputs)
sitsot_outs = op.inner_sitsot_outs(op.inner_outputs)
outer_sitsot = op.outer_sitsot_outs(node.outputs)
......@@ -2416,9 +2420,13 @@ def scan_push_out_dot1(fgraph, node):
inner_sitsot_outs = op.inner_sitsot_outs(op.inner_outputs)
outer_nitsot = op.outer_nitsot(node.inputs)
inner_nitsot_outs = op.inner_nitsot_outs(op.inner_outputs)
inner_shared = op.inner_shared(op.inner_inputs)
outer_shared = op.outer_shared(node.inputs)
inner_shared_outs = op.inner_shared_outs(op.inner_outputs)
inner_untraced_sitsot = op.inner_untraced_sitsot(op.inner_inputs)
outer_untraced_sitsot_outs = op.outer_untraced_sitsot_outs(
node.inputs
)
inner_untraced_sitsot_outs = op.inner_untraced_sitsot_outs(
op.inner_outputs
)
inner_non_seqs = op.inner_non_seqs(op.inner_inputs)
outer_non_seqs = op.outer_non_seqs(node.inputs)
......@@ -2441,7 +2449,7 @@ def scan_push_out_dot1(fgraph, node):
+ inner_mitmot
+ inner_mitsot
+ inner_sitsot
+ inner_shared
+ inner_untraced_sitsot
+ inner_non_seqs
)
_new_inner_outs = (
......@@ -2449,7 +2457,7 @@ def scan_push_out_dot1(fgraph, node):
+ inner_mitsot_outs
+ inner_sitsot_outs
+ inner_nitsot_outs
+ inner_shared_outs
+ inner_untraced_sitsot_outs
)
new_inner_inps, new_inner_outs = reconstruct_graph(
_new_inner_inps, _new_inner_outs
......@@ -2471,7 +2479,7 @@ def scan_push_out_dot1(fgraph, node):
*outer_mitmot,
*outer_mitsot,
*outer_sitsot,
*outer_shared,
*outer_untraced_sitsot_outs,
*outer_nitsot,
node.inputs[0],
*outer_non_seqs,
......
pytensor/scan/utils.py
浏览文件 @ 1d19c375
......@@ -370,7 +370,9 @@ def scan_can_remove_outs(op, out_idxs):
out_ins += [op.inner_inputs[offset : offset + n_ins]]
offset += n_ins
out_ins += [[] for k in range(op.info.n_nit_sot)]
out_ins += [[op.inner_inputs[offset + k]] for k in range(op.info.n_shared_outs)]
out_ins += [
[op.inner_inputs[offset + k]] for k in range(op.info.n_untraced_sit_sot_outs)
]
added = True
out_idxs_mask = [1 for idx in out_idxs]
......@@ -409,7 +411,7 @@ def compress_outs(op, not_required, inputs):
mit_sot_in_slices=(),
sit_sot_in_slices=(),
n_nit_sot=0,
n_shared_outs=0,
n_untraced_sit_sot_outs=0,
n_non_seqs=0,
as_while=op_info.as_while,
)
......@@ -515,17 +517,19 @@ def compress_outs(op, not_required, inputs):
info = dataclasses.replace(info, n_nit_sot=info.n_nit_sot + 1)
op_outputs += [op.inner_outputs[o_offset]]
o_offset += 1
nit_sot_ins += [inputs[ni_offset + idx + op_info.n_shared_outs]]
nit_sot_ins += [inputs[ni_offset + idx + op_info.n_untraced_sit_sot_outs]]
else:
o_offset += 1
offset += op_info.n_nit_sot
shared_ins = []
for idx in range(op_info.n_shared_outs):
for idx in range(op_info.n_untraced_sit_sot_outs):
if offset + idx not in not_required:
map_old_new[offset + idx] = curr_pos
curr_pos += 1
info = dataclasses.replace(info, n_shared_outs=info.n_shared_outs + 1)
info = dataclasses.replace(
info, n_untraced_sit_sot_outs=info.n_untraced_sit_sot_outs + 1
)
op_outputs += [op.inner_outputs[o_offset]]
o_offset += 1
op_inputs += [op.inner_inputs[i_offset]]
......@@ -539,7 +543,9 @@ def compress_outs(op, not_required, inputs):
# other stuff
op_inputs += op.inner_inputs[i_offset:]
info = dataclasses.replace(info, n_non_seqs=len(op.inner_inputs[i_offset:]))
node_inputs += inputs[ni_offset + op_info.n_shared_outs + op_info.n_nit_sot :]
node_inputs += inputs[
ni_offset + op_info.n_untraced_sit_sot_outs + op_info.n_nit_sot :
]
if op_info.as_while:
op_outputs += [op.inner_outputs[o_offset]]
map_old_new[o_offset] = len(op_outputs) - 1
......@@ -658,11 +664,11 @@ class ScanArgs:
p += n_sit_sot
q += n_sit_sot
n_shared_outs = info.n_shared_outs
self.outer_in_shared = list(outer_inputs[p : p + n_shared_outs])
self.inner_in_shared = list(inner_inputs[q : q + n_shared_outs])
p += n_shared_outs
q += n_shared_outs
n_untraced_sit_sot_outs = info.n_untraced_sit_sot_outs
self.outer_in_shared = list(outer_inputs[p : p + n_untraced_sit_sot_outs])
self.inner_in_shared = list(inner_inputs[q : q + n_untraced_sit_sot_outs])
p += n_untraced_sit_sot_outs
q += n_untraced_sit_sot_outs
n_nit_sot = info.n_nit_sot
self.outer_in_nit_sot = list(outer_inputs[p : p + n_nit_sot])
......@@ -702,10 +708,10 @@ class ScanArgs:
p += n_nit_sot
q += n_nit_sot
self.outer_out_shared = list(outer_outputs[p : p + n_shared_outs])
self.inner_out_shared = list(inner_outputs[q : q + n_shared_outs])
p += n_shared_outs
q += n_shared_outs
self.outer_out_shared = list(outer_outputs[p : p + n_untraced_sit_sot_outs])
self.inner_out_shared = list(inner_outputs[q : q + n_untraced_sit_sot_outs])
p += n_untraced_sit_sot_outs
q += n_untraced_sit_sot_outs
assert p == len(outer_outputs)
assert q == len(inner_outputs)
......@@ -816,7 +822,7 @@ class ScanArgs:
mit_sot_in_slices=tuple(tuple(v) for v in self.mit_sot_in_slices),
sit_sot_in_slices=((-1,),) * len(self.inner_in_sit_sot),
n_nit_sot=len(self.outer_in_nit_sot),
n_shared_outs=len(self.outer_in_shared),
n_untraced_sit_sot_outs=len(self.outer_in_shared),
n_non_seqs=len(self.inner_in_non_seqs),
as_while=self.as_while,
)
......
tests/link/numba/test_scan.py
浏览文件 @ 1d19c375
......@@ -85,7 +85,7 @@ from tests.link.numba.test_basic import compare_numba_and_py
3,
[],
[np.array([0.50100236, 2.16822932, 1.36326596])],
lambda op: op.info.n_shared_outs > 0,
lambda op: op.info.n_untraced_sit_sot_outs > 0,
),
# mit-sot (that's also a type of sit-sot)
(
......
tests/scan/test_basic.py
浏览文件 @ 1d19c375
......@@ -42,11 +42,13 @@ from pytensor.tensor.math import all as pt_all
from pytensor.tensor.math import dot, exp, mean, sigmoid, tanh
from pytensor.tensor.math import sum as pt_sum
from pytensor.tensor.random import normal
from pytensor.tensor.random.type import RandomGeneratorType, random_generator_type
from pytensor.tensor.random.utils import RandomStream
from pytensor.tensor.shape import Shape_i, reshape, specify_shape
from pytensor.tensor.sharedvar import SharedVariable
from pytensor.tensor.subtensor import Subtensor
from pytensor.tensor.type import (
TensorType,
dcol,
dmatrix,
dscalar,
......@@ -4007,7 +4009,7 @@ class TestExamples:
[{}],
[],
3,
lambda op: op.info.n_shared_outs > 0,
lambda op: op.info.n_untraced_sit_sot_outs > 0,
),
# mit-sot (that's also a type of sit-sot)
(
......@@ -4106,3 +4108,34 @@ def test_output_storage_reuse(linker_mode):
res = f_cvm()
assert np.array_equal(res, np.array([3, 1, 0]))
def test_rng_outputs_info():
rng_init = random_generator_type("rng")
rng_x0, x0 = pt.random.normal(0, rng=rng_init, dtype="float64").owner.outputs
def step(prev_x, prev_rng):
next_rng, next_x = pt.random.normal(
prev_x, rng=prev_rng, dtype="float64"
).owner.outputs
return next_x, next_rng
[xs, rng_final], updates = scan(
fn=step,
outputs_info=[x0, rng_x0],
n_steps=10,
)
assert isinstance(xs.type, TensorType)
assert isinstance(rng_final.type, RandomGeneratorType)
assert not updates
fn = function([rng_init], [xs, rng_final])
xs_eval, rng_final_eval = fn(np.random.default_rng(0))
rng_ref = np.random.default_rng(0)
assert not random_generator_type.values_eq(rng_ref, rng_final_eval)
xs_ref = [rng_ref.normal(0)]
for i in range(10):
xs_ref.append(rng_ref.normal(xs_ref[-1]))
assert random_generator_type.values_eq(rng_ref, rng_final_eval)
np.testing.assert_allclose(xs_eval, xs_ref[1:])
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论