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Complete xlayers.rearrange

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This commit is contained in:
D-X-Y 2021-06-08 23:47:52 -07:00
parent f9bbf974de
commit 744ce97bc5
10 changed files with 218 additions and 96 deletions
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1
tests/test_super_att.py
View File

@ -29,6 +29,7 @@ class TestSuperSelfAttention(unittest.TestCase):
abstract_child = abstract_space.random(reuse_last=True)
print("The abstract child program is:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.enable_candidate()
model.apply_candidate(abstract_child)
outputs = model(inputs)
return abstract_child, outputs

1
tests/test_super_container.py
View File

@ -25,6 +25,7 @@ def _internal_func(inputs, model):
abstract_space.clean_last()
abstract_child = abstract_space.random(reuse_last=True)
print("The abstract child program is:\n{:}".format(abstract_child))
model.enable_candidate()
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.apply_candidate(abstract_child)
outputs = model(inputs)

4
tests/test_super_mlp.py
View File

@ -37,6 +37,7 @@ class TestSuperLinear(unittest.TestCase):
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.enable_candidate()
model.apply_candidate(abstract_child)
output_shape = (20, abstract_child["_out_features"].value)
@ -77,6 +78,7 @@ class TestSuperLinear(unittest.TestCase):
)
mlp.set_super_run_type(super_core.SuperRunMode.Candidate)
mlp.enable_candidate()
mlp.apply_candidate(abstract_child)
outputs = mlp(inputs)
output_shape = (4, abstract_child["fc2"]["_out_features"].value)
@ -103,6 +105,7 @@ class TestSuperLinear(unittest.TestCase):
print("The abstract child program is:\n{:}".format(abstract_child))
mlp.set_super_run_type(super_core.SuperRunMode.Candidate)
mlp.enable_candidate()
mlp.apply_candidate(abstract_child)
outputs = mlp(inputs)
output_shape = (4, abstract_child["_out_features"].value)
@ -120,6 +123,7 @@ class TestSuperLinear(unittest.TestCase):
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.enable_candidate()
model.apply_candidate(abstract_child)
outputs = model(inputs)
output_shape = (4, 60, abstract_child["_embed_dim"].value)

2
tests/test_super_norm.py
View File

@ -38,6 +38,7 @@ class TestSuperSimpleNorm(unittest.TestCase):
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.enable_candidate()
model.apply_candidate(abstract_child)
output_shape = (20, abstract_child["1"]["_out_features"].value)
@ -70,6 +71,7 @@ class TestSuperSimpleNorm(unittest.TestCase):
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.enable_candidate()
model.apply_candidate(abstract_child)
output_shape = (20, abstract_child["2"]["_out_features"].value)

7
tests/test_super_rearrange.py
View File

@ -5,12 +5,6 @@
#####################################################
import sys
import unittest
from pathlib import Path
lib_dir = (Path(__file__).parent / "..").resolve()
print("LIB-DIR: {:}".format(lib_dir))
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
import torch
from xautodl import xlayers
@ -28,3 +22,4 @@ class TestSuperReArrange(unittest.TestCase):
print(layer)
outs = layer(tensor)
print("The output tensor shape: {:}".format(outs.shape))
assert tuple(outs.shape) == (8, 32 * 32 // 16, 4 * 4 * 4)

36
tests/test_super_transformer.py
View File

@ -1,36 +0,0 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
# pytest ./tests/test_super_model.py -s #
#####################################################
import unittest
import torch
from xautodl.xlayers.super_core import SuperRunMode
from xautodl.trade_models import get_transformer
class TestSuperTransformer(unittest.TestCase):
"""Test the super transformer."""
def test_super_transformer(self):
model = get_transformer(None)
model.apply_verbose(False)
print(model)
inputs = torch.rand(10, 360)
print("Input shape: {:}".format(inputs.shape))
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), (10,))
abstract_space = model.abstract_search_space
abstract_space.clean_last()
abstract_child = abstract_space.random(reuse_last=True)
print("The abstract searc space:\n{:}".format(abstract_space))
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(SuperRunMode.Candidate)
model.apply_candidate(abstract_child)
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), (10,))

91
xautodl/xlayers/misc_utils.py
View File

@ -1,4 +1,28 @@
# borrowed from https://github.com/arogozhnikov/einops/blob/master/einops/parsing.py
import warnings
import keyword
from typing import List
class AnonymousAxis:
"""Important thing: all instances of this class are not equal to each other"""
def __init__(self, value: str):
self.value = int(value)
if self.value <= 1:
if self.value == 1:
raise EinopsError(
"No need to create anonymous axis of length 1. Report this as an issue"
)
else:
raise EinopsError(
"Anonymous axis should have positive length, not {}".format(
self.value
)
)
def __repr__(self):
return "{}-axis".format(str(self.value))
class ParsedExpression:
@ -8,24 +32,13 @@ class ParsedExpression:
"""
def __init__(self, expression):
self.has_ellipsis = False
self.has_ellipsis_parenthesized = None
self.identifiers = set()
# that's axes like 2, 3 or 5. Axes with size 1 are exceptional and replaced with empty composition
self.has_non_unitary_anonymous_axes = False
# composition keeps structure of composite axes, see how different corner cases are handled in tests
self.composition = []
if "." in expression:
if "..." not in expression:
raise ValueError(
"Expression may contain dots only inside ellipsis (...)"
)
if str.count(expression, "...") != 1 or str.count(expression, ".") != 3:
raise ValueError(
"Expression may contain dots only inside ellipsis (...); only one ellipsis for tensor "
)
expression = expression.replace("...", _ellipsis)
self.has_ellipsis = True
raise ValueError("Does not support . in the expression.")
bracket_group = None
@ -37,37 +50,28 @@ class ParsedExpression:
x
)
)
if x == _ellipsis:
self.identifiers.add(_ellipsis)
is_number = str.isdecimal(x)
if is_number and int(x) == 1:
# handling the case of anonymous axis of length 1
if bracket_group is None:
self.composition.append(_ellipsis)
self.has_ellipsis_parenthesized = False
self.composition.append([])
else:
bracket_group.append(_ellipsis)
self.has_ellipsis_parenthesized = True
pass # no need to think about 1s inside parenthesis
return
is_axis_name, reason = self.check_axis_name(x, return_reason=True)
if not (is_number or is_axis_name):
raise ValueError(
"Invalid axis identifier: {}\n{}".format(x, reason)
)
if is_number:
x = AnonymousAxis(x)
self.identifiers.add(x)
if is_number:
self.has_non_unitary_anonymous_axes = True
if bracket_group is None:
self.composition.append([x])
else:
is_number = str.isdecimal(x)
if is_number and int(x) == 1:
# handling the case of anonymous axis of length 1
if bracket_group is None:
self.composition.append([])
else:
pass # no need to think about 1s inside parenthesis
return
is_axis_name, reason = self.check_axis_name(x, return_reason=True)
if not (is_number or is_axis_name):
raise ValueError(
"Invalid axis identifier: {}\n{}".format(x, reason)
)
if is_number:
x = AnonymousAxis(x)
self.identifiers.add(x)
if is_number:
self.has_non_unitary_anonymous_axes = True
if bracket_group is None:
self.composition.append([x])
else:
bracket_group.append(x)
bracket_group.append(x)
current_identifier = None
for char in expression:
@ -85,7 +89,7 @@ class ParsedExpression:
raise ValueError("Brackets are not balanced")
self.composition.append(bracket_group)
bracket_group = None
elif str.isalnum(char) or char in ["_", _ellipsis]:
elif str.isalnum(char) or char == "_":
if current_identifier is None:
current_identifier = char
else:
@ -143,3 +147,8 @@ class ParsedExpression:
return result
else:
return result[0]
def __repr__(self) -> str:
return "{name}({composition})".format(
name=self.__class__.__name__, composition=self.composition
)

21
xautodl/xlayers/super_module.py
View File

@ -21,6 +21,8 @@ from .super_utils import ShapeContainer
BEST_DIR_KEY = "best_model_dir"
BEST_NAME_KEY = "best_model_name"
BEST_SCORE_KEY = "best_model_score"
ENABLE_CANDIDATE = 0
DISABLE_CANDIDATE = 1
class SuperModule(abc.ABC, nn.Module):
@ -32,6 +34,7 @@ class SuperModule(abc.ABC, nn.Module):
self._abstract_child = None
self._verbose = False
self._meta_info = {}
self._candidate_mode = DISABLE_CANDIDATE
def set_super_run_type(self, super_run_type):
def _reset_super_run(m):
@ -65,6 +68,20 @@ class SuperModule(abc.ABC, nn.Module):
)
self._abstract_child = abstract_child
def enable_candidate(self):
def _enable_candidate(m):
if isinstance(m, SuperModule):
m._candidate_mode = ENABLE_CANDIDATE
self.apply(_enable_candidate)
def disable_candidate(self):
def _disable_candidate(m):
if isinstance(m, SuperModule):
m._candidate_mode = DISABLE_CANDIDATE
self.apply(_disable_candidate)
def get_w_container(self):
container = TensorContainer()
for name, param in self.named_parameters():
@ -191,9 +208,11 @@ class SuperModule(abc.ABC, nn.Module):
if self.super_run_type == SuperRunMode.FullModel:
outputs = self.forward_raw(*inputs)
elif self.super_run_type == SuperRunMode.Candidate:
if self._candidate_mode == DISABLE_CANDIDATE:
raise ValueError("candidate mode is disabled")
outputs = self.forward_candidate(*inputs)
else:
raise ModeError(
raise ValueError(
"Unknown Super Model Run Mode: {:}".format(self.super_run_type)
)
if self.verbose:

145
xautodl/xlayers/super_rearrange.py
View File

@ -8,10 +8,14 @@ import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
import itertools
import functools
from collections import OrderedDict
from typing import Optional, Callable
from xautodl import spaces
from .misc_utils import ParsedExpression
from .misc_utils import ParsedExpression, AnonymousAxis
from .super_module import SuperModule
from .super_module import IntSpaceType
from .super_module import BoolSpaceType
@ -31,11 +35,133 @@ class SuperReArrange(SuperModule):
left, right = pattern.split("->")
left = ParsedExpression(left)
right = ParsedExpression(right)
difference = set.symmetric_difference(left.identifiers, right.identifiers)
if difference:
raise ValueError(
"Identifiers only on one side of expression (should be on both): {}".format(
difference
)
)
import pdb
# parsing all dimensions to find out lengths
axis_name2known_length = OrderedDict()
for composite_axis in left.composition:
for axis_name in composite_axis:
if isinstance(axis_name, AnonymousAxis):
axis_name2known_length[axis_name] = axis_name.value
else:
axis_name2known_length[axis_name] = None
for axis_name in right.identifiers:
if axis_name not in axis_name2known_length:
if isinstance(axis_name, AnonymousAxis):
axis_name2known_length[axis_name] = axis_name.value
else:
axis_name2known_length[axis_name] = None
pdb.set_trace()
print("-")
axis_name2position = {
name: position for position, name in enumerate(axis_name2known_length)
}
for elementary_axis, axis_length in axes_lengths:
if not ParsedExpression.check_axis_name(elementary_axis):
raise ValueError("Invalid name for an axis", elementary_axis)
if elementary_axis not in axis_name2known_length:
raise ValueError(
"Axis {} is not used in transform".format(elementary_axis)
)
axis_name2known_length[elementary_axis] = axis_length
input_composite_axes = []
# some of shapes will be inferred later - all information is prepared for faster inference
for composite_axis in left.composition:
known = {
axis
for axis in composite_axis
if axis_name2known_length[axis] is not None
}
unknown = {
axis for axis in composite_axis if axis_name2known_length[axis] is None
}
if len(unknown) > 1:
raise ValueError("Could not infer sizes for {}".format(unknown))
assert len(unknown) + len(known) == len(composite_axis)
input_composite_axes.append(
(
[axis_name2position[axis] for axis in known],
[axis_name2position[axis] for axis in unknown],
)
)
axis_position_after_reduction = {}
for axis_name in itertools.chain(*left.composition):
if axis_name in right.identifiers:
axis_position_after_reduction[axis_name] = len(
axis_position_after_reduction
)
result_axes_grouping = []
for composite_axis in right.composition:
result_axes_grouping.append(
[axis_name2position[axis] for axis in composite_axis]
)
ordered_axis_right = list(itertools.chain(*right.composition))
axes_permutation = tuple(
axis_position_after_reduction[axis]
for axis in ordered_axis_right
if axis in left.identifiers
)
#
self.input_composite_axes = input_composite_axes
self.output_composite_axes = result_axes_grouping
self.elementary_axes_lengths = list(axis_name2known_length.values())
self.axes_permutation = axes_permutation
@functools.lru_cache(maxsize=1024)
def reconstruct_from_shape(self, shape):
if len(shape) != len(self.input_composite_axes):
raise ValueError(
"Expected {} dimensions, got {}".format(
len(self.input_composite_axes), len(shape)
)
)
axes_lengths = list(self.elementary_axes_lengths)
for input_axis, (known_axes, unknown_axes) in enumerate(
self.input_composite_axes
):
length = shape[input_axis]
known_product = 1
for axis in known_axes:
known_product *= axes_lengths[axis]
if len(unknown_axes) == 0:
if (
isinstance(length, int)
and isinstance(known_product, int)
and length != known_product
):
raise ValueError(
"Shape mismatch, {} != {}".format(length, known_product)
)
else:
if (
isinstance(length, int)
and isinstance(known_product, int)
and length % known_product != 0
):
raise ValueError(
"Shape mismatch, can't divide axis of length {} in chunks of {}".format(
length, known_product
)
)
(unknown_axis,) = unknown_axes
axes_lengths[unknown_axis] = length // known_product
# at this point all axes_lengths are computed (either have values or variables, but not Nones)
final_shape = []
for output_axis, grouping in enumerate(self.output_composite_axes):
lengths = [axes_lengths[elementary_axis] for elementary_axis in grouping]
final_shape.append(int(np.prod(lengths)))
axes_reordering = self.axes_permutation
return axes_lengths, axes_reordering, final_shape
@property
def abstract_search_space(self):
@ -46,10 +172,13 @@ class SuperReArrange(SuperModule):
self.forward_raw(input)
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
import pdb
pdb.set_trace()
raise NotImplementedError
init_shape, axes_reordering, final_shape = self.reconstruct_from_shape(
tuple(input.shape)
)
tensor = torch.reshape(input, init_shape)
tensor = tensor.permute(axes_reordering)
tensor = torch.reshape(tensor, final_shape)
return tensor
def extra_repr(self) -> str:
params = repr(self._pattern)

6
xautodl/xmodels/transformers.py
View File

@ -1,8 +1,6 @@
opyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
from __future__ import division
from __future__ import print_function
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
#####################################################
import math
from functools import partial
from typing import Optional, Text, List