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import pickle
from collections import namedtuple
import numpy
import pytest
from einops import rearrange, reduce, EinopsError
from einops.tests import collect_test_backends, is_backend_tested, FLOAT_REDUCTIONS as REDUCTIONS
__author__ = "Alex Rogozhnikov"
testcase = namedtuple("testcase", ["pattern", "axes_lengths", "input_shape", "wrong_shapes"])
rearrangement_patterns = [
testcase(
"b c h w -> b (c h w)",
dict(c=20),
(10, 20, 30, 40),
[(), (10,), (10, 10, 10), (10, 21, 30, 40), [1, 20, 1, 1, 1]],
),
testcase(
"b c (h1 h2) (w1 w2) -> b (c h2 w2) h1 w1",
dict(h2=2, w2=2),
(10, 20, 30, 40),
[(), (1, 1, 1, 1), (1, 10, 3), ()],
),
testcase(
"b ... c -> c b ...",
dict(b=10),
(10, 20, 30),
[(), (10,), (5, 10)],
),
]
def test_rearrange_imperative():
for backend in collect_test_backends(symbolic=False, layers=True):
print("Test layer for ", backend.framework_name)
for pattern, axes_lengths, input_shape, wrong_shapes in rearrangement_patterns:
x = numpy.arange(numpy.prod(input_shape), dtype="float32").reshape(input_shape)
result_numpy = rearrange(x, pattern, **axes_lengths)
layer = backend.layers().Rearrange(pattern, **axes_lengths)
for shape in wrong_shapes:
try:
layer(backend.from_numpy(numpy.zeros(shape, dtype="float32")))
except BaseException:
pass
else:
raise AssertionError("Failure expected")
# simple pickling / unpickling
layer2 = pickle.loads(pickle.dumps(layer))
result1 = backend.to_numpy(layer(backend.from_numpy(x)))
result2 = backend.to_numpy(layer2(backend.from_numpy(x)))
assert numpy.allclose(result_numpy, result1)
assert numpy.allclose(result1, result2)
just_sum = backend.layers().Reduce("...->", reduction="sum")
variable = backend.from_numpy(x)
result = just_sum(layer(variable))
result.backward()
assert numpy.allclose(backend.to_numpy(variable.grad), 1)
def test_rearrange_symbolic():
for backend in collect_test_backends(symbolic=True, layers=True):
print("Test layer for ", backend.framework_name)
for pattern, axes_lengths, input_shape, wrong_shapes in rearrangement_patterns:
x = numpy.arange(numpy.prod(input_shape), dtype="float32").reshape(input_shape)
result_numpy = rearrange(x, pattern, **axes_lengths)
layer = backend.layers().Rearrange(pattern, **axes_lengths)
input_shape_of_nones = [None] * len(input_shape)
shapes = [input_shape, input_shape_of_nones]
for shape in shapes:
symbol = backend.create_symbol(shape)
eval_inputs = [(symbol, x)]
result_symbol1 = layer(symbol)
result1 = backend.eval_symbol(result_symbol1, eval_inputs)
assert numpy.allclose(result_numpy, result1)
layer2 = pickle.loads(pickle.dumps(layer))
result_symbol2 = layer2(symbol)
result2 = backend.eval_symbol(result_symbol2, eval_inputs)
assert numpy.allclose(result1, result2)
# now testing back-propagation
just_sum = backend.layers().Reduce("...->", reduction="sum")
result_sum1 = backend.eval_symbol(just_sum(result_symbol1), eval_inputs)
result_sum2 = numpy.sum(x)
assert numpy.allclose(result_sum1, result_sum2)
reduction_patterns = rearrangement_patterns + [
testcase("b c h w -> b ()", dict(b=10), (10, 20, 30, 40), [(10,), (10, 20, 30)]),
testcase("b c (h1 h2) (w1 w2) -> b c h1 w1", dict(h1=15, h2=2, w2=2), (10, 20, 30, 40), [(10, 20, 31, 40)]),
testcase("b ... c -> b", dict(b=10), (10, 20, 30, 40), [(10,), (11, 10)]),
]
def test_reduce_imperative():
for backend in collect_test_backends(symbolic=False, layers=True):
print("Test layer for ", backend.framework_name)
for reduction in REDUCTIONS:
for pattern, axes_lengths, input_shape, wrong_shapes in reduction_patterns:
print(backend, reduction, pattern, axes_lengths, input_shape, wrong_shapes)
x = numpy.arange(1, 1 + numpy.prod(input_shape), dtype="float32").reshape(input_shape)
x /= x.mean()
result_numpy = reduce(x, pattern, reduction, **axes_lengths)
layer = backend.layers().Reduce(pattern, reduction, **axes_lengths)
for shape in wrong_shapes:
try:
layer(backend.from_numpy(numpy.zeros(shape, dtype="float32")))
except BaseException:
pass
else:
raise AssertionError("Failure expected")
# simple pickling / unpickling
layer2 = pickle.loads(pickle.dumps(layer))
result1 = backend.to_numpy(layer(backend.from_numpy(x)))
result2 = backend.to_numpy(layer2(backend.from_numpy(x)))
assert numpy.allclose(result_numpy, result1)
assert numpy.allclose(result1, result2)
just_sum = backend.layers().Reduce("...->", reduction="sum")
variable = backend.from_numpy(x)
result = just_sum(layer(variable))
result.backward()
grad = backend.to_numpy(variable.grad)
if reduction == "sum":
assert numpy.allclose(grad, 1)
if reduction == "mean":
assert numpy.allclose(grad, grad.min())
if reduction in ["max", "min"]:
assert numpy.all(numpy.in1d(grad, [0, 1]))
assert numpy.sum(grad) > 0.5
def test_reduce_symbolic():
for backend in collect_test_backends(symbolic=True, layers=True):
print("Test layer for ", backend.framework_name)
for reduction in REDUCTIONS:
for pattern, axes_lengths, input_shape, wrong_shapes in reduction_patterns:
x = numpy.arange(1, 1 + numpy.prod(input_shape), dtype="float32").reshape(input_shape)
x /= x.mean()
result_numpy = reduce(x, pattern, reduction, **axes_lengths)
layer = backend.layers().Reduce(pattern, reduction, **axes_lengths)
input_shape_of_nones = [None] * len(input_shape)
shapes = [input_shape, input_shape_of_nones]
for shape in shapes:
symbol = backend.create_symbol(shape)
eval_inputs = [(symbol, x)]
result_symbol1 = layer(symbol)
result1 = backend.eval_symbol(result_symbol1, eval_inputs)
assert numpy.allclose(result_numpy, result1)
layer2 = pickle.loads(pickle.dumps(layer))
result_symbol2 = layer2(symbol)
result2 = backend.eval_symbol(result_symbol2, eval_inputs)
assert numpy.allclose(result1, result2)
def create_torch_model(use_reduce=False, add_scripted_layer=False):
if not is_backend_tested("torch"):
pytest.skip()
else:
from torch.nn import Sequential, Conv2d, MaxPool2d, Linear, ReLU
from einops.layers.torch import Rearrange, Reduce, EinMix
import torch.jit
return Sequential(
Conv2d(3, 6, kernel_size=(5, 5)),
Reduce("b c (h h2) (w w2) -> b c h w", "max", h2=2, w2=2) if use_reduce else MaxPool2d(kernel_size=2),
Conv2d(6, 16, kernel_size=(5, 5)),
Reduce("b c (h h2) (w w2) -> b c h w", "max", h2=2, w2=2),
torch.jit.script(Rearrange("b c h w -> b (c h w)"))
if add_scripted_layer
else Rearrange("b c h w -> b (c h w)"),
Linear(16 * 5 * 5, 120),
ReLU(),
Linear(120, 84),
ReLU(),
EinMix("b c1 -> (b c2)", weight_shape="c1 c2", bias_shape="c2", c1=84, c2=84),
EinMix("(b c2) -> b c3", weight_shape="c2 c3", bias_shape="c3", c2=84, c3=84),
Linear(84, 10),
)
def test_torch_layer():
if not is_backend_tested("torch"):
pytest.skip()
else:
# checked that torch present
import torch
import torch.jit
model1 = create_torch_model(use_reduce=True)
model2 = create_torch_model(use_reduce=False)
input = torch.randn([10, 3, 32, 32])
# random models have different predictions
assert not torch.allclose(model1(input), model2(input))
model2.load_state_dict(pickle.loads(pickle.dumps(model1.state_dict())))
assert torch.allclose(model1(input), model2(input))
# tracing (freezing)
model3 = torch.jit.trace(model2, example_inputs=input)
torch.testing.assert_close(model1(input), model3(input), atol=1e-3, rtol=1e-3)
torch.testing.assert_close(model1(input + 1), model3(input + 1), atol=1e-3, rtol=1e-3)
model4 = torch.jit.trace(model2, example_inputs=input)
torch.testing.assert_close(model1(input), model4(input), atol=1e-3, rtol=1e-3)
torch.testing.assert_close(model1(input + 1), model4(input + 1), atol=1e-3, rtol=1e-3)
def test_torch_layers_scripting():
if not is_backend_tested("torch"):
pytest.skip()
else:
import torch
for script_layer in [False, True]:
model1 = create_torch_model(use_reduce=True, add_scripted_layer=script_layer)
model2 = torch.jit.script(model1)
input = torch.randn([10, 3, 32, 32])
torch.testing.assert_close(model1(input), model2(input), atol=1e-3, rtol=1e-3)
def test_keras_layer():
if not is_backend_tested("tensorflow"):
pytest.skip()
else:
import tensorflow as tf
if tf.__version__ < "2.16.":
# current implementation of layers follows new TF interface
pytest.skip()
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D as Conv2d, Dense as Linear, ReLU
from einops.layers.keras import Rearrange, Reduce, EinMix, keras_custom_objects
def create_keras_model():
return Sequential(
[
Conv2d(6, kernel_size=5, input_shape=[32, 32, 3]),
Reduce("b c (h h2) (w w2) -> b c h w", "max", h2=2, w2=2),
Conv2d(16, kernel_size=5),
Reduce("b c (h h2) (w w2) -> b c h w", "max", h2=2, w2=2),
Rearrange("b c h w -> b (c h w)"),
Linear(120),
ReLU(),
Linear(84),
ReLU(),
EinMix("b c1 -> (b c2)", weight_shape="c1 c2", bias_shape="c2", c1=84, c2=84),
EinMix("(b c2) -> b c3", weight_shape="c2 c3", bias_shape="c3", c2=84, c3=84),
Linear(10),
]
)
model1 = create_keras_model()
model2 = create_keras_model()
input = numpy.random.normal(size=[10, 32, 32, 3]).astype("float32")
# two randomly init models should provide different outputs
assert not numpy.allclose(model1.predict_on_batch(input), model2.predict_on_batch(input))
# get some temp filename
tmp_model_filename = "/tmp/einops_tf_model.h5"
# save arch + weights
print("temp_path_keras1", tmp_model_filename)
tf.keras.models.save_model(model1, tmp_model_filename)
model3 = tf.keras.models.load_model(tmp_model_filename, custom_objects=keras_custom_objects)
numpy.testing.assert_allclose(model1.predict_on_batch(input), model3.predict_on_batch(input))
weight_filename = "/tmp/einops_tf_model.weights.h5"
# save arch as json
model4 = tf.keras.models.model_from_json(model1.to_json(), custom_objects=keras_custom_objects)
model1.save_weights(weight_filename)
model4.load_weights(weight_filename)
model2.load_weights(weight_filename)
# check that differently-inialized model receives same weights
numpy.testing.assert_allclose(model1.predict_on_batch(input), model2.predict_on_batch(input))
# ulimate test
# save-load architecture, and then load weights - should return same result
numpy.testing.assert_allclose(model1.predict_on_batch(input), model4.predict_on_batch(input))
def test_flax_layers():
"""
One-off simple tests for Flax layers.
Unfortunately, Flax layers have a different interface from other layers.
"""
if not is_backend_tested("jax"):
pytest.skip()
else:
import jax
import jax.numpy as jnp
import flax
from flax import linen as nn
from einops.layers.flax import EinMix, Reduce, Rearrange
class NN(nn.Module):
@nn.compact
def __call__(self, x):
x = EinMix(
"b (h h2) (w w2) c -> b h w c_out", "h2 w2 c c_out", "c_out", sizes=dict(h2=2, w2=3, c=4, c_out=5)
)(x)
x = Rearrange("b h w c -> b (w h c)", sizes=dict(c=5))(x)
x = Reduce("b hwc -> b", "mean", dict(hwc=2 * 3 * 5))(x)
return x
model = NN()
fixed_input = jnp.ones([10, 2 * 2, 3 * 3, 4])
params = model.init(jax.random.PRNGKey(0), fixed_input)
def eval_at_point(params):
return jnp.linalg.norm(model.apply(params, fixed_input))
vandg = jax.value_and_grad(eval_at_point)
value0 = eval_at_point(params)
value1, grad1 = vandg(params)
assert jnp.allclose(value0, value1)
params2 = jax.tree_map(lambda x1, x2: x1 - x2 * 0.001, params, grad1)
value2 = eval_at_point(params2)
assert value0 >= value2, (value0, value2)
# check serialization
fbytes = flax.serialization.to_bytes(params)
_loaded = flax.serialization.from_bytes(params, fbytes)
def test_einmix_decomposition():
"""
Testing that einmix correctly decomposes into smaller transformations.
"""
from einops.layers._einmix import _EinmixDebugger
mixin1 = _EinmixDebugger(
"a b c d e -> e d c b a",
weight_shape="d a b",
d=2, a=3, b=5,
) # fmt: off
assert mixin1.pre_reshape_pattern is None
assert mixin1.post_reshape_pattern is None
assert mixin1.einsum_pattern == "abcde,dab->edcba"
assert mixin1.saved_weight_shape == [2, 3, 5]
assert mixin1.saved_bias_shape is None
mixin2 = _EinmixDebugger(
"a b c d e -> e d c b a",
weight_shape="d a b",
bias_shape="a b c d e",
a=1, b=2, c=3, d=4, e=5,
) # fmt: off
assert mixin2.pre_reshape_pattern is None
assert mixin2.post_reshape_pattern is None
assert mixin2.einsum_pattern == "abcde,dab->edcba"
assert mixin2.saved_weight_shape == [4, 1, 2]
assert mixin2.saved_bias_shape == [5, 4, 3, 2, 1]
mixin3 = _EinmixDebugger(
"... -> ...",
weight_shape="",
bias_shape="",
) # fmt: off
assert mixin3.pre_reshape_pattern is None
assert mixin3.post_reshape_pattern is None
assert mixin3.einsum_pattern == "...,->..."
assert mixin3.saved_weight_shape == []
assert mixin3.saved_bias_shape == []
mixin4 = _EinmixDebugger(
"b a ... -> b c ...",
weight_shape="b a c",
a=1, b=2, c=3,
) # fmt: off
assert mixin4.pre_reshape_pattern is None
assert mixin4.post_reshape_pattern is None
assert mixin4.einsum_pattern == "ba...,bac->bc..."
assert mixin4.saved_weight_shape == [2, 1, 3]
assert mixin4.saved_bias_shape is None
mixin5 = _EinmixDebugger(
"(b a) ... -> b c (...)",
weight_shape="b a c",
a=1, b=2, c=3,
) # fmt: off
assert mixin5.pre_reshape_pattern == "(b a) ... -> b a ..."
assert mixin5.pre_reshape_lengths == dict(a=1, b=2)
assert mixin5.post_reshape_pattern == "b c ... -> b c (...)"
assert mixin5.einsum_pattern == "ba...,bac->bc..."
assert mixin5.saved_weight_shape == [2, 1, 3]
assert mixin5.saved_bias_shape is None
mixin6 = _EinmixDebugger(
"b ... (a c) -> b ... (a d)",
weight_shape="c d",
bias_shape="a d",
a=1, c=3, d=4,
) # fmt: off
assert mixin6.pre_reshape_pattern == "b ... (a c) -> b ... a c"
assert mixin6.pre_reshape_lengths == dict(a=1, c=3)
assert mixin6.post_reshape_pattern == "b ... a d -> b ... (a d)"
assert mixin6.einsum_pattern == "b...ac,cd->b...ad"
assert mixin6.saved_weight_shape == [3, 4]
assert mixin6.saved_bias_shape == [1, 1, 4] # (b) a d, ellipsis does not participate
mixin7 = _EinmixDebugger(
"a ... (b c) -> a (... d b)",
weight_shape="c d b",
bias_shape="d b",
b=2, c=3, d=4,
) # fmt: off
assert mixin7.pre_reshape_pattern == "a ... (b c) -> a ... b c"
assert mixin7.pre_reshape_lengths == dict(b=2, c=3)
assert mixin7.post_reshape_pattern == "a ... d b -> a (... d b)"
assert mixin7.einsum_pattern == "a...bc,cdb->a...db"
assert mixin7.saved_weight_shape == [3, 4, 2]
assert mixin7.saved_bias_shape == [1, 4, 2] # (a) d b, ellipsis does not participate
def test_einmix_restrictions():
"""
Testing different cases
"""
from einops.layers._einmix import _EinmixDebugger
with pytest.raises(EinopsError):
_EinmixDebugger(
"a b c d e -> e d c b a",
weight_shape="d a b",
d=2, a=3, # missing b
) # fmt: off
with pytest.raises(EinopsError):
_EinmixDebugger(
"a b c d e -> e d c b a",
weight_shape="w a b",
d=2, a=3, b=1 # missing d
) # fmt: off
with pytest.raises(EinopsError):
_EinmixDebugger(
"(...) a -> ... a",
weight_shape="a", a=1, # ellipsis on the left
) # fmt: off
with pytest.raises(EinopsError):
_EinmixDebugger(
"(...) a -> a ...",
weight_shape="a", a=1, # ellipsis on the right side after bias axis
bias_shape='a',
) # fmt: off
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