wehub-resource-sync
467 lines
16 KiB
Python
467 lines
16 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import get_places
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from utils import static_guard
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import paddle
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from paddle import base
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from paddle.base import Program, program_guard
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class TestDygraphLayerNormv2(unittest.TestCase):
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def test_dygraph(self):
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for p in get_places():
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shape = [4, 10, 4, 4]
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def compute_v1(x):
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with base.dygraph.guard(p):
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ln = paddle.nn.LayerNorm(shape[1:])
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y = ln(paddle.to_tensor(x))
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return y.numpy()
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def compute_v2(x):
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with base.dygraph.guard(p):
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ln = paddle.nn.LayerNorm(shape[1:])
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y = ln(paddle.to_tensor(x))
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return y.numpy()
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x)
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y2 = compute_v2(x)
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np.testing.assert_allclose(y1, y2, rtol=1e-05)
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def test_eager(self):
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for p in get_places():
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shape = [4, 10, 4, 4]
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def compute_v1(x):
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with base.dygraph.guard(p):
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ln = paddle.nn.LayerNorm(shape[1:])
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x1 = paddle.to_tensor(x)
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x1.stop_gradient = False
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y = ln(x1)
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y.backward()
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return y.numpy(), x1.gradient()
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def compute_v2(x):
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with base.dygraph.guard(p):
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ln = paddle.nn.LayerNorm(shape[1:])
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x1 = paddle.to_tensor(x)
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x1.stop_gradient = False
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y = ln(x1)
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y.backward()
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return y.numpy(), x1.gradient()
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x = np.random.randn(*shape).astype("float32")
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y1, g1 = compute_v1(x)
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y2, g2 = compute_v2(x)
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np.testing.assert_allclose(y1, y2, rtol=1e-05)
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np.testing.assert_allclose(g1, g2, rtol=1e-05)
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def test_static(self):
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paddle.enable_static()
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for p in get_places():
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exe = base.Executor(p)
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shape = [4, 10, 16, 16]
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def compute_v1(x_np):
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with program_guard(Program(), Program()):
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ln = paddle.nn.LayerNorm(shape[1:])
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x = paddle.static.data(
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name='x', shape=x_np.shape, dtype=x_np.dtype
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)
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y = ln(x)
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exe.run(base.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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def compute_v2(x_np):
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with program_guard(Program(), Program()):
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ln = paddle.nn.LayerNorm(shape[1:])
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x = paddle.static.data(
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name='x', shape=x_np.shape, dtype=x_np.dtype
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)
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y = ln(x)
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exe.run(base.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x)
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y2 = compute_v2(x)
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np.testing.assert_allclose(y1, y2, rtol=1e-05)
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class TestLayerNormFunction(unittest.TestCase):
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def test_dygraph(self):
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for p in get_places():
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shape = [4, 10, 4, 4]
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def compute_v0(x):
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with base.dygraph.guard(p):
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ln = paddle.nn.LayerNorm(shape[1:])
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y = ln(paddle.to_tensor(x))
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return y.numpy()
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def compute_v1(x):
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with base.dygraph.guard(p):
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x = paddle.to_tensor(x)
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y = paddle.nn.functional.layer_norm(x, shape[1:])
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return y.numpy()
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def compute_v2(x):
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with base.dygraph.guard(p):
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x = paddle.to_tensor(x)
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y = paddle.nn.functional.layer_norm(x, tuple(shape[1:]))
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return y.numpy()
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def compute_v3(x):
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with base.dygraph.guard(p):
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ln = paddle.nn.LayerNorm(shape[-1])
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y = ln(paddle.to_tensor(x))
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return y.numpy()
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def compute_v4(x):
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with base.dygraph.guard(p):
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x = paddle.to_tensor(x)
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y = paddle.nn.functional.layer_norm(x, shape[-1])
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return y.numpy()
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x = np.random.randn(*shape).astype("float32")
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y0 = compute_v0(x)
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y1 = compute_v1(x)
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y2 = compute_v2(x)
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np.testing.assert_allclose(y0, y1, rtol=1e-05)
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np.testing.assert_allclose(y0, y2, rtol=1e-05)
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y3 = compute_v3(x)
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y4 = compute_v4(x)
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np.testing.assert_allclose(y3, y4, rtol=1e-05)
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self.assertRaises(
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ValueError,
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paddle.nn.functional.layer_norm,
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x=x,
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normalized_shape=1.0,
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)
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class TestLayerNormParamDygraph(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.normalized_shape = [6]
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self.x_shape = [2, 4, 4, 6]
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self.places = get_places()
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def _run_test_on_places(self, test_func):
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"""Helper to run the test function on all places."""
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for p in self.places:
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with base.dygraph.guard(p):
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test_func(p)
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def test_elementwise_affine_false(self):
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"""test that when elementwise_affine=False, weight and bias parameters are not created."""
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def run_test(p):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape, elementwise_affine=False
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)
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assert layer.weight is None
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assert layer.bias is None
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x_tensor = paddle.randn(self.x_shape)
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out = layer(x_tensor)
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assert out.shape == self.x_shape
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self._run_test_on_places(run_test)
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def test_elementwise_affine_true(self):
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"""test that when elementwise_affine=True and attr=None, parameters are created with default initialization."""
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def run_test(p):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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)
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assert layer.weight is not None
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assert layer.bias is not None
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expected_weight = paddle.ones(self.normalized_shape)
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expected_bias = paddle.zeros(self.normalized_shape)
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np.testing.assert_allclose(
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layer.weight.numpy(), expected_weight.numpy()
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)
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np.testing.assert_allclose(
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layer.bias.numpy(), expected_bias.numpy()
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)
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self._run_test_on_places(run_test)
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def test_bias_false(self):
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"""test that when bias=False, the bias parameter is disabled even if elementwise_affine=True."""
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def run_test(p):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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bias=False,
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)
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assert layer.weight is not None
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assert layer.bias is None
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self._run_test_on_places(run_test)
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def test_weight_and_bias_false(self):
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"""test that when weight_attr=False and bias_attr=False, both parameters are disabled."""
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def run_test(p):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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weight_attr=False,
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bias_attr=False,
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)
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assert layer.weight is None
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assert layer.bias is None
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self._run_test_on_places(run_test)
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def test_alias(self):
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"""test parameter alias epsilon/eps"""
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def run_test(p):
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layer_epsilon = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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epsilon=1e-5,
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)
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layer_eps = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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eps=1e-5,
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)
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x_tensor = paddle.randn(self.x_shape)
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out_epsilon = layer_epsilon(x_tensor)
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out_eps = layer_eps(x_tensor)
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np.testing.assert_array_equal(out_epsilon.numpy(), out_eps.numpy())
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self._run_test_on_places(run_test)
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def test_errors(self):
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"""test for errors."""
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def run_test(p):
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with self.assertRaises(ValueError):
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layer_norm = paddle.nn.LayerNorm(self.normalized_shape)
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x1 = np.random.random([3, *self.normalized_shape]).astype(
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'float32'
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)
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layer_norm(x1)
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layer_norm = paddle.nn.LayerNorm(self.normalized_shape, 1e-5, False)
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self.assertIsNone(layer_norm.weight)
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self.assertIsNone(layer_norm.bias)
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layer_norm = paddle.nn.LayerNorm(
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self.normalized_shape, 1e-5, True, False, None, paddle.float32
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)
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self.assertIsNotNone(layer_norm.weight)
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self.assertIsNone(layer_norm.bias)
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self._run_test_on_places(run_test)
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class TestLayerNormParamStatic(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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self.normalized_shape = [6]
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self.x_shape = [2, 4, 4, 6]
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self.places = get_places()
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def test_static_elementwise_affine_false(self):
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"""test elementwise_affine=False in static graph mode."""
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for p in self.places:
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with static_guard():
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=False,
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)
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x = paddle.static.data(
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name='x', shape=self.x_shape, dtype='float32'
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)
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out = layer(x)
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exe = base.Executor(p)
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exe.run(start)
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input_np = np.random.randn(*self.x_shape).astype('float32')
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result = exe.run(main, feed={'x': input_np}, fetch_list=[out])[
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0
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]
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assert result.shape == tuple(self.x_shape)
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def test_static_elementwise_affine_true(self):
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"""test elementwise_affine=True with default init in static graph mode."""
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for p in self.places:
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with static_guard():
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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)
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exe = base.Executor(p)
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exe.run(start)
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weight_np, bias_np = exe.run(
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main, fetch_list=[layer.weight, layer.bias]
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)
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assert weight_np is not None
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assert bias_np is not None
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expected_weight = np.ones(self.normalized_shape)
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expected_bias = np.zeros(self.normalized_shape)
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np.testing.assert_allclose(weight_np, expected_weight)
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np.testing.assert_allclose(bias_np, expected_bias)
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def test_static_bias_false(self):
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"""test bias=False in static graph mode."""
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for p in self.places:
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with static_guard():
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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bias=False,
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)
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assert layer.bias is None
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exe = base.Executor(p)
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exe.run(start)
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weight_np = exe.run(main, fetch_list=[layer.weight])[0]
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assert weight_np is not None
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assert weight_np.shape == tuple(self.normalized_shape)
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def test_static_weight_and_bias_false(self):
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"""test weight_attr=False and bias_attr=False in static graph mode."""
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for p in self.places:
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with static_guard():
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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layer = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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weight_attr=False,
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bias_attr=False,
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)
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assert layer.weight is None
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assert layer.bias is None
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def test_static_alias(self):
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"""test parameter alias epsilon/eps in static graph mode."""
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for p in self.places:
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with static_guard():
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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layer_epsilon = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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epsilon=1e-5,
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)
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layer_eps = paddle.nn.LayerNorm(
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normalized_shape=self.normalized_shape,
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elementwise_affine=True,
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eps=1e-5,
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)
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x = paddle.static.data(
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name='x', shape=self.x_shape, dtype='float32'
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)
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out_epsilon = layer_epsilon(x)
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out_eps = layer_eps(x)
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exe = base.Executor(p)
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exe.run(start)
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input_np = np.random.randn(*self.x_shape).astype('float32')
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out_eps_val, out_epsilon_val = exe.run(
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main,
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feed={'x': input_np},
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fetch_list=[out_eps, out_epsilon],
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)
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np.testing.assert_array_equal(out_epsilon_val, out_eps_val)
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def test_static_errors(self):
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"""test errors in static graph mode."""
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for p in self.places:
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with static_guard():
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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layer_norm = paddle.nn.LayerNorm(
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self.normalized_shape, 1e-5, False
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)
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self.assertIsNone(layer_norm.weight)
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self.assertIsNone(layer_norm.bias)
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layer_norm = paddle.nn.LayerNorm(
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self.normalized_shape,
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1e-5,
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True,
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False,
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None,
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paddle.float32,
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)
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self.assertIsNotNone(layer_norm.weight)
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self.assertIsNone(layer_norm.bias)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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