wehub-resource-sync
348 lines
11 KiB
Python
348 lines
11 KiB
Python
# Copyright (c) 2023 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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import paddle
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from paddle.distributed.fleet.recompute.recompute_hybrid import recompute_hybrid
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from paddle.distributed.fleet.utils import recompute
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class Layer(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear1 = paddle.nn.Linear(10, 10)
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self.linear2 = paddle.nn.Linear(10, 10)
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self.linear3 = paddle.nn.Linear(10, 10)
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self.silu1 = paddle.nn.Silu()
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self.silu2 = paddle.nn.Silu()
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self.silu3 = paddle.nn.Silu()
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def forward(self, x, y):
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assert type(x) is tuple
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assert len(x) == 2
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o1 = self.silu1(self.linear1(x[0]))
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o2 = self.silu2(self.linear2(x[1]))
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o3 = self.silu3(self.linear3(y))
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o = o1 + o2 + o3
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return o
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class TestPyLayer(unittest.TestCase):
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def test_tuple_input(self):
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layer = Layer()
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x1 = paddle.rand(shape=[10, 10])
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x1.stop_gradient = False
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x2 = paddle.rand(shape=[10, 10])
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x2.stop_gradient = False
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y = paddle.rand(shape=[10, 10])
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y.stop_gradient = False
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o = recompute(layer, (x1, x2), y)
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loss = paddle.mean(o, keepdim=True)
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loss.backward()
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def test_tuple_input_with_non_tensor(self):
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layer = Layer()
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x1 = paddle.rand(shape=[10, 10])
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x1.stop_gradient = False
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y = paddle.rand(shape=[10, 10])
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y.stop_gradient = False
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try:
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o = recompute(layer, (x1, True), y)
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except ValueError:
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pass
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def test_tuple_input_with_different_stop_gradient(self):
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layer = Layer()
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x1 = paddle.rand(shape=[10, 10])
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x1.stop_gradient = False
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x2 = paddle.rand(shape=[10, 10])
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y = paddle.rand(shape=[10, 10])
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y.stop_gradient = False
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try:
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o = recompute(layer, (x1, True), y)
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except ValueError:
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pass
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def test_tuple_input_all_no_gradient(self):
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layer = Layer()
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x1 = paddle.rand(shape=[10, 10])
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x2 = paddle.rand(shape=[10, 10])
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y = paddle.rand(shape=[10, 10])
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y.stop_gradient = False
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o = recompute(layer, (x1, x2), y)
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loss = paddle.mean(o, keepdim=True)
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loss.backward()
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class _MockMpGroup:
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"""Minimal mock of a model-parallel group for single-GPU unit tests.
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Only used when partition=False, so nranks/rank are never actually accessed.
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"""
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nranks = 1
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rank = 0
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class TestRecomputeHybridProtectTensors(unittest.TestCase):
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"""Tests for recompute_hybrid() with various tensor inputs.
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Uses a MockMpGroup so no distributed init is needed,
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and partition=False so mp_group is never actually invoked.
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"""
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@classmethod
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def setUpClass(cls):
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if not paddle.is_compiled_with_cuda():
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raise unittest.SkipTest("Requires GPU")
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def test_forward_backward_plain_tensor(self):
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"""recompute_hybrid with a plain tensor input completes correctly."""
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linear = paddle.nn.Linear(8, 8)
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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ctx = {'mp_group': _MockMpGroup(), 'offload': False, 'partition': False}
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out = recompute_hybrid(ctx, linear, x)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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def test_forward_backward_multiple_tensors(self):
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"""recompute_hybrid with multiple tensor inputs completes correctly."""
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class TwoInputLayer(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(8, 8)
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def forward(self, x, y):
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return self.linear(x) + self.linear(y)
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layer = TwoInputLayer()
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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y = paddle.rand([4, 8])
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y.stop_gradient = False
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ctx = {'mp_group': _MockMpGroup(), 'offload': False, 'partition': False}
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out = recompute_hybrid(ctx, layer, x, y)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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self.assertIsNotNone(y.grad)
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class TestRecomputeWithClosureTensors(unittest.TestCase):
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"""End-to-end tests for recompute() with closure-captured tensors."""
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# ------------------------------------------------------------------
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# basic: plain function with a closure tensor, no release
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# ------------------------------------------------------------------
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def test_plain_function_with_closure_tensor(self):
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"""recompute() on a plain function that captures a tensor in its
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closure must complete forward and backward correctly."""
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grid = paddle.rand([4, 8])
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def fn(x):
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return x * grid
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(fn, x)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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# ------------------------------------------------------------------
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# basic: no closure at all
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# ------------------------------------------------------------------
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def test_function_without_closure(self):
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"""recompute() on a function that has no closure must not raise."""
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def simple_fn(x):
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return x * 2.0
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(simple_fn, x)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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# ------------------------------------------------------------------
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# basic: closure with only non-tensor values
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# ------------------------------------------------------------------
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def test_function_with_non_tensor_closure(self):
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"""Closure holding only non-tensor values must be handled safely."""
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scale = 3.0
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def fn(x):
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return x * scale
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(fn, x)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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# ------------------------------------------------------------------
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# basic: nn.Layer (uses run_function.forward.__closure__)
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# ------------------------------------------------------------------
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def test_layer_with_closure_in_forward(self):
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"""recompute() on an nn.Layer that captures a tensor in forward's
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closure must complete forward and backward correctly."""
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class ClosureLayer(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(8, 8)
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mask = paddle.ones([4, 8])
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# Define forward as a closure over `mask`
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def _forward_impl(x):
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return self.linear(x) * mask
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self._forward_impl = _forward_impl
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def forward(self, x):
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return self._forward_impl(x)
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layer = ClosureLayer()
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(layer, x)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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# ------------------------------------------------------------------
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# gradient correctness with closure tensor
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# ------------------------------------------------------------------
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def test_gradient_correctness_with_closure_tensor(self):
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"""Gradients computed via recompute (with closure tensor) must match
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those computed without recompute."""
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paddle.seed(42)
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grid = paddle.rand([4, 8])
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linear = paddle.nn.Linear(8, 8)
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def fn(x):
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return linear(x) + grid
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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# reference: no recompute
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out_ref = fn(x)
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loss_ref = out_ref.mean()
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loss_ref.backward()
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grad_ref = x.grad.numpy().copy()
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x.clear_gradient()
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# with recompute
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out_rc = recompute(fn, x)
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loss_rc = out_rc.mean()
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loss_rc.backward()
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np.testing.assert_allclose(x.grad.numpy(), grad_ref, rtol=1e-5)
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def test_non_reentrant_with_closure_tensor(self):
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"""use_reentrant=False path with a closure-captured tensor must
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complete forward and backward correctly."""
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grid = paddle.rand([4, 8])
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def fn(x):
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return x * grid
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(fn, x, use_reentrant=False)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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def test_closure_tensor_preserve_rng_state_false(self):
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"""recompute() with preserve_rng_state=False and a closure tensor must
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execute the else-branch in backward (L441), completing forward and
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backward correctly."""
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grid = paddle.rand([4, 8])
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def fn(x):
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return x * grid
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(fn, x, preserve_rng_state=False)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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class TestRecomputeRegression(unittest.TestCase):
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"""Regression tests for recompute() basic scenarios.
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These cover the plain-tensor, tuple-only-input, and keyword-argument
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code paths and serve as a safety net against regressions regardless of
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the internal protection mechanism used.
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"""
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def test_plain_tensor(self):
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"""recompute() with a plain tensor arg completes forward/backward."""
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linear = paddle.nn.Linear(8, 8)
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(linear, x)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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def test_tuple_only_input(self):
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"""recompute() with a tuple-only tensor arg completes forward/backward."""
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class TupleInputLayer(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(8, 8)
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def forward(self, xy):
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x, y = xy
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return self.linear(x) + self.linear(y)
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layer = TupleInputLayer()
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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y = paddle.rand([4, 8])
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y.stop_gradient = False
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out = recompute(layer, (x, y))
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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self.assertIsNotNone(y.grad)
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def test_with_kwargs(self):
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"""recompute() with extra keyword arguments completes forward/backward."""
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class ScaledLinear(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(8, 8)
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def forward(self, x, scale=1.0):
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return self.linear(x) * scale
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layer = ScaledLinear()
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x = paddle.rand([4, 8])
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x.stop_gradient = False
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out = recompute(layer, x, scale=2.0)
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out.mean().backward()
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self.assertIsNotNone(x.grad)
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if __name__ == '__main__':
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unittest.main()
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