chore: import upstream snapshot with attribution

test/xpu/parallel_dygraph_gradient_check.py

@@ -0,0 +1,142 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+import paddle
+import paddle.distributed as dist
+from paddle.nn import Linear
+
+paddle.seed(1024)
+np.random.seed(2021)
+
+batch = 5
+in_dim = 10
+out_dim = 20
+
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self, train_id):
+        super().__init__()
+        self.w1 = self.create_parameter(
+            shape=[in_dim, out_dim], dtype="float32"
+        )
+        self.w2 = self.create_parameter(
+            shape=[in_dim, out_dim], dtype="float32"
+        )
+        self.share_net = Linear(out_dim, 10)
+
+        self.unused_param = self.create_parameter(
+            shape=[out_dim, in_dim], dtype="float64"
+        )
+
+        # just for test sync_params_buffers
+        self.register_buffer("queue", paddle.randn([10, 5]))
+        self.queue = paddle.nn.functional.normalize(self.queue, axis=0)
+        self.register_buffer("queue_ptr", paddle.zeros([1], 'int64'))
+
+        self.trainer_id = train_id
+
+    def forward(self, x):
+        is_use = (
+            paddle.equal_all(x, paddle.ones(shape=(batch, in_dim))).item()
+            and self.trainer_id == 1
+        )
+
+        if is_use:
+            tmp = paddle.matmul(x, self.w1)
+        else:
+            tmp = paddle.matmul(x, self.w2)
+
+        return self.share_net(tmp)
+
+
+class TestDistTraining(unittest.TestCase):
+    def test_multiple_xpus(self):
+        dist.init_parallel_env()
+        self.trainer_id = dist.get_rank()
+
+        model_a = SimpleNet(self.trainer_id)
+        model_b = SimpleNet(self.trainer_id)
+
+        state_dict = model_a.state_dict()
+        model_b.set_state_dict(state_dict)
+
+        model_a = paddle.DataParallel(model_a, find_unused_parameters=True)
+        model_b = paddle.DataParallel(model_b, find_unused_parameters=True)
+
+        ones_input = paddle.ones(shape=(batch, in_dim))
+        ones_input.stop_gradient = True
+
+        w1_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
+        w2_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
+
+        for step_id in range(5):
+            random_input = paddle.rand(shape=(batch, in_dim))
+            random_input.stop_gradient = True
+
+            if step_id % 2 == 0:
+                out_a = model_a(random_input)
+                out_b = model_b(random_input)
+            else:
+                out_a = model_a(ones_input)
+                out_b = model_b(ones_input)
+
+            out_a.sum().backward()
+            out_b.sum().backward()
+
+            self.check_gradient(model_a.parameters())
+            self.check_gradient(model_b.parameters())
+
+            # test acc gradient
+            w1_grad_sum = self.check_acc(
+                model_a._layers.w1.grad, w1_grad_sum, model_b._layers.w1.grad
+            )
+            w2_grad_sum = self.check_acc(
+                model_a._layers.w2.grad, w2_grad_sum, model_b._layers.w2.grad
+            )
+
+            model_a.clear_gradients()
+
+    def check_acc(self, grad, grad_sum, acc_grad):
+        if grad is not None:
+            grad_sum = grad_sum + grad.numpy(False)
+            acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
+            np.testing.assert_allclose(grad_sum, acc_grad, rtol=1e-6)
+        return grad_sum
+
+    def print_trainer_0(self, *args):
+        if self.trainer_id == 0:
+            print(*args)
+
+    def broadcast_param(self, param, root):
+        paddle.distributed.broadcast(param, root)
+        return param
+
+    def check_gradient(self, params):
+        other_param = []
+        for param in params:
+            if param.trainable and (param._grad_ivar() is not None):
+                grad = param._grad_ivar()
+                other_grad = self.broadcast_param(grad.clone(), root=1)
+                if self.trainer_id == 0:
+                    np.testing.assert_allclose(
+                        other_grad.numpy(False), grad.numpy(False)
+                    )
+
+
+if __name__ == '__main__':
+    unittest.main()