chore: import upstream snapshot with attribution

test/auto_parallel/reshard_api.py

@@ -0,0 +1,191 @@
+# Copyright (c) 2023 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 os
+
+import numpy as np
+
+import paddle
+import paddle.distributed as dist
+from paddle import nn
+from paddle.distributed import Partial, Replicate, Shard
+
+
+class TestReshardAPI:
+    def __init__(self):
+        self._shape = eval(os.getenv("shape"))
+        self._dtype = os.getenv("dtype")
+        self._seeds = eval(os.getenv("seeds"))
+        self._backend = os.getenv("backend")
+        self._shard = eval(os.getenv("shard"))
+        self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
+
+    def run_test_cases(self):
+        if self._backend == "cpu":
+            paddle.set_device("cpu")
+        self.test_case_p_to_r()
+        self.test_case_r_to_s()
+        self.test_case_forward_and_backward()
+        self.test_case_p_to_s_reshard_grad()
+        self.test_case_p_to_r_reshard_grad()
+
+    def test_case_p_to_r(self):
+        a = paddle.ones(self._shape)
+        in_shard_specs = [None for i in range(len(self._shape))]
+        out_shard_specs = [None for i in range(len(self._shape))]
+
+        input_tensor = dist.shard_tensor(a, self._mesh, [Partial()])
+        output_tensor = dist.reshard(input_tensor, self._mesh, [Replicate()])
+
+        input_tensor = dist.shard_tensor(a, self._mesh, [Replicate()])
+        assert np.equal(output_tensor.shape, input_tensor.shape).all()
+        np.testing.assert_equal(output_tensor._local_value().numpy(), a.numpy())
+
+    def test_case_r_to_s(self):
+        a = paddle.ones(self._shape)
+
+        input_tensor = dist.shard_tensor(a, self._mesh, [Replicate()])
+        output_tensor = dist.reshard(input_tensor, self._mesh, [Shard(0)])
+
+        out_shape = list(self._shape)
+        if out_shape[self._shard] % 2 == 0:
+            out_shape[self._shard] = out_shape[self._shard] // 2
+            np.testing.assert_equal(output_tensor.numpy(), input_tensor.numpy())
+        else:
+            out_shape[self._shard] = (
+                out_shape[self._shard] // 2
+                if dist.get_rank() == 1
+                else out_shape[self._shard] // 2 + 1
+            )
+
+        assert np.equal(output_tensor.shape, input_tensor.shape).all()
+        assert np.equal(output_tensor._local_shape, out_shape).all()
+
+    def test_case_forward_and_backward(self):
+        if self._backend == "cpu":
+            return
+
+        np.random.seed(1901)
+        input_numpy = np.random.random(self._shape).astype("float32")
+        label_numpy = np.random.random(self._shape).astype('float32')
+
+        local_input = paddle.to_tensor(input_numpy)
+        dist_input = dist.shard_tensor(
+            paddle.to_tensor(input_numpy),
+            dist.ProcessMesh([0, 1], dim_names=["x"]),
+            [Replicate()],
+        )
+
+        local_input.stop_gradient = False
+        dist_input.stop_gradient = False
+
+        local_output = local_input + paddle.ones(self._shape)
+        dist_output = dist_input + dist.shard_tensor(
+            paddle.ones(self._shape),
+            dist.ProcessMesh([0, 1], dim_names=["x"]),
+            [Replicate()],
+        )
+        dist_output.stop_gradient = False
+
+        dist_output = dist.reshard(
+            dist_output, dist.ProcessMesh([0, 1], dim_names=["x"]), [Shard(0)]
+        )
+
+        local_label = paddle.to_tensor(label_numpy)
+        dist_label = dist.shard_tensor(
+            paddle.to_tensor(label_numpy),
+            dist.ProcessMesh([0, 1], dim_names=["x"]),
+            [Shard(0)],
+        )
+
+        local_loss_fn = nn.MSELoss()
+        dist_loss_fn = nn.MSELoss()
+
+        local_loss = local_loss_fn(local_output, local_label)
+        dist_loss = dist_loss_fn(dist_output, dist_label)
+
+        np.testing.assert_allclose(
+            local_loss.numpy(), dist_loss.numpy(), rtol=1e-5, atol=1e-5
+        )
+
+        local_loss.backward()
+        dist_loss.backward()
+        np.testing.assert_allclose(
+            local_input.grad.numpy(),
+            dist_input.grad.numpy(),
+            rtol=1e-5,
+            atol=1e-5,
+        )
+
+    def test_case_p_to_s_reshard_grad(self):
+        if self._backend == "cpu":
+            return
+
+        np.random.seed(1901)
+        input_numpy = np.random.random(self._shape).astype("float32")
+        label_numpy = np.random.random(self._shape).astype('float32')
+
+        dist_input = dist.shard_tensor(
+            paddle.to_tensor(input_numpy),
+            dist.ProcessMesh([0, 1], dim_names=["x"]),
+            [Partial()],
+        )
+        dist_input.stop_gradient = False
+        dist_output = dist.reshard(
+            dist_input, dist.ProcessMesh([0, 1], dim_names=["x"]), [Shard(0)]
+        )
+        dist_label = dist.shard_tensor(
+            paddle.to_tensor(label_numpy),
+            dist.ProcessMesh([0, 1], dim_names=["x"]),
+            [Shard(0)],
+        )
+
+        dist_loss_fn = nn.MSELoss()
+        dist_loss = dist_loss_fn(dist_output, dist_label)
+        dist_loss.backward()
+
+        np.testing.assert_equal(dist_input.grad.placements, [dist.Replicate()])
+
+    def test_case_p_to_r_reshard_grad(self):
+        if self._backend == "cpu":
+            return
+
+        np.random.seed(1901)
+        input_numpy = np.random.random(self._shape).astype("float32")
+        label_numpy = np.random.random(self._shape).astype('float32')
+
+        dist_input = dist.shard_tensor(
+            paddle.to_tensor(input_numpy),
+            dist.ProcessMesh([0, 1], dim_names=["x"]),
+            [Partial()],
+        )
+        dist_input.stop_gradient = False
+        dist_output = dist.reshard(
+            dist_input, dist.ProcessMesh([0, 1], dim_names=["x"]), [Replicate()]
+        )
+        dist_label = dist.shard_tensor(
+            paddle.to_tensor(label_numpy),
+            dist.ProcessMesh([0, 1], dim_names=["x"]),
+            [Replicate()],
+        )
+
+        dist_loss_fn = nn.MSELoss()
+        dist_loss = dist_loss_fn(dist_output, dist_label)
+        dist_loss.backward()
+
+        np.testing.assert_equal(dist_input.grad.placements, [dist.Replicate()])
+
+
+if __name__ == '__main__':
+    TestReshardAPI().run_test_cases()