wehub-resource-sync
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
# Copyright (c) 2024 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 paddle
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import paddle.distributed as dist
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from paddle.distributed.auto_parallel.api import (
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dtensor_from_local,
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dtensor_to_local,
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)
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class TestLocalViewCompute:
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def __init__(self):
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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def run_test_cases(self):
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self.test_local_view_compute()
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def masked_lm_loss_func(self, pred, label, ignored_idx=-100):
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pred_sub = pred[:, 0:1] # shape [B,1]
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label_float = paddle.cast(label, 'float32') # shape [B,1]
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raw_loss = paddle.abs(pred_sub - label_float)
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lossmask = label != ignored_idx
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lossmask_ = lossmask.reshape([-1]).cast('float32')
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raw_loss_flat = raw_loss.reshape([-1]).cast('float32')
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masked_lm_loss_sum = paddle.sum(raw_loss_flat * lossmask_)
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valid_count = paddle.sum(lossmask_)
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loss = masked_lm_loss_sum / (valid_count + 1e-8)
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return loss
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def local_view_compute(self, local_pred, local_label):
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# do not use dist.shard_tensor here
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local_pred = local_pred + 1
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local_loss = self.masked_lm_loss_func(
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local_pred, local_label, ignored_idx=-100
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)
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return local_loss
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def test_local_view_compute(self):
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dist.init_parallel_env()
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cur_rank = dist.get_rank()
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# prepare data and label for mask_lm_loss
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if cur_rank == 0:
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pred = paddle.to_tensor([[1.0, 2.0], [4.0, 4.0]], dtype='float32')
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label = paddle.to_tensor([[1], [3]], dtype='int64')
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elif cur_rank == 1:
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pred = paddle.to_tensor([[2.0, 2.0], [7.0, 8.0]], dtype='float32')
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label = paddle.to_tensor([[2], [-100]], dtype='int64')
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local_result = self.local_view_compute(pred.clone(), label.clone())
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dist_pred = dist.shard_tensor(pred, self._mesh, [dist.Replicate()])
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dist_label = dist.shard_tensor(label, self._mesh, [dist.Replicate()])
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local_pred = dtensor_to_local(
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dist_pred, dist_pred.process_mesh, dist_pred.placements
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)
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local_label = dtensor_to_local(
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dist_label, dist_label.process_mesh, dist_label.placements
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)
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local_pred = local_pred + 1
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local_loss = self.masked_lm_loss_func(
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local_pred, local_label, ignored_idx=-100
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)
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assert local_result == local_loss, "local_result != local_loss"
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tensor_list = []
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dist.all_gather(tensor_list, local_loss)
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loss_sum = paddle.sum(paddle.stack(tensor_list))
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dist_loss = dtensor_from_local(
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local_loss, self._mesh, [dist.Partial(dist.ReduceType.kRedSum)]
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)
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assert loss_sum == dist_loss, "loss_sum != dist_loss"
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if __name__ == '__main__':
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TestLocalViewCompute().run_test_cases()
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