wehub-resource-sync
220 lines
8.5 KiB
Python
220 lines
8.5 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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from collections import OrderedDict
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from paddle.distributed.auto_parallel.static.dist_attribute import (
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DistTensorSpec,
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TensorDistAttr,
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)
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from paddle.distributed.fleet import auto
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from paddle.framework import core
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class TestEmbeddingSPMDRule(unittest.TestCase):
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def setUp(self):
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self.rule1 = core.get_phi_spmd_rule("lookup_table_v2")
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def test_embedding_infer_forward(self):
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# forward setup
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x_shape = [4, 1024] # [B,S]
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table_shape = [512, 768] # [V,H]
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process_mesh = auto.ProcessMesh(mesh=[[0, 1, 2, 3], [4, 5, 6, 7]])
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x_tensor_dist_attr = TensorDistAttr()
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x_tensor_dist_attr.process_mesh = process_mesh
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self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
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table_tensor_dist_attr = TensorDistAttr()
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table_tensor_dist_attr.process_mesh = process_mesh
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self.table_dist_tensor_spec = DistTensorSpec(
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table_shape, table_tensor_dist_attr
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)
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self.attrs = OrderedDict([('padding_idx', -1), ('sparse', False)])
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# data parallel
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self.x_dist_tensor_spec.set_dims_mapping([1, -1])
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self.table_dist_tensor_spec.set_dims_mapping([-1, -1])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec,
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self.table_dist_tensor_spec,
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self.attrs['padding_idx'],
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self.attrs['sparse'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(result_dist_attrs), 2)
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self.assertEqual(len(inferred_input_dist_attrs), 2)
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self.assertEqual(len(inferred_output_dist_attrs), 1)
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1]
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)
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# table col-wise parallel & dp
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self.x_dist_tensor_spec.set_dims_mapping([1, -1])
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self.table_dist_tensor_spec.set_dims_mapping([-1, 0])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec,
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self.table_dist_tensor_spec,
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self.attrs['padding_idx'],
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self.attrs['sparse'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, 0])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, -1, 0])
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# table row-wise parallel & dp
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self.x_dist_tensor_spec.set_dims_mapping([1, -1])
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self.table_dist_tensor_spec.set_dims_mapping([0, -1])
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec,
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self.table_dist_tensor_spec,
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self.attrs['padding_idx'],
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self.attrs['sparse'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [0, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1]
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)
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self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True)
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self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {0})
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# table row-wise parallel & padding_idx
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self.x_dist_tensor_spec.set_dims_mapping([1, -1])
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self.table_dist_tensor_spec.set_dims_mapping([0, -1])
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self.attrs['padding_idx'] = 128
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with self.assertRaises(ValueError):
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result_dist_attrs = self.rule1.infer_forward(
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[self.x_dist_tensor_spec, self.table_dist_tensor_spec],
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self.attrs,
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)
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# table row-wise parallel & sparse
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self.x_dist_tensor_spec.set_dims_mapping([1, -1])
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self.table_dist_tensor_spec.set_dims_mapping([0, -1])
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self.attrs['padding_idx'] = -1
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self.attrs['sparse'] = True
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with self.assertRaises(ValueError):
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result_dist_attrs = self.rule1.infer_forward(
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self.x_dist_tensor_spec,
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self.table_dist_tensor_spec,
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self.attrs['padding_idx'],
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self.attrs['sparse'],
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)
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def test_embedding_infer_backward(self):
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# backward setup
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process_mesh = auto.ProcessMesh(mesh=[[0, 1, 2, 3], [4, 5, 6, 7]])
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x_shape = [4, 1024] # [B,S]
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table_shape = [512, 768] # [V,H]
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x_tensor_dist_attr = TensorDistAttr()
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x_tensor_dist_attr.process_mesh = (
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process_mesh # not set the dims mapping is ok.
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)
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self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
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table_tensor_dist_attr = TensorDistAttr()
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table_tensor_dist_attr.process_mesh = (
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process_mesh # not set the dims mapping is ok.
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)
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self.table_dist_tensor_spec = DistTensorSpec(
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table_shape, table_tensor_dist_attr
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)
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out_shape = [4, 1024, 768] # [B,S, H]
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out_tensor_dist_attr = TensorDistAttr()
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out_tensor_dist_attr.process_mesh = process_mesh
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self.out_dist_tensor_spec = DistTensorSpec(
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out_shape, out_tensor_dist_attr
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)
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self.attrs = OrderedDict([('padding_idx', -1), ('sparse', False)])
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# data parallel
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self.out_dist_tensor_spec.set_dims_mapping([1, -1, -1])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.table_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['padding_idx'],
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self.attrs['sparse'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(result_dist_attrs), 2)
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self.assertEqual(len(inferred_input_dist_attrs), 2)
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self.assertEqual(len(inferred_output_dist_attrs), 1)
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1])
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self.assertEqual(
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inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1]
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)
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# table col-wise parallel & dp
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self.out_dist_tensor_spec.set_dims_mapping([-1, 0, 1])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.table_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['padding_idx'],
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self.attrs['sparse'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, 0])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, 1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1, 0, 1])
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# sharded on multiple broadcast axes
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self.out_dist_tensor_spec.set_dims_mapping([1, 0, -1])
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result_dist_attrs = self.rule1.infer_backward(
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self.x_dist_tensor_spec,
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self.table_dist_tensor_spec,
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self.out_dist_tensor_spec,
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self.attrs['padding_idx'],
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self.attrs['sparse'],
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, 0])
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self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, 0, -1])
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# table row-wise parallel
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# skipped
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if __name__ == "__main__":
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unittest.main()
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