# Copyright (c) 2024 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 from collections import OrderedDict from paddle.distributed.auto_parallel.static.dist_attribute import ( DistTensorSpec, TensorDistAttr, ) from paddle.distributed.fleet import auto from paddle.framework import core class TestEmbeddingSPMDRule(unittest.TestCase): def setUp(self): self.rule = core.get_phi_spmd_rule("c_embedding") def test_c_embedding_infer_forward(self): # forward setup table_shape = [512, 768] # [V,H] x_shape = [4, 1024] # [B,S] process_mesh = auto.ProcessMesh(mesh=[[0, 1, 2, 3], [4, 5, 6, 7]]) table_tensor_dist_attr = TensorDistAttr() table_tensor_dist_attr.process_mesh = process_mesh self.table_dist_tensor_spec = DistTensorSpec( table_shape, table_tensor_dist_attr ) x_tensor_dist_attr = TensorDistAttr() x_tensor_dist_attr.process_mesh = process_mesh self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr) self.attrs = OrderedDict([('start_index', 0), ('vocab_size', -1)]) # data parallel self.table_dist_tensor_spec.set_dims_mapping([-1, -1]) self.x_dist_tensor_spec.set_dims_mapping([1, -1]) result_dist_attrs = self.rule.infer_forward( self.table_dist_tensor_spec, self.x_dist_tensor_spec, self.attrs['start_index'], self.attrs['vocab_size'], ) inferred_input_dist_attrs = result_dist_attrs[0] inferred_output_dist_attrs = result_dist_attrs[1] self.assertEqual(len(result_dist_attrs), 2) self.assertEqual(len(inferred_input_dist_attrs), 2) self.assertEqual(len(inferred_output_dist_attrs), 1) self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1]) self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [1, -1]) self.assertEqual( inferred_output_dist_attrs[0].dims_mapping, [1, -1, -1] ) # table row-wise parallel self.table_dist_tensor_spec.set_dims_mapping([1, -1]) self.x_dist_tensor_spec.set_dims_mapping([-1, -1]) result_dist_attrs = self.rule.infer_forward( self.table_dist_tensor_spec, self.x_dist_tensor_spec, self.attrs['start_index'], self.attrs['vocab_size'], ) inferred_input_dist_attrs = result_dist_attrs[0] inferred_output_dist_attrs = result_dist_attrs[1] self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1]) self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1]) self.assertEqual( inferred_output_dist_attrs[0].dims_mapping, [-1, -1, -1] ) self.assertEqual(inferred_output_dist_attrs[0]._is_partial(), True) self.assertEqual(inferred_output_dist_attrs[0]._partial_dims(), {1}) def test_c_embedding_infer_backward(self): # backward setup process_mesh = auto.ProcessMesh(mesh=[[0, 1, 2, 3], [4, 5, 6, 7]]) table_shape = [512, 768] # [V,H] x_shape = [4, 1024] # [B,S] table_tensor_dist_attr = TensorDistAttr() table_tensor_dist_attr.process_mesh = process_mesh self.table_dist_tensor_spec = DistTensorSpec( table_shape, table_tensor_dist_attr ) x_tensor_dist_attr = TensorDistAttr() x_tensor_dist_attr.process_mesh = process_mesh self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr) out_shape = [4, 1024, 768] # [B,S,H] out_tensor_dist_attr = TensorDistAttr() out_tensor_dist_attr.process_mesh = process_mesh self.out_dist_tensor_spec = DistTensorSpec( out_shape, out_tensor_dist_attr ) self.attrs = OrderedDict([('start_index', 0), ('vocab_size', -1)]) # table row-wise parallel self.table_dist_tensor_spec.set_dims_mapping([1, -1]) self.x_dist_tensor_spec.set_dims_mapping([-1, -1]) self.out_dist_tensor_spec.set_dims_mapping([-1, -1, -1]) result_dist_attrs = self.rule.infer_backward( self.table_dist_tensor_spec, self.x_dist_tensor_spec, self.out_dist_tensor_spec, self.attrs['start_index'], self.attrs['vocab_size'], ) inferred_input_dist_attrs = result_dist_attrs[0] inferred_output_dist_attrs = result_dist_attrs[1] self.assertEqual(len(result_dist_attrs), 2) self.assertEqual(len(inferred_input_dist_attrs), 3) self.assertEqual(len(inferred_output_dist_attrs), 1) self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1]) self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [-1, -1]) self.assertEqual( inferred_input_dist_attrs[2].dims_mapping, [-1, -1, -1] ) self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, -1]) # data parallel self.x_dist_tensor_spec.set_dims_mapping([0, -1]) self.out_dist_tensor_spec.set_dims_mapping([0, -1, -1]) result_dist_attrs = self.rule.infer_backward( self.table_dist_tensor_spec, self.x_dist_tensor_spec, self.out_dist_tensor_spec, self.attrs['start_index'], self.attrs['vocab_size'], ) inferred_input_dist_attrs = result_dist_attrs[0] inferred_output_dist_attrs = result_dist_attrs[1] self.assertEqual(len(result_dist_attrs), 2) self.assertEqual(len(inferred_input_dist_attrs), 3) self.assertEqual(len(inferred_output_dist_attrs), 1) self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [1, -1]) self.assertEqual(inferred_input_dist_attrs[1].dims_mapping, [0, -1]) self.assertEqual(inferred_input_dist_attrs[2].dims_mapping, [0, -1, -1]) self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [1, -1]) if __name__ == "__main__": unittest.main()