# 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 import paddle import paddle.distributed as dist from paddle.base.libpaddle.pir import apply_dist2dense_pass from paddle.distributed.auto_parallel.static.mix_to_dist_pass import ( apply_mix2dist_pass, ) BATCH_SIZE = 2 SEQ_LEN = 4 HIDDEN_SIZE = 8 MP_SIZE = 2 class TestBuildFakeProgram(unittest.TestCase): def test_build_with_shard_tensor(self): paddle.enable_static() with paddle.pir_utils.IrGuard(): main_program = paddle.base.Program() with paddle.base.program_guard(main_program): mesh = dist.ProcessMesh([0, 1], dim_names=['mp']) input = paddle.static.data( name='input', shape=[BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE], ) w0 = paddle.create_parameter( dtype="float32", shape=[HIDDEN_SIZE, HIDDEN_SIZE], name="w0", default_initializer=paddle.nn.initializer.Uniform(), ) w1 = paddle.create_parameter( dtype="float32", shape=[HIDDEN_SIZE, HIDDEN_SIZE], name="w1", default_initializer=paddle.nn.initializer.Uniform(), ) self.assertTrue(input.is_dense_tensor_type()) self.assertTrue(w0.is_dense_tensor_type()) dist_input = dist.shard_tensor(input, mesh, [dist.Replicate()]) dist_w0 = dist.shard_tensor(w0, mesh, [dist.Shard(0)]) dist_w1 = dist.shard_tensor(w1, mesh, [dist.Shard(1)]) self.assertTrue(main_program.num_ops() == 6) self.assertFalse(input.use_empty()) self.assertFalse(w0.use_empty()) self.assertFalse(w1.use_empty()) self.assertTrue(dist_input.use_empty()) self.assertTrue(dist_w0.use_empty()) self.assertTrue(dist_w1.use_empty()) self.assertTrue(w0.is_dense_tensor_type()) self.assertTrue(w1.is_dense_tensor_type()) self.assertTrue(input.is_dense_tensor_type()) # check dist type self.assertTrue(dist_input.is_dist_dense_tensor_type()) self.assertTrue(dist_w0.is_dist_dense_tensor_type()) self.assertTrue(dist_w1.is_dist_dense_tensor_type()) # check global shape self.assertEqual(dist_input.shape, [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE]) self.assertEqual(dist_w0.shape, [HIDDEN_SIZE, HIDDEN_SIZE]) self.assertEqual(dist_w1.shape, [HIDDEN_SIZE, HIDDEN_SIZE]) # check local shape self.assertTrue( dist_input._local_shape == dist_input.shape ) # replicated, local = global self.assertTrue( dist_w0._local_shape == [HIDDEN_SIZE // MP_SIZE, HIDDEN_SIZE] ) # sharded, local != global, sharded by mesh size self.assertTrue( dist_w1._local_shape == [HIDDEN_SIZE, HIDDEN_SIZE // MP_SIZE] ) # sharded, local != global, sharded by mesh size # check op dist_attr self.assertFalse(input.get_defining_op().has_attr("op_dist_attr")) self.assertFalse(w0.get_defining_op().has_attr("op_dist_attr")) self.assertFalse(w1.get_defining_op().has_attr("op_dist_attr")) dist_input_op_dist_attr = dist_input.get_defining_op().dist_attr # #check attrs self.assertEqual(dist_input_op_dist_attr.process_mesh, mesh) self.assertEqual(dist_input_op_dist_attr.num_operands(), 0) self.assertEqual(dist_input_op_dist_attr.num_results(), 1) dist_w0_op_dist_attr = dist_w0.get_defining_op().dist_attr self.assertEqual(dist_w0_op_dist_attr.process_mesh, mesh) self.assertEqual(dist_w0_op_dist_attr.num_operands(), 0) self.assertEqual(dist_w0_op_dist_attr.num_results(), 1) dist_w1_op_dist_attr = dist_w1.get_defining_op().dist_attr self.assertEqual(dist_w1_op_dist_attr.process_mesh, mesh) self.assertEqual(dist_w1_op_dist_attr.num_operands(), 0) self.assertEqual(dist_w1_op_dist_attr.num_results(), 1) attrs_op_dist_attr = ( dist_input.get_defining_op().attrs().get("op_dist_attr") ) self.assertEqual(attrs_op_dist_attr.process_mesh, mesh) # check op result dist_attr tensor_dist_attr = dist_input_op_dist_attr.result( 0 ).as_tensor_dist_attr() self.assertEqual(tensor_dist_attr.process_mesh, mesh) self.assertEqual( tensor_dist_attr.dims_mapping, [-1, -1, -1], ) tensor_dist_attr = dist_w0_op_dist_attr.result(0).as_tensor_dist_attr() self.assertEqual(tensor_dist_attr.process_mesh, mesh) self.assertEqual(tensor_dist_attr.dims_mapping, [0, -1]) tensor_dist_attr = dist_w1_op_dist_attr.result(0).as_tensor_dist_attr() self.assertEqual(tensor_dist_attr.process_mesh, mesh) self.assertEqual(tensor_dist_attr.dims_mapping, [-1, 0]) # check value dist_attr self.assertEqual(dist_input.dist_attr().process_mesh, mesh) self.assertEqual(dist_input.dist_attr().dims_mapping, [-1, -1, -1]) self.assertEqual(dist_w0.dist_attr().process_mesh, mesh) self.assertEqual(dist_w0.dist_attr().dims_mapping, [0, -1]) self.assertEqual(dist_w1.dist_attr().process_mesh, mesh) self.assertEqual(dist_w1.dist_attr().dims_mapping, [-1, 0]) def test_build_with_apply_mix2dist_pass(self): paddle.enable_static() with paddle.pir_utils.IrGuard(): main_program = paddle.base.Program() with paddle.base.program_guard(main_program): mesh = dist.ProcessMesh([0, 1], dim_names=['dp']) input1 = paddle.randint(low=0, high=1000, shape=[8, 4]) output1 = dist.shard_tensor(input1, mesh, [dist.Shard(0)]) input2 = paddle.randn([4, 8]) output2 = dist.shard_tensor(input2, mesh, [dist.Shard(1)]) self.assertTrue(input1.is_dense_tensor_type()) self.assertTrue(input2.is_dense_tensor_type()) self.assertTrue(main_program.num_ops() == 6) self.assertFalse(input1.use_empty()) self.assertFalse(input2.use_empty()) self.assertTrue(output1.use_empty()) self.assertTrue(output2.use_empty()) self.assertFalse(input1.get_defining_op().has_attr("op_dist_attr")) self.assertFalse(input2.get_defining_op().has_attr("op_dist_attr")) # check dist type self.assertTrue(output1.is_dist_dense_tensor_type()) self.assertTrue(output2.is_dist_dense_tensor_type()) # run apply_mix2dist_pass apply_mix2dist_pass(main_program) # after apply_mix2dist_pass, the program changed self.assertTrue(main_program.num_ops() == 4) self.assertTrue(input1.is_dist_dense_tensor_type()) self.assertTrue(input2.is_dist_dense_tensor_type()) self.assertTrue(input1.get_defining_op().has_attr("op_dist_attr")) self.assertTrue(input2.get_defining_op().has_attr("op_dist_attr")) # check op result dist_attr input1_op_dist_attr = input1.get_defining_op().dist_attr tensor_dist_attr = input1_op_dist_attr.result(0).as_tensor_dist_attr() self.assertEqual(tensor_dist_attr.process_mesh, mesh) self.assertEqual(tensor_dist_attr.dims_mapping, [0, -1]) input2_op_dist_attr = input2.get_defining_op().dist_attr tensor_dist_attr = input2_op_dist_attr.result(0).as_tensor_dist_attr() self.assertEqual(tensor_dist_attr.process_mesh, mesh) self.assertEqual(tensor_dist_attr.dims_mapping, [-1, 0]) # check value dist_attr self.assertEqual(input1.dist_attr().process_mesh, mesh) self.assertEqual(input1.dist_attr().dims_mapping, [0, -1]) self.assertEqual(input2.dist_attr().process_mesh, mesh) self.assertEqual(input2.dist_attr().dims_mapping, [-1, 0]) # check full_int_array op result dist_attr input1_shape = input1.get_defining_op().operand_source(0) input1_shape_op_dist_attr = input1_shape.get_defining_op().dist_attr tensor_dist_attr = input1_shape_op_dist_attr.result( 0 ).as_tensor_dist_attr() self.assertEqual(tensor_dist_attr.process_mesh, mesh) self.assertEqual(tensor_dist_attr.dims_mapping, [-1]) input2_shape = input2.get_defining_op().operand_source(0) input2_shape_op_dist_attr = input2_shape.get_defining_op().dist_attr tensor_dist_attr = input2_shape_op_dist_attr.result( 0 ).as_tensor_dist_attr() self.assertEqual(tensor_dist_attr.process_mesh, mesh) self.assertEqual(tensor_dist_attr.dims_mapping, [-1]) # check shape value dist_attr self.assertEqual(input1_shape.dist_attr().process_mesh, mesh) self.assertEqual(input1_shape.dist_attr().dims_mapping, [-1]) self.assertEqual(input2_shape.dist_attr().process_mesh, mesh) self.assertEqual(input2_shape.dist_attr().dims_mapping, [-1]) def test_build_with_apply_dist2dense_pass(self): paddle.enable_static() with paddle.pir_utils.IrGuard(): main_program = paddle.base.Program() with paddle.base.program_guard(main_program): mesh = dist.ProcessMesh([0, 1], dim_names=['dp']) input1 = paddle.randint(low=0, high=1000, shape=[8, 4]) output1 = dist.shard_tensor(input1, mesh, [dist.Shard(0)]) input2 = paddle.randn([4, 8]) output2 = dist.shard_tensor(input2, mesh, [dist.Shard(1)]) self.assertTrue(input1.is_dense_tensor_type()) self.assertTrue(input2.is_dense_tensor_type()) self.assertTrue(main_program.num_ops() == 6) self.assertFalse(input1.use_empty()) self.assertFalse(input2.use_empty()) self.assertTrue(output1.use_empty()) self.assertTrue(output2.use_empty()) self.assertFalse(input1.get_defining_op().has_attr("op_dist_attr")) self.assertFalse(input2.get_defining_op().has_attr("op_dist_attr")) # check dist type self.assertTrue(output1.is_dist_dense_tensor_type()) self.assertTrue(output2.is_dist_dense_tensor_type()) # run apply_mix2dist_pass and apply_dist2dense_pass apply_mix2dist_pass(main_program) apply_dist2dense_pass(main_program) # after apply_mix2dist_pass, the program changed # and after apply_dist2dense_pass, the operator in program do not have dist_attr self.assertTrue(main_program.num_ops() == 4) self.assertTrue(input1.is_dense_tensor_type()) self.assertTrue(input2.is_dense_tensor_type()) self.assertFalse(input1.get_defining_op().has_attr("op_dist_attr")) self.assertFalse(input2.get_defining_op().has_attr("op_dist_attr")) # check shape attribute of full_int_array op input1_shape = input1.get_defining_op().operand_source(0) input1_shape_op = input1_shape.get_defining_op() self.assertFalse(input1_shape_op.has_attr("op_dist_attr")) input1_shape_op_attr = input1_shape_op.attrs() self.assertEqual(input1_shape_op_attr['value'], [4, 4]) input2_shape = input2.get_defining_op().operand_source(0) input2_shape_op = input2_shape.get_defining_op() self.assertFalse(input2_shape_op.has_attr("op_dist_attr")) input2_shape_op_attr = input2_shape_op.attrs() self.assertEqual(input2_shape_op_attr['value'], [4, 4]) # check shape of input1 and input2 self.assertEqual(input1.shape, [4, 4]) self.assertEqual(input2.shape, [4, 4]) if __name__ == "__main__": unittest.main()