# Copyright (c) 2020 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. from __future__ import print_function import numpy as np import unittest import time import argparse import os import sys import subprocess import traceback import functools import pickle import tempfile from contextlib import closing import paddle import paddle.fluid as fluid import paddle.fluid.unique_name as nameGen from paddle.fluid import core def create_bool_test_data(shape=None, seed=None): if seed: np.random.seed(seed) data = np.random.choice([True, False], size=shape) return data def create_float_test_data(shape=None, dtype=None, seed=None): if seed: np.random.seed(seed) data = np.random.random(shape).astype(dtype) return data def create_int_test_data(shape=None, dtype=None, seed=None): if seed: np.random.seed(seed) data = np.random.randint(0, high=100, size=shape).astype(dtype) return data def create_complex_test_data(shape=None, dtype=None, seed=None): if seed: np.random.seed(seed) data = np.random.random(shape).astype(dtype) data.imag = np.random.random(shape) return data def create_pylist_test_data(shape=None, seed=None): if seed: np.random.seed(seed) # Generate random shape test case for xxx_object api shape = np.random.randint(0, high=100, size=(2)).tolist() data = np.random.random(shape).tolist() return data def create_pydict_test_data(shape=None, seed=None): if seed: np.random.seed(seed) key = [i for i in range(0, shape[0])] value = np.random.random(shape).tolist() data = dict(zip(key, value)) return data def create_test_data(shape=None, dtype=None, seed=None): assert shape, "Shape should be specified" if dtype == "float32" or dtype == "float16" or dtype == "float64": return create_float_test_data(shape=shape, dtype=dtype, seed=seed) elif dtype == "bool": return create_bool_test_data(shape=shape, seed=seed) elif dtype == "int32" or dtype == "int64" or dtype == "int8" or dtype == "uint8": return create_int_test_data(shape=shape, dtype=dtype, seed=seed) elif dtype == "complex64" or dtype == "complex128": return create_complex_test_data(shape=shape, dtype=dtype, seed=seed) elif dtype == "pylist": return create_pylist_test_data(shape=shape, seed=seed) elif dtype == "pydict": return create_pydict_test_data(shape=shape, seed=seed) else: raise NotImplementedError("Unsupported dtype for creating test data.") class TestCollectiveAPIRunnerBase(object): def get_model(self, train_prog, startup_prog, rank, indata=None, dtype=None): raise NotImplementedError( "get model should be implemented by child class.") def run_trainer(self, args): train_prog = fluid.Program() startup_prog = fluid.Program() endpoints = args["endpoints"].split(",") rank = args["trainerid"] current_endpoint = args["currentendpoint"] nranks = 2 paddle.distributed.init_parallel_env() if args['backend'] == 'nccl': device_id = int(os.getenv("FLAGS_selected_gpus", "0")) place = fluid.CUDAPlace( device_id) #if args.use_gpu else fluid.CPUPlace() elif args['backend'] == 'bkcl': device_id = int(os.getenv("FLAGS_selected_xpus", "0")) place = fluid.XPUPlace(device_id) else: place = fluid.CPUPlace() indata = create_test_data(shape=(10, 1000), dtype=args["dtype"], seed=os.getpid()) if args['static_mode']: result = self.get_model(train_prog, startup_prog, rank) exe = fluid.Executor(place) exe.run(startup_prog) fetch_list = [] for elem in result: fetch_list.append(elem.name) out = exe.run(train_prog, feed={'tindata': indata}, fetch_list=fetch_list) else: out = self.get_model(train_prog, startup_prog, rank, indata) #print(out, sys.stderr) sys.stdout.buffer.write(pickle.dumps(out)) def runtime_main(test_class, col_type): args = {} model = test_class() args["trainerid"] = int(os.getenv("PADDLE_TRAINER_ID")) args["trainernum"] = int(os.getenv("PADDLE_TRAINERS_NUM")) args["endpoints"] = os.getenv('PADDLE_TRAINER_ENDPOINTS') args["currentendpoint"] = os.getenv("PADDLE_CURRENT_ENDPOINT") args["col_type"] = col_type args["backend"] = os.getenv("BACKEND") args["path_id"] = int(os.getenv("PATH_ID")) args["static_mode"] = int(os.getenv("STATIC_MODE")) args["dtype"] = os.getenv("DTYPE") model.run_trainer(args) import paddle.compat as cpt import socket from contextlib import closing class TestDistBase(unittest.TestCase): def setUp(self): self._port_set = set() self._trainers = 2 self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % ( self._find_free_port(), self._find_free_port()) self._python_interp = sys.executable self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def _find_free_port(self): def __free_port(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) return s.getsockname()[1] while True: port = __free_port() if port not in self._port_set: self._port_set.add(port) return port def _run_cluster(self, model_file, envs): worker_endpoints = self._ps_endpoints.split(",") w0_ep, w1_ep = worker_endpoints #print("w0_ep:",w0_ep," w1_ep:",w1_ep) if core.is_compiled_with_cuda(): env0 = { "FLAGS_selected_gpus": "0", "PADDLE_TRAINER_ID": "0", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints, "PADDLE_CURRENT_ENDPOINT": w0_ep } env1 = { "FLAGS_selected_gpus": "1", "PADDLE_TRAINER_ID": "1", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints, "PADDLE_CURRENT_ENDPOINT": w1_ep } elif core.is_compiled_with_xpu(): env0 = { "FLAGS_selected_xpus": "0", "PADDLE_TRAINER_ID": "0", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints, "PADDLE_CURRENT_ENDPOINT": w0_ep } env1 = { "FLAGS_selected_xpus": "1", "PADDLE_TRAINER_ID": "1", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints, "PADDLE_CURRENT_ENDPOINT": w1_ep } #update environment env0.update(envs) env1.update(envs) if os.getenv('WITH_COVERAGE', 'OFF') == 'ON': tr_cmd = "%s -m coverage run --branch -p %s" else: tr_cmd = "%s %s" tr0_cmd = tr_cmd % (self._python_interp, model_file) tr1_cmd = tr_cmd % (self._python_interp, model_file) path0 = os.path.join(self.temp_dir.name, "/tmp/tr0_err_%d.log" % os.getpid()) path1 = os.path.join(self.temp_dir.name, "/tmp/tr1_err_%d.log" % os.getpid()) tr0_pipe = open(path0, "w") tr1_pipe = open(path1, "w") #print(tr0_cmd) tr0_proc = subprocess.Popen(tr0_cmd.strip().split(), stdout=subprocess.PIPE, stderr=tr0_pipe, env=env0) tr1_proc = subprocess.Popen(tr0_cmd.strip().split(), stdout=subprocess.PIPE, stderr=tr1_pipe, env=env1) tr0_out, tr0_err = tr0_proc.communicate() tr1_out, tr1_err = tr1_proc.communicate() sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err) sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err) # close trainer file tr0_pipe.close() tr1_pipe.close() with open(path0, "r") as f: sys.stderr.write('trainer 0 stderr file: %s\n' % f.read()) with open(path1, "r") as f: sys.stderr.write('trainer 1 stderr file: %s\n' % f.read()) return pickle.loads(tr0_out), pickle.loads( tr1_out), tr0_proc.pid, tr1_proc.pid def check_with_place(self, model_file, col_type, backend="nccl", path_id="0", static_mode="1", check_error_log=False, need_envs={}, eager_mode=True, dtype=None): if backend == "nccl" or backend == "bkcl": with_gloo = '0' else: with_gloo = '1' required_envs = os.environ.copy() dtype = "float32" if dtype is None else dtype additional_envs = { "NCCL_P2P_DISABLE": "1", "STATIC_MODE": static_mode, "PADDLE_WITH_GLOO": with_gloo, "PADDLE_DISTRI_BACKEND": backend, "BACKEND": backend, "PATH_ID": path_id, "DTYPE": dtype } required_envs.update(additional_envs) required_envs.update(need_envs) if check_error_log: required_envs["GLOG_v"] = "3" required_envs["GLOG_logtostderr"] = "1" required_envs["GLOO_LOG_LEVEL"] = "TRACE" if os.getenv('NVIDIA_TF32_OVERRIDE', '') is not None: required_envs['NVIDIA_TF32_OVERRIDE'] = os.getenv( 'NVIDIA_TF32_OVERRIDE', '') if eager_mode: required_envs["FLAGS_enable_eager_mode"] = "%d" % 1 else: required_envs["FLAGS_enable_eager_mode"] = "%d" % 0 tr0_out, tr1_out, pid0, pid1 = self._run_cluster( model_file, required_envs) input1 = create_test_data(shape=(10, 1000), dtype=dtype, seed=pid0) input2 = create_test_data(shape=(10, 1000), dtype=dtype, seed=pid1) if col_type == "allgather": need_result = np.vstack((input1, input2)) tr_out0 = np.vstack((tr0_out[0], tr0_out[1])) tr_out1 = np.vstack((tr1_out[0], tr1_out[1])) np.testing.assert_allclose(tr_out0, need_result, rtol=1e-05) np.testing.assert_allclose(tr_out1, need_result, rtol=1e-05) if col_type == "allgather_object": need_result = [input1, input2] self.assertEqual(need_result, tr0_out) self.assertEqual(need_result, tr1_out) elif col_type == "broadcast": need_result = input2 np.testing.assert_allclose(tr0_out[0], need_result, rtol=1e-05) np.testing.assert_allclose(tr1_out[0], need_result, rtol=1e-05) elif col_type == "reduce": need_result = input1 + input2 np.testing.assert_allclose(tr0_out[0], need_result, rtol=1e-05) elif col_type == "scatter": need_result = input2 need_result1 = need_result[0:need_result.shape[0] // 2] need_result2 = need_result[need_result.shape[0] // 2:] np.testing.assert_allclose(tr0_out[0], need_result1, rtol=1e-05) np.testing.assert_allclose(tr1_out[0], need_result2, rtol=1e-05) elif col_type == "allreduce": need_result = input1 + input2 np.testing.assert_allclose(tr0_out[0], need_result, rtol=1e-05, atol=1e-05) np.testing.assert_allclose(tr1_out[0], need_result, rtol=1e-05, atol=1e-05) elif col_type == "parallel_embedding": result_data = tr0_out[0] np.random.seed(2020) need_result = np.random.rand(12, 8) for i in range(result_data.shape[0]): for j in range(result_data.shape[1]): data = result_data[i][j] assert np.allclose(tr0_out[1][i][j], need_result[data], atol=1e-08) elif col_type == "row_parallel_linear": result_data = tr0_out[0] np.random.seed(2020) weight = np.random.rand(1000, 16) need_result = np.matmul(input1, weight) np.testing.assert_allclose(result_data, need_result, rtol=1e-05, atol=1e-05) elif col_type == "column_parallel_linear": result_data = tr0_out[0] np.random.seed(2020) weight = np.random.rand(1000, 16) need_result = np.matmul(input1, weight) np.testing.assert_allclose(result_data, need_result, rtol=1e-05, atol=1e-05) elif col_type == "alltoall": need_result1 = np.vstack((input1[0:input1.shape[0] // 2, :], input2[0:input2.shape[0] // 2, :])) need_result2 = np.vstack((input1[input1.shape[0] // 2:, :], input2[input2.shape[0] // 2:, :])) tr0_out = np.vstack(tr0_out) tr1_out = np.vstack(tr1_out) np.testing.assert_allclose(tr0_out, need_result1, rtol=1e-05, atol=1e-05) np.testing.assert_allclose(tr1_out, need_result2, rtol=1e-05, atol=1e-05) elif col_type == "sendrecv": result_data = tr1_out[0] np.testing.assert_allclose(input1, result_data, rtol=1e-05, atol=1e-05) elif col_type == "global_gather": in_feat = 2 n_expert = 2 world_size = 2 tot_expert = n_expert * world_size np.random.seed(pid0) local_expert_count1 = np.random.randint( 1, 4, size=tot_expert).astype("int") expert_ptr1 = np.ones(tot_expert, dtype=np.int32) expert_ptr1[0] = 0 for i in range(1, tot_expert): expert_ptr1[i] = expert_ptr1[i - 1] + local_expert_count1[i - 1] np.random.seed(pid1) local_expert_count2 = np.random.randint( 1, 4, size=tot_expert).astype("int") expert_ptr2 = np.ones(tot_expert, dtype=np.int32) expert_ptr2[0] = 0 for i in range(1, tot_expert): expert_ptr2[i] = expert_ptr2[i - 1] + local_expert_count2[i - 1] global_expert_count1 = np.zeros(tot_expert).astype("int") global_expert_count2 = np.zeros(tot_expert).astype("int") global_expert_count1[0:n_expert] = local_expert_count1[0:n_expert] global_expert_count1[n_expert:] = local_expert_count2[0:n_expert] global_expert_count2[0:n_expert] = local_expert_count1[n_expert:] global_expert_count2[n_expert:] = local_expert_count2[n_expert:] np.random.seed(pid0) fwd_expert_count = sum(global_expert_count1).astype("int") local_input_buf1 = np.random.rand(fwd_expert_count, in_feat).astype("float32") np.random.seed(pid1) fwd_expert_count = sum(global_expert_count2).astype("int") local_input_buf2 = np.random.rand(fwd_expert_count, in_feat).astype("float32") output1 = [[], [], [], []] output2 = [[], [], [], []] send_ptr1 = 0 send_ptr2 = 0 for i in range(n_expert): for j in range(world_size): idx = j * n_expert + i if j == 0: output1_part1 = local_input_buf1[send_ptr1: \ send_ptr1 + global_expert_count1[idx], :] output1_part2 = local_input_buf2[send_ptr2: \ send_ptr2 + global_expert_count2[idx], :] output1[i].extend(output1_part1) output1[i + n_expert].extend(output1_part2) else: output2_part1 = local_input_buf1[send_ptr1: \ send_ptr1 + global_expert_count1[idx]] output2_part2 = local_input_buf2[send_ptr2: \ send_ptr2 + global_expert_count2[idx]] output2[i].extend(output2_part1) output2[i + n_expert].extend(output2_part2) send_ptr1 = send_ptr1 + global_expert_count1[idx] send_ptr2 = send_ptr2 + global_expert_count2[idx] result1 = [] result2 = [] for i in range(tot_expert): for arr in output1[i]: if arr == []: continue result1.append(arr) for i in range(tot_expert): for arr in output2[i]: if arr == []: continue result2.append(arr) if result1 == []: output1 = np.array([]) else: output1 = np.concatenate(result1, axis=0).reshape( sum(local_expert_count1), in_feat) if result2 == []: output2 = np.array([]) else: output2 = np.concatenate(result2, axis=0).reshape( sum(local_expert_count2), in_feat) if tr0_out[0] is None or tr0_out[0].shape[0] == 0: tr0_out[0] = np.array([]) if tr1_out[0] is None or tr1_out[0].shape[0] == 0: tr1_out[0] = np.array([]) np.testing.assert_allclose(tr0_out[0], output1, rtol=1e-05, atol=1e-05) np.testing.assert_allclose(tr1_out[0], output2, rtol=1e-05, atol=1e-05) if static_mode == 0: np.testing.assert_allclose(tr0_out[1], 2 * local_input_buf1, rtol=1e-05, atol=1e-05) np.testing.assert_allclose(tr1_out[1], 2 * local_input_buf2, rtol=1e-05, atol=1e-05) elif col_type == "global_scatter": np.random.seed(pid0) local_expert_count1 = np.random.randint(1, 4, size=4).astype("int") fwd_expert_count = sum(local_expert_count1) local_input_buf1 = np.random.rand(fwd_expert_count, 2).astype("float32") expert_ptr1 = np.ones(4, dtype=np.int32) expert_ptr1[0] = 0 for i in range(1, 4): expert_ptr1[i] = expert_ptr1[i - 1] + local_expert_count1[i - 1] np.random.seed(pid1) local_expert_count2 = np.random.randint(1, 4, size=4).astype("int") fwd_expert_count = sum(local_expert_count2) local_input_buf2 = np.random.rand(fwd_expert_count, 2).astype("float32") expert_ptr2 = np.ones(4, dtype=np.int32) expert_ptr2[0] = 0 for i in range(1, 4): expert_ptr2[i] = expert_ptr2[i - 1] + local_expert_count2[i - 1] output1 = [] output2 = [] for i in range(2): for j in range(2): idx = j * 2 + i if j == 0: # send data to 0 card output1.append(local_input_buf1[expert_ptr1[idx]: \ expert_ptr1[idx]+local_expert_count1[idx]]) output1.append(local_input_buf2[expert_ptr2[idx]:\ expert_ptr2[idx]+local_expert_count2[idx]]) else: output2.append(local_input_buf1[expert_ptr1[idx]: \ expert_ptr1[idx]+local_expert_count1[idx]]) output2.append(local_input_buf2[expert_ptr2[idx]:\ expert_ptr2[idx]+local_expert_count2[idx]]) if output1 == []: output1 = np.array([]) else: output1 = np.concatenate(output1) if output2 == []: output2 = np.array([]) else: output2 = np.concatenate(output2) if tr0_out[0] is None or tr0_out[0].shape[0] == 0: tr0_out[0] = np.array([]) if tr1_out[0] is None or tr1_out[0].shape[0] == 0: tr1_out[0] = np.array([]) np.testing.assert_allclose(tr0_out[0], output1, rtol=1e-05, atol=1e-05) np.testing.assert_allclose(tr1_out[0], output2, rtol=1e-05, atol=1e-05) if static_mode == 0: np.testing.assert_allclose(tr0_out[1], 2 * local_input_buf1, rtol=1e-05, atol=1e-05) np.testing.assert_allclose(tr1_out[1], 2 * local_input_buf2, rtol=1e-05, atol=1e-05) else: pass