# 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. import os import pickle import socket import subprocess import sys import tempfile import unittest from contextlib import closing import numpy as np from paddle_bfloat import bfloat16 import paddle import paddle.fluid as fluid from paddle.distributed.utils.nccl_utils import get_nccl_version_str 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_pyobject_test_data(shape=None, seed=None): if seed: np.random.seed(seed) list_shape = np.random.randint(0, high=100, size=(2)).tolist() list_data = np.random.random(shape).tolist() dict_key = [i for i in range(0, shape[0])] dict_val = np.random.random(shape).tolist() dict_data = dict(zip(dict_key, dict_val)) return [list_data, dict_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 == "bfloat16": # since numpy does not support bfloat16 yet, use `paddle_bfloat` to replace return create_float_test_data(shape=shape, dtype=bfloat16, 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 == "pyobject": return create_pyobject_test_data(shape=shape, seed=seed) else: raise NotImplementedError("Unsupported dtype for creating test data.") class TestCollectiveAPIRunnerBase: 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 if args["use_comm_context"]: paddle.distributed.collective._init_parallel_env(args["backend"]) else: 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_new(train_prog, startup_prog, rank) if args["use_comm_context"] else 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") args["use_comm_context"] = bool(int(os.getenv("USE_COMM_CONTEXT", "0"))) model.run_trainer(args) 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._master_endpoints = "127.0.0.1:%s" % (self._find_free_port()) self.temp_dir = tempfile.TemporaryDirectory() # NOTE: this is a hack to get int format nccl version, like 2134 # if current platform is not linux, version number will be 0 nccl_version_str = get_nccl_version_str() self._nccl_version = ( int("".join(nccl_version_str.split("."))) if nccl_version_str else 0 ) 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, "PADDLE_MASTER": self._master_endpoints, } env1 = { "FLAGS_selected_gpus": "1", "PADDLE_TRAINER_ID": "1", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints, "PADDLE_CURRENT_ENDPOINT": w1_ep, "PADDLE_MASTER": self._master_endpoints, } 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', '' ) 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) # cast bfloat16 to float32 for numeric comparison if dtype == "bfloat16": input1 = input1.astype("float32") input2 = input2.astype("float32") 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) elif 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 == "broadcast_object_list": need_result = [input2] self.assertEqual(need_result, tr0_out) self.assertEqual(need_result, tr1_out) elif col_type == "reduce": need_result = input1 + input2 # bfloat16 precision loss comes from truncating the last 16 bits of float32, # which sums (\sum_{i=-23}^{-8}2^{i}) to about 0.0078 if dtype == "bfloat16": rtol = 8e-03 else: rtol = 1e-05 np.testing.assert_allclose(tr0_out[0], need_result, rtol=rtol) 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 == "scatter_object_list": need_result = input2 need_result1 = [need_result[0 : len(need_result) // 2]] need_result2 = [need_result[len(need_result) // 2 :]] self.assertEqual(need_result1, tr0_out) self.assertEqual(need_result2, tr1_out) elif col_type == "reduce_scatter": need_result = input1 + input2 need_result1 = need_result[0 : need_result.shape[0] // 2] need_result2 = need_result[need_result.shape[0] // 2 :] if dtype == "bfloat16": rtol = 8e-03 else: rtol = 1e-05 np.testing.assert_allclose(tr0_out[0], need_result1, rtol=rtol) np.testing.assert_allclose(tr1_out[0], need_result2, rtol=rtol) elif col_type == "allreduce": need_result = input1 + input2 if dtype == "bfloat16": rtol = 8e-03 atol = 8e-03 else: rtol = 1e-05 atol = 1e-05 np.testing.assert_allclose( tr0_out[0], need_result, rtol=rtol, atol=atol ) np.testing.assert_allclose( tr1_out[0], need_result, rtol=rtol, atol=atol ) 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] np.testing.assert_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