test_collective_api_base.py

# 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 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 == "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:]
            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