test_fp16.py

import torch
import deepspeed
import argparse
import pytest
import json
import os
from common import distributed_test
from simple_model import SimpleModel, random_dataloader, args_from_dict


def test_lamb_fp16_basic(tmpdir):
    config_dict = {
        "train_batch_size": 2,
        "steps_per_print": 1,
        "optimizer": {
            "type": "Lamb",
            "params": {
                "lr": 0.00015,
                "max_grad_norm": 1.0
            }
        },
        "fp16": {
            "enabled": True
        }
    }
    args = args_from_dict(tmpdir, config_dict)
    hidden_dim = 10

    model = SimpleModel(hidden_dim, empty_grad=False)

    @distributed_test(world_size=[1, 2])
    def _test_lamb_fp16_basic(args, model, hidden_dim):
        model, _, _,_ = deepspeed.initialize(args=args,
                                             model=model,
                                             model_parameters=model.parameters())
        data_loader = random_dataloader(model=model,
                                        total_samples=50,
                                        hidden_dim=hidden_dim,
                                        device=model.device)
        for n, batch in enumerate(data_loader):
            loss = model(batch[0], batch[1])
            model.backward(loss)
            model.step()

    _test_lamb_fp16_basic(args=args, model=model, hidden_dim=hidden_dim)


def test_lamb_fp16_empty_grad(tmpdir):
    config_dict = {
        "train_batch_size": 1,
        "steps_per_print": 1,
        "optimizer": {
            "type": "Lamb",
            "params": {
                "lr": 0.00015,
                "max_grad_norm": 1.0
            }
        },
        "fp16": {
            "enabled": True
        }
    }
    args = args_from_dict(tmpdir, config_dict)
    hidden_dim = 10

    model = SimpleModel(hidden_dim, empty_grad=True)

    @distributed_test(world_size=[1])
    def _test_lamb_fp16_empty_grad(args, model, hidden_dim):
        model, _, _,_ = deepspeed.initialize(args=args,
                                             model=model,
                                             model_parameters=model.parameters())
        data_loader = random_dataloader(model=model,
                                        total_samples=50,
                                        hidden_dim=hidden_dim,
                                        device=model.device)
        for n, batch in enumerate(data_loader):
            loss = model(batch[0], batch[1])
            model.backward(loss)
            model.step()

    _test_lamb_fp16_empty_grad(args=args, model=model, hidden_dim=hidden_dim)


def test_adamw_fp16_basic(tmpdir):
    config_dict = {
        "train_batch_size": 1,
        "steps_per_print": 1,
        "fp16": {
            "enabled": True
        }
    }
    args = args_from_dict(tmpdir, config_dict)
    hidden_dim = 10

    model = SimpleModel(hidden_dim, empty_grad=False)

    @distributed_test(world_size=[1])
    def _test_adamw_fp16_basic(args, model, hidden_dim):
        optimizer = torch.optim.AdamW(params=model.parameters())
        model, _, _,_ = deepspeed.initialize(args=args,
                                             model=model,
                                             optimizer=optimizer)
        data_loader = random_dataloader(model=model,
                                        total_samples=50,
                                        hidden_dim=hidden_dim,
                                        device=model.device)
        for n, batch in enumerate(data_loader):
            loss = model(batch[0], batch[1])
            model.backward(loss)
            model.step()

    _test_adamw_fp16_basic(args=args, model=model, hidden_dim=hidden_dim)


def test_adamw_fp16_empty_grad(tmpdir):
    config_dict = {
        "train_batch_size": 1,
        "steps_per_print": 1,
        "fp16": {
            "enabled": True
        }
    }
    args = args_from_dict(tmpdir, config_dict)
    hidden_dim = 10

    model = SimpleModel(hidden_dim, empty_grad=True)

    @distributed_test(world_size=[1])
    def _test_adamw_fp16_empty_grad(args, model, hidden_dim):
        optimizer = torch.optim.AdamW(params=model.parameters())
        model, _, _,_ = deepspeed.initialize(args=args,
                                             model=model,
                                             optimizer=optimizer)
        data_loader = random_dataloader(model=model,
                                        total_samples=50,
                                        hidden_dim=hidden_dim,
                                        device=model.device)
        for n, batch in enumerate(data_loader):
            loss = model(batch[0], batch[1])
            model.backward(loss)
            model.step()

    _test_adamw_fp16_empty_grad(args=args, model=model, hidden_dim=hidden_dim)