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test_multi_output_model.py 5.45 KB
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Olatunji Ruwase's avatar
Support multi-output models (#170)  
Olatunji Ruwase committed
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import torch
import deepspeed
import argparse
import pytest
from pytest import approx
import json
import os
from common import distributed_test
from simple_model import args_from_dict
from multi_output_model import MultiOutputModel, multi_output_dataloader


def create_config_dict(micro_batch_size, grad_accumulation_steps, world_size):
    return {
        "train_micro_batch_size_per_gpu": micro_batch_size,
        "gradient_accumulation_steps": grad_accumulation_steps,
        "train_batch_size": micro_batch_size * grad_accumulation_steps * world_size,
        "steps_per_print": 1,
        "optimizer": {
            "type": "Adam",
            "params": {
                "lr": 0.00015
            }
        },
        "fp16": {
            "enabled": True
        }
    }


def test_two_output_model(tmpdir):
    gradient_accumulation_steps = 2
    micro_batch_size = 1
    world_size = 1
    config_dict = create_config_dict(micro_batch_size,
                                     gradient_accumulation_steps,
                                     world_size)

    hidden_dim = 10
    weight_value = 0.1
    args = args_from_dict(tmpdir, config_dict)

    model = MultiOutputModel(hidden_dim, weight_value)

    @distributed_test(world_size=[1])
    def _test_two_output_model(args, model, hidden_dim):
        model, _, _, _ = deepspeed.initialize(args=args,
                                              model=model,
                                              model_parameters=model.parameters())
        total_samples = 4
        data_loader = multi_output_dataloader(model=model,
                                              total_samples=total_samples,
                                              hidden_dim=hidden_dim,
                                              device=model.device,
                                              inputs=[1.0,
                                                      2.0],
                                              targets=[1,
                                                       2])
        for n, batch in enumerate(data_loader):
            assert len(batch) % 2 == 0, \
                 f"multi_output_dataloader failed to return even number of data samples (input+target)"

            midpoint = len(batch) // 2
            inputs, targets = batch[:midpoint], batch[midpoint:]
            loss_tuple = model(inputs, targets)

            expected_loss = torch.tensor(2.302734375,
                                         dtype=torch.half,
                                         device=model.device)
            for loss in loss_tuple:
                assert loss.shape == torch.Size([])
                assert loss.item() == approx(expected_loss.item())

            summed_loss = sum(loss_tuple)
            scaled_loss = model.backward(summed_loss)
            expected_scaled_loss = summed_loss / gradient_accumulation_steps
            assert scaled_loss.item() == approx(expected_scaled_loss.item())

            model.step()

    _test_two_output_model(args=args, model=model, hidden_dim=hidden_dim)


def test_three_output_model(tmpdir):
    gradient_accumulation_steps = 3
    micro_batch_size = 1
    world_size = 1
    config_dict = create_config_dict(micro_batch_size,
                                     gradient_accumulation_steps,
                                     world_size)

    hidden_dim = 10
    weight_value = 0.1
    args = args_from_dict(tmpdir, config_dict)

    model = MultiOutputModel(hidden_dim, weight_value)

    @distributed_test(world_size=[1])
    def _test_three_output_model(args, model, hidden_dim):
        model, _, _, _ = deepspeed.initialize(args=args,
                                              model=model,
                                              model_parameters=model.parameters())

        total_samples = gradient_accumulation_steps * micro_batch_size * 2
        data_loader = multi_output_dataloader(model=model,
                                              total_samples=total_samples,
                                              hidden_dim=hidden_dim,
                                              device=model.device,
                                              inputs=[1.0,
                                                      2.0,
                                                      3.0],
                                              targets=[1,
                                                       2,
                                                       3])
        for n, batch in enumerate(data_loader):
            assert len(batch) % 2 == 0, \
                 f"multi_output_dataloader failed to return even number of data samples (input+target)"

            midpoint = len(batch) // 2
            inputs, targets = batch[:midpoint], batch[midpoint:]
            loss_tuple = model(inputs, targets)
            assert len(loss_tuple) == 3

            expected_loss = torch.tensor(2.302734375,
                                         dtype=torch.half,
                                         device=model.device)

            for loss in loss_tuple:
                assert loss.shape == torch.Size([])
                assert loss.item() == approx(expected_loss.item())

            summed_loss = sum(loss_tuple)
            scaled_loss = model.backward(summed_loss)
            expected_scaled_loss = summed_loss / gradient_accumulation_steps
            assert scaled_loss.item() == approx(expected_scaled_loss.item())

            model.step()

    _test_three_output_model(args=args, model=model, hidden_dim=hidden_dim)