test_basic_train.py

"""
module: basic_train.py - Model fitting methods
docs  : https://fastai1.fast.ai/train.html
"""

import pytest
from fastai.vision import *
from fastai.utils.mem import *
from fastai.gen_doc.doctest import this_tests
from fastai.utils.fakes import *
from fastai.utils.text import *
from fastai.utils.mem import *
from math import isclose

@pytest.fixture(scope="module")
def data():
    path = untar_data(URLs.MNIST_TINY)
    data = ImageDataBunch.from_folder(path, ds_tfms=([], []), bs=2)
    return data

# this is not a fixture on purpose - the memory measurement tests are very sensitive, so
# they need to be able to get a fresh learn object and not one modified by other tests.
def learn_large_unfit(data):
    learn = cnn_learner(data, models.resnet18, metrics=accuracy)
    return learn

@pytest.fixture(scope="module")
def learn(data): return learn_large_unfit(data)

def test_get_preds():
    learn = fake_learner()
    this_tests(learn.get_preds)
    with CaptureStdout() as cs:
        a = learn.get_preds()
    assert learn.data.batch_size == len(a[1])

def test_freeze_to():
    learn = fake_learner(layer_group_count=3)
    this_tests(learn.freeze_to)
    learn.freeze_to(1)
    for i, param in enumerate(learn.model.parameters()):
        # param 0 is weights in layer_group 0 and param 1 is bias in layer_group 0
        # all params other than those should be frozen
        if i >= 2: assert param.requires_grad == True
        else:      assert param.requires_grad == False

def test_freeze():
    learn = fake_learner(layer_group_count=3)
    this_tests(learn.freeze)
    learn.freeze()
    for i, param in enumerate(learn.model.parameters()):
        # 2 layer groups with 1 param in each should be frozen
        if i >= 4: assert param.requires_grad == True
        else:      assert param.requires_grad == False

def test_unfreeze():
    learn = fake_learner(layer_group_count=4)
    this_tests(learn.unfreeze)
    for param in learn.model.parameters(): param.requires_grad=False
    learn.unfreeze()
    for param in learn.model.parameters(): assert param.requires_grad == True

def check_learner(learn, model_summary_before, train_items_before):
    # basic checks
    #assert learn.recorder
    assert learn.model
    assert train_items_before == len(learn.data.train_ds.items)

    if model_summary_before is not None:
        assert model_summary_before == learn.summary(), f"model summary before and after"

    # XXX: could use more sanity checks

def test_purge():
    learn = fake_learner() # don't use fixture - we mess with the object
    this_tests(learn.purge)

    # just testing we can run each of these
    learn.purge()
    learn.purge(clear_opt=False)

    # writable dir
    model_dir_orig = learn.model_dir
    learn.model_dir = "." # should succeed
    learn.purge()
    learn.model_dir = model_dir_orig

def test_save_load(learn):
    this_tests(learn.save, learn.load, learn.purge)
    name = 'mnist-tiny-test-save-load'
    train_items_before = len(learn.data.train_ds.items)
    model_summary_before = learn.summary()

    # testing that all these various sequences don't break each other
    model_path = learn.save(name, return_path=True)
    _ = learn.load(name, purge=True)
    learn.data.sanity_check()
    check_learner(learn, model_summary_before, train_items_before)

    learn.purge()
    _ = learn.load(name)
    _ = learn.load(name)
    model_path = learn.save(name, return_path=True)
    _ = learn.load(name, purge=True)
    check_learner(learn, model_summary_before, train_items_before)

    # Test save/load using bytes streams
    output_buffer = io.BytesIO()
    learn.save(output_buffer)
    learn.purge()
    input_buffer = io.BytesIO(output_buffer.getvalue())
    _ = learn.load(input_buffer)
    check_learner(learn, model_summary_before, train_items_before)

    # cleanup
    if os.path.exists(model_path): os.remove(model_path)

def subtest_save_load_mem(data):
    learn = learn_large_unfit(data)
    name = 'mnist-tiny-test-save-load'
    #learn.fit_one_cycle(1)

    # save should consume no extra used or peaked memory
    with GPUMemTrace(on_exit_report=False) as mtrace:
        model_path = learn.save(name, return_path=True)
    check_mtrace(used_exp=0, peaked_exp=0, mtrace=mtrace, abs_tol=10, ctx="save")

    # load w/ purge still leaks some the first time it's run
    with GPUMemTrace(on_exit_report=False) as mtrace:
        learn.load(name, purge=True)
    # XXX: very different numbers if done w/o fit first 42 8, w/ fit 24 16
    check_mtrace(used_exp=18, peaked_exp=8, mtrace=mtrace, abs_tol=10, ctx="load")

    # subsequent multiple load w/o purge should consume no extra used memory
    with GPUMemTrace(on_exit_report=False) as mtrace:
        learn.load(name, purge=False)
        learn.load(name, purge=False)
    check_mtrace(used_exp=0, peaked_exp=20, mtrace=mtrace, abs_tol=10, ctx="load x 2")

    # subsequent multiple load w/ purge should consume no extra used memory
    with GPUMemTrace(on_exit_report=False) as mtrace:
        learn.load(name, purge=True)
        learn.load(name, purge=True)
    check_mtrace(used_exp=0, peaked_exp=20, mtrace=mtrace, abs_tol=10, ctx="load x 2 2nd time")

    # purge + load w/ default purge should consume no extra used memory
    with GPUMemTrace(on_exit_report=False) as mtrace:
        learn.purge()
        learn.load(name)
    check_mtrace(used_exp=0, peaked_exp=0, mtrace=mtrace, abs_tol=10, ctx="purge+load")

    if os.path.exists(model_path): os.remove(model_path)

def test_destroy():
    msg = "this object has been destroyed"
    learn = fake_learner()
    this_tests(learn.destroy)
    with CaptureStdout() as cs: learn.destroy()
    assert "this Learner object self-destroyed" in cs.out

    # should be able to re-run learn.destroy multiple times for nb convenience
    with CaptureStdout() as cs: learn.destroy()
    assert msg in cs.out

    # should be able to run normal methods, except they are no-ops and say that they are
    with CaptureStdout() as cs: learn.fit(1)
    assert msg in cs.out

    # should be able to call attributes, except they are gone and say so
    # unless they are __getattr__' loaded from Learner, in which case they are still normal
    for attr in ['data', 'model', 'callbacks']:
        with CaptureStdout() as cs: val = getattr(learn, attr, None)
        assert msg in cs.out, attr
        assert val is None, attr

    # check that `destroy` didn't break the Learner class
    learn = fake_learner()
    with CaptureStdout() as cs: learn.fit(1)
    assert "epoch" in cs.out
    assert "train_loss" in cs.out

def subtest_destroy_mem(data):
    with GPUMemTrace(on_exit_report=False) as mtrace:
        learn = learn_large_unfit(data)
    load_used, load_peaked = mtrace.data()

    # destroy should free most of the memory that was allocated during load (training, etc.)
    with GPUMemTrace(on_exit_report=False) as mtrace:
        with CaptureStdout() as cs: learn.destroy()
    check_mtrace(used_exp=-load_used, peaked_exp=-load_peaked, mtrace=mtrace, abs_tol=10, ctx="destroy")

# memory tests behave differently when run individually and in a row, since
# memory utilization patterns are very inconsistent - would require a full gpu
# card reset before each test to be able to test consistently, so will run them
# all in a precise sequence
@pytest.mark.cuda
def test_memory(data):
    this_tests(Learner.save, Learner.load, Learner.purge, Learner.destroy)

    # A big difficulty with measuring memory consumption is that it varies quite
    # wildly from one GPU model to another.
    #
    # Perhaps we need sets of different expected numbers per developer's GPUs?
    # override check_mem above in tests.utils.mem with report_mem to acquire a new set
    #
    # So for now just testing the specific card I have until a better way is found.
    dev_name = torch.cuda.get_device_name(None)
    if dev_name != 'GeForce GTX 1070 Ti':
        pytest.skip(f"currently only matched for mem usage on specific GPU models, {dev_name} is not one of them")

    subtest_save_load_mem(data)
    subtest_destroy_mem(data)

def test_export_load_learner():
    export_file = 'export.pkl'
    for should_destroy in [False, True]:
        learn = fake_learner()
        this_tests(learn.export, load_learner, learn.summary)
        path = learn.path
        model_summary_before = learn.summary()

        print(f"\n*** Testing w/ learn.export(destroy={should_destroy})")
        with CaptureStdout() as cs: learn.export(destroy=should_destroy)
        learn = load_learner(path)
        check_empty_learner(learn)
        if os.path.exists(export_file): os.remove(export_file)

    print(f"\n*** Testing learn.export to buffer")
    learn = fake_learner()
    path = learn.path

    output_buffer = io.BytesIO()
    with CaptureStdout() as cs:
        learn.export(output_buffer, destroy=should_destroy)
    input_buffer = io.BytesIO(output_buffer.getvalue())
    learn = load_learner(path, input_buffer)
    check_empty_learner(learn)


def check_empty_learner(learn):
    # export removes data, so train_items_before=0
    # also testing learn.summary here on learn created from `load_learner`
    check_learner(learn, model_summary_before=None, train_items_before=0)

    try:
        learn.summary()
    except:
        assert "This is an empty `Learner`" in str(sys.exc_info()[1])
    else:
        assert False, "should have failed"


# XXX: dupe with test_memory - integrate (moved from test_vision_train.py)
def test_model_load_mem_leak():
    "testing memory leak on load"
    pytest.xfail("memory leak in learn.load()")

    path = untar_data(URLs.MNIST_TINY)
    data = ImageDataBunch.from_folder(path, ds_tfms=([], []), bs=2)
    learn = cnn_learner(data, models.resnet18, metrics=accuracy)
    this_tests(learn.load)
    gpu_mem_reclaim() # baseline
    used_before = gpu_mem_get_used()

    name = 'mnist-tiny-test-load-mem-leak'
    model_path = learn.save(name, return_path=True)
    _ = learn.load(name)
    if os.path.exists(model_path): os.remove(model_path)
    used_after = gpu_mem_get_used()

    # models.resnet18 loaded in GPU RAM is about 50MB
    # calling learn.load() of a saved and then instantly re-loaded model shouldn't require more GPU RAM
    # XXX: currently w/o running gc.collect() this temporarily leaks memory and causes fragmentation - the fragmentation can't be tested from here, but it'll get automatically fixed once load is fixed. load() must unload first the previous model, gc.collect() and only then load the new one onto cuda.
    assert isclose(used_before, used_after, abs_tol=6), f"load() and used GPU RAM: before load(): {used_before}, after: {used_after}"

    # this shows how it should have been
    gc.collect()
    gpu_cache_clear()
    used_after_reclaimed = gpu_mem_get_used()
    # XXX: not sure where 6MB get lost still but for now it's a small leak - need to test with a bigger model
    assert isclose(used_before, used_after_reclaimed, abs_tol=6),f"load() and used GPU RAM: before load(): {used_before}, after: {used_after}, after gc.collect() {used_after_reclaimed} used"