зеркало из https://github.com/microsoft/DeepSpeed.git
Re-enable GPT-J unit tests and refactor inference tests (#3618)
This commit is contained in:
Michael Wyatt
GitHub
Родитель
7726fc8d54
Коммит
78b7693591
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@ -0,0 +1,5 @@
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[flake8]
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ignore = E,F403,F405,F541,F841,W
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select = E9,F,W6
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per-file-ignores =
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__init__.py:F401
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@ -67,7 +67,7 @@ repos:
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rev: 4.0.1
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hooks:
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- id: flake8
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args: ['--ignore=E,F403,F405,F541,F841,W', '--select=E9,F,W6', '--per-file-ignores=__init__.py:F401']
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args: ['--config=.flake8']
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- repo: local
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hooks:
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@ -6,3 +6,4 @@ markers =
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inference_ops:Individual inference operator tests
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seq_inference:Inference model tests to run sequentially
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nightly:Tests that should be run nightly
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world_size:Change world size of individual tests in a class
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@ -49,75 +49,57 @@ _gpt_models = [
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"gpt2",
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"distilgpt2",
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"Norod78/hebrew-bad_wiki-gpt_neo-tiny",
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"EleutherAI/gpt-j-6B", # bring back this model as we did not catch an error before by merging some changes! TODO: we need to fix the OOM issue later!
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"EleutherAI/gpt-j-6b",
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"EleutherAI/pythia-70m-deduped",
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"bigscience/bloom-560m",
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]
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_opt_models = [
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"facebook/opt-125m", # 125m, 1.7B, ..., 175B variants have the same model architecture.
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"facebook/opt-350m", # 350m applies layer norm after attention layer which is different than other variants.
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]
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_all_models = HfApi().list_models()
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test_models = set(_bert_models + _roberta_models + _gpt_models + _opt_models)
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test_tasks = [
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_test_models = set(_bert_models + _roberta_models + _gpt_models + _opt_models)
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_test_tasks = [
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"fill-mask", "question-answering", "text-classification", "token-classification", "text-generation",
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"text2text-generation", "summarization", "translation"
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]
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pytest.all_models = {task: [m.modelId for m in _all_models if m.pipeline_tag == task] for task in test_tasks}
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_model_w_tasks = itertools.product(*[test_models, test_tasks])
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# Get a list of all models and mapping from task to supported models
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_hf_models = HfApi().list_models()
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_hf_model_names = [m.modelId for m in _hf_models]
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_hf_task_to_models = {task: [m.modelId for m in _hf_models if m.pipeline_tag == task] for task in _test_tasks}
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# Get all combinations of task:model to test
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_model_w_tasks = [(m, t) for m, t in itertools.product(*[_test_models, _test_tasks]) if m in _hf_task_to_models[t]]
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# Assign to pytest variables for testing
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pytest.model_w_tasks = _model_w_tasks
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pytest.mt_names = [f"{m}-{t}" for m, t in pytest.model_w_tasks]
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def _valid_model_task(model_task):
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m, t = model_task
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return m in pytest.all_models[t]
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pytest.models_w_tasks = list(filter(_valid_model_task, _model_w_tasks))
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pytest.mt_names = [f"{m}-{t}" for m, t in pytest.models_w_tasks]
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"""
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These fixtures iterate all combinations of tasks and models, dtype, & cuda_graph
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"""
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@pytest.fixture(params=pytest.models_w_tasks, ids=pytest.mt_names)
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def model_w_task(request):
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return request.param
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@pytest.fixture(params=[torch.float, torch.half], ids=["fp32", "fp16"])
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def dtype(request):
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return request.param
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@pytest.fixture(params=[True, False], ids=["CG", "noCG"])
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def enable_cuda_graph(request):
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return request.param
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@pytest.fixture(params=[True, False], ids=["Triton", "noTriton"])
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def enable_triton(request):
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return request.param
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"""
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This fixture will validate the configuration
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"""
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@pytest.fixture(scope="module", autouse=True)
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def verify_models():
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# Verify all test models are registered in HF
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_test_models_not_found = [m for m in _test_models if m not in _hf_model_names]
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if _test_models_not_found:
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pytest.fail(f"Model(s) not found in HuggingFace: {_test_models_not_found}")
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# Verify all models are assigned to at least one task
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_models_to_be_tested = set(m for m, t in _model_w_tasks)
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_missing_task_models = _models_to_be_tested.difference(_test_models)
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if _missing_task_models:
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pytest.fail(f"Model(s) do not have an assigned task: {_missing_task_models}")
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# Fixture to add skips for certain configurations
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@pytest.fixture()
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def invalid_model_task_config(model_w_task, dtype, enable_cuda_graph, enable_triton):
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def invalid_test(model_w_task, dtype, enable_cuda_graph, enable_triton):
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model, task = model_w_task
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msg = ""
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if pkg_version.parse(torch.__version__) <= pkg_version.parse("1.2"):
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msg = "DS inference injection doesn't work well on older torch versions"
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elif model not in pytest.all_models[task]:
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msg = f"Not a valid model / task combination: {model} / {task}"
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elif enable_cuda_graph and (torch_info["cuda_version"] == "0.0"):
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if enable_cuda_graph and (torch_info["cuda_version"] == "0.0"):
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msg = "CUDA not detected, cannot use CUDA Graph"
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elif enable_cuda_graph and pkg_version.parse(torch.__version__) < pkg_version.parse("1.10"):
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msg = "CUDA Graph is only available in torch versions >= 1.10"
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elif "gpt-j-6B" in model:
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elif "gpt-j-6b" in model:
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if dtype != torch.half:
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msg = f"Not enough GPU memory to run {model} with dtype {dtype}"
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elif enable_cuda_graph:
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@ -139,10 +121,30 @@ def invalid_model_task_config(model_w_task, dtype, enable_cuda_graph, enable_tri
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return msg
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"""
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These fixtures can be used to customize the query, inference args, and assert
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statement for each combination of model /task
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"""
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""" Fixtures for inference config """
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@pytest.fixture(params=pytest.model_w_tasks, ids=pytest.mt_names)
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def model_w_task(request):
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return request.param
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@pytest.fixture(params=[torch.float, torch.half], ids=["fp32", "fp16"])
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def dtype(request):
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return request.param
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@pytest.fixture(params=[True, False], ids=["CG", "noCG"])
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def enable_cuda_graph(request):
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return request.param
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@pytest.fixture(params=[True, False], ids=["Triton", "noTriton"])
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def enable_triton(request):
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return request.param
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""" Fixtures for running query """
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@pytest.fixture
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@ -178,7 +180,7 @@ def query(model_w_task):
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def inf_kwargs(model_w_task):
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model, task = model_w_task
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if task == "text-generation":
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if model == "EleutherAI/gpt-j-6B":
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if model == "EleutherAI/gpt-j-6b":
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# This model on V100 is hitting memory problems that limit the number of output tokens
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return {"do_sample": False, "max_length": 12}
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return {"do_sample": False, "max_length": 20}
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@ -186,6 +188,9 @@ def inf_kwargs(model_w_task):
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return {}
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""" Assertion fixture for verifying model outputs """
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def fill_mask_assert(x, y):
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return set(res["token_str"] for res in x) == set(res["token_str"] for res in y)
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@ -237,6 +242,7 @@ def assert_fn(model_w_task):
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return assert_fn
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# Used to verify DeepSpeed kernel injection worked with a model
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def check_injection(model):
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def verify_injection(module):
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@ -251,16 +257,12 @@ def check_injection(model):
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verify_injection(model)
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"""
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Tests
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"""
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@pytest.mark.inference
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class TestModelTask(DistributedTest):
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world_size = 1
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def test(self,
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def test(
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self,
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model_w_task,
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dtype,
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enable_cuda_graph,
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@ -268,10 +270,11 @@ class TestModelTask(DistributedTest):
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query,
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inf_kwargs,
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assert_fn,
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invalid_model_task_config,
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perf_meas=True):
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if invalid_model_task_config:
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pytest.skip(invalid_model_task_config)
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invalid_test,
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perf_meas=True,
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):
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if invalid_test:
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pytest.skip(invalid_test)
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model, task = model_w_task
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local_rank = int(os.getenv("LOCAL_RANK", "0"))
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@ -338,10 +341,10 @@ class TestModelTask(DistributedTest):
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@pytest.mark.parametrize("model_w_task", [("EleutherAI/gpt-neo-1.3B", "text-generation"),
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("EleutherAI/gpt-neox-20b", "text-generation"),
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("bigscience/bloom-3b", "text-generation"),
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("EleutherAI/gpt-j-6B", "text-generation")],
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("EleutherAI/gpt-j-6b", "text-generation")],
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ids=["gpt-neo", "gpt-neox", "bloom", "gpt-j"])
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class TestMPSize(DistributedTest):
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world_size = 4
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world_size = 2
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def test(
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self,
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@ -350,10 +353,10 @@ class TestMPSize(DistributedTest):
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query,
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inf_kwargs,
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assert_fn,
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invalid_model_task_config,
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invalid_test,
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):
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if invalid_model_task_config:
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pytest.skip(invalid_model_task_config)
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if invalid_test:
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pytest.skip(invalid_test)
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model, task = model_w_task
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local_rank = int(os.getenv("LOCAL_RANK", "0"))
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@ -402,12 +405,12 @@ class TestInjectionPolicy(DistributedTest):
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query,
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inf_kwargs,
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assert_fn,
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invalid_model_task_config,
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invalid_test,
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dtype,
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enable_cuda_graph,
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):
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if invalid_model_task_config:
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pytest.skip(invalid_model_task_config)
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if invalid_test:
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pytest.skip(invalid_test)
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model, task = model_w_task
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local_rank = int(os.getenv("LOCAL_RANK", "0"))
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@ -452,12 +455,12 @@ class TestAutoTensorParallelism(DistributedTest):
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query,
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inf_kwargs,
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assert_fn,
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invalid_model_task_config,
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invalid_test,
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dtype,
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enable_cuda_graph,
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):
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if invalid_model_task_config:
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pytest.skip(invalid_model_task_config)
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if invalid_test:
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pytest.skip(invalid_test)
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model, task = model_w_task
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local_rank = int(os.getenv("LOCAL_RANK", "0"))
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@ -483,7 +486,7 @@ class TestAutoTensorParallelism(DistributedTest):
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"model_family, model_name",
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(
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["gpt2", "EleutherAI/gpt-neo-2.7B"],
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["gpt2", "EleutherAI/gpt-j-6B"],
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["gpt2", "EleutherAI/gpt-j-6b"],
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["gpt2", "gpt2-xl"],
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),
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)
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@ -503,7 +506,7 @@ class TestLMCorrectness(DistributedTest):
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dtype = torch.float
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task_dict = lm_eval.tasks.get_task_dict([task])
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if 'gpt-j-6B' in model_name:
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if 'gpt-j-6b' in model_name:
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dtype = torch.half
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lm = lm_eval.models.get_model(model_family).create_from_arg_string(f"pretrained={model_name}",
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{"device": "cpu"})
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@ -13,43 +13,17 @@ from unit.common import DistributedTest
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from deepspeed.accelerator import get_accelerator
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@pytest.fixture
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def query(model, task):
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if task == "text-generation":
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return "DeepSpeed is"
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elif task == "fill-mask":
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if "roberta" in model:
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return "I am a <mask> model"
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else:
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return "I am a [MASK] model"
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else:
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raise NotImplementedError
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@pytest.fixture
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def inf_kwargs(task):
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if task == "text-generation":
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return {"do_sample": False, "min_length": 50, "max_length": 50}
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else:
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return {}
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@pytest.mark.inference
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@pytest.mark.parametrize("model,task", [
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("bert-base-cased", "fill-mask"),
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("roberta-base", "fill-mask"),
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("gpt2", "text-generation"),
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("facebook/opt-125m", "text-generation"),
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("bigscience/bloom-560m", "text-generation"),
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])
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@pytest.mark.parametrize("cuda_graphs", [True, False])
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@pytest.mark.parametrize("use_cuda_events", [True, False])
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@pytest.mark.parametrize("enable_cuda_graph", [True, False])
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class TestModelProfiling(DistributedTest):
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world_size = 1
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def test(self, model, task, query, inf_kwargs, cuda_graphs, use_cuda_events, dtype=torch.float16):
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if cuda_graphs and "bert" not in model:
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pytest.skip(f"CUDA Graph not supported for {model}")
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def test(self, enable_cuda_graph, use_cuda_events):
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task = "fill-mask"
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model = "bert-base-cased"
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dtype = torch.float16
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query = "I am a [MASK] model"
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local_rank = int(os.getenv("LOCAL_RANK", "0"))
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world_size = int(os.getenv("WORLD_SIZE", "1"))
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@ -59,7 +33,7 @@ class TestModelProfiling(DistributedTest):
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dtype=dtype,
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mp_size=world_size,
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replace_with_kernel_inject=True,
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enable_cuda_graph=cuda_graphs)
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enable_cuda_graph=enable_cuda_graph)
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pipe.model.profile_model_time(use_cuda_events=use_cuda_events)
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e2e_times = []
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@ -68,7 +42,7 @@ class TestModelProfiling(DistributedTest):
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get_accelerator().synchronize()
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start = time.perf_counter_ns()
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r = pipe(query, **inf_kwargs)
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r = pipe(query)
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get_accelerator().synchronize()
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end = time.perf_counter_ns()
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