benchmarks/fp8/transformer_engine/distrib_deepspeed.py

# Copyright 2024 The HuggingFace Inc. team. 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.

"""
This script tests to ensure that `accelerate` performs at the same level as raw `TransformersEngine`.

This particular script verifies this for DDP training.
"""

from unittest.mock import patch

import deepspeed
import evaluate
import torch
import transformer_engine.common.recipe as te_recipe
import transformer_engine.pytorch as te
from fp8_utils import evaluate_model, get_named_parameters, get_training_utilities
from transformer_engine.common.recipe import DelayedScaling

from accelerate import Accelerator, DeepSpeedPlugin
from accelerate.state import AcceleratorState
from accelerate.utils import FP8RecipeKwargs, set_seed
from accelerate.utils.transformer_engine import convert_model


MODEL_NAME = "bert-base-cased"
METRIC = evaluate.load("glue", "mrpc")


def train_baseline(zero_stage: int = 1):
    # This forces transformers to think Zero-3 Init should be used
    with patch("transformers.integrations.deepspeed.is_deepspeed_zero3_enabled") as mock:
        mock.return_value = zero_stage == 3
    set_seed(42)

    accelerator = Accelerator()
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
        MODEL_NAME, accelerator=accelerator
    )

    # Convert the model to TE
    old_named_params = get_named_parameters(model)

    with torch.no_grad():
        convert_model(model)
    new_named_params = get_named_parameters(model)

    mapping = {p: new_named_params[n] for n, p in old_named_params.items()}
    for param_group in optimizer.param_groups:
        param_group["params"] = [mapping[p] for p in param_group["params"]]

    FP8_RECIPE_KWARGS = {"fp8_format": te_recipe.Format.HYBRID, "amax_history_len": 32, "amax_compute_algo": "max"}
    fp8_recipe = DelayedScaling(**FP8_RECIPE_KWARGS)

    import numpy as np

    config = {
        "train_batch_size": 16,
        "train_micro_batch_size_per_gpu": 16,
        "gradient_accumulation_steps": 1,
        "zero_optimization": {
            "stage": zero_stage,
            "offload_optimizer": {"device": "none", "nvme_path": None},
            "offload_param": {"device": "none", "nvme_path": None},
            "stage3_gather_16bit_weights_on_model_save": False,
        },
        "gradient_clipping": 1.0,
        "steps_per_print": np.inf,
        "bf16": {"enabled": True},
        "fp16": {"enabled": False},
        "zero_allow_untested_optimizer": True,
    }

    (
        model,
        optimizer,
        _,
        _,
    ) = deepspeed.initialize(
        model=model,
        optimizer=optimizer,
        config_params=config,
    )

    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
    model.train()

    model_outputs = []
    data = []

    for _ in range(2):
        for batch in train_dataloader:
            with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
                outputs = model(**batch)
                data.append(batch.to("cpu"))
            model_outputs.append(outputs.logits.to("cpu"))
            loss = outputs.loss
            model.backward(loss)
            model.step()
            for _ in range(accelerator.num_processes):
                lr_scheduler.step()

    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
    model.destroy()
    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
    )
    assert trained_model_results["f1"] > base_model_results["f1"], (
        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
    )

    return base_model_results, trained_model_results, model_outputs, data


def train_integration(zero_stage: int = 1):
    set_seed(42)
    FP8_RECIPE_KWARGS = {"fp8_format": "HYBRID", "amax_history_len": 32, "amax_compute_algo": "max"}
    kwargs_handlers = [FP8RecipeKwargs(backend="TE", **FP8_RECIPE_KWARGS)]
    AcceleratorState()._reset_state(True)
    deepspeed_plugin = DeepSpeedPlugin(
        zero_stage=zero_stage,
        zero3_init_flag=zero_stage == 3,
    )
    accelerator = Accelerator(
        mixed_precision="fp8", kwargs_handlers=kwargs_handlers, deepspeed_plugin=deepspeed_plugin
    )
    accelerator.state.deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"] = 16

    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
        MODEL_NAME, accelerator=accelerator
    )

    model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
    model.train()
    model_outputs = []
    data = []
    for _ in range(2):
        for batch in train_dataloader:
            outputs = model(**batch)
            data.append(batch.to("cpu"))
            model_outputs.append(outputs.logits.to("cpu"))
            loss = outputs.loss
            accelerator.backward(loss)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
    model.destroy()
    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
    )
    assert trained_model_results["f1"] > base_model_results["f1"], (
        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
    )

    return base_model_results, trained_model_results, model_outputs, data


if __name__ == "__main__":
    for zero_stage in [1, 2, 3]:
        baseline_not_trained, baseline_trained, baseline_outputs, baseline_data = train_baseline(zero_stage)
        accelerator_not_trained, accelerator_trained, accelerator_outputs, accelerator_data = train_integration(
            zero_stage
        )
        assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
            f"ZERO stage {zero_stage}: Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
        )
        assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
            f"ZERO stage {zero_stage}: F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
        )
        assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
            f"ZERO stage {zero_stage}: Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
        )
        assert baseline_trained["f1"] == accelerator_trained["f1"], (
            f"ZERO stage {zero_stage}: F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
        )

        torch.distributed.destroy_process_group()