ColossalAI/tests/test_shardformer/test_model/test_shard_whisper.py

import pytest
import torch

import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.testing import (
    assert_hf_output_close,
    clear_cache_before_run,
    parameterize,
    rerun_if_address_is_in_use,
    spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import (
    build_model_from_hybrid_plugin,
    check_all_grad_tensors,
    check_loss,
    check_output_hidden_state,
    check_weight,
    get_grad_tensors_for_check,
    run_forward_backward_with_hybrid_plugin,
)


def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
    # check forward
    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \
        build_model_from_hybrid_plugin(model_fn, loss_fn, test_config)

    org_loss, org_output, sharded_loss, sharded_output = \
        run_forward_backward_with_hybrid_plugin(
            org_model,
            sharded_model,
            sharded_optimizer,
            data_gen_fn,
            output_transform_fn,
            criterion,
            booster)

    stage_manager = booster.plugin.stage_manager
    tp_group = booster.plugin.tp_group

    # unwarp the model
    if org_model.__class__.__name__ == 'WhisperForConditionalGeneration':
        whisper = org_model.model
        sharded_whisper = sharded_model.unwrap().model
    else:
        whisper = org_model
        sharded_whisper = sharded_model.unwrap()

    # check grad
    if org_model.__class__.__name__ == 'WhisperForAudioClassification':
        col_layer_for_check = ['encoder.layers[0].self_attn.q_proj']
        row_layer_for_check = ['encoder.layers[0].self_attn.out_proj']
    else:
        col_layer_for_check = [
            'encoder.layers[0].self_attn.q_proj',
        # 'decoder.layers[0].self_attn.q_proj'
        ]
        row_layer_for_check = [
            'encoder.layers[0].self_attn.out_proj',
        #'decoder.layers[0].self_attn.out_proj'
        ]

    # Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
    grads_to_check = {}
    if test_config['precision'] == 'fp32':
        atol, rtol = 2e-4, 2e-4
    else:
        atol, rtol = 5e-3, 5e-3

    if stage_manager is None or stage_manager.is_first_stage():
        row_layer_grads = get_grad_tensors_for_check(whisper,
                                                     sharded_whisper,
                                                     row_layer_for_check,
                                                     tp_group,
                                                     atol=atol,
                                                     rtol=rtol,
                                                     dim=1)
        col_layer_grads = get_grad_tensors_for_check(whisper,
                                                     sharded_whisper,
                                                     col_layer_for_check,
                                                     tp_group,
                                                     atol=atol,
                                                     rtol=rtol,
                                                     dim=0)
        grads_to_check.update(col_layer_grads)
        grads_to_check.update(row_layer_grads)

    # optimizer executes step
    org_optimizer.step()
    sharded_optimizer.step()

    # check last hidden state & loss
    if stage_manager is None or stage_manager.is_last_stage():
        if test_config['precision'] == 'fp32':
            atol, rtol = 2e-4, 2e-4
        else:
            atol, rtol = 5e-3, 5e-3

        if org_model.__class__.__name__ == 'WhisperModel':
            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)

        check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)

    # check weights
    if test_config['precision'] == 'fp32':
        atol, rtol = 5e-4, 5e-4
    else:
        atol, rtol = 5e-3, 5e-3
    if stage_manager is None or stage_manager.is_first_stage():
        check_weight(whisper,
                     sharded_whisper,
                     row_layer_for_check,
                     tp_group,
                     atol=atol,
                     rtol=rtol,
                     dim=1,
                     verbose=False)
        check_weight(whisper,
                     sharded_whisper,
                     col_layer_for_check,
                     tp_group,
                     atol=atol,
                     rtol=rtol,
                     dim=0,
                     verbose=False)

    # check grads
    check_all_grad_tensors(grads_to_check)

    torch.cuda.empty_cache()


#TODO fix WhisperForConditionalGeneration enable jit fused operato
# TODO（jianghai) fix fp16
@parameterize(
    'test_config',
    [
        {
            'tp_size': 2,
            'pp_size': 2,
            'num_microbatches': 2,
            'enable_all_optimization': True,
            'use_lazy_init': True,
            'precision': 'fp32',
            'initial_scale': 1,
        },
        {
            'tp_size': 1,
            'pp_size': 2,
            'num_microbatches': 4,
            'use_lazy_init': False,
            'precision': 'fp32',
            'initial_scale': 1,
        },
        {
            'tp_size': 4,
            'pp_size': 1,
            'enable_all_optimization': True,
            'use_lazy_init': False,
            'precision': 'fp32',
        },
        {
            'tp_size': 1,
            'pp_size': 4,
            'num_microbatches': 4,
            'use_lazy_init': False,
            'precision': 'fp32',
        },
    # whisper is not supported fp16 for now.
    ])
def run_whisper_test(test_config):
    sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper')
    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():

        if test_config['pp_size'] > 2 and name == 'transformers_whisper_for_audio_classification':
            continue
        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)

    clear_layout_converter()
    Randomizer.reset_index()
    torch.cuda.empty_cache()


@parameterize('test_config', [
    {
        'tp_size': 2,
        'pp_size': 2,
        'num_microbatches': 4,
        'enable_all_optimization': False,
        'use_lazy_init': False,
        'precision': 'fp32',
        'initial_scale': 1,
    },
])
def run_whisper_3d_test(test_config):
    sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper')

    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)

    clear_layout_converter()
    torch.cuda.empty_cache()


def check_whisper(rank, world_size, port):
    disable_existing_loggers()
    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    run_whisper_test()


def check_whisper_3d(rank, world_size, port):
    disable_existing_loggers()
    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    run_whisper_3d_test()


@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_whisper():
    spawn(check_whisper, 4)


@pytest.mark.largedist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_whisper_3d():
    spawn(check_whisper_3d, 8)


if __name__ == "__main__":
    test_whisper()
    test_whisper_3d()