ColossalAI/tests/test_shardformer/test_model/test_shard_mixtral.py

# modified from test_shard_mistral.py
import os

import pytest
import torch

import colossalai
from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
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 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_weight,
    get_grad_tensors_for_check,
    run_forward_backward_with_hybrid_plugin,
    unwrap_model,
)

os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"


def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
    # TODO: SGD failed for full dp
    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
        model_fn, loss_fn, test_config, pluggin_cls=MoeHybridParallelPlugin, optim_class=torch.optim.Adam
    )
    with torch.autograd.set_detect_anomaly(True):
        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

    # check last hidden state & loss
    if stage_manager is None or stage_manager.is_last_stage():
        if test_config["precision"] == "fp32":
            atol, rtol = 1e-5, 1e-3
        else:
            atol, rtol = 5e-3, 5e-3

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

    # unwrap model
    mixtral_model = unwrap_model(org_model, "MixtralModel", "model")
    shard_mixtral_model = unwrap_model(sharded_model, "MixtralModel", "model")

    row_layer_for_check = ["layers[0].self_attn.q_proj", "embed_tokens"]
    col_layer_for_check = ["layers[0].self_attn.o_proj"]

    # Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
    grads_to_check = {}
    if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
        if test_config["precision"] == "fp32":
            atol, rtol = 5e-5, 1e-4
        else:
            atol, rtol = 5e-3, 5e-3
        row_layer_grads = get_grad_tensors_for_check(
            mixtral_model,
            shard_mixtral_model,
            row_layer_for_check,
            tp_group,
            atol=atol,
            rtol=rtol,
            dim=0,
            verbose=False,
        )
        col_layer_grads = get_grad_tensors_for_check(
            mixtral_model,
            shard_mixtral_model,
            col_layer_for_check,
            tp_group,
            atol=atol,
            rtol=rtol,
            dim=1,
            verbose=False,
        )
        grads_to_check.update(col_layer_grads)
        grads_to_check.update(row_layer_grads)

    # check grads
    check_all_grad_tensors(grads_to_check)

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

    # check weights
    if stage_manager is None or stage_manager.is_first_stage():
        if test_config["precision"] == "fp32":
            atol, rtol = 2e-4, 1e-3
        else:
            atol, rtol = 5e-3, 5e-3
        check_weight(
            mixtral_model,
            shard_mixtral_model,
            col_layer_for_check,
            tp_group,
            atol=atol,
            rtol=rtol,
            dim=1,
            verbose=False,
        )

    torch.cuda.empty_cache()


@parameterize(
    "test_config",
    [
        {
            "tp_size": 1,
            "pp_size": 1,
            "ep_size": 1,
            "zero_stage": 1,
            "overlap_communication": False,
            "precision": "fp32",
        },  # [dp(4)] + [moe_dp(4)]
        {
            "tp_size": 1,
            "pp_size": 2,
            "num_microbatches": 2,
            "ep_size": 1,
            "zero_stage": 1,
            "overlap_communication": False,
            "precision": "fp32",
        },  # [dp(2) + pp(2)] + [moe_pp(2)]
        {
            "tp_size": 2,
            "pp_size": 2,
            "num_microbatches": 2,
            "ep_size": 1,
            "zero_stage": 1,
            "overlap_communication": False,
            "precision": "fp32",
        },  # [pp(2) + tp(2)] + [pp(2), replicate(2)] pass
        # {
        #     "tp_size": 1,
        #     "pp_size": 2,
        #     "num_microbatches": 2,
        #     "ep_size": 2,
        #     "zero_stage": 1,
        #     "overlap_communication": False,
        #     "precision": "fp32",
        # },  # [dp(2) + pp(2)] + [ep(4))]
        # {
        #     "tp_size": 1,
        #     "pp_size": 1,
        #     "ep_size": 2,
        #     "zero_stage": 0,
        #     "overlap_communication": False,
        #     "precision": "fp32",
        # },  # [dp(4)] + [ep(2) + moe_tp(2)]
        # {
        #     "tp_size": 1,
        #     "pp_size": 1,
        #     "ep_size": 4,
        #     "overlap_communication": False,
        #     "zero_stage": 0,
        #     "precision": "fp32"
        # },  # full dp for non-moe and full ep for moe
    ],
)
def run_mixtral_test(test_config):
    sub_model_zoo = model_zoo.get_sub_registry("transformers_mixtral")

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


def check_mixtral(rank, world_size, port):
    disable_existing_loggers()
    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
    run_mixtral_test()


@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_mixtral():
    spawn(check_mixtral, 4)


if __name__ == "__main__":
    test_mixtral()