mirror of https://github.com/hpcaitech/ColossalAI
226 lines
7.7 KiB
Python
226 lines
7.7 KiB
Python
# modified from test_shard_mistral.py
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import os
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import pytest
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import torch
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import torch.distributed as dist
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from torch.testing import assert_close
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import colossalai
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from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
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from colossalai.logging import disable_existing_loggers
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from colossalai.shardformer.layer.utils import Randomizer
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from colossalai.tensor.d_tensor.api import clear_layout_converter
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from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
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from tests.kit.model_zoo import model_zoo
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from tests.test_shardformer.test_model._utils import (
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build_model_from_hybrid_plugin,
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check_all_grad_tensors,
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check_loss,
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check_output_hidden_state,
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check_weight,
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get_grad_tensors_for_check,
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run_forward_backward_with_hybrid_plugin,
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unwrap_model,
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)
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os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
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def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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# TODO: SGD failed for full dp
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org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
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model_fn, loss_fn, test_config, pluggin_cls=MoeHybridParallelPlugin, optim_class=torch.optim.SGD
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)
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org_model = org_model.to(torch.float16)
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org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
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org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
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)
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print(org_output.last_hidden_state.shape, sharded_output.last_hidden_state.shape)
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stage_manager = booster.plugin.stage_manager
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tp_group = booster.plugin.tp_group
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# check last hidden state & loss
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if stage_manager is None or stage_manager.is_last_stage():
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if test_config["precision"] == "fp32":
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atol, rtol = 1e-5, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
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check_output_hidden_state(org_output, sharded_output, stage_manager, atol, rtol)
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# unwrap model
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mixtral_model = unwrap_model(org_model, "MixtralModel", "model")
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shard_mixtral_model = unwrap_model(sharded_model, "MixtralModel", "model")
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row_layer_for_check = ["layers[0].self_attn.q_proj", "embed_tokens"]
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col_layer_for_check = ["layers[0].self_attn.o_proj"]
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# Check the grad when using ZeRO-1 and ZeRO-2
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if (
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# booster.plugin.zero_stage in [1, 2]
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booster.plugin.shard_config.enable_sequence_parallelism
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and booster.plugin.shard_config.sequence_parallelism_mode == "all_to_all"
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):
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rank = dist.get_rank()
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# for p1, p2 in zip(mixtral_model.parameters(), sharded_optimizer._master_param_groups_of_current_rank[0]):
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for (n1, p1), (n2, p2) in zip(mixtral_model.named_parameters(), shard_mixtral_model.named_parameters()):
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assert_close(p1.grad, p2.grad, atol=5e-3, rtol=5e-3, check_dtype=False)
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# Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
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grads_to_check = {}
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if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
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if test_config["precision"] == "fp32":
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atol, rtol = 5e-5, 1e-4
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else:
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atol, rtol = 5e-3, 5e-3
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row_layer_grads = get_grad_tensors_for_check(
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mixtral_model,
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shard_mixtral_model,
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row_layer_for_check,
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tp_group,
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atol=atol,
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rtol=rtol,
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dim=0,
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verbose=False,
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)
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col_layer_grads = get_grad_tensors_for_check(
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mixtral_model,
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shard_mixtral_model,
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col_layer_for_check,
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tp_group,
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atol=atol,
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rtol=rtol,
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dim=1,
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verbose=False,
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)
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grads_to_check.update(col_layer_grads)
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grads_to_check.update(row_layer_grads)
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# check grads
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# print(grads_to_check)
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check_all_grad_tensors(grads_to_check)
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for (n1, p1), (n2, p2) in zip(mixtral_model.named_parameters(), shard_mixtral_model.named_parameters()):
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assert_close(p1, p2, atol=5e-3, rtol=5e-3, check_dtype=False)
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# optimizer executes step
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org_optimizer.step()
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sharded_optimizer.step()
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for (n1, p1), (n2, p2) in zip(mixtral_model.named_parameters(), shard_mixtral_model.named_parameters()):
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assert_close(p1, p2, atol=5e-3, rtol=5e-3, check_dtype=False)
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# check weights
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if stage_manager is None or stage_manager.is_first_stage():
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if test_config["precision"] == "fp32":
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atol, rtol = 2e-4, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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try:
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check_weight(
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mixtral_model,
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shard_mixtral_model,
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col_layer_for_check,
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tp_group,
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atol=atol,
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rtol=rtol,
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dim=1,
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verbose=False,
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)
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except Exception as e:
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rank = dist.get_rank()
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print(f"{rank=}, Failed config: {test_config}")
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raise e
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torch.cuda.empty_cache()
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@parameterize(
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"test_config",
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[
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# {
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# "tp_size": 1,
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# "pp_size": 2,
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# "num_microbatches": 2,
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# "ep_size": 2,
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# "zero_stage": 1,
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# "overlap_communication": False,
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# "precision": "fp32",
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# }, # [dp(4)] + [moe_dp(4)]
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# {
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# "tp_size": 1,
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# "pp_size": 2,
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# "num_microbatches": 2,
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# "ep_size": 2,
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# "zero_stage": 1,
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# "overlap_communication": False,
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# "precision": "fp32",
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# }, # [dp(2) + pp(2)] + [moe_pp(2)]
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# {
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# "tp_size": 2,
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# "pp_size": 2,
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# "num_microbatches": 2,
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# "ep_size": 2,
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# "zero_stage": 1,
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# "overlap_communication": False,
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# "precision": "fp32",
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# }, # [pp(2) + tp(2)] + [pp(2), replicate(2)] pass
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{ # Ulysess + Flash attention
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"tp_size": 1,
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"pp_size": 1,
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"sp_size": 4,
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"ep_size": 1,
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"enable_sequence_parallelism": True,
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"sequence_parallelism_mode": "all_to_all",
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"zero_stage": 0,
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"overlap_communication": False,
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"precision": "fp16",
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"initial_scale": 1,
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"find_unused_parameters": True,
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},
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# {
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# "tp_size": 1,
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# "pp_size": 1,
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# "ep_size": 2,
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# "zero_stage": 0,
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# "overlap_communication": False,
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# "precision": "fp32",
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# }, # [dp(4)] + [ep(2) + moe_tp(2)]
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# {
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# "tp_size": 1,
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# "pp_size": 1,
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# "ep_size": 4,
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# "overlap_communication": False,
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# "zero_stage": 0,
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# "precision": "fp32"
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# }, # full dp for non-moe and full ep for moe
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],
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)
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def run_mixtral_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_mixtral")
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for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
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check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
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clear_layout_converter()
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Randomizer.reset_index()
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torch.cuda.empty_cache()
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def check_mixtral(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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run_mixtral_test()
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_mixtral():
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spawn(check_mixtral, 4)
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if __name__ == "__main__":
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test_mixtral()
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