mirror of
https://github.com/hpcaitech/ColossalAI.git
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[feature] Add clip_grad_norm for hybrid_parallel_plugin (#4837)
* Add clip_grad_norm for hibrid_parallel_plugin * polish code * add unittests * Move tp to a higher-level optimizer interface. * bug fix * polish code
This commit is contained in:
littsk
GitHub
@@ -0,0 +1,258 @@
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import pytest
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import torch
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from torch.nn.utils.clip_grad import clip_grad_norm_
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import colossalai
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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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def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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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
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)
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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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stage_manager = booster.plugin.stage_manager
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tp_group = booster.plugin.tp_group
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bert = unwrap_model(org_model, "BertModel", "bert")
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sharded_bert = unwrap_model(sharded_model, "BertModel", "bert")
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col_layer_for_check = ["encoder.layer[0].output.dense"]
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row_layer_for_check = ["embeddings.word_embeddings", "encoder.layer[0].intermediate.dense"]
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if test_config["precision"] == "fp32":
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atol, rtol = 1e-4, 1e-3
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elif test_config["precision"] == "fp16":
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atol, rtol = 5e-3, 5e-3
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else:
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atol, rtol = 2e-2, 2e-2
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# Check grads
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# Save gradient tensors for comparison between the original model and the sharded model.
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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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col_layer_grads = get_grad_tensors_for_check(
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bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
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)
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row_layer_grads = get_grad_tensors_for_check(
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bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, 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_all_grad_tensors(grads_to_check)
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# Check gradient norm
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# Convert the gradient data of the working parameter to float and assign it to the master parameter's gradient
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# Note that this operation should have been done in the 'step' function, but it is performed here in advance for gradient norm calculation purposes.
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# Although it will be done again in the 'step' function, it does not affect correctness.
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for group in sharded_optimizer.optim.param_groups:
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for p in group["params"]:
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working_param = sharded_optimizer.master_to_working_map[p]
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if p is working_param:
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continue
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if working_param.grad is not None:
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p.grad = working_param.grad.data.float()
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working_param.grad = None
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# Create a list of parameter-gradient pairs containing working parameters and their gradients
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param_gradient_pairs = [
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(sharded_optimizer.master_to_working_map[p], p.grad)
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for group in sharded_optimizer.param_groups
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for p in group["params"]
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if p.grad is not None
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]
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origin_norm = clip_grad_norm_(org_model.parameters(), test_config["max_norm"])
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# Calculate the gradient norm of the sharded optimizer
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device = origin_norm.device
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hybrid_norm = torch.tensor(sharded_optimizer._compute_grad_norm(param_gradient_pairs)).to(device)
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# If using fp16 precision, divide by the initial scale
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if test_config["precision"] == "fp16":
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hybrid_norm /= test_config["initial_scale"]
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# Assert that the gradient norm of the original model is close to the gradient norm of the hybrid model
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assert torch.allclose(
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origin_norm, hybrid_norm, atol=atol, rtol=rtol
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), f"Original model grad norm is not equal to sharded model grad norm\n{origin_norm}\n{hybrid_norm}"
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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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# 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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elif test_config["precision"] == "fp16":
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atol, rtol = 5e-3, 5e-3
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else:
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atol, rtol = 2e-2, 2e-2
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if org_model.__class__.__name__ == "BertModel":
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check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
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check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
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# Check weights
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if test_config["precision"] == "fp32":
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atol, rtol = 5e-3, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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if stage_manager is None or stage_manager.is_first_stage():
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check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
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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": 4,
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"enable_all_optimization": False,
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"use_lazy_init": True,
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"precision": "fp16",
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"max_norm": 5,
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"initial_scale": 1,
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},
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{
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"tp_size": 2,
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"pp_size": 1,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "fp16",
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"max_norm": 5,
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"initial_scale": 1,
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},
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{
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"tp_size": 2,
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"pp_size": 2,
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"num_microbatches": 4,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "fp16",
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"max_norm": 5,
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"initial_scale": 1,
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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": 4,
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"enable_all_optimization": False,
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"use_lazy_init": True,
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"precision": "bf16",
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"max_norm": 5,
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},
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{
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"tp_size": 2,
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"pp_size": 1,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "bf16",
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"max_norm": 5,
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},
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{
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"tp_size": 2,
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"pp_size": 2,
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"num_microbatches": 4,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "bf16",
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"max_norm": 5,
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},
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],
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)
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def run_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
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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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@parameterize(
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"test_config",
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[
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{
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"tp_size": 2,
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"pp_size": 2,
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"num_microbatches": 4,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "bf16",
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"max_norm": 5,
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},
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{
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"tp_size": 2,
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"pp_size": 2,
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"num_microbatches": 4,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "fp16",
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"max_norm": 5,
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"initial_scale": 1,
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},
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],
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)
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def run_3d_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
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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_grad_clip_norm(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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run_test()
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def check_grad_clip_norm_3d(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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run_3d_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_grad_clip_norm():
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spawn(check_grad_clip_norm, 4)
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@pytest.mark.largedist
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_grad_clip_norm_3d():
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spawn(check_grad_clip_norm_3d, 8)
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if __name__ == "__main__":
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test_grad_clip_norm()
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test_grad_clip_norm_3d()
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@@ -0,0 +1,197 @@
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import pytest
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import torch
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from torch.nn.utils.clip_grad import clip_grad_norm_
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import colossalai
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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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def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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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
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)
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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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stage_manager = booster.plugin.stage_manager
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tp_group = booster.plugin.tp_group
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bert = unwrap_model(org_model, "BertModel", "bert")
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sharded_bert = unwrap_model(sharded_model, "BertModel", "bert")
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col_layer_for_check = ["encoder.layer[0].output.dense"]
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row_layer_for_check = ["embeddings.word_embeddings", "encoder.layer[0].intermediate.dense"]
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if test_config["precision"] == "fp32":
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atol, rtol = 1e-4, 1e-3
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elif test_config["precision"] == "fp16":
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atol, rtol = 5e-3, 5e-3
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else:
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atol, rtol = 2e-2, 2e-2
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# Check grads
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# Save gradient tensors for comparison between the original model and the sharded model.
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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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col_layer_grads = get_grad_tensors_for_check(
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bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
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)
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row_layer_grads = get_grad_tensors_for_check(
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bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, 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_all_grad_tensors(grads_to_check)
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# Check grad norm
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param_gradient_pairs = [
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(p, p.grad) for group in sharded_optimizer.param_groups for p in group["params"] if p.grad is not None
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]
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origin_norm = clip_grad_norm_(org_model.parameters(), test_config["max_norm"])
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device = origin_norm.device
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hybrid_norm = torch.tensor(sharded_optimizer._compute_grad_norm(param_gradient_pairs)).to(device)
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assert torch.allclose(
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origin_norm, hybrid_norm, atol=atol, rtol=rtol
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), f"orgin origin model grad norm is not equal to shard model grad norm\n{origin_norm}\n{hybrid_norm}"
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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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# 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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elif test_config["precision"] == "fp16":
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atol, rtol = 5e-3, 5e-3
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else:
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atol, rtol = 2e-2, 2e-2
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if org_model.__class__.__name__ == "BertModel":
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check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
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check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
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# check weights
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if test_config["precision"] == "fp32":
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atol, rtol = 5e-3, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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if stage_manager is None or stage_manager.is_first_stage():
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check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
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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": 4,
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"enable_all_optimization": False,
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"use_lazy_init": True,
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"precision": "fp32",
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"max_norm": 5,
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},
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{
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"tp_size": 2,
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"pp_size": 1,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "fp32",
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"max_norm": 5,
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},
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{
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"tp_size": 2,
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"pp_size": 2,
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"num_microbatches": 4,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "fp32",
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"max_norm": 5,
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},
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],
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)
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def run_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
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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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@parameterize(
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"test_config",
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[
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{
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"tp_size": 2,
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"pp_size": 2,
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"num_microbatches": 4,
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"enable_all_optimization": False,
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"use_lazy_init": False,
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"precision": "fp32",
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"max_norm": 5,
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},
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],
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)
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def run_3d_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
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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_grad_clip_norm(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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run_test()
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||||
|
||||
|
||||
def check_grad_clip_norm_3d(rank, world_size, port):
|
||||
disable_existing_loggers()
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
||||
run_3d_test()
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@rerun_if_address_is_in_use()
|
||||
@clear_cache_before_run()
|
||||
def test_grad_clip_norm():
|
||||
spawn(check_grad_clip_norm, 4)
|
||||
|
||||
|
||||
@pytest.mark.largedist
|
||||
@rerun_if_address_is_in_use()
|
||||
@clear_cache_before_run()
|
||||
def test_grad_clip_norm_3d():
|
||||
spawn(check_grad_clip_norm_3d, 8)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_grad_clip_norm()
|
||||
test_grad_clip_norm_3d()
|
||||
@@ -0,0 +1,241 @@
|
||||
import math
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from torch.nn.utils.clip_grad import clip_grad_norm_
|
||||
|
||||
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 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_loss,
|
||||
check_output_hidden_state,
|
||||
check_weight,
|
||||
run_forward_backward_with_hybrid_plugin,
|
||||
unwrap_model,
|
||||
)
|
||||
|
||||
|
||||
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
|
||||
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
|
||||
|
||||
bert = unwrap_model(org_model, "BertModel", "bert")
|
||||
sharded_bert = unwrap_model(sharded_model, "BertModel", "bert")
|
||||
|
||||
col_layer_for_check = ["encoder.layer[0].output.dense"]
|
||||
|
||||
if test_config["precision"] == "fp32":
|
||||
atol, rtol = 1e-4, 1e-3
|
||||
elif test_config["precision"] == "fp16":
|
||||
atol, rtol = 5e-3, 5e-3
|
||||
else:
|
||||
atol, rtol = 2e-2, 2e-2
|
||||
|
||||
dist.barrier()
|
||||
# Check gradient norm
|
||||
origin_norm = clip_grad_norm_(org_model.parameters(), test_config["max_norm"])
|
||||
|
||||
# Calculate the gradient norm of the sharded optimizer
|
||||
device = origin_norm.device
|
||||
norm_groups = []
|
||||
for group_id in range(sharded_optimizer.num_param_groups):
|
||||
working_grads = sharded_optimizer._grad_store.get_working_grads_by_group_id(group_id)
|
||||
norm_group = sharded_optimizer._compute_grad_norm(gradients=working_grads)
|
||||
norm_groups.append(norm_group)
|
||||
total_norm = 0.0
|
||||
for norm in norm_groups:
|
||||
total_norm += norm**2.0
|
||||
hybrid_norm = torch.tensor(math.sqrt(total_norm)).to(device)
|
||||
|
||||
# If using fp16 precision, divide by the initial scale
|
||||
if test_config["precision"] == "fp16":
|
||||
hybrid_norm /= test_config["initial_scale"]
|
||||
|
||||
# Assert that the gradient norm of the original model is close to the gradient norm of the hybrid model
|
||||
assert torch.allclose(
|
||||
origin_norm, hybrid_norm, atol=atol, rtol=rtol
|
||||
), f"Original model grad norm is not equal to sharded model grad norm\n{origin_norm}\n{hybrid_norm}"
|
||||
|
||||
# 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 = 1e-5, 1e-3
|
||||
elif test_config["precision"] == "fp16":
|
||||
atol, rtol = 5e-3, 5e-3
|
||||
else:
|
||||
atol, rtol = 2e-2, 2e-2
|
||||
if org_model.__class__.__name__ == "BertModel":
|
||||
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-3, 1e-3
|
||||
else:
|
||||
atol, rtol = 5e-3, 5e-3
|
||||
if stage_manager is None or stage_manager.is_first_stage():
|
||||
check_weight(bert, sharded_bert, 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": 2,
|
||||
"num_microbatches": 4,
|
||||
"zero_stage": 1,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": True,
|
||||
"precision": "fp16",
|
||||
"max_norm": 5,
|
||||
"initial_scale": 1,
|
||||
},
|
||||
{
|
||||
"tp_size": 2,
|
||||
"pp_size": 1,
|
||||
"zero_stage": 1,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": False,
|
||||
"precision": "fp16",
|
||||
"max_norm": 5,
|
||||
"initial_scale": 1,
|
||||
},
|
||||
{
|
||||
"tp_size": 2,
|
||||
"pp_size": 1,
|
||||
"zero_stage": 2,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": False,
|
||||
"precision": "fp16",
|
||||
"max_norm": 5,
|
||||
"initial_scale": 1,
|
||||
},
|
||||
{
|
||||
"tp_size": 1,
|
||||
"pp_size": 2,
|
||||
"num_microbatches": 4,
|
||||
"zero_stage": 1,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": True,
|
||||
"precision": "bf16",
|
||||
"max_norm": 5,
|
||||
},
|
||||
{
|
||||
"tp_size": 2,
|
||||
"pp_size": 1,
|
||||
"zero_stage": 1,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": False,
|
||||
"precision": "bf16",
|
||||
"max_norm": 5,
|
||||
},
|
||||
{
|
||||
"tp_size": 2,
|
||||
"pp_size": 1,
|
||||
"zero_stage": 2,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": False,
|
||||
"precision": "bf16",
|
||||
"max_norm": 5,
|
||||
},
|
||||
],
|
||||
)
|
||||
def run_test(test_config):
|
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
|
||||
|
||||
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()
|
||||
|
||||
|
||||
@parameterize(
|
||||
"test_config",
|
||||
[
|
||||
{
|
||||
"tp_size": 2,
|
||||
"pp_size": 2,
|
||||
"num_microbatches": 4,
|
||||
"zero_stage": 1,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": False,
|
||||
"precision": "bf16",
|
||||
"max_norm": 5,
|
||||
},
|
||||
{
|
||||
"tp_size": 2,
|
||||
"pp_size": 2,
|
||||
"num_microbatches": 4,
|
||||
"zero_stage": 1,
|
||||
"enable_all_optimization": False,
|
||||
"use_lazy_init": False,
|
||||
"precision": "fp16",
|
||||
"max_norm": 5,
|
||||
"initial_scale": 1,
|
||||
},
|
||||
],
|
||||
)
|
||||
def run_3d_test(test_config):
|
||||
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
|
||||
|
||||
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_grad_clip_norm(rank, world_size, port):
|
||||
disable_existing_loggers()
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
||||
run_test()
|
||||
|
||||
|
||||
def check_grad_clip_norm_3d(rank, world_size, port):
|
||||
disable_existing_loggers()
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
||||
run_3d_test()
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@rerun_if_address_is_in_use()
|
||||
@clear_cache_before_run()
|
||||
def test_grad_clip_norm():
|
||||
spawn(check_grad_clip_norm, 4)
|
||||
|
||||
|
||||
@pytest.mark.largedist
|
||||
@rerun_if_address_is_in_use()
|
||||
@clear_cache_before_run()
|
||||
def test_grad_clip_norm_3d():
|
||||
spawn(check_grad_clip_norm_3d, 8)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_grad_clip_norm()
|
||||
test_grad_clip_norm_3d()
|
||||
Reference in New Issue
Block a user