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GuangyaoZhang
301
tests/test_shardformer/test_model/test_shard_command.py
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301
tests/test_shardformer/test_model/test_shard_command.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.logging import disable_existing_loggers
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from colossalai.shardformer import PipelineGradientCheckpointConfig
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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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enable_gradient_checkpointing = test_config.pop("enable_gradient_checkpointing", False)
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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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if enable_gradient_checkpointing:
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# org_model.gradient_checkpointing_enable()
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sharded_model.unwrap().gradient_checkpointing_enable()
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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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# unwrap model
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command_model = unwrap_model(org_model, "CohereModel", "model")
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shard_command_model = unwrap_model(sharded_model, "CohereModel", "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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# Here we check the grad of layernorm because an all-reduce operation should be performed during sequence parallelism
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norm_layer_for_check = ["layers[0].input_layernorm", "layers[1].input_layernorm"]
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# During pipeline parallelism, we cannot get the grad of norm layer during first stage, so we only check this when pp is not enbaled
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if stage_manager is None:
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norm_layer_for_check.append("norm")
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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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and 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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for p1, p2 in zip(command_model.parameters(), sharded_optimizer._master_param_groups_of_current_rank[0]):
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working_p = sharded_optimizer._param_store.master_to_working_param[id(p2)]
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grads = sharded_optimizer._grad_store.get_partitioned_gradients_by_param_id(0, id(working_p))
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grad_index = (
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0 if sharded_optimizer._grad_store._partition_grads else sharded_optimizer._bucket_store.zero_local_rank
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)
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grad = grads[grad_index]
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sharded_grad = p1.grad.view(-1).chunk(dist.get_world_size())[dist.get_rank()]
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assert_close(sharded_grad, grad[: sharded_grad.shape[0]], 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(ignore_chunk=True)) and booster.plugin.zero_stage == 0:
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if test_config["precision"] == "fp32":
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atol, rtol = 1e-6, 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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command_model, shard_command_model, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False
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)
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col_layer_grads = get_grad_tensors_for_check(
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command_model, shard_command_model, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
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)
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norm_layer_grads = get_grad_tensors_for_check(
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command_model,
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shard_command_model,
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norm_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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grads_to_check.update(norm_layer_grads)
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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(ignore_chunk=True):
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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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if org_model.__class__.__name__ == "CohereModel":
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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 stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True):
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if test_config["precision"] == "fp32":
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atol, rtol = 1e-4, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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check_weight(
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command_model, shard_command_model, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
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)
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# check grads
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check_all_grad_tensors(grads_to_check)
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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": 1,
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"num_microbatches": 1,
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"enable_sequence_parallelism": True,
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"sequence_parallelism_mode": "ring",
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"enable_flash_attention": True,
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"use_lazy_init": True,
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"zero_stage": 2,
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"precision": "fp16",
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"initial_scale": 1,
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},
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{
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"tp_size": 4,
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"pp_size": 1,
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"num_microbatches": 1,
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"enable_sequence_parallelism": True,
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"sequence_parallelism_mode": "split_gather",
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"enable_flash_attention": False,
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"use_lazy_init": True,
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"precision": "fp16",
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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": 1,
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"sp_size": 2,
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"num_microbatches": 1,
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"enable_sequence_parallelism": True,
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"sequence_parallelism_mode": "all_to_all",
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"use_lazy_init": True,
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"zero_stage": 2,
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"precision": "fp16",
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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": 2,
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"enable_all_optimization": True,
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"use_lazy_init": True,
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"precision": "fp16",
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"initial_scale": 1,
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"enable_gradient_checkpointing": True,
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"gradient_checkpoint_config": PipelineGradientCheckpointConfig(gradient_checkpointing_ratio=0.5),
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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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"use_lazy_init": False,
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"precision": "fp32",
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"enable_gradient_checkpointing": True,
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"gradient_checkpoint_config": PipelineGradientCheckpointConfig(num_ckpt_layers_per_stage=[4, 0]),
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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": True,
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"use_lazy_init": True,
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"zero_stage": 2,
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"precision": "fp16",
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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": 2,
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"enable_all_optimization": True,
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"use_lazy_init": True,
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"zero_stage": 1,
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"precision": "fp16",
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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_command_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_command", "transformers_command_for_casual_lm")
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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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"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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"zero_stage": 1,
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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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"pp_style": "interleaved",
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"num_model_chunks": 2,
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"num_microbatches": 4,
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"enable_all_optimization": False,
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"precision": "fp16",
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"zero_stage": 1,
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"initial_scale": 1,
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"enable_gradient_checkpointing": True,
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"gradient_checkpoint_config": PipelineGradientCheckpointConfig(
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num_ckpt_layers_per_stage=[0, 1, 2, 2],
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),
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},
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],
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)
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def run_command_3d_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_command", "transformers_command_for_casual_lm")
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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_command(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_command_test()
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def check_command_3d(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_command_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_command():
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spawn(check_command, 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_command_3d():
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spawn(check_command_3d, 8)
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if __name__ == "__main__":
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test_command()
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test_command_3d()
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