mirror of https://github.com/hpcaitech/ColossalAI
botbw
4 months ago
committed by
Hongxin Liu
3 changed files with 1 additions and 234 deletions
@ -1,232 +0,0 @@
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# 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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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, "DeepseekModel", "model") |
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shard_mixtral_model = unwrap_model(sharded_model, "DeepseekModel", "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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name_to_p = {n: p for n, p in mixtral_model.named_parameters()} |
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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 n, p in shard_mixtral_model.named_parameters(): |
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zero_grad = sharded_optimizer.get_param_grad(p) |
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if name_to_p[n].grad is None: |
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name_to_p[n].grad = torch.zeros_like(name_to_p[n].data) |
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continue |
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assert_close(name_to_p[n].grad, zero_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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check_all_grad_tensors(grads_to_check) |
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for n, p in shard_mixtral_model.named_parameters(): |
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assert_close(name_to_p[n], p, 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 n, p in shard_mixtral_model.named_parameters(): |
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assert_close(name_to_p[n], p, 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": 1, |
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# "num_microbatches": 2, |
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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": "fp16", |
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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": 1, |
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# "pp_size": 2, |
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# "ep_size": 2, |
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# "num_microbatches": 2, |
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# "zero_stage": 1, |
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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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# }, # [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": 2, |
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"ep_size": 2, |
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"enable_sequence_parallelism": True, |
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"sequence_parallelism_mode": "all_to_all", |
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"zero_stage": 1, |
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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_deepseek_test(test_config): |
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sub_model_zoo = model_zoo.get_sub_registry("transformers_deepseek") |
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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_deepseek(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_deepseek_test() |
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@pytest.mark.skip("redundant") |
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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_deepseek, 4) |
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if __name__ == "__main__": |
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test_mixtral() |
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