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[chore] remove redundant test case, print string & reduce test tokens

colossalchat
botbw 2024-07-30 02:56:39 +00:00 committed by Hongxin Liu
parent d1d1ab871e
commit 62cdac6b7b
3 changed files with 1 additions and 234 deletions
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1
colossalai/shardformer/modeling/mixtral.py
View File

@ -245,7 +245,6 @@ class MixtralPipelineForwards:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
print("input_ids", input_ids.shape)
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape

2
tests/test_shardformer/test_model/test_shard_deepseek.py
View File

@ -17,7 +17,7 @@ from colossalai.testing.random import seed_all
from tests.test_moe.moe_utils import assert_loose_close, check_model_equal
NUM_BATCH = 8
NUM_TOK_PER_BATCH, NUM_EXPERTS = 4000, 2
NUM_TOK_PER_BATCH, NUM_EXPERTS = 4, 2
NUM_LAYERS = 4
HIDDEN_SIZE_PER_HEAD = 4
NUM_HEADS = 4

232
tests/test_shardformer/test_model/test_shard_deepseek_skip.py
View File

@ -1,232 +0,0 @@
# modified from test_shard_mistral.py
import os
import pytest
import torch
import torch.distributed as dist
from torch.testing import assert_close
import colossalai
from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import (
build_model_from_hybrid_plugin,
check_all_grad_tensors,
check_loss,
check_output_hidden_state,
check_weight,
get_grad_tensors_for_check,
run_forward_backward_with_hybrid_plugin,
unwrap_model,
)
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
# TODO: SGD failed for full dp
org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
model_fn, loss_fn, test_config, pluggin_cls=MoeHybridParallelPlugin, optim_class=torch.optim.SGD
)
org_model = org_model.to(torch.float16)
org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
)
stage_manager = booster.plugin.stage_manager
tp_group = booster.plugin.tp_group
# check last hidden state & loss
if stage_manager is None or stage_manager.is_last_stage():
if test_config["precision"] == "fp32":
atol, rtol = 1e-5, 1e-3
else:
atol, rtol = 5e-3, 5e-3
check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
check_output_hidden_state(org_output, sharded_output, stage_manager, atol, rtol)
# unwrap model
mixtral_model = unwrap_model(org_model, "DeepseekModel", "model")
shard_mixtral_model = unwrap_model(sharded_model, "DeepseekModel", "model")
row_layer_for_check = ["layers[0].self_attn.q_proj", "embed_tokens"]
col_layer_for_check = ["layers[0].self_attn.o_proj"]
name_to_p = {n: p for n, p in mixtral_model.named_parameters()}
# Check the grad when using ZeRO-1 and ZeRO-2
if (
# booster.plugin.zero_stage in [1, 2]
booster.plugin.shard_config.enable_sequence_parallelism
and booster.plugin.shard_config.sequence_parallelism_mode == "all_to_all"
):
rank = dist.get_rank()
for n, p in shard_mixtral_model.named_parameters():
zero_grad = sharded_optimizer.get_param_grad(p)
if name_to_p[n].grad is None:
name_to_p[n].grad = torch.zeros_like(name_to_p[n].data)
continue
assert_close(name_to_p[n].grad, zero_grad, atol=5e-3, rtol=5e-3, check_dtype=False)
# Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
grads_to_check = {}
if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
if test_config["precision"] == "fp32":
atol, rtol = 5e-5, 1e-4
else:
atol, rtol = 5e-3, 5e-3
row_layer_grads = get_grad_tensors_for_check(
mixtral_model,
shard_mixtral_model,
row_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=0,
verbose=False,
)
col_layer_grads = get_grad_tensors_for_check(
mixtral_model,
shard_mixtral_model,
col_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=1,
verbose=False,
)
grads_to_check.update(col_layer_grads)
grads_to_check.update(row_layer_grads)
# check grads
check_all_grad_tensors(grads_to_check)
for n, p in shard_mixtral_model.named_parameters():
assert_close(name_to_p[n], p, atol=5e-3, rtol=5e-3, check_dtype=False)
# optimizer executes step
org_optimizer.step()
sharded_optimizer.step()
for n, p in shard_mixtral_model.named_parameters():
assert_close(name_to_p[n], p, atol=5e-3, rtol=5e-3, check_dtype=False)
# check weights
if stage_manager is None or stage_manager.is_first_stage():
if test_config["precision"] == "fp32":
atol, rtol = 2e-4, 1e-3
else:
atol, rtol = 5e-3, 5e-3
try:
check_weight(
mixtral_model,
shard_mixtral_model,
col_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=1,
verbose=False,
)
except Exception as e:
rank = dist.get_rank()
print(f"{rank=}, Failed config: {test_config}")
raise e
torch.cuda.empty_cache()
@parameterize(
"test_config",
[
# {
# "tp_size": 1,
# "pp_size": 1,
# "num_microbatches": 2,
# "ep_size": 2,
# "zero_stage": 0,
# "overlap_communication": False,
# "precision": "fp16",
# }, # [dp(4)] + [moe_dp(4)]
# {
# "tp_size": 1,
# "pp_size": 2,
# "num_microbatches": 2,
# "ep_size": 2,
# "zero_stage": 1,
# "overlap_communication": False,
# "precision": "fp32",
# }, # [dp(2) + pp(2)] + [moe_pp(2)]
# {
# "tp_size": 1,
# "pp_size": 2,
# "ep_size": 2,
# "num_microbatches": 2,
# "zero_stage": 1,
# "overlap_communication": False,
# "precision": "fp16",
# "initial_scale": 1,
# "find_unused_parameters": True,
# }, # [pp(2) + tp(2)] + [pp(2), replicate(2)] pass
{ # Ulysess + Flash attention
"tp_size": 1,
"pp_size": 1,
"sp_size": 2,
"ep_size": 2,
"enable_sequence_parallelism": True,
"sequence_parallelism_mode": "all_to_all",
"zero_stage": 1,
"overlap_communication": False,
"precision": "fp16",
"initial_scale": 1,
"find_unused_parameters": True,
},
# {
# "tp_size": 1,
# "pp_size": 1,
# "ep_size": 2,
# "zero_stage": 0,
# "overlap_communication": False,
# "precision": "fp32",
# }, # [dp(4)] + [ep(2) + moe_tp(2)]
# {
# "tp_size": 1,
# "pp_size": 1,
# "ep_size": 4,
# "overlap_communication": False,
# "zero_stage": 0,
# "precision": "fp32"
# }, # full dp for non-moe and full ep for moe
],
)
def run_deepseek_test(test_config):
sub_model_zoo = model_zoo.get_sub_registry("transformers_deepseek")
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_deepseek(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
run_deepseek_test()
@pytest.mark.skip("redundant")
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_mixtral():
spawn(check_deepseek, 4)
if __name__ == "__main__":
test_mixtral()