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change command

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This commit is contained in:
GuangyaoZhang
2024-06-14 03:04:56 +00:00
parent 0b81163bc0
commit f656d61778
9 changed files with 778 additions and 435 deletions
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6
tests/kit/model_zoo/transformers/__init__.py
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@@ -22,3 +22,9 @@ try:
from .qwen2 import *
except ImportError:
print("This version of transformers doesn't support qwen2.")
try:
from .command import *
except ImportError:
print("This version of transformers doesn't support Command-R.")

81
tests/kit/model_zoo/transformers/command.py Normal file
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@@ -0,0 +1,81 @@
import torch
import transformers
from ..registry import ModelAttribute, model_zoo
try:
from transformers import CohereConfig
HAS_COMMAND = True
except ImportError:
HAS_COMMAND = False
if HAS_COMMAND:
# ===============================
# Register Command-R
# ===============================
def data_gen():
input_ids = torch.Tensor(
[
[1, 15043, 29892, 590, 11203, 338, 274, 1082, 1, 15043, 29892, 590, 11203, 338, 274, 1082],
[1, 15043, 29892, 590, 11203, 338, 274, 1082, 1, 15043, 29892, 590, 11203, 338, 274, 1082],
]
).long()
attention_mask = torch.Tensor(
[
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
]
).long()
return dict(input_ids=input_ids, attention_mask=attention_mask)
# label is needed for casual lm
def data_gen_for_casual_lm():
data = data_gen()
labels = data["input_ids"].clone()
data["labels"] = labels
return data
# transform the output to a dict
output_transform_fn = lambda x: x
# function to get the loss
loss_fn = lambda output: output["last_hidden_state"].mean()
loss_fn_for_casual_lm = lambda output: output["loss"]
loss_fn_for_seq_classification = lambda output: output["logits"].mean()
config = CohereConfig(
num_hidden_layers=8,
hidden_size=32,
intermediate_size=64,
num_attention_heads=4,
max_position_embeddings=128,
)
if hasattr(config, "pad_token_id"):
config.pad_token_id = config.eos_token_id
# register the following models
# transformers.CohereModel,
# transformers.CohereForCausalLM,
model_zoo.register(
name="transformers_command",
model_fn=lambda: transformers.CohereModel(config),
data_gen_fn=data_gen,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
name="transformers_command_for_casual_lm",
model_fn=lambda: transformers.CohereForCausalLM(config),
data_gen_fn=data_gen_for_casual_lm,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn_for_casual_lm,
model_attribute=ModelAttribute(has_control_flow=True),
)

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