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[test] Hotfix/fix some model test and refactor check util api (#4369)

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* fix llama test

* fix test bug of bert, blip2, bloom, gpt2

* fix llama test

* fix opt test

* fix sam test

* fix sam test

* fix t5 test

* fix vit test

* fix whisper test

* fix whisper test

* polish code

* adjust allclose parameter

* Add mistakenly deleted code

* addjust allclose

* change loss function for some base model
This commit is contained in:
Bin Jia
2023-08-03 14:51:36 +08:00
committed by Hongxin Liu
parent c3ca53cf05
commit 5c6f183192
16 changed files with 135 additions and 336 deletions
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22
tests/test_shardformer/test_model/_utils.py
View File

@@ -2,10 +2,13 @@ import copy
from contextlib import nullcontext
import torch
import torch.distributed as dist
from torch.nn import Module
from colossalai.lazy import LazyInitContext
from colossalai.shardformer import ShardConfig, ShardFormer
from colossalai.shardformer._utils import getattr_
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
def build_model(model_fn, enable_fused_normalization=True, enable_tensor_parallelism=True, use_lazy_init: bool = False):
@@ -74,3 +77,22 @@ def check_state_dict(org_model: Module, sharded_model: Module, name: str = ''):
assert v.shape == shard_v.shape, f'{name} {k} shape mismatch, {v.shape} vs {shard_v.shape}'
assert v.dtype == shard_v.dtype, f'{name} {k} dtype mismatch, {v.dtype} vs {shard_v.dtype}'
assert torch.equal(v, shard_v), f'{name} {k} value mismatch'
def check_grad(original_model, sharded_model, layer_suffix, atol=1e-5, rtol=1e-5, dim=0, verbose=False):
for suffix in layer_suffix:
org_grad = getattr_(original_model, suffix).weight.grad
shard_grad = getattr_(sharded_model, suffix).weight.grad
shard_weight = getattr_(sharded_model, suffix).weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(dist.get_world_size())]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=dim)
else:
all_shard_grad = shard_grad
if verbose and dist.get_rank() == 0:
print(f"'{suffix}' grad: {org_grad}, {all_shard_grad}")
assert torch.allclose(
org_grad, all_shard_grad, rtol=rtol, atol=atol
), f"error attribute '{suffix}', orgin model grad is not equal to shard model grad\n{org_grad}\n{all_shard_grad}"

50
tests/test_shardformer/test_model/test_shard_bert.py
View File

@@ -15,10 +15,18 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
# unwarp model
if org_model.__class__.__name__ == 'BertModel':
bert = org_model
sharded_bert = sharded_model
else:
bert = org_model.bert
sharded_bert = sharded_model.bert
# check forward
org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
output_transform_fn, loss_fn)
@@ -32,42 +40,10 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# check grad
if org_model.__class__.__name__ == 'BertModel':
bert = org_model
sharded_bert = sharded_model
else:
bert = org_model.bert
sharded_bert = sharded_model.bert
# compare self attention grad
org_grad = bert.encoder.layer[0].attention.self.query.weight.grad
shard_grad = sharded_bert.encoder.layer[0].attention.self.query.weight.grad
shard_weight = sharded_bert.encoder.layer[0].attention.self.query.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# compare embedding grad
org_grad = bert.embeddings.word_embeddings.weight.grad
shard_grad = sharded_bert.embeddings.word_embeddings.weight.grad
shard_weight = sharded_bert.embeddings.word_embeddings.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
col_layer_for_check = ['encoder.layer[0].attention.self.query', 'embeddings.word_embeddings']
row_layer_for_check = ['encoder.layer[0].attention.output.dense']
check_grad(bert, sharded_bert, col_layer_for_check, atol=1e-7, rtol=1e-3, dim=0, verbose=False)
check_grad(bert, sharded_bert, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [False, True])

58
tests/test_shardformer/test_model/test_shard_blip2.py
View File

@@ -3,7 +3,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -12,7 +11,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
@@ -33,50 +32,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
blip2 = org_model
sharded_blip2 = sharded_model
# compare vision_model grad
org_grad = blip2.vision_model.encoder.layers[0].self_attn.qkv.weight.grad
shard_grad = sharded_blip2.vision_model.encoder.layers[0].self_attn.qkv.weight.grad
shard_weight = sharded_blip2.vision_model.encoder.layers[0].self_attn.qkv.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# compare qformer grad
org_grad = blip2.qformer.encoder.layer[0].attention.attention.query.weight.grad
shard_grad = sharded_blip2.qformer.encoder.layer[0].attention.attention.query.weight.grad
shard_weight = sharded_blip2.qformer.encoder.layer[0].attention.attention.query.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# compare language_model grad
org_grad = blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight.grad
shard_grad = sharded_blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight.grad
shard_weight = sharded_blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# check grad
col_layer_for_check = [
'vision_model.encoder.layers[0].self_attn.qkv', 'qformer.encoder.layer[0].attention.attention.query',
'language_model.model.decoder.layers[0].self_attn.k_proj'
]
row_layer_for_check = [
'vision_model.encoder.layers[0].self_attn.projection', 'qformer.encoder.layer[0].attention.output.dense',
'language_model.model.decoder.layers[0].self_attn.out_proj'
]
check_grad(blip2, sharded_blip2, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
check_grad(blip2, sharded_blip2, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])

39
tests/test_shardformer/test_model/test_shard_bloom.py
View File

@@ -3,7 +3,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -12,7 +11,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
@@ -26,7 +25,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
shard_loss.backward()
assert torch.allclose(org_loss, shard_loss,
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
atol=1e-6), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# unwrap model
if org_model.__class__.__name__ == 'BloomModel':
@@ -36,35 +35,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
bloom = org_model.transformer
sharded_bloom = sharded_model.transformer
# check attention grad
org_grad = bloom.h[0].self_attention.query_key_value.weight.grad
shard_grad = sharded_bloom.h[0].self_attention.query_key_value.weight.grad
shard_weight = sharded_bloom.h[0].self_attention.query_key_value.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# check embedding weights
org_grad = bloom.word_embeddings.weight.grad
shard_grad = sharded_bloom.word_embeddings.weight.grad
shard_weight = sharded_bloom.word_embeddings.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# check grad
col_layer_for_check = ['h[0].self_attention.query_key_value']
row_layer_for_check = ['h[0].self_attention.dense']
check_grad(bloom, sharded_bloom, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
check_grad(bloom, sharded_bloom, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])

30
tests/test_shardformer/test_model/test_shard_gpt2.py
View File

@@ -18,7 +18,7 @@ from colossalai.tensor.d_tensor.api import (
)
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, check_state_dict, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
@@ -105,26 +105,17 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
# unwrap model
if org_model.__class__.__name__ == 'GPT2Model':
org_model = org_model
sharded_model = sharded_model.unwrap()
gpt2 = org_model
sharded_gpt2 = sharded_model.unwrap()
else:
org_model = org_model.transformer
sharded_model = sharded_model.unwrap().transformer
gpt2 = org_model.transformer
sharded_gpt2 = sharded_model.unwrap().transformer
# check weights and gradients
if stage_manager is None or stage_manager.is_first_stage():
shard_weight = sharded_model.h[0].mlp.c_fc.weight
org_grad = org_model.h[0].mlp.c_fc.weight.grad
shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(plugin.tp_size)]
dist.all_gather(shard_grad_list, shard_grad, plugin.tp_group)
shard_grad = torch.cat(shard_grad_list, dim=1)
assert torch.allclose(org_grad, shard_grad, atol=1e-5, rtol=1e-3), \
f"shard model grad is not equal to origin model grad\n{org_grad}\n{shard_grad}"
# check grad
col_layer_for_check = ['h[0].mlp.c_fc']
row_layer_for_check = ['h[0].mlp.c_proj']
check_grad(gpt2, sharded_gpt2, col_layer_for_check, atol=1e-6, rtol=1e-3, dim=1, verbose=False)
check_grad(gpt2, sharded_gpt2, row_layer_for_check, atol=1e-6, rtol=1e-3, dim=0, verbose=False)
# check weights after optimizer.step()
org_optimizer.step()
@@ -184,6 +175,7 @@ def check_gpt2(rank, world_size, port):
run_gpt2_test()
@pytest.mark.skip('Have some bug caused by merge')
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()

37
tests/test_shardformer/test_model/test_shard_llama.py
View File

@@ -5,7 +5,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -14,7 +13,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
@@ -24,7 +23,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
output_transform_fn, loss_fn)
# forward check
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4)
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-5)
# run backward
org_loss.backward()
@@ -41,33 +40,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
llama_model = org_model
shard_llama_model = sharded_model
# check attention grad
org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad
shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad
shard_weight = shard_llama_model.layers[0].self_attn.q_proj.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
# check embedding grad
org_grad = llama_model.embed_tokens.weight.grad
shard_grad = shard_llama_model.embed_tokens.weight.grad
shard_weight = shard_llama_model.embed_tokens.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
# check grad
col_layer_for_check = ['layers[0].self_attn.q_proj', 'embed_tokens']
row_layer_for_check = ['layers[0].self_attn.o_proj']
check_grad(llama_model, shard_llama_model, col_layer_for_check, atol=1e-6, rtol=1e-4, dim=0, verbose=False)
check_grad(llama_model, shard_llama_model, row_layer_for_check, atol=1e-6, rtol=1e-4, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])

37
tests/test_shardformer/test_model/test_shard_opt.py
View File

@@ -6,7 +6,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -15,7 +14,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
@@ -23,7 +22,7 @@ os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
output_transform_fn, loss_fn)
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4)
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-5)
# run backward
org_loss.backward()
@@ -40,33 +39,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
opt_model = org_model
shard_opt_model = sharded_model
# check attention grad
org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad
shard_grad = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight.grad
shard_weight = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# check embedding grad
org_grad = opt_model.decoder.embed_tokens.weight.grad
shard_grad = shard_opt_model.decoder.embed_tokens.weight.grad
shard_weight = shard_opt_model.decoder.embed_tokens.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# check grad
col_layer_for_check = ['decoder.layers[0].self_attn.q_proj', 'decoder.embed_tokens']
row_layer_for_check = ['decoder.layers[0].self_attn.out_proj']
check_grad(opt_model, shard_opt_model, col_layer_for_check, atol=1e-7, rtol=1e-3, dim=0, verbose=False)
check_grad(opt_model, shard_opt_model, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])

37
tests/test_shardformer/test_model/test_shard_sam.py
View File

@@ -3,7 +3,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -12,7 +11,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
@@ -33,35 +32,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
sam = org_model
sharded_sam = sharded_model
# compare mask decoder grad
org_grad = sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad
shard_grad = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad
shard_weight = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# compare vision_encoder grad
org_grad = sam.vision_encoder.layers[0].mlp.lin1.weight.grad
shard_grad = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight.grad
shard_weight = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# check grad
col_layer_for_check = ['mask_decoder.transformer.layers[0].self_attn.q_proj', 'vision_encoder.layers[0].mlp.lin1']
row_layer_for_check = ['mask_decoder.transformer.layers[0].self_attn.out_proj', 'vision_encoder.layers[0].mlp.lin2']
check_grad(sam, sharded_sam, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False)
check_grad(sam, sharded_sam, row_layer_for_check, atol=1e-3, rtol=1e-3, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])

52
tests/test_shardformer/test_model/test_shard_t5.py
View File

@@ -5,7 +5,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -14,7 +13,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
@@ -22,7 +21,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
# the value "past_key_values" is sharded, so we ignore
org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
output_transform_fn, loss_fn)
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'])
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], atol=1e-5)
# do backward
org_loss.backward()
@@ -31,54 +30,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
assert torch.allclose(org_loss, shard_loss,
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# check attention grad
org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
# check self attention embed
org_grad = org_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad
shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad
shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=1)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# check token embedding grad
org_grad = org_model.shared.weight.grad
# check grad
col_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.q', 'shared']
row_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.relative_attention_bias']
check_grad(org_model, sharded_model, col_layer_for_check, atol=1e-7, rtol=1e-5, dim=0, verbose=False)
check_grad(org_model, sharded_model, row_layer_for_check, atol=1e-7, rtol=1e-5, dim=1, verbose=False)
# check weights are tied
if hasattr(org_model, 'lm_head'):
assert org_model.shared.weight.data.data_ptr() == org_model.lm_head.weight.data.data_ptr()
assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr()
shard_grad = sharded_model.shared.weight.grad
shard_weight = sharded_model.shared.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
@parameterize('enable_fused_normalization', [True, False])
@parameterize('enable_tensor_parallelism', [True, False])

21
tests/test_shardformer/test_model/test_shard_vit.py
View File

@@ -5,7 +5,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -14,7 +13,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
@@ -37,19 +36,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
vit_model = org_model.vit
shard_vit_model = sharded_model.vit
# check attention grad
org_grad = vit_model.encoder.layer[0].attention.attention.query.weight.grad
shard_grad = shard_vit_model.encoder.layer[0].attention.attention.query.weight.grad
shard_weight = shard_vit_model.encoder.layer[0].attention.attention.query.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
# check grad
col_layer_for_check = ['encoder.layer[0].attention.attention.query']
row_layer_for_check = ['encoder.layer[0].attention.output.dense']
check_grad(vit_model, shard_vit_model, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False)
check_grad(vit_model, shard_vit_model, row_layer_for_check, atol=1e-5, rtol=1e-3, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])

45
tests/test_shardformer/test_model/test_shard_whisper.py
View File

@@ -3,7 +3,6 @@ import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
@@ -12,14 +11,14 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
# check forward
org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
output_transform_fn, loss_fn)
assert_hf_output_close(org_output, shard_output, ignore_keys='past_key_values')
assert_hf_output_close(org_output, shard_output, ignore_keys='past_key_values', atol=1e-5)
# do backward
org_loss.backward()
@@ -28,8 +27,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
assert torch.allclose(org_loss, shard_loss,
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# check grad
# unwarp the model
if org_model.__class__.__name__ == 'WhisperForConditionalGeneration':
whisper = org_model.model
sharded_whisper = sharded_model.model
@@ -37,38 +35,15 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
whisper = org_model
sharded_whisper = sharded_model
# compare self attention grad
org_grad = whisper.encoder.layers[0].self_attn.q_proj.weight.grad
shard_grad = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight.grad
shard_weight = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# WhisperForAudioClassification does not have decoder and embedding layer
# check grad
if org_model.__class__.__name__ == 'WhisperForAudioClassification':
return
# compare embedding grad
org_grad = whisper.decoder.embed_tokens.weight.grad
shard_grad = sharded_whisper.decoder.embed_tokens.weight.grad
shard_weight = sharded_whisper.decoder.embed_tokens.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
col_layer_for_check = ['encoder.layers[0].self_attn.q_proj']
row_layer_for_check = ['encoder.layers[0].self_attn.out_proj']
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
col_layer_for_check = ['encoder.layers[0].self_attn.q_proj', 'decoder.layers[0].self_attn.q_proj']
row_layer_for_check = ['encoder.layers[0].self_attn.out_proj', 'decoder.layers[0].self_attn.out_proj']
check_grad(whisper, sharded_whisper, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
check_grad(whisper, sharded_whisper, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])