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[shardformer] Support the T5ForTokenClassification model (#5816)

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* t5 token, still pytest fail

* Resolve T5 Pytest Failure

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix typos

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Guangyao Zhang
2024-06-27 16:40:38 +08:00
committed by GitHub
parent 5dfbcd7746
commit d9d5e7ea1f
5 changed files with 166 additions and 11 deletions
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16
tests/test_shardformer/test_model/test_shard_t5.py
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@@ -41,14 +41,17 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
t5 = unwrap_model(org_model)
sharded_t5 = unwrap_model(sharded_model)
row_layer_for_check = ["shared", "encoder.block[0].layer[0].SelfAttention.q"]
if t5.__class__.__name__ == "T5ForTokenClassification":
row_layer_for_check = ["transformer.shared", "transformer.encoder.block[0].layer[0].SelfAttention.q"]
else:
row_layer_for_check = ["shared", "encoder.block[0].layer[0].SelfAttention.q"]
# Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
grads_to_check = {}
if test_config["precision"] == "fp32":
atol, rtol = 1e-5, 1e-3
else:
atol, rtol = 5e-3, 5e-3
atol, rtol = 5e-2, 5e-2
if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
row_layer_grads = get_grad_tensors_for_check(
t5, sharded_t5, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0
@@ -66,7 +69,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
else:
atol, rtol = 5e-3, 5e-3
if org_model.__class__.__name__ != "T5ForConditionalGeneration":
if org_model.__class__.__name__ not in ["T5ForConditionalGeneration", "T5ForTokenClassification"]:
check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
@@ -157,7 +160,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
)
@clear_cache_before_run()
def run_t5_test(test_config):
sub_model_zoo = model_zoo.get_sub_registry("transformers_t5")
sub_model_zoo = model_zoo.get_sub_registry(["transformers_t5_for_token_classification"])
for name, (
model_fn,
@@ -167,7 +170,10 @@ def run_t5_test(test_config):
_,
) in sub_model_zoo.items():
# skip 4-stage pp test for t5_encoder
if test_config["pp_size"] > 2 and name == "transformers_t5_encoder_model":
if test_config["pp_size"] > 2 and name in [
"transformers_t5_encoder_model",
"transformers_t5_for_token_classification",
]:
continue
check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)