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T5 compile compatibilty (#34089)

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* this worked in normal generation, needs more tests

* fix almost all tests in t5

* nit

* longt5, umt5, mt5

* style

* udop, pix2struct

* more models

* fix some tests

* fix onnx tests

* tracing tests fixed

* compile enabled and tested for t5 models

* fix small bug in slow tests

* [run-slow] t5

* uncomment

* style

* update with new generation refactoring

* nit

* fix copies

* this is the fix, had to change t5 to fix copies

* update

* [run-slow] t5

* [run-slow] t5

* update

* add test for encoder only T5

* clean up after rebase

* fix pop2piano

* add comment

* style

* fix copies after rebase

* fix copies  missed this one
This commit is contained in:
Raushan Turganbay
2024-10-22 08:23:53 +02:00
committed by GitHub
parent 5077bc034f
commit 73d65e637b
22 changed files with 2744 additions and 1179 deletions
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39
tests/models/umt5/test_modeling_umt5.py
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@@ -41,6 +41,7 @@ if is_torch_fx_available():
if is_torch_available():
import torch
import torch.nn.functional as F
from transformers import (
AutoTokenizer,
@@ -316,6 +317,9 @@ class UMT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
# The small UMT5 model needs higher percentages for CPU/MP tests
model_split_percents = [0.5, 0.8, 0.9]
# used in `test_torch_compile`
_torch_compile_test_ckpt = "google/umt5-small"
def setUp(self):
self.model_tester = UMT5ModelTester(self)
@@ -486,6 +490,41 @@ class UMT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
with torch.no_grad():
model(**inputs)[0]
# overwrite because T5 doesn't accept position ids as input and expects `decoder_input_ids`
def test_custom_4d_attention_mask(self):
for model_class in self.all_generative_model_classes:
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config).to(device=torch_device, dtype=torch.float32)
(
input_ids,
_,
input_ids_shared_prefix,
mask_shared_prefix,
_,
) = self._get_custom_4d_mask_test_data()
logits = model.forward(
decoder_input_ids=input_ids,
input_ids=input_dict["input_ids"][:3],
).logits
# logits.shape == torch.Size([3, 4, ...])
logits_shared_prefix = model(
input_ids=input_dict["input_ids"][:1],
decoder_input_ids=input_ids_shared_prefix,
decoder_attention_mask=mask_shared_prefix,
)[0]
# logits_shared_prefix.shape == torch.Size([1, 6, ...])
out_last_tokens = logits[:, -1, :] # last tokens in each batch line
out_shared_prefix_last_tokens = logits_shared_prefix[0, -3:, :] # last three tokens
# comparing softmax-normalized logits:
normalized_0 = F.softmax(out_last_tokens)
normalized_1 = F.softmax(out_shared_prefix_last_tokens)
torch.testing.assert_close(normalized_0, normalized_1, rtol=1e-3, atol=1e-4)
def test_with_sequence_classification_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)