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Device agnostic testing (#25870)

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* adds agnostic decorators and availability fns

* renaming decorators and fixing imports

* updating some representative example tests
bloom, opt, and reformer for now

* wip device agnostic functions

* lru cache to device checking functions

* adds `TRANSFORMERS_TEST_DEVICE_SPEC`
if present, imports the target file and updates device to function
mappings

* comments `TRANSFORMERS_TEST_DEVICE_SPEC` code

* extra checks on device name

* `make style; make quality`

* updates default functions for agnostic calls

* applies suggestions from review

* adds `is_torch_available` guard

* Add spec file to docs, rename function dispatch names to backend_*

* add backend import to docs example for spec file

* change instances of  to

* Move register backend to before device check as per @statelesshz changes

* make style

* make opt test require fp16 to run

---------

Co-authored-by: arsalanu <arsalanu@graphcore.ai>
Co-authored-by: arsalanu <hzji210@gmail.com>
This commit is contained in:
Alex McKinney
2023-10-24 15:49:26 +01:00
committed by GitHub
parent 41496b95da
commit 9da451713d
8 changed files with 188 additions and 25 deletions
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10
tests/models/bloom/test_modeling_bloom.py
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@@ -18,7 +18,7 @@ import math
import unittest
from transformers import BloomConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
@@ -401,7 +401,7 @@ class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
self.assertIsNotNone(model)
@slow
@require_torch_gpu
@require_torch_accelerator
def test_simple_generation(self):
# This test is a bit flaky. For some GPU architectures, pytorch sets by default allow_fp16_reduced_precision_reduction = True and some operations
# do not give the same results under this configuration, especially torch.baddmm and torch.bmm. https://pytorch.org/docs/stable/notes/numerical_accuracy.html#fp16-on-mi200
@@ -440,7 +440,7 @@ class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
self.assertEqual(tokenizer.decode(greedy_output[0], skip_special_tokens=True), EXPECTED_OUTPUT)
@slow
@require_torch_gpu
@require_torch_accelerator
def test_batch_generation(self):
path_560m = "bigscience/bloom-560m"
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
@@ -460,7 +460,7 @@ class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
)
@slow
@require_torch_gpu
@require_torch_accelerator
def test_batch_generation_padd(self):
path_560m = "bigscience/bloom-560m"
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
@@ -489,7 +489,7 @@ class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
)
@slow
@require_torch_gpu
@require_torch_accelerator
def test_batch_generated_text(self):
path_560m = "bigscience/bloom-560m"