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enable generation fsdp/utils cases on XPU (#38009)

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* enable generation fsdp/utils test cases on XPU

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

* xx

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

* use backend_xx APIs

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

---------

Signed-off-by: Yao Matrix <matrix.yao@intel.com>
This commit is contained in:
Yao Matrix
2025-05-10 04:52:41 +08:00
committed by GitHub
parent 87e971e14d
commit 8f08318769
4 changed files with 48 additions and 35 deletions
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27
tests/generation/test_utils.py
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@@ -41,7 +41,6 @@ from transformers.testing_utils import (
require_torch_gpu,
require_torch_greater_or_equal,
require_torch_multi_accelerator,
require_torch_multi_gpu,
require_torch_sdpa,
set_config_for_less_flaky_test,
set_model_for_less_flaky_test,
@@ -2954,7 +2953,7 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_stop_sequence_stopping_criteria(self):
prompt = """Hello I believe in"""
generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-bart")
output = generator(prompt)
output = generator(prompt, max_new_tokens=10)
self.assertEqual(
output,
[{"generated_text": ("Hello I believe in we we we we we we we we we")}],
@@ -3860,7 +3859,7 @@ class GenerationIntegrationTests(unittest.TestCase):
@slow
@require_torch_multi_accelerator
def test_assisted_decoding_in_different_gpu(self):
def test_assisted_decoding_in_different_accelerator(self):
device_0 = f"{torch_device}:0" if torch_device != "cpu" else "cpu"
device_1 = f"{torch_device}:1" if torch_device != "cpu" else "cpu"
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM").to(device_0)
@@ -3885,7 +3884,7 @@ class GenerationIntegrationTests(unittest.TestCase):
@slow
@require_torch_accelerator
def test_assisted_decoding_model_in_gpu_assistant_in_cpu(self):
def test_assisted_decoding_model_in_accelerator_assistant_in_cpu(self):
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM").to(
torch_device
)
@@ -3970,10 +3969,10 @@ class GenerationIntegrationTests(unittest.TestCase):
self.assertTrue((expected_out == predicted_out).all().item())
@pytest.mark.generate
@require_torch_multi_gpu
def test_generate_with_static_cache_multi_gpu(self):
@require_torch_multi_accelerator
def test_generate_with_static_cache_multi_accelerator(self):
"""
Tests if the static cache has been set correctly and if generate works correctly when we are using multi-gpus.
Tests if the static cache has been set correctly and if generate works correctly when we are using multi-acceleratorss.
"""
# need to split manually as auto doesn't work well with unbalanced model
device_map = {"model.embed_tokens": 0, "model.layers.0": 0, "model.layers.1": 1, "model.norm": 1, "lm_head": 0}
@@ -4005,10 +4004,10 @@ class GenerationIntegrationTests(unittest.TestCase):
self.assertTrue(key_cache_1.device == value_cache_1.device == torch.device(1))
@pytest.mark.generate
@require_torch_multi_gpu
def test_generate_multi_gpu_causal_mask(self):
@require_torch_multi_accelerator
def test_generate_multi_accelerator_causal_mask(self):
"""
Tests that cache position device doesn't clash with causal mask device when we are using multi-gpus.
Tests that cache position device doesn't clash with causal mask device when we are using multi-accelerators.
In real life happens only when multimodal encoder size is big, so `embed_tokens` gets allocated to the next device.
The error will be triggered whenever a bacthed input is used, so that `causal_mask` is actually prepared instead of
being `None`.
@@ -4033,10 +4032,10 @@ class GenerationIntegrationTests(unittest.TestCase):
_ = model.generate(**inputs, max_new_tokens=20)
@pytest.mark.generate
@require_torch_multi_gpu
def test_init_static_cache_multi_gpu(self):
@require_torch_multi_accelerator
def test_init_static_cache_multi_accelerator(self):
"""
Tests if the static cache has been set correctly when we initialize it manually in a multi-gpu setup.
Tests if the static cache has been set correctly when we initialize it manually in a multi-accelerator setup.
"""
# need to split manually as auto doesn't work well with unbalanced model
device_map = {"model.embed_tokens": 0, "model.layers.0": 0, "model.layers.1": 1, "model.norm": 1, "lm_head": 0}
@@ -4870,7 +4869,7 @@ class GenerationIntegrationTests(unittest.TestCase):
@require_read_token
@slow
@require_torch_gpu
@require_torch_accelerator
def test_cache_device_map_with_vision_layer_device_map(self):
"""
Test that the cache device map is correctly set when the vision layer has a device map. Regression test for