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fix test_air_top_p_sampling name (#3211)

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chen
2025-08-05 15:47:50 +08:00
committed by GitHub
parent 9f1936ae28
commit 04fc7eb931
1 changed files with 5 additions and 5 deletions
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10
test/operators/test_air_topp_sampling.py → test/operators/test_air_top_p_sampling.py
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@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
"""UT for air_topp_sampling kernel""" """UT for air_top_p_sampling kernel"""
import subprocess import subprocess
import unittest import unittest
@@ -36,19 +36,19 @@ class Test(unittest.TestCase):
release_idx = output.index("release") + 1 release_idx = output.index("release") + 1
self.nvcc_cuda_version = float(output[release_idx].split(",")[0]) self.nvcc_cuda_version = float(output[release_idx].split(",")[0])
def test_air_topp_sampling(self): def test_air_top_p_sampling(self):
""" """
Check air_topp_sampling output with paddle.tensor.top_p_sampling. Check air_top_p_sampling output with paddle.tensor.top_p_sampling.
""" """
if self.nvcc_cuda_version < 12.0: if self.nvcc_cuda_version < 12.0:
self.skipTest("air_topp_sampling only support cu12+") self.skipTest("air_top_p_sampling only support cu12+")
bsz = 8 bsz = 8
vocab_size = 103424 vocab_size = 103424
x = paddle.randn([bsz, vocab_size]) x = paddle.randn([bsz, vocab_size])
x = paddle.nn.functional.softmax(x) x = paddle.nn.functional.softmax(x)
x = paddle.cast(x, "float32") x = paddle.cast(x, "float32")
top_ps = paddle.to_tensor(np.random.uniform(0, 1, [bsz]).astype(np.float32)) top_ps = paddle.to_tensor(np.random.uniform(0, 1, [bsz]).astype(np.float32))
_, next_tokens = fastdeploy.model_executor.ops.gpu.air_topp_sampling( _, next_tokens = fastdeploy.model_executor.ops.gpu.air_top_p_sampling(
x.cuda(), top_ps.cuda(), None, None, seed=0, k=1, mode="truncated" x.cuda(), top_ps.cuda(), None, None, seed=0, k=1, mode="truncated"
) )
print(next_tokens) print(next_tokens)