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fix: torch-native LoRA for multi-adapter case (#20564)

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Co-authored-by: Satyam Kumar <satyamk@linkedin.com>
This commit is contained in:
satyamk7054
2026-03-26 14:34:16 -07:00
committed by GitHub
co-authored by Satyam Kumar
parent fb90c9d298
commit e59ea4f6e9
2 changed files with 19 additions and 14 deletions
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test/manual/lora/test_torch_backend.py
+13 -11
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@@ -11,20 +11,22 @@ from sglang.test.test_utils import CustomTestCase
class TestTorchNativeLoRABackend(CustomTestCase):
device = "cpu"
weight_indices = [0, 1]
# set duplicate weights to test merging during prepare_lora_batch
weight_indices = [0, 0, 1]
lora_ranks = [1, 1]
scalings = [1.0, 0.5]
seq_lens = [1, 1]
seq_lens = [1, 1, 1]
use_cuda_graph = False
forward_batch = ForwardBatch(
forward_mode=ForwardMode.EXTEND,
batch_size=2,
input_ids=torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32),
batch_size=3,
input_ids=torch.tensor([[1], [2], [3]], dtype=torch.int32),
req_pool_indices=None,
seq_lens=None,
out_cache_loc=None,
seq_lens_sum=6,
seq_lens_sum=3,
extend_seq_lens=torch.tensor(seq_lens, dtype=torch.int32),
extend_seq_lens_cpu=seq_lens,
)
@@ -41,7 +43,7 @@ class TestTorchNativeLoRABackend(CustomTestCase):
)
def test_run_lora_a_sgemm(self):
batch_size = 2
batch_size = 3
input_dim = 4
output_dim = 6
num_loras = 3
@@ -80,7 +82,7 @@ class TestTorchNativeLoRABackend(CustomTestCase):
self.assertTrue(torch.allclose(actual_output, expect_output))
def test_run_lora_b_sgemm(self):
batch_size = 2
batch_size = 3
input_dim = 6
output_dim = 4
num_loras = 3
@@ -118,12 +120,12 @@ class TestTorchNativeLoRABackend(CustomTestCase):
self.assertTrue(torch.allclose(actual_output, expect_output))
def test_run_qkv_lora(self):
batch_size = 2
batch_size = 3
num_loras = 3
input_dim = 6
output_offset = [0, 3, 6, 9, 12]
output_offset = [0, 3, 6, 9]
output_dim = output_offset[-1]
num_slices = len(output_offset) - 1
num_slices = len(output_offset) - 1 # 3 slices for Q, K, V
max_lora_rank = max(self.lora_ranks)
dtype = torch.float32
@@ -177,7 +179,7 @@ class TestTorchNativeLoRABackend(CustomTestCase):
self.assertTrue(torch.allclose(actual_output, expect_output))
def test_run_gate_up_lora(self):
batch_size = 2
batch_size = 3
input_dim = 6
output_dim = 4
num_loras = 3