xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2025-12-15 18:06:03 +08:00
Code Issues Packages Projects Releases Wiki Activity

[BugFix] Enhance test_pos_encoding to support execution on multi-devices (#13187)

Browse Source 
Signed-off-by: wchen61 <wchen61@foxmail.com>
This commit is contained in:
wchen61 2025-02-16 16:59:49 +08:00 committed by GitHub
parent d3d547e057
commit dc0f7ccf8b
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
tests/kernels/test_pos_encoding.py
View File

@ -70,7 +70,7 @@ def test_rotary_embedding(
if rotary_dim is None: if rotary_dim is None:
rotary_dim = head_size rotary_dim = head_size
rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style) rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style)
rope = rope.to(dtype=dtype) rope = rope.to(dtype=dtype, device=torch.get_default_device())
positions = torch.randint(0, max_position, (batch_size, seq_len)) positions = torch.randint(0, max_position, (batch_size, seq_len))
query_shape = tensor_shape_fn(batch_size, seq_len, num_heads, head_size) query_shape = tensor_shape_fn(batch_size, seq_len, num_heads, head_size)
@ -125,7 +125,7 @@ def test_batched_rotary_embedding(
"rope_type": "linear", "rope_type": "linear",
"factor": (1, ) "factor": (1, )
}) })
rope = rope.to(dtype=dtype) rope = rope.to(dtype=dtype, device=torch.get_default_device())
positions = torch.randint(0, max_position, (batch_size, seq_len)) positions = torch.randint(0, max_position, (batch_size, seq_len))
query_shape = tensor_shape_fn(batch_size, seq_len, num_heads, head_size) query_shape = tensor_shape_fn(batch_size, seq_len, num_heads, head_size)
@ -184,7 +184,7 @@ def test_batched_rotary_embedding_multi_lora(
"rope_type": "linear", "rope_type": "linear",
"factor": tuple(scaling_factors) "factor": tuple(scaling_factors)
}) })
rope = rope.to(dtype=dtype) rope = rope.to(dtype=dtype, device=torch.get_default_device())
positions = torch.randint(0, max_position, (batch_size, seq_len)) positions = torch.randint(0, max_position, (batch_size, seq_len))
query = torch.randn(batch_size, query = torch.randn(batch_size,