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Revert "Support nvidia/NVIDIA-Nemotron-Nano-9B-v2-FP8/NVFP4" (#12015)

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This commit is contained in:
Liangsheng Yin
2025-10-23 21:27:58 +08:00
committed by GitHub
parent 32852fe9e9
commit 6c18addb6f
10 changed files with 127 additions and 207 deletions
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43
test/srt/layers/attention/mamba/test_mamba_ssm_ssd.py
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@@ -8,12 +8,13 @@ from einops import rearrange, repeat
from sglang.srt.layers.attention.mamba.mamba2_metadata import Mamba2Metadata
from sglang.srt.layers.attention.mamba.ops import mamba_chunk_scan_combined
from sglang.utils import is_in_ci
# Added by the IBM Team, 2024
# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/modules/ssd_minimal.py
# TODO: These take a long time to run - we should cut down on some of the parameterized matrix.
# this is the segsum implementation taken from above
def segsum(x):
@@ -190,22 +191,10 @@ def generate_continuous_batched_examples(
)
SINGLE_ITYPE = [torch.float32, torch.float16, torch.bfloat16]
SINGLE_NHEADS = [3, 4, 11, 16, 32]
SINGLE_DHEAD = [5, 8, 19, 32, 128]
SINGLE_SEQ_LEN_CHUNK_SIZE = [(112, 16), (128, 32)]
if is_in_ci():
SINGLE_ITYPE = [torch.float32, torch.bfloat16]
SINGLE_NHEADS = [3, 32]
SINGLE_DHEAD = [5, 128]
SINGLE_SEQ_LEN_CHUNK_SIZE = [(112, 16)]
@pytest.mark.parametrize("itype", SINGLE_ITYPE)
@pytest.mark.parametrize("n_heads", SINGLE_NHEADS)
@pytest.mark.parametrize("d_head", SINGLE_DHEAD)
@pytest.mark.parametrize("seq_len_chunk_size", SINGLE_SEQ_LEN_CHUNK_SIZE)
@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
@pytest.mark.parametrize("n_heads", [3, 4, 11, 16, 32])
@pytest.mark.parametrize("d_head", [5, 8, 19, 32, 128])
@pytest.mark.parametrize("seq_len_chunk_size", [(112, 16), (128, 32)])
def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, itype):
if not torch.cuda.is_available():
pytest.skip("CUDA device not available")
@@ -249,19 +238,9 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, it
)
BATCHED_ITYPE = [torch.float32, torch.float16]
BATCHED_NHEADS = [4, 8, 13]
BATCHED_DHEAD = [5, 16, 21, 32]
if is_in_ci():
BATCHED_ITYPE = [torch.float32]
BATCHED_NHEADS = [4, 13]
BATCHED_DHEAD = [5, 32]
@pytest.mark.parametrize("itype", BATCHED_ITYPE)
@pytest.mark.parametrize("n_heads", BATCHED_NHEADS)
@pytest.mark.parametrize("d_head", BATCHED_DHEAD)
@pytest.mark.parametrize("itype", [torch.float32, torch.float16])
@pytest.mark.parametrize("n_heads", [4, 8, 13])
@pytest.mark.parametrize("d_head", [5, 16, 21, 32])
@pytest.mark.parametrize(
"seq_len_chunk_size_cases",
[
@@ -600,7 +579,3 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens):
rtol=rtol,
msg=lambda x: f"seq{i} state " + x,
) # noqa: B023
if __name__ == "__main__":
pytest.main([__file__])