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[Bugfix][Mamba] - Fix Conv State Kernel FP32 Support (#24883)

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Signed-off-by: asafg <39553475+Josephasafg@users.noreply.github.com>
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Asaf Joseph Gardin 2025-09-18 15:21:17 +03:00 committed by GitHub
parent 3ed1ec4af2
commit 66072b36db
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GPG Key ID: B5690EEEBB952194
2 changed files with 14 additions and 5 deletions
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9
tests/models/language/generation/test_hybrid.py
View File

@ -418,7 +418,9 @@ def test_full_cuda_graph(
@pytest.mark.parametrize("model", FP32_STATE_MODELS)
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_fp32_state(
@pytest.mark.parametrize("cache_dtype_param",
["mamba_ssm_cache_dtype", "mamba_cache_dtype"])
def test_fp32_cache_state(
hf_runner,
vllm_runner,
example_prompts,
@ -426,6 +428,7 @@ def test_fp32_state(
model: str,
max_tokens: int,
num_logprobs: int,
cache_dtype_param: str,
) -> None:
try:
@ -443,13 +446,13 @@ def test_fp32_state(
m.setenv("VLLM_USE_V1", "0")
with vllm_runner(model,
max_num_seqs=MAX_NUM_SEQS,
mamba_ssm_cache_dtype="float32") as vllm_model:
**{cache_dtype_param: "float32"}) as vllm_model:
vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs)
with vllm_runner(model,
max_num_seqs=MAX_NUM_SEQS,
mamba_ssm_cache_dtype="float32") as vllm_model:
**{cache_dtype_param: "float32"}) as vllm_model:
vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs)

10
vllm/model_executor/layers/mamba/ops/causal_conv1d.py
View File

@ -415,6 +415,9 @@ def causal_conv1d_fn(
activation = "silu"
args = None
# Store original dtype to cast back at the end
original_x_dtype = x.dtype
x = x.to(conv_states.dtype)
out = torch.empty_like(x)
if metadata is not None:
cu_seqlen = metadata.cu_seqlen
@ -613,7 +616,7 @@ def causal_conv1d_fn(
BLOCK_N=256,
num_stages=2,
)
return out
return out.to(original_x_dtype)
@triton.jit()
@ -973,6 +976,9 @@ def causal_conv1d_update(
activation = "silu" if activation is True else None
elif activation is not None:
assert activation in ["silu", "swish"]
original_x_dtype = x.dtype
x = x.to(conv_state.dtype)
unsqueeze = query_start_loc is None and x.dim() == 2
if unsqueeze:
# make it (batch, dim, seqlen) with seqlen == 1
@ -1081,4 +1087,4 @@ def causal_conv1d_update(
)
if unsqueeze:
out = out.squeeze(-1)
return out
return out.to(original_x_dtype)