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[ROCm][CI] fix fp8 support for test_deepep_moe #45302

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Merged
AndreasKaratzas merged 1 commit into from
Jun 12, 2026
Merged

[ROCm][CI] fix fp8 support for test_deepep_moe #45302

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21 changes: 12 additions & 9 deletions tests/kernels/moe/test_deepep_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
per_token_group_quant_fp8,
)
from vllm.platforms import current_platform
from vllm.utils.import_utils import has_deep_ep
from vllm.utils.torch_utils import set_random_seed
from vllm.v1.worker.workspace import init_workspace_manager
Expand Down Expand Up @@ -64,7 +65,7 @@ def make_weights(
return w1, w2, None, None

# per-out-channel weight quantization
assert dtype == torch.float8_e4m3fn
assert dtype == current_platform.fp8_dtype()
w1 = torch.empty((e, 2 * n, k), device="cuda", dtype=torch.float16)
w2 = torch.empty((e, k, n), device="cuda", dtype=torch.float16)

Expand Down Expand Up @@ -105,9 +106,11 @@ class TestTensors:
@staticmethod
def make(config: TestConfig, low_latency_mode: bool) -> "TestTensors":
# TODO (varun) - check that float16 works ?
assert config.dtype in [torch.bfloat16, torch.float8_e4m3fn]
assert config.dtype in [torch.bfloat16, current_platform.fp8_dtype()]
token_dtype = (
torch.bfloat16 if config.dtype == torch.float8_e4m3fn else config.dtype
torch.bfloat16
if config.dtype == current_platform.fp8_dtype()
else config.dtype
)
rank_tokens = (
torch.randn((config.m, config.k), device="cuda", dtype=token_dtype) / 10
Expand Down Expand Up @@ -216,10 +219,10 @@ def build_expert_map():
return expert_map.to(device=device, dtype=torch.int32)

hidden_size = test_tensors.rank_tokens.size(1)
is_quantized = w1.dtype == torch.float8_e4m3fn
is_quantized = w1.dtype == current_platform.fp8_dtype()
q_dtype = None
if is_quantized:
q_dtype = torch.float8_e4m3fn
q_dtype = current_platform.fp8_dtype()

out_hidden_states = torch.empty_like(test_tensors.rank_tokens)
total_num_tokens = test_tensors.rank_tokens.size(0)
Expand Down Expand Up @@ -318,7 +321,7 @@ def torch_moe_impl(
.to(a.dtype)
)

is_quantized = w1.dtype == torch.float8_e4m3fn
is_quantized = w1.dtype == current_platform.fp8_dtype()
a_dtype = a.dtype
if is_quantized:
w1 = w1.to(dtype=torch.float32) * w1_scale
Expand Down Expand Up @@ -367,7 +370,7 @@ def _deep_ep_moe(
"FP8 dispatch interface is available only in low-latency mode"
)

is_quantized = w1.dtype == torch.float8_e4m3fn
is_quantized = w1.dtype == current_platform.fp8_dtype()
device_idx = torch.accelerator.current_device_index()
w1 = w1.to(device=device_idx)
w2 = w2.to(device=device_idx)
Expand Down Expand Up @@ -441,7 +444,7 @@ def _deep_ep_moe(
(222, 1024, 2048),
]

DTYPES = [torch.bfloat16, torch.float8_e4m3fn]
DTYPES = [torch.bfloat16, current_platform.fp8_dtype()]


@pytest.mark.parametrize("dtype", DTYPES)
Expand Down Expand Up @@ -496,7 +499,7 @@ def test_deep_ep_moe(
(64, 1024, 2560),
(222, 1024, 2560),
]
DTYPES = [torch.float8_e4m3fn, torch.bfloat16]
DTYPES = [current_platform.fp8_dtype(), torch.bfloat16]
USE_FP8_DISPATCH = [True, False]


Expand Down
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