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Merged
xin3he merged 2 commits into from
Jan 21, 2026
Merged

cherry-pick two internal PRs for vllm-gaudi #2392

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abde0f1
[SW-228723] Added flag if op supports dynamic quant to ModuleInfo (#314)
HolyFalafel Oct 22, 2025
e06af2f
[GAUDISW-244137] Set FusedMoE and VllmMixtureOfExperts to support dyn…
HolyFalafel Dec 4, 2025
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40 changes: 20 additions & 20 deletions neural_compressor/torch/algorithms/fp8_quant/_core/patching_common.py
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Expand Up @@ -67,43 +67,43 @@ def create_mod_info_recursion(parent):

_mod_default_dict = {
"Matmul": ModuleInfo("matmul", PatchedMatmul),
"Linear": ModuleInfo("linear", PatchedLinear),
"ParallelLMHead": ModuleInfo("linear", PatchedParallelLMHead),
"RowParallelLinear": ModuleInfo("row_parallel_linear", PatchedRowParallelLinear),
"ColumnParallelLinear": ModuleInfo("linear", PatchedColumnParallelLinear),
"MergedColumnParallelLinear": ModuleInfo("linear", PatchedColumnParallelLinear),
"QKVParallelLinear": ModuleInfo("linear", PatchedColumnParallelLinear),
"FalconLinear": ModuleInfo("linear", PatchedLinear),
"Linear": ModuleInfo("linear", PatchedLinear, supports_dynamic_quantization=True),
"ParallelLMHead": ModuleInfo("linear", PatchedParallelLMHead, supports_dynamic_quantization=True),
"RowParallelLinear": ModuleInfo("row_parallel_linear", PatchedRowParallelLinear, supports_dynamic_quantization=True),
"ColumnParallelLinear": ModuleInfo("linear", PatchedColumnParallelLinear, supports_dynamic_quantization=True),
"MergedColumnParallelLinear": ModuleInfo("linear", PatchedColumnParallelLinear, supports_dynamic_quantization=True),
"QKVParallelLinear": ModuleInfo("linear", PatchedColumnParallelLinear, supports_dynamic_quantization=True),
"FalconLinear": ModuleInfo("linear", PatchedLinear, supports_dynamic_quantization=True),
"KVCache": ModuleInfo("kv_cache", PatchedKVCache),
"VLLMKVCache": ModuleInfo("kv_cache", PatchedVLLMKVCache),
"Conv2d": ModuleInfo("linear", PatchedConv2d),
"LoRACompatibleLinear": ModuleInfo("linear", PatchedLoRACompatibleLinear),
"LoRACompatibleLinear": ModuleInfo("linear", PatchedLoRACompatibleLinear, supports_dynamic_quantization=True),
"LoRACompatibleConv": ModuleInfo("linear", PatchedLoRACompatibleConv),
"Softmax": ModuleInfo("softmax", PatchedSoftmax),
"BlockSoftmaxConstMax": ModuleInfo("softmax", PatchedBlockSoftmaxConstMax),
"ModuleFusedSDPA": ModuleInfo("fused_sdpa", PatchedModuleFusedSDPA),
"MoeMatmul": ModuleInfo("linear", PatchedMoeMatmul),
"MoeFP8Matmul": ModuleInfo("linear", PatchedMoeFP8Matmul),
"ReplicatedLinear": ModuleInfo("linear", PatchedReplicatedLinear),
"MoeMatmul": ModuleInfo("linear", PatchedMoeMatmul, supports_dynamic_quantization=True),
"MoeFP8Matmul": ModuleInfo("linear", PatchedMoeFP8Matmul, supports_dynamic_quantization=True),
"ReplicatedLinear": ModuleInfo("linear", PatchedReplicatedLinear, supports_dynamic_quantization=True),
# Note: `no_quantize_op` indicates that this module is patched but does not require measurement or quantization.
"FusedMoE": ModuleInfo("no_quantize_op", PatchedMixtralMoE, False),
"SharedFusedMoE": ModuleInfo("no_quantize_op", PatchedMixtralMoE, False),
"FusedMoE": ModuleInfo("no_quantize_op", PatchedMixtralMoE, False, supports_dynamic_quantization=True),
"SharedFusedMoE": ModuleInfo("no_quantize_op", PatchedMixtralMoE, False, supports_dynamic_quantization=True),
"GaudiMixtralSparseMoeBlock": ModuleInfo("dynamic_moe", PatchedGaudiMixtralSparseMoeBlock),
"GaudiDeepseekV3MoE": ModuleInfo("dynamic_moe", PatchedGaudiDeepseekV3MoE),
"GaudiFP8Linear": ModuleInfo("linear", PatchedMoeFP8Matmul),
"VllmMixtureOfExpertsOp": ModuleInfo("dynamic_moe", PatchedVllmMixtureOfExpertsOp),
"VllmMixtureOfExpertsOpFP8": ModuleInfo("dynamic_moe", PatchedVllmMixtureOfExpertsOpFP8),
"VllmMixtureOfExpertsOpFP8PerChannel": ModuleInfo("dynamic_moe", PatchedVllmMixtureOfExpertsOpFP8),
"VllmMixtureOfExpertsOp": ModuleInfo("dynamic_moe", PatchedVllmMixtureOfExpertsOp, supports_dynamic_quantization=True),
"VllmMixtureOfExpertsOpFP8": ModuleInfo("dynamic_moe", PatchedVllmMixtureOfExpertsOpFP8, supports_dynamic_quantization=True),
"VllmMixtureOfExpertsOpFP8PerChannel": ModuleInfo("dynamic_moe", PatchedVllmMixtureOfExpertsOpFP8, supports_dynamic_quantization=True),
}


if deepspeed_exists:
_mod_default_dict.update(
{
"LinearLayer": ModuleInfo("linear", PatchedLinear),
"LinearAllreduce": ModuleInfo("linear", PatchedLinearAllReduce),
"ScopedLinearAllReduce": ModuleInfo("linear", PatchedLinearAllReduce),
"LmHeadLinearAllreduce": ModuleInfo("linear", PatchedLmHeadLinearAllreduce),
"LinearLayer": ModuleInfo("linear", PatchedLinear, supports_dynamic_quantization=True),
"LinearAllreduce": ModuleInfo("linear", PatchedLinearAllReduce, supports_dynamic_quantization=True),
"ScopedLinearAllReduce": ModuleInfo("linear", PatchedLinearAllReduce, supports_dynamic_quantization=True),
"LmHeadLinearAllreduce": ModuleInfo("linear", PatchedLmHeadLinearAllreduce, supports_dynamic_quantization=True),
}
)

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12 changes: 9 additions & 3 deletions neural_compressor/torch/algorithms/fp8_quant/_quant_common/quant_config.py
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Expand Up @@ -98,10 +98,16 @@ class DeviceForScalesType(Enum):
]

# TODO [SW-217813]: support dynamic quantization in all ops and remove
# TODO [SW-228723]: get a better way to list all linear ops, like set in ModuleInfo if supports dynamic
supported_dynamic_ops = ["Linear", "RowParallelLinear", "ColumnParallelLinear", "MergedColumnParallelLinear", "QKVParallelLinear", "FalconLinear", "LoRACompatibleLinear", "ReplicatedLinear", "LinearLayer", "LinearAllreduce", "ScopedLinearAllReduce", "LmHeadLinearAllreduce", "FusedMoE", "GaudiMixtralSparseMoeBlock", "VllmMixtureOfExpertsOp", "VllmMixtureOfExpertsOpFP8", "GaudiDeepseekV3MoE", "GaudiFP8Linear"]
from neural_compressor.torch.algorithms.fp8_quant.model_configs import get_patched_module_table, ModuleInfo

def is_supported_dynamic_op(op_str):
ret = op_str in supported_dynamic_ops
"""
Dynamically checks if the given op supports dynamic quantization
by looking up its ModuleInfo and checking for a 'supports_dynamic_quantization' attribute.
"""
patched_table = get_patched_module_table()
info = patched_table.get(op_str)
ret = getattr(info, "supports_dynamic_quantization", False) if info is not None else False
logger.trace("Checking if %s is supported for dynamic quantization: %s", op_str, ret)
return ret

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6 changes: 4 additions & 2 deletions neural_compressor/torch/algorithms/fp8_quant/model_configs.py
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Expand Up @@ -40,16 +40,18 @@ class ModuleInfo:
Configures a relation between a ModuleType key (from `_mod_types` dict in `common.py`)
to a PatchedModule class.
"""
def __init__(self, type, patched_module, should_measure_and_quant=True):
def __init__(self, type, patched_module, should_measure_and_quant=True, *, supports_dynamic_quantization=False):
self.type = type
self.patched_module = patched_module
self.should_measure_and_quant = should_measure_and_quant
self.supports_dynamic_quantization = supports_dynamic_quantization

def __repr__(self):
return (
f"ModuleInfo(type={self.type}, "
f"patched_module={self.patched_module.__name__}), "
f"should_measure_and_quant={self.should_measure_and_quant}"
f"should_measure_and_quant={self.should_measure_and_quant}, "
f"supports_dynamic_quantization={self.supports_dynamic_quantization}"
)


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