[Feat] 310p support MoE W8A8 quantizaition (#6641)
### What this PR does / why we need it?
This PR introduces support for W8A8 dynamic quantization for
Mixture-of-Experts (MoE) models on Ascend 310P devices. This is achieved
by:
- Implementing a new quantization scheme
`AscendW8A8DynamicFusedMoEMethod310`.
- Adding a unified MLP implementation (`unified_apply_mlp`) for 310P
that handles both quantized and unquantized paths.
- Refactoring the MoE and quantization configuration logic to correctly
route to the new 310P-specific implementations.
- Adding new e2e and unit tests to verify the functionality of MoE W8A8
quantization.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Added a new e2e test `test_qwen3_moe_tp2_w8a8` to test MoE W8A8
quantization in a multi-card setup.
- Added several new unit tests for the 310P-specific MoE components,
including `experts_selector`, `fused_moe`, `moe_comm_method`, `moe_mlp`,
and the new `w8a8_dynamic` quantization method.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
This commit is contained in:
pu-zhe
@@ -31,14 +31,13 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding,
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)
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# Important: trigger 310P method registrations (register into 310P-local registry)
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from vllm_ascend._310p.quantization import methods as _methods_310p # noqa: F401
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from vllm_ascend._310p.quantization.methods.registry import get_scheme_class as get_scheme_class_310p
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from vllm_ascend.quantization.method_adapters import (
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AscendLinearMethod,
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from vllm_ascend._310p.quantization.methods.registry import (
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get_scheme_class,
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)
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from vllm_ascend.quantization.method_adapters import AscendFusedMoEMethod, AscendLinearMethod
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from vllm_ascend.quantization.modelslim_config import (
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AscendModelSlimConfig,
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get_quant_type_for_layer,
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packed_modules_model_mapping,
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)
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from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD
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@@ -47,31 +46,34 @@ logger = init_logger(__name__)
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def create_scheme_for_layer(
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cfg: AscendModelSlimConfig,
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quant_description: dict[str, Any],
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prefix: str,
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layer_type: str,
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packed_modules_mapping: dict[str, Any] | None = None,
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):
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"""Create 310P quant scheme (mainline-like).
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"""Create a quantization scheme instance for a layer.
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- If quant_type cannot be determined: raise ValueError
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- If quant_type is determined but not supported on 310P: raise NotImplementedError
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Args:
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quant_description: The quantization description dictionary.
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prefix: The layer prefix.
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layer_type: The type of layer ("linear", "moe", "attention").
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packed_modules_mapping: Mapping for packed/fused modules.
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Returns:
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An instance of the appropriate quantization scheme class.
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"""
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logger.info_once("Using 310P ModelSlim Quantization routing.")
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logger.info_once("Using the vLLM Ascend modelslim Quantization now!")
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quant_type = get_quant_type_for_layer(quant_description, prefix, layer_type, packed_modules_mapping)
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if layer_type != "linear":
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raise NotImplementedError(f"310P quantization: layer_type={layer_type} is not supported yet (TODO).")
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quant_type = cfg._get_linear_quant_type(prefix)
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if quant_type is None:
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raise ValueError(f"310P quantization: could not determine quant_type for layer={prefix}.")
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raise ValueError(f"Could not determine quantization type for layer {prefix}.")
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scheme_cls = get_scheme_class_310p(quant_type, "linear")
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if scheme_cls is None:
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raise NotImplementedError(f"310P quantization: quant_type={quant_type} for linear is not supported yet (TODO).")
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return scheme_cls()
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# Use registry to get scheme class
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scheme_cls = get_scheme_class(quant_type, layer_type)
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if scheme_cls is not None:
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return scheme_cls()
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else:
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raise NotImplementedError(f"Currently, vLLM Ascend doesn't support {quant_type} for {layer_type}.")
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@register_quantization_config(ASCEND_QUANTIZATION_METHOD)
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@@ -84,40 +86,6 @@ class AscendModelSlimConfig310(AscendModelSlimConfig):
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causing NZ/transpose issues on 310P.
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"""
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def _get_linear_quant_type(self, prefix: str) -> str | None:
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"""Packed-aware quant type lookup.
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ModelSlim may describe fused modules by their shards.
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Example:
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prefix = "...qkv_proj" -> shards "...q_proj.weight", "...k_proj.weight", "...v_proj.weight"
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"""
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fused_mapping = getattr(self, "packed_modules_mapping", {}) or {}
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proj_name = prefix.split(".")[-1]
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if proj_name in fused_mapping:
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shard_prefixes = [
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prefix.replace(proj_name, shard_proj_name) for shard_proj_name in fused_mapping[proj_name]
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]
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quant_types: list[str] = []
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for sp in shard_prefixes:
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qt = self.quant_description.get(sp + ".weight")
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if isinstance(qt, str):
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quant_types.append(qt)
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if not quant_types:
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return None
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first = quant_types[0]
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if any(q != first for q in quant_types[1:]):
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raise ValueError(
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f"310P quantization: not all shards of fused layer '{prefix}' "
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f"share the same quant type. shards={shard_prefixes}, types={quant_types}"
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)
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return first
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qt = self.quant_description.get(prefix + ".weight")
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return qt if isinstance(qt, str) else None
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def get_quant_method(
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self,
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layer: torch.nn.Module,
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@@ -141,7 +109,6 @@ class AscendModelSlimConfig310(AscendModelSlimConfig):
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return AscendUnquantizedLinearMethod()
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scheme = create_scheme_for_layer(
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cfg=self,
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quant_description=self.quant_description,
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prefix=prefix,
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layer_type="linear",
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@@ -149,14 +116,15 @@ class AscendModelSlimConfig310(AscendModelSlimConfig):
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)
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return AscendLinearMethod(scheme)
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if isinstance(layer, VocabParallelEmbedding):
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elif isinstance(layer, FusedMoE):
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if self.is_layer_skipped_ascend(prefix, self.packed_modules_mapping):
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from vllm_ascend._310p.fused_moe.fused_moe import AscendUnquantizedFusedMoEMethod310
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return AscendUnquantizedFusedMoEMethod310(layer.moe_config)
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scheme = create_scheme_for_layer(self.quant_description, prefix, "moe", self.packed_modules_mapping)
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return AscendFusedMoEMethod(scheme, layer.moe_config)
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elif isinstance(layer, VocabParallelEmbedding):
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return UnquantizedEmbeddingMethod()
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if isinstance(layer, FusedMoE):
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raise NotImplementedError(
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"310P quantization: FusedMoE is not supported yet. "
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"TODO: add 310P MoE quant schemes and routing. "
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"Workaround: use a non-MoE model."
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)
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return super().get_quant_method(layer, prefix)
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