68d8d20ca2
### What this PR does / why we need it?
`mxfp_compat` only provides dtype/symbol compatibility helpers for
different `torch_npu` versions, but it was placed under
`vllm_ascend.quantization`. Importing it from device/ops paths could
trigger `quantization/__init__.py` and pull in heavy quantization method
dependencies, increasing startup coupling and causing import-cycle risk
(especially on 310P paths).
### Does this PR introduce _any_ user-facing change?
No functional behavior change intended.
### How was this patch tested?
CI passed.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
import torch
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from vllm_ascend.device.mxfp_compat import (
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FLOAT4_E2M1FN_X2_DTYPE,
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FLOAT8_E8M0FNU_DTYPE,
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ensure_mxfp4_dtype_available,
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ensure_mxfp8_scale_dtype_available,
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)
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class QuantTypeMapping:
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quant_configs = {
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"W8A8_MXFP8": {
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"act_quant_type": torch.float8_e4m3fn,
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"weight_quant_type": None,
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"scale_dtype": FLOAT8_E8M0FNU_DTYPE,
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"per_token_scale_dtype": FLOAT8_E8M0FNU_DTYPE,
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},
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"W4A4_MXFP4": {
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"act_quant_type": FLOAT4_E2M1FN_X2_DTYPE,
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"weight_quant_type": FLOAT4_E2M1FN_X2_DTYPE,
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"scale_dtype": FLOAT8_E8M0FNU_DTYPE,
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"per_token_scale_dtype": FLOAT8_E8M0FNU_DTYPE,
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},
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"W4A8_MXFP": {
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"act_quant_type": torch.float8_e4m3fn,
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"weight_quant_type": FLOAT4_E2M1FN_X2_DTYPE,
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"scale_dtype": FLOAT8_E8M0FNU_DTYPE,
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"per_token_scale_dtype": FLOAT8_E8M0FNU_DTYPE,
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},
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}
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@staticmethod
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def get_quant_settings():
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return QuantTypeMapping.quant_configs
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def get_rollback_quant_type(rollback_quant_config):
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rollback_quant_type = "W8A8_MXFP8"
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for k, v in rollback_quant_config.items():
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if "down_proj" in k:
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rollback_quant_type = v
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return rollback_quant_type
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def parse_mxfp_quant_params(**kwargs):
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act_quant_type = kwargs.get("act_quant_type", torch.float8_e4m3fn)
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weight_quant_type = kwargs.get("weight_quant_type", torch.float8_e4m3fn)
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scale_type = kwargs.get("scale_type")
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per_token_scale_type = kwargs.get("per_token_scale_type")
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round_mode = kwargs.get("round_mode", "rint")
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return act_quant_type, weight_quant_type, scale_type, per_token_scale_type, round_mode
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def parse_quant_moe_down_proj_params(rollback_quant_type, parsed_round_mode):
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if rollback_quant_type == "W4A4_MXFP4":
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ensure_mxfp4_dtype_available("W4A4_MXFP4 quantization")
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elif rollback_quant_type in ("W8A8_MXFP8", "W4A8_MXFP"):
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ensure_mxfp8_scale_dtype_available(f"{rollback_quant_type} quantization")
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quant_type_mapping = QuantTypeMapping.get_quant_settings()
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cur_rollback_quant_config = quant_type_mapping[rollback_quant_type]
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if rollback_quant_type in ["W4A4_MXFP4"]: # w4a4mxfp4 round mode support round、rint
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round_mode = parsed_round_mode
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else: # mxfp8 only support rint
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round_mode = "rint"
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return (
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cur_rollback_quant_config["act_quant_type"],
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cur_rollback_quant_config["weight_quant_type"],
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cur_rollback_quant_config["scale_dtype"],
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cur_rollback_quant_config["per_token_scale_dtype"],
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round_mode,
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)
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