246 lines
10 KiB
Python
246 lines
10 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from collections.abc import Callable
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from typing import Any
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import torch
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import torch_npu
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from vllm.config import CompilationMode, get_current_vllm_config
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from vllm.distributed import get_ep_group
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from vllm.forward_context import get_forward_context
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.device.mxfp_compat import (
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FLOAT8_E8M0FNU_DTYPE,
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ensure_mxfp4_linear_available,
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)
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from vllm_ascend.ops.fused_moe.experts_selector import select_experts
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from vllm_ascend.ops.fused_moe.moe_runtime_args import build_fused_experts_input
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from .base import AscendLinearScheme, AscendMoEScheme, QuantType, get_moe_num_logical_experts
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from .registry import register_scheme
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@register_scheme("W4A8_MXFP", "linear")
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class AscendW4A8MXFPDynamicLinearMethod(AscendLinearScheme):
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"""Linear method for Ascend W4A8_MXFP (Microscaling) quantization."""
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def __init__(self):
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ensure_mxfp4_linear_available("W8A8_MXFP8 linear quantization")
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vllm_config = get_current_vllm_config()
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self.group_size = vllm_config.quant_config.quant_description.get("group_size", 32)
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@staticmethod
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def get_weight(input_size: int, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
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params_dict = {"weight": torch.empty(output_size, input_size // 2, dtype=torch.uint8)}
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return params_dict
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def get_pergroup_param(
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self, input_size: int, output_size: int, params_dtype: torch.dtype, layer_type: str | None = None
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) -> dict[str, Any]:
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params_dict = {}
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params_dict["weight_scale"] = torch.empty(output_size, input_size // self.group_size, dtype=torch.uint8)
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return params_dict
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
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bias: torch.Tensor | None = None,
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tp_rank: int | None = 0,
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) -> torch.Tensor:
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if isinstance(x, tuple):
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quantized_x, dynamic_scale = x
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output_dtype = torch.bfloat16
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else:
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quantized_x, dynamic_scale = torch_npu.npu_dynamic_mx_quant(x, dst_type=torch.float8_e4m3fn)
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output_dtype = x.dtype
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output = torch_npu.npu_quant_matmul(
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quantized_x,
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layer.weight,
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layer.weight_scale,
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scale_dtype=torch_npu.float8_e8m0fnu,
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pertoken_scale=dynamic_scale,
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pertoken_scale_dtype=torch_npu.float8_e8m0fnu,
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bias=bias,
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output_dtype=output_dtype,
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x2_dtype=torch_npu.float4_e2m1fn_x2,
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group_sizes=[0, 0, self.group_size],
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)
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return output
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def process_weights_after_loading(self, layer):
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layer.weight.data = torch_npu.npu_format_cast(
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layer.weight.data, 29, customize_dtype=torch.float8_e4m3fn, input_dtype=torch_npu.float4_e2m1fn_x2
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)
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layer.weight.data = layer.weight.data.transpose(-1, -2)
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n, k = layer.weight_scale.shape
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layer.weight_scale.data = layer.weight_scale.data.reshape(n, k // 2, 2).transpose(-3, -2)
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@register_scheme("W4A8_MXFP", "moe")
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class AscendW4A8MXFPDynamicFusedMoEMethod(AscendMoEScheme):
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"""FusedMoe method for Ascend W4A8_DYNAMIC."""
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quant_type: QuantType = QuantType.W4A8MXFP
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def __init__(self):
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self.ep_group = get_ep_group()
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vllm_config = get_current_vllm_config()
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self.group_size = vllm_config.quant_config.quant_description.get("group_size", 32)
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ascend_config = get_ascend_config()
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self.use_aclgraph = (
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vllm_config.compilation_config.mode == CompilationMode.VLLM_COMPILE
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and not vllm_config.model_config.enforce_eager
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)
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self.dynamic_eplb = ascend_config.eplb_config.dynamic_eplb
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@staticmethod
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def get_weight(
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num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype
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) -> dict[str, Any]:
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param_dict = {}
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param_dict["w13_weight"] = torch.empty(
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num_experts, 2 * intermediate_size_per_partition, hidden_sizes // 2, dtype=torch.uint8
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)
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param_dict["w2_weight"] = torch.empty(
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num_experts, hidden_sizes, intermediate_size_per_partition // 2, dtype=torch.uint8
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)
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return param_dict
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def get_dynamic_quant_param(
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self, num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype
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) -> dict[str, Any]:
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param_dict = {}
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param_dict["w13_weight_scale"] = torch.empty(
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num_experts, 2 * intermediate_size_per_partition, hidden_sizes // self.group_size, dtype=torch.uint8
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)
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param_dict["w2_weight_scale"] = torch.empty(
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num_experts, hidden_sizes, intermediate_size_per_partition // self.group_size, dtype=torch.uint8
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)
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return param_dict
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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num_experts: int = -1,
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expert_map: torch.Tensor | None = None,
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topk_group: int | None = None,
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num_expert_group: int | None = None,
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custom_routing_function: Callable | None = None,
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scoring_func: str = "softmax",
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routed_scaling_factor: float = 1.0,
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e_score_correction_bias: torch.Tensor | None = None,
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is_prefill: bool = True,
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enable_force_load_balance: bool = True,
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log2phy: torch.Tensor = None,
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global_redundant_expert_num: int = 0,
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pertoken_scale: Any | None = None,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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mc2_mask: torch.Tensor | None = None,
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tid2eid: torch.Tensor | None = None,
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) -> torch.Tensor:
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num_shared_experts = getattr(layer, "n_shared_experts", 0)
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if num_shared_experts is None:
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num_shared_experts = 0
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num_logical_experts = get_moe_num_logical_experts(
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layer,
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num_experts,
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global_redundant_expert_num=global_redundant_expert_num,
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num_shared_experts=num_shared_experts,
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)
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assert router_logits.shape[1] == num_logical_experts, "Number of global experts mismatch (excluding redundancy)"
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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routed_scaling_factor=routed_scaling_factor,
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num_experts=num_logical_experts,
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tid2eid=tid2eid,
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)
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# this is a naive implementation for experts load balance so as
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# to avoid accumulating too much tokens on a single rank.
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# currently it is only activated when doing profile runs.
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if enable_force_load_balance:
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random_matrix = torch.rand(topk_ids.size(0), num_logical_experts, device=topk_ids.device)
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topk_ids = torch.argsort(random_matrix, dim=1)[:, : topk_ids.size(1)].to(topk_ids.dtype)
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if x.dtype not in [torch.float8_e4m3fn]:
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topk_weights = topk_weights.to(x.dtype)
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moe_comm_method = get_forward_context().moe_comm_method
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return moe_comm_method.fused_experts(
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fused_experts_input=build_fused_experts_input(
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hidden_states=x,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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quant_type=self.quant_type,
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dynamic_eplb=self.dynamic_eplb,
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expert_map=expert_map,
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global_redundant_expert_num=global_redundant_expert_num,
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mc2_mask=mc2_mask,
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apply_router_weight_on_input=apply_router_weight_on_input,
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log2phy=log2phy,
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pertoken_scale=pertoken_scale,
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activation=activation,
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mxfp_act_quant_type=torch.float8_e4m3fn,
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mxfp_weight_quant_type=torch_npu.float4_e2m1fn_x2,
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mxfp_scale_dtype=FLOAT8_E8M0FNU_DTYPE,
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mxfp_per_token_scale_dtype=FLOAT8_E8M0FNU_DTYPE,
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mxfp_use_bf16=(x.dtype in [torch.bfloat16, torch.float8_e4m3fn]),
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w1_scale=layer.w13_weight_scale,
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w2_scale=layer.w2_weight_scale,
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swiglu_limit=layer.swiglu_limit,
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)
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)
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def process_weights_after_loading(self, layer):
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layer.w13_weight.data = torch_npu.npu_format_cast(
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layer.w13_weight.data, 29, customize_dtype=torch.float8_e4m3fn, input_dtype=torch_npu.float4_e2m1fn_x2
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)
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layer.w2_weight.data = torch_npu.npu_format_cast(
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layer.w2_weight.data, 29, customize_dtype=torch.float8_e4m3fn, input_dtype=torch_npu.float4_e2m1fn_x2
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)
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layer.w13_weight.data = layer.w13_weight.data.transpose(1, 2)
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layer.w2_weight.data = layer.w2_weight.data.transpose(1, 2)
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g, n, k = layer.w13_weight_scale.shape
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layer.w13_weight_scale.data = layer.w13_weight_scale.data.reshape(g, n, k // 2, 2).transpose(-3, -2)
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g, n, k = layer.w2_weight_scale.shape
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layer.w2_weight_scale.data = layer.w2_weight_scale.data.reshape(g, n, k // 2, 2).transpose(-3, -2)
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