mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
Longzhi Wang
daaf498213
* [Feature] support compute shared experts before combine for better overlap * fix test * fix xpu * fix
613 lines
23 KiB
Python
613 lines
23 KiB
Python
"""
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# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
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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 typing import Callable, Optional
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import paddle
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from paddle import nn
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from paddleformers.utils.log import logger
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import fastdeploy
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from fastdeploy import envs
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from fastdeploy.model_executor.layers.moe import FusedMoE
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from fastdeploy.model_executor.utils import (
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create_parameter_and_copy,
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free_tensor,
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set_weight_attrs,
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)
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from .quant_base import QuantConfigBase, QuantMethodBase
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paddle.compat.enable_torch_proxy(scope={"flashinfer"})
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def next_power_of_2(n: int):
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return 1 << (n - 1).bit_length() if n > 0 else 1
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def _process_scale_interleaved(scales):
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scale_dim = len(scales.shape)
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if scale_dim == 2:
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scales = scales.unsqueeze(0)
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assert len(scales.shape) == 3
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B, M, K = scales.shape
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round_up_multiple = lambda x, m: (x + m - 1) // m * m
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M_padded = round_up_multiple(M, 128)
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K_padded = round_up_multiple(K, 4)
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padded_scales = paddle.empty([B, M_padded, K_padded], dtype=scales.dtype)
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padded_scales[:B, :M, :K].copy_(scales)
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batches, rows, cols = padded_scales.shape
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assert rows % 128 == 0
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assert cols % 4 == 0
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padded_scales = padded_scales.reshape(batches, rows // 128, 4, 32, cols // 4, 4)
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padded_scales = padded_scales.transpose([0, 1, 4, 3, 2, 5])
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# [batches, rows // 128, cols // 4, 32, 4, 4]
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padded_scales = padded_scales.contiguous().to(paddle.device.get_device())
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padded_scales = (
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padded_scales.reshape(M_padded, K_padded) if scale_dim == 2 else padded_scales.reshape(B, M_padded, K_padded)
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)
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return padded_scales
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class ModelOptNvFp4Config(QuantConfigBase):
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"""
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quantization config for ModelOpt Nvfp4 datatype
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"""
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def __init__(
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self,
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is_checkpoint_nvfp4_serialized: bool,
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kv_cache_quant_algo: str | None,
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exclude_modules: list[str],
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group_size: int = 16,
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is_checkpoint_bf16: bool = False,
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) -> None:
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self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
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if is_checkpoint_nvfp4_serialized:
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logger.warning(
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"Detected ModelOpt NVFP4 checkpoint. Please note that"
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" the format is experimental and could change in future."
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)
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self.group_size = group_size
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self.kv_cache_quant_algo = kv_cache_quant_algo
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self.exclude_modules = exclude_modules
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self.quant_max_bound = 6
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self.quant_min_bound = -6
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self.quant_round_type = 1
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self.is_checkpoint_bf16 = is_checkpoint_bf16
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def name(self) -> str:
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return "modelopt_fp4"
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@classmethod
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def from_config(cls, config: dict) -> "ModelOptNvFp4Config":
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quant_config = config
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quant_method = quant_config.get("quant_algo", "")
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if not quant_method:
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raise ValueError("Missing 'quant_algo' in quantization config")
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# Handle kv_cache_quant_algo with proper type validation
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kv_cache_quant_algo_raw = quant_config.get("kv_cache_quant_algo")
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if kv_cache_quant_algo_raw is None:
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# No KV cache quantization by default
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kv_cache_quant_algo = None
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elif isinstance(kv_cache_quant_algo_raw, str):
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kv_cache_quant_algo = kv_cache_quant_algo_raw
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else:
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raise ValueError(f"kv_cache_quant_algo must be a string, got " f"{type(kv_cache_quant_algo_raw)}")
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# Handle group_size with proper type validation
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group_size_raw = quant_config.get("group_size")
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if group_size_raw is None:
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group_size = 16 # Default value
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elif isinstance(group_size_raw, int):
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group_size = group_size_raw
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else:
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try:
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group_size = int(group_size_raw)
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except (ValueError, TypeError):
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raise ValueError(f"group_size must be an integer, got {type(group_size_raw)}") from None
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# "exclude_modules" is the key in the legacy hf_quant_config.json
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exclude_modules = quant_config.get("exclude_modules", [])
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if not isinstance(exclude_modules, list):
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raise ValueError(f"exclude_modules must be a list, got {type(exclude_modules)}")
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is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
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# For FP4, these fields are required
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if is_checkpoint_nvfp4_serialized and "quantization" in config:
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# Check if required fields are present in the quantization config
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quant_config = config["quantization"]
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required_fields = ["group_size", "kv_cache_quant_algo", "exclude_modules"]
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missing_fields = [field for field in required_fields if field not in quant_config]
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if missing_fields:
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raise ValueError(
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f"NVFP4 quantization requires the following fields in " f"hf_quant_config.json: {missing_fields}"
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)
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return cls(
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is_checkpoint_nvfp4_serialized=is_checkpoint_nvfp4_serialized,
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kv_cache_quant_algo=kv_cache_quant_algo,
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exclude_modules=exclude_modules,
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group_size=group_size,
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)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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"""
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Get quantization method.
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"""
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if isinstance(layer, FusedMoE):
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return ModelOptNvFp4FusedMoE(self)
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else:
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return ModelOptNvFp4LinearMethod(self)
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class ModelOptNvFp4LinearMethod(QuantMethodBase):
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"""Linear method for Model Optimizer NVFP4.
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Supports loading NVFP4 checkpoints with the following structure:
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input_scale: paddle.float32, scalar ,
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weight: NVFP4(represented as byte) Shape: [1, X, y/2]
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weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
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weight_scale_2: paddle.float32, scalar,
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Args: quant_config: The ModelOpt quantization config.
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"""
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def __init__(self, quant_config: ModelOptNvFp4Config) -> None:
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self.quant_config = quant_config
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self.backend = "none"
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if envs.FD_NVFP4_GEMM_BACKEND is None:
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self.backend = "flashinfer-cutlass"
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elif envs.FD_NVFP4_GEMM_BACKEND.startswith("flashinfer-"):
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self.backend = envs.FD_NVFP4_GEMM_BACKEND
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if self.backend == "none":
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raise ValueError(
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"No valid NVFP4 GEMM backend found. Please check your platform capability and installtion of Flashinfer."
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)
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logger.info(f"Using {self.backend} for NVFP4 GEMM")
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def create_weights(
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self,
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layer,
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**extra_weight_attrs,
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):
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# 因为模型存储是列存储的，所以这里需要not一下！
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extra_weight_attrs["output_dim"] = not extra_weight_attrs["output_dim"]
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K = layer.weight_shape[0]
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N = layer.weight_shape[1]
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# 因为模型的存储时候权重是[N,K//2]
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# 所以这里创建的权重是为了契合模型存储的权重！
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weight_shape = [N, K // 2]
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layer.weight_dtype = "uint8"
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input_scale_shape = [1]
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weight_scale_shape = [N, K // self.quant_config.group_size]
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weight_scale_2_shape = [1]
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self._create_main_weight(layer, weight_shape, extra_weight_attrs)
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self._create_input_scale(layer, input_scale_shape)
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self._create_weight_scales(layer, weight_scale_shape, weight_scale_2_shape, extra_weight_attrs)
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def _create_main_weight(self, layer, weight_shape, extra_weight_attrs):
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"""创建主权重参数
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参数:
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layer: 当前层对象
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weight_shape: 权重形状
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extra_weight_attrs: 额外权重属性
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"""
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layer.weight = layer.create_parameter(
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shape=weight_shape,
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dtype=layer.weight_dtype,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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set_weight_attrs(
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layer.weight,
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extra_weight_attrs,
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)
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def _create_input_scale(self, layer, input_scale_shape):
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"""创建输入缩放参数
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参数:
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layer: 当前层对象
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input_scale_shape: 输入缩放形状
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"""
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layer.input_scale = layer.create_parameter(
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shape=input_scale_shape,
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dtype=paddle.float32,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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def _create_weight_scales(self, layer, weight_scale_shape, weight_scale_2_shape, extra_weight_attrs):
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"""创建权重缩放参数
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参数:
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layer: 当前层对象
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weight_scale_shape: 权重缩放形状
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weight_scale_2_shape: 权重缩放2形状
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extra_weight_attrs: 额外权重属性
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"""
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layer.weight_scale = layer.create_parameter(
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shape=weight_scale_shape,
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dtype=paddle.float8_e4m3fn,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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set_weight_attrs(
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layer.weight_scale,
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extra_weight_attrs,
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)
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layer.weight_scale_2 = layer.create_parameter(
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shape=weight_scale_2_shape,
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dtype=paddle.float32,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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def process_weights_after_loading(self, layer) -> None:
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input_scale_2 = layer.input_scale.max().to(paddle.float32)
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weight_scale_2 = layer.weight_scale_2.max().to(paddle.float32)
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alpha = input_scale_2 * weight_scale_2
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input_scale_inv = (1 / input_scale_2).to(paddle.float32)
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weight_scale_interleaved = _process_scale_interleaved(layer.weight_scale)
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free_tensor(layer.input_scale)
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free_tensor(layer.weight_scale_2)
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layer.weight_scale_2 = layer.create_parameter(
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shape=weight_scale_2.shape,
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dtype=weight_scale_2.dtype,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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layer.input_scale = layer.create_parameter(
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shape=input_scale_2.shape,
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dtype=input_scale_2.dtype,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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layer.alpha = layer.create_parameter(
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shape=alpha.shape,
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dtype=alpha.dtype,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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layer.input_scale_inv = layer.create_parameter(
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shape=input_scale_inv.shape,
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dtype=input_scale_inv.dtype,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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layer.weight_scale_interleaved = layer.create_parameter(
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shape=weight_scale_interleaved.shape,
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dtype=weight_scale_interleaved.dtype,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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layer.weight_scale_2.copy_(weight_scale_2, False)
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layer.input_scale.copy_(input_scale_2, False)
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layer.alpha.copy_(alpha, False)
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layer.input_scale_inv.copy_(input_scale_inv, False)
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layer.weight_scale_interleaved.copy_(weight_scale_interleaved, False)
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def apply(
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self,
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layer,
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x,
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):
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x_m, _ = x.shape
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w_n, _ = layer.weight.shape
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output_shape = [x_m, w_n]
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output_dtype = x.dtype
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# Quantize BF16 or FP16 to (FP4 and interleaved block scale)
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from flashinfer import fp4_quantize
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x_fp4, x_scale_interleaved = fp4_quantize(x, layer.input_scale_inv)
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assert x_fp4.dtype == paddle.uint8
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assert layer.weight.dtype == paddle.uint8
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assert layer.weight_scale_interleaved.dtype == paddle.float8_e4m3fn
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assert layer.alpha.dtype == paddle.float32
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if self.backend.startswith("flashinfer-"):
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backend = self.backend[len("flashinfer-") :]
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else:
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raise ValueError(f"Unsupported backend: {self.backend}.")
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# shape 恢复到[K//2,N]
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w = layer.weight.T
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# shape 恢复到[K//group_size, N]
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w_scale_interleaved = layer.weight_scale_interleaved.T
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if backend == "cutlass":
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x_scale_interleaved = x_scale_interleaved.view(paddle.uint8)
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w_scale_interleaved = w_scale_interleaved.view(paddle.uint8)
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from flashinfer import mm_fp4 as fp4_gemm
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out = fp4_gemm(x_fp4, w, x_scale_interleaved, w_scale_interleaved, layer.alpha, output_dtype, backend=backend)
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if layer.with_bias:
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out = paddle.add(out, layer.bias)
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assert out.shape == output_shape
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return out
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class ModelOptNvFp4FusedMoE(QuantMethodBase):
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"""Fused MoE method for Model Optimizer NVFP4.
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Supports loading NVFP4 checkpoints with the following structure:
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input_scale: paddle.float32, scalar ,
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weight: NVFP4(represented as byte) Shape: [1, X, y/2]
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weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
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weight_scale_2: paddle.float32, scalar,
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Args:
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quant_config: The ModelOpt quantization config.
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moe_config: The MoE configuration.
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layer: The linear layer.
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"""
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def __init__(self, quant_config: ModelOptNvFp4Config):
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self.quant_config = quant_config
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self.added_weight_attrs = ["up_gate_proj_weight", "down_proj_weight"]
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self.added_scale_attrs = [
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"up_gate_proj_weight_scale",
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"down_proj_weight_scale",
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]
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self.quant_config = quant_config
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self.backend = "none"
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if envs.FD_MOE_BACKEND is None:
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# currently support flashinfer-cutlass, flashinfer-trtllm will support in the future
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self.backend = "flashinfer-cutlass"
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elif envs.FD_MOE_BACKEND.startswith("flashinfer-"):
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self.backend = envs.FD_MOE_BACKEND
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if self.backend == "none":
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raise ValueError(
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"No valid NVFP4 flashinfer MoE backend found. Please check your platform capability and installtion of FlashInfer."
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)
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logger.info(f"Using {self.backend} for NVFP4 FusedMoE")
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def create_weights(self, layer, **extra_weight_attrs):
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"""
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NVFP4 MoE create weight.
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"""
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self.up_gate_proj_weight_shape = [
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layer.num_local_experts,
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layer.moe_intermediate_size * 2,
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layer.hidden_size // 2,
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]
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self.down_proj_weight_shape = [
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layer.num_local_experts,
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layer.hidden_size,
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layer.moe_intermediate_size // 2,
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]
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self.up_gate_proj_scale_shape = self.up_gate_proj_weight_shape[0:2] + [
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layer.hidden_size // self.quant_config.group_size
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]
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self.down_proj_scale_shape = self.down_proj_weight_shape[0:2] + [
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layer.moe_intermediate_size // self.quant_config.group_size
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]
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self.weight_scale_dtype = paddle.float8_e4m3fn
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self.weight_dtype = paddle.uint8
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up_gate_proj_weight_name = self.added_weight_attrs[0]
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down_proj_weight_name = self.added_weight_attrs[1]
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up_gate_proj_scale_name = self.added_scale_attrs[0]
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down_proj_scale_name = self.added_scale_attrs[1]
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setattr(
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layer,
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up_gate_proj_weight_name,
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layer.create_parameter(
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shape=self.up_gate_proj_weight_shape,
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dtype=self.weight_dtype,
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default_initializer=paddle.nn.initializer.Constant(0),
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),
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)
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setattr(
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layer,
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down_proj_weight_name,
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layer.create_parameter(
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shape=self.down_proj_weight_shape,
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dtype=self.weight_dtype,
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default_initializer=paddle.nn.initializer.Constant(0),
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),
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)
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# weight_scale
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setattr(
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layer,
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up_gate_proj_scale_name,
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layer.create_parameter(
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shape=self.up_gate_proj_scale_shape,
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dtype=self.weight_scale_dtype,
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default_initializer=paddle.nn.initializer.Constant(0),
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),
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)
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setattr(
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layer,
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down_proj_scale_name,
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layer.create_parameter(
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shape=self.down_proj_scale_shape,
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dtype=self.weight_scale_dtype,
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default_initializer=paddle.nn.initializer.Constant(0),
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),
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)
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# weight_scale_2
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layer.up_gate_proj_weight_scale_2 = layer.create_parameter(
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shape=[layer.num_local_experts, 2],
|
|
dtype="float32",
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
layer.down_proj_weight_scale_2 = layer.create_parameter(
|
|
shape=[layer.num_local_experts],
|
|
dtype="float32",
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
# input_scale
|
|
layer.up_gate_proj_input_scale = layer.create_parameter(
|
|
shape=[layer.num_local_experts, 2],
|
|
dtype="float32",
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
layer.down_proj_input_scale = layer.create_parameter(
|
|
shape=[layer.num_local_experts],
|
|
dtype="float32",
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
|
|
set_weight_attrs(
|
|
getattr(layer, up_gate_proj_weight_name),
|
|
{**extra_weight_attrs, "SHARD_ID_TO_SHARDED_DIM": {"gate": 0, "down": 1, "up": 0}},
|
|
)
|
|
set_weight_attrs(
|
|
getattr(layer, up_gate_proj_scale_name),
|
|
{**extra_weight_attrs, "SHARD_ID_TO_SHARDED_DIM": {"gate": 0, "down": 1, "up": 0}},
|
|
)
|
|
|
|
set_weight_attrs(
|
|
getattr(layer, down_proj_weight_name),
|
|
{**extra_weight_attrs, "SHARD_ID_TO_SHARDED_DIM": {"gate": 0, "down": 1, "up": 0}},
|
|
)
|
|
set_weight_attrs(
|
|
getattr(layer, down_proj_scale_name),
|
|
{**extra_weight_attrs, "SHARD_ID_TO_SHARDED_DIM": {"gate": 0, "down": 1, "up": 0}},
|
|
)
|
|
|
|
set_weight_attrs(layer.up_gate_proj_weight_scale_2, {**extra_weight_attrs, "weight_type": "weight_scale_2"})
|
|
set_weight_attrs(layer.down_proj_weight_scale_2, {**extra_weight_attrs, "weight_type": "weight_scale_2"})
|
|
set_weight_attrs(layer.up_gate_proj_input_scale, {**extra_weight_attrs, "weight_type": "input_scale"})
|
|
set_weight_attrs(layer.down_proj_input_scale, {**extra_weight_attrs, "weight_type": "input_scale"})
|
|
|
|
@property
|
|
def load_up_proj_weight_first(self) -> bool:
|
|
# FlashInfer CUTLASS kernel assumes [Up, Gate] Proj as W13
|
|
# 目前默认给True
|
|
return True
|
|
|
|
def process_weights_after_loading(self, layer):
|
|
""" """
|
|
up_gate_proj_weight_scale_2 = layer.up_gate_proj_weight_scale_2[:, 0]
|
|
free_tensor(layer.up_gate_proj_weight_scale_2)
|
|
create_parameter_and_copy(layer, name="up_gate_proj_weight_scale_2", weight=up_gate_proj_weight_scale_2)
|
|
up_gate_proj_input_scale = paddle.max(layer.up_gate_proj_input_scale).cast("float32")
|
|
down_proj_input_scale = paddle.max(layer.down_proj_input_scale).cast("float32")
|
|
|
|
# Create shared parameters
|
|
create_parameter_and_copy(
|
|
layer, "g1_alphas", (up_gate_proj_input_scale * up_gate_proj_weight_scale_2).cast("float32")
|
|
)
|
|
create_parameter_and_copy(
|
|
layer, "g2_alphas", (down_proj_input_scale * layer.down_proj_weight_scale_2).cast("float32")
|
|
)
|
|
create_parameter_and_copy(
|
|
layer, "up_gate_proj_input_scale_quant", (1 / up_gate_proj_input_scale).cast("float32")
|
|
)
|
|
create_parameter_and_copy(layer, "down_proj_input_scale_quant", (1 / down_proj_input_scale).cast("float32"))
|
|
|
|
for name, weight_scale in [
|
|
("up_gate", layer.up_gate_proj_weight_scale),
|
|
("down", layer.down_proj_weight_scale),
|
|
]:
|
|
assert weight_scale.shape[2] % 16 == 0, f"Expected {name}_weight_scale.dim(2) to be divisible by 16"
|
|
assert (
|
|
weight_scale.dtype == paddle.float8_e4m3fn
|
|
), f"{name} Weight Blockscale must be represented as FP8-E4M3"
|
|
|
|
up_gate_proj_blockscale_swizzled = _process_scale_interleaved(layer.up_gate_proj_weight_scale)
|
|
free_tensor(layer.up_gate_proj_weight_scale)
|
|
layer.up_gate_proj_weight_scale = None
|
|
create_parameter_and_copy(
|
|
layer, name="up_gate_proj_blockscale_swizzled", weight=up_gate_proj_blockscale_swizzled
|
|
)
|
|
down_proj_blockscale_swizzled = _process_scale_interleaved(layer.down_proj_weight_scale)
|
|
free_tensor(layer.down_proj_weight_scale)
|
|
layer.down_proj_weight_scale = None
|
|
create_parameter_and_copy(layer, name="down_proj_blockscale_swizzled", weight=down_proj_blockscale_swizzled)
|
|
|
|
def apply(
|
|
self,
|
|
layer,
|
|
x,
|
|
gate,
|
|
topk_ids_hookfunc: Callable = None,
|
|
shared_experts: nn.Layer = None,
|
|
):
|
|
"""
|
|
flashinfer nvfp4 fusedmoe for Model Optimizer
|
|
"""
|
|
gate_out = gate(x.cast("float32"))
|
|
topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
|
|
gate_out,
|
|
layer.gate_correction_bias,
|
|
layer.top_k,
|
|
True, # apply_norm_weight,
|
|
False,
|
|
)
|
|
|
|
if topk_ids_hookfunc is not None:
|
|
topk_ids_hookfunc(topk_ids)
|
|
|
|
output_dtype = x.dtype
|
|
x_sf = None
|
|
output = paddle.empty_like(x)
|
|
|
|
if self.backend == "flashinfer-cutlass":
|
|
# flashinfer cutlass
|
|
from flashinfer.fused_moe import (
|
|
cutlass_fused_moe as flashinfer_cutlass_fused_moe,
|
|
)
|
|
|
|
_ = flashinfer_cutlass_fused_moe(
|
|
input=x,
|
|
token_selected_experts=topk_ids.to(paddle.int),
|
|
token_final_scales=topk_weights,
|
|
fc1_expert_weights=getattr(layer, self.added_weight_attrs[0]).view(paddle.long),
|
|
fc2_expert_weights=getattr(layer, self.added_weight_attrs[1]).view(paddle.long),
|
|
output_dtype=output_dtype,
|
|
input_sf=x_sf,
|
|
quant_scales=[
|
|
layer.up_gate_proj_input_scale_quant,
|
|
layer.up_gate_proj_blockscale_swizzled.view(paddle.int32),
|
|
layer.g1_alphas,
|
|
layer.down_proj_input_scale_quant,
|
|
layer.down_proj_blockscale_swizzled.view(paddle.int32),
|
|
layer.g2_alphas,
|
|
],
|
|
ep_size=layer.ep_size,
|
|
ep_rank=layer.ep_rank,
|
|
tp_size=layer.tp_size,
|
|
tp_rank=layer.tp_rank,
|
|
tune_max_num_tokens=next_power_of_2(x.shape[0]),
|
|
output=output,
|
|
)
|
|
|
|
return output
|
|
|
|
# flashinfer-trtllm
|
|
return output
|