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95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
"""
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# Copyright (c) 2025 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 Optional
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import paddle
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import fastdeploy
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import fastdeploy.model_executor.ops.gpu.deep_gemm as deep_gemm
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from ..utils import per_block_cast_to_fp8
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from .quant_base import QuantConfigBase, QuantMethodBase
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QUANT_ALIGNMENT_OFFSET = 127
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QUANT_BLOCK_SIZE = 128
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class BlockWiseConfig(QuantConfigBase):
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"""
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block wise quantization config, only support fp8 quant and only supports loading weights in BF16 format.
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After loading the weights, it will automatically compute quantization sparsity and dynamically perform
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per-token quantization of activations during inference.
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"""
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def __init__(self, weight_block_size: list = [-1, -1]) -> None:
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super().__init__()
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self.weight_block_size = weight_block_size
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def get_name(self) -> str:
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return "block_wise"
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@classmethod
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def from_config(cls, config: dict) -> "BlockWiseConfig":
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weight_block_size = config["weight_block_size"]
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return cls(weight_block_size)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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return BlockWiseLinearMethod(self)
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class BlockWiseLinearMethod(QuantMethodBase):
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"""
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block wise quantization method for linear
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"""
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def __init__(
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self,
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quant_config: BlockWiseConfig,
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) -> None:
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super().__init__()
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self.quant_config = quant_config
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def create_weights(self, layer):
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layer.linear_weight_scale = self.create_parameter(
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shape=[
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(layer.embed_dim + QUANT_ALIGNMENT_OFFSET) // QUANT_BLOCK_SIZE,
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(layer.num_heads * layer.head_dim + QUANT_ALIGNMENT_OFFSET) //
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QUANT_BLOCK_SIZE,
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],
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dtype="float32",
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is_bias=False,
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)
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def process_loaded_weights(self, layer, weights) -> None:
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weight_tensor = weights.transpose([1, 0])
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quanted_weight_tensor, weight_block_scale_tensor = (
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per_block_cast_to_fp8(weight_tensor))
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layer.linear_weight.copy_(quanted_weight_tensor, False)
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layer.linear_weight_scale.set_value(weight_block_scale_tensor)
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def apply(self, layer, x):
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x, x_scale_tensor = fastdeploy.model_executor.ops.gpu.per_token_quant_padding(
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x, self.quant_config.weight_block_size[0])
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linear_out = paddle.empty(
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(x.shape[0], layer.llm_config.model_config.hidden_size),
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dtype=paddle.bfloat16)
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deep_gemm.gemm_fp8_fp8_bf16_nt(
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(x, x_scale_tensor),
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(layer.linear_weight, layer.linear_weight_scale),
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linear_out,
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)
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return linear_out
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