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94 lines
3.7 KiB
Python
94 lines
3.7 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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!! This file will be deleted after the platform is fully functional
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"""
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from typing import Tuple
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import numpy as np
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import paddle
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def xpu_clip_and_round(x: np.ndarray) -> np.ndarray:
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"""
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Clip and round the input array to the range [-127, 127] and convert to int8.
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Args:
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x (numpy.ndarray): The input array to be clipped and rounded.
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Returns:
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numpy.ndarray: The clipped and rounded array with dtype int8.
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"""
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return np.clip(np.around(x), -127, 127).astype("int8")
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def xpu_quant_qkv_weight(
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weight_np: np.ndarray,
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) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""
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Quantize the query, key, and value weights for the Transformer model.
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Args:
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weight_np (numpy.ndarray): The original weights of query, key, and value in numpy format.
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It should be a 2D or higher dimensional tensor, where the last dimension represents the
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embedding dimension.
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Returns:
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tuple: A tuple containing:
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quanted_weight (paddle.Tensor): The quantized weights in paddle tensor format,
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with the same shape as the input weight_np.
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weight_scales (paddle.Tensor): The scaling factors for each element in the last dimension
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of the input, used to recover the original value range from the quantized weights.
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"""
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dim_embed = weight_np.shape[-1]
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weight = np.reshape(weight_np, [-1, dim_embed])
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max_value = np.max(np.abs(weight), axis=1).reshape(-1, 1)
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quanted_weight = xpu_clip_and_round(weight / max_value * 127.0)
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quanted_weight = np.reshape(quanted_weight, weight_np.shape)
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quanted_weight = paddle.to_tensor(quanted_weight, place=paddle.CPUPlace())
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weight_scales = (max_value / 127.0).astype(weight_np.dtype).reshape(-1)
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weight_scales = paddle.to_tensor(weight_scales, place=paddle.CPUPlace())
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weight_scales = paddle.cast(weight_scales, paddle.get_default_dtype())
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return quanted_weight, weight_scales
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def xpu_quant_weight(
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weight_np: np.ndarray,
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) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""
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Quantize the weight tensor for XPU devices.
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Args:
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weight_np (numpy.ndarray): The original weight tensor in numpy format,
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expected to be a 2D array.
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Returns:
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tuple: A tuple containing two elements:
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quanted_weight (paddle.Tensor): The quantized weight tensor,
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converted to a Paddle Tensor on CPU.
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weight_scales (paddle.Tensor): The corresponding scales for the quantized
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weights, also converted to a Paddle Tensor on CPU and cast to the
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default data type.
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"""
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weight = np.transpose(weight_np, [1, 0])
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max_value = np.max(np.abs(weight), axis=1).reshape(-1, 1)
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quanted_weight = xpu_clip_and_round(weight / max_value * 127.0)
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quanted_weight = paddle.to_tensor(quanted_weight, place=paddle.CPUPlace())
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weight_scales = (max_value / 127.0).astype(weight_np.dtype).reshape(-1)
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weight_scales = paddle.to_tensor(weight_scales, place=paddle.CPUPlace())
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weight_scales = paddle.cast(weight_scales, paddle.get_default_dtype())
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return quanted_weight, weight_scales
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