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89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
# 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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import numpy as np
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import paddle
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from fastdeploy.model_executor.ops.xpu import weight_quantize_xpu
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np.random.seed(2025)
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def np_clip_and_round(x, abs_max=127):
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return np.clip(np.around(x), -abs_max, abs_max).astype("int8")
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def np_quant_weight_int4(weight_np):
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assert weight_np.dtype == np.float32 # k,n
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weight = np.transpose(weight_np, [1, 0]) # n,k
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max_value = np.max(np.abs(weight), axis=1).reshape(-1, 1) # k => k,1
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quanted_weight = np_clip_and_round(weight / max_value * 7.0, 7) # n,k
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quanted_weight = (quanted_weight[:, 1::2] & 0xF) << 4 | (quanted_weight[:, ::2] & 0xF) # pack int4, [n,k//2]
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weight_scales = (max_value).astype(weight_np.dtype).reshape(-1)
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return quanted_weight, weight_scales.astype(np.float32)
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def np_quant_weight(weight_np, algo="weight_only_int8"):
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assert weight_np.dtype == np.float32
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if algo == "weight_only_int4":
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return np_quant_weight_int4(weight_np)
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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 = np_clip_and_round(weight / max_value * 127.0)
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weight_scales = (max_value).astype(weight_np.dtype).reshape(-1)
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return quanted_weight, weight_scales.astype(np.float32)
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def int8_to_bin_np(value):
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value_np = np.int8(value)
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return np.binary_repr(value_np, width=8)
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def int8_to_bin(value):
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if not -128 <= value <= 127:
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raise ValueError("int8 值必须在 -128 到 127 之间")
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return format(value & 0xFF, "08b") # '08b' 表示 8 位二进制，高位补零
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# 1) preparation
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k, n = 128, 256
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algo = "weight_only_int8"
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k, n = 8192, 57344
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w_np = (np.random.random((k, n)).astype(np.float32) - 0.5) * 10
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# 2) np calculation
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qw_np, wscale_np = np_quant_weight(w_np, algo)
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# 3) xpu calculation
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dtype = "float32"
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x_pd = paddle.to_tensor(w_np, dtype=dtype)
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qw_pd, wscale_pd = weight_quantize_xpu(x_pd, algo, -1, -1)
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qw_pd_trans = paddle.transpose(qw_pd, [1, 0])
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# print("w_np:\n{}".format(w_np))
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# print("qw_np:\n{}".format(qw_np))
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# print("qw_pd:\n{}".format(qw_pd_trans))
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# print("wscale_pd:\n{}".format(wscale_pd))
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# print("wscale_np:\n{}".format(wscale_np))
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# comparison
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print(f"wscale_pd, mean={wscale_pd.mean()}, std={wscale_pd.std()}")
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print(f"wscale_np, mean={wscale_np.mean()}, std={wscale_np.std()}")
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print(f"qw_np, mean={qw_np.astype(np.float32).mean()}, std={qw_np.astype(np.float32).std()}")
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print(f"qw_pd_trans, mean={qw_pd_trans.astype('float32').mean()}, std={qw_pd_trans.astype('float32').std()}")
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sum_diff = np.sum(np.abs(qw_pd_trans.astype("float32").numpy() - qw_np.astype("float32")))
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print(f"sum_diff: {sum_diff}")
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