wehub-resource-sync
122 lines
3.2 KiB
Python
122 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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def bench_split(fn1, fn2, num_warmups: int = 50, num_tests: int = 50):
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# clear
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cache = paddle.empty((int(256e6 // 4),), dtype="int32")
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cache.zero_()
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# Warmup
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for _ in range(num_warmups):
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fn1()
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fn2()
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# Flush L2
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cache.zero_()
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del cache
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# Testing
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start_events_fn1 = [
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paddle.device.Event(enable_timing=True) for _ in range(num_tests)
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]
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end_events_fn1 = [
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paddle.device.Event(enable_timing=True) for _ in range(num_tests)
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]
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start_events_fn2 = [
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paddle.device.Event(enable_timing=True) for _ in range(num_tests)
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]
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end_events_fn2 = [
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paddle.device.Event(enable_timing=True) for _ in range(num_tests)
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]
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for i in range(num_tests):
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# Record
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start_events_fn1[i].record()
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fn1()
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end_events_fn1[i].record()
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start_events_fn2[i].record()
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fn2()
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end_events_fn2[i].record()
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paddle.device.synchronize()
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times_fn1 = np.array(
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[
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s.elapsed_time(e) / 1e3
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for s, e in zip(start_events_fn1, end_events_fn1)
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]
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)[1:]
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times_fn2 = np.array(
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[
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s.elapsed_time(e) / 1e3
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for s, e in zip(start_events_fn2, end_events_fn2)
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]
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)[1:]
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return (
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np.average(times_fn1),
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np.min(times_fn1),
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np.max(times_fn1),
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np.average(times_fn2),
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np.min(times_fn2),
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np.max(times_fn2),
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)
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def bench(fn, num_warmups: int = 50, num_tests: int = 50):
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# clear
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cache = paddle.empty((int(256e6 // 4),), dtype="int32")
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cache.zero_()
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# Warmup
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for _ in range(num_warmups):
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fn()
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# Flush L2
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cache.zero_()
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del cache
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# Testing
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start_events_fn = [
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paddle.device.Event(enable_timing=True) for _ in range(num_tests)
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]
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end_events_fn = [
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paddle.device.Event(enable_timing=True) for _ in range(num_tests)
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]
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for i in range(num_tests):
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start_events_fn[i].record()
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fn()
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end_events_fn[i].record()
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paddle.device.synchronize()
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times_fn = np.array(
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[
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s.elapsed_time(e) / 1e3
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for s, e in zip(start_events_fn, end_events_fn)
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]
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)[1:]
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return (
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np.average(times_fn),
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np.min(times_fn),
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np.max(times_fn),
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)
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def per_token_cast_back(x_fp8: paddle.Tensor, x_scales: paddle.Tensor):
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x_fp32 = x_fp8.to("float32").view((x_fp8.shape[0], -1, 128))
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x_scales = x_scales.view((x_fp8.shape[0], -1, 1))
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return (x_fp32 * x_scales).view(x_fp8.shape).to("bfloat16")
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