wehub-resource-sync
128 lines
3.6 KiB
Python
128 lines
3.6 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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# The file has been adapted from DeepSeek DeepEP project
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# Copyright (c) 2025 DeepSeek
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# Licensed under the MIT License - https://github.com/deepseek-ai/DeepEP/blob/main/LICENSE
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import os
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import sys
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import paddle
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def bench(fn, num_warmups: int = 5, num_tests: int = 10, high_precision: bool = False):
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# Flush L2 cache with 256 MB data
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paddle.device.cuda.synchronize()
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cache = paddle.empty(int(256e6 // 4), dtype=paddle.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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# Add a large kernel to eliminate the CPU launch overhead
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if high_precision:
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x = paddle.randn((8192, 8192), dtype=paddle.float32)
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y = paddle.randn((8192, 8192), dtype=paddle.float32)
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x @ y
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# Testing
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start_event = paddle.device.cuda.Event(enable_timing=True)
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end_event = paddle.device.cuda.Event(enable_timing=True)
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start_event.record()
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for i in range(num_tests):
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fn()
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end_event.record()
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paddle.cuda.synchronize()
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return start_event.elapsed_time(end_event) / num_tests
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def get_cuda_home():
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cuda_home = os.environ.get("CUDA_HOME") or os.environ.get("CUDA_PATH")
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if cuda_home:
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return cuda_home
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try:
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which_cmd = "which nvcc"
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nvcc_path = os.popen(which_cmd).read().strip()
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if nvcc_path:
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return os.path.dirname(os.path.dirname(nvcc_path))
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except Exception:
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pass
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return None
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class empty_suppress:
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def __enter__(self):
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return self
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def __exit__(self, *_):
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pass
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class suppress_stdout_stderr:
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def __enter__(self):
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self.outnull_file = open(os.devnull, "w")
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self.errnull_file = open(os.devnull, "w")
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self.old_stdout_fileno_undup = sys.stdout.fileno()
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self.old_stderr_fileno_undup = sys.stderr.fileno()
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self.old_stdout_fileno = os.dup(sys.stdout.fileno())
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self.old_stderr_fileno = os.dup(sys.stderr.fileno())
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self.old_stdout = sys.stdout
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self.old_stderr = sys.stderr
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os.dup2(self.outnull_file.fileno(), self.old_stdout_fileno_undup)
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os.dup2(self.errnull_file.fileno(), self.old_stderr_fileno_undup)
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sys.stdout = self.outnull_file
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sys.stderr = self.errnull_file
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return self
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def __exit__(self, *_):
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sys.stdout = self.old_stdout
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sys.stderr = self.old_stderr
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os.dup2(self.old_stdout_fileno, self.old_stdout_fileno_undup)
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os.dup2(self.old_stderr_fileno, self.old_stderr_fileno_undup)
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os.close(self.old_stdout_fileno)
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os.close(self.old_stderr_fileno)
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self.outnull_file.close()
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self.errnull_file.close()
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def calc_diff(x, y):
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x, y = x.astype(paddle.float64), y.astype(paddle.float64)
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denominator = (x * x + y * y).sum()
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sim = 2 * (x * y).sum() / denominator
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return 1 - sim
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def count_bytes(tensors):
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total = 0
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for t in tensors:
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if isinstance(t, tuple):
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total += count_bytes(t)
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else:
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total += t.numel() * t.element_size()
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return total
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