e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
205 lines
8.0 KiB
Python
205 lines
8.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import math
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from unittest.mock import MagicMock
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import pytest
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import torch
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from vllm.spec_decode.metrics import AsyncMetricsCollector
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def test_initial_call_returns_none():
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"""Expect first call to get metrics to return None.
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"""
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spec_decode_sampler = MagicMock()
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spec_decode_sampler.num_accepted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_emitted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_draft_tokens = 0
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collector = AsyncMetricsCollector(spec_decode_sampler)
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collector.init_gpu_tensors(rank=0)
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maybe_metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert maybe_metrics is None
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def test_second_call_returns_metrics():
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"""Expect second call to not return None.
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"""
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spec_decode_sampler = MagicMock()
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spec_decode_sampler.num_accepted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_emitted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_draft_tokens = 0
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collect_interval_s = 5.0
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timer = MagicMock()
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timer.side_effect = [
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0.0, collect_interval_s + 0.1, collect_interval_s + 0.2
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]
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collector = AsyncMetricsCollector(spec_decode_sampler=spec_decode_sampler,
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timer=timer,
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collect_interval_s=collect_interval_s)
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collector.init_gpu_tensors(rank=0)
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_ = collector.maybe_collect_rejsample_metrics(k=5)
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metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert metrics is not None
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@pytest.mark.parametrize("rank", [1, 2, 3, 4])
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def test_nonzero_rank_noop(rank):
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"""Verify nonzero ranks don't collect metrics.
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"""
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spec_decode_sampler = MagicMock()
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spec_decode_sampler.num_accepted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_emitted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_draft_tokens = 0
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collector = AsyncMetricsCollector(spec_decode_sampler)
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collector.init_gpu_tensors(rank=rank)
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_ = collector.maybe_collect_rejsample_metrics(k=5)
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metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert metrics is None
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def test_noop_until_time():
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"""Verify metrics aren't collected until enough time passes.
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"""
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spec_decode_sampler = MagicMock()
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spec_decode_sampler.num_accepted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_emitted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_draft_tokens = 0
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collect_interval_s = 5.0
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timer = MagicMock()
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timer.side_effect = [
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0.0, collect_interval_s - 0.1, collect_interval_s - 0.1,
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collect_interval_s + 0.1, collect_interval_s + 0.1
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]
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collector = AsyncMetricsCollector(spec_decode_sampler=spec_decode_sampler,
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timer=timer,
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collect_interval_s=collect_interval_s)
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collector.init_gpu_tensors(rank=0)
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_ = collector.maybe_collect_rejsample_metrics(k=5)
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metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert metrics is None
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_ = collector.maybe_collect_rejsample_metrics(k=5)
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metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert metrics is not None
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def test_timer_is_reset():
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"""Verify that the internal timer inside AsyncMetricsCollector
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is reset after collection.
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"""
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spec_decode_sampler = MagicMock()
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spec_decode_sampler.num_accepted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_emitted_tokens = torch.tensor(0,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_draft_tokens = 0
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collect_interval_s = 5.0
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timer = MagicMock()
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timer.side_effect = [
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0.0,
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collect_interval_s + 0.1,
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collect_interval_s + 0.1,
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collect_interval_s + 0.2,
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collect_interval_s + 0.2,
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2 * collect_interval_s + 0.1,
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2 * collect_interval_s + 0.1,
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]
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collector = AsyncMetricsCollector(spec_decode_sampler=spec_decode_sampler,
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timer=timer,
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collect_interval_s=collect_interval_s)
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collector.init_gpu_tensors(rank=0)
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_ = collector.maybe_collect_rejsample_metrics(k=5)
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metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert metrics is not None
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_ = collector.maybe_collect_rejsample_metrics(k=5)
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metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert metrics is None
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_ = collector.maybe_collect_rejsample_metrics(k=5)
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metrics = collector.maybe_collect_rejsample_metrics(k=5)
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assert metrics is not None
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@pytest.mark.parametrize("has_data", [True, False])
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def test_initial_metrics_has_correct_values(has_data: bool):
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"""Test correctness of metrics data.
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"""
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if has_data:
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num_accepted_tokens = 103
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num_emitted_tokens = 104
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num_draft_tokens = 105
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else:
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num_accepted_tokens = 0
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num_emitted_tokens = 0
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num_draft_tokens = 0
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k = 5
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max_num_emitted_tokens = AsyncMetricsCollector.get_max_num_emitted_tokens(
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num_draft_tokens, k)
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spec_decode_sampler = MagicMock()
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spec_decode_sampler.num_accepted_tokens = torch.tensor(num_accepted_tokens,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_emitted_tokens = torch.tensor(num_emitted_tokens,
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dtype=torch.long,
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device='cuda')
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spec_decode_sampler.num_draft_tokens = num_draft_tokens
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collect_interval_s = 5.0
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timer = MagicMock()
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timer.side_effect = [
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0.0, collect_interval_s + 0.1, collect_interval_s + 0.2
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]
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collector = AsyncMetricsCollector(spec_decode_sampler=spec_decode_sampler,
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timer=timer,
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collect_interval_s=collect_interval_s)
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collector.init_gpu_tensors(rank=0)
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_ = collector.maybe_collect_rejsample_metrics(k)
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metrics = collector.maybe_collect_rejsample_metrics(k)
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assert metrics.num_spec_tokens == k
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assert metrics.accepted_tokens == num_accepted_tokens
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assert metrics.draft_tokens == num_draft_tokens
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assert metrics.emitted_tokens == num_emitted_tokens
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if has_data:
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assert (metrics.draft_acceptance_rate == num_accepted_tokens /
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num_draft_tokens)
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assert (metrics.system_efficiency == num_emitted_tokens /
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max_num_emitted_tokens)
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else:
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assert math.isnan(metrics.draft_acceptance_rate)
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assert math.isnan(metrics.system_efficiency)
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