Add cache watermark to avoid frequent cache eviction #11
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WoosukKwon
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@zhuohan123 I'm merging this PR as it does not conflict with any other and it (slightly) improves the system performance. |
bigPYJ1151
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* add pos_encoding impl * add benchmark and add open mp parallel
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Oct 25, 2023
* Comments done above worker * format * fixed missing arguments * fix * format
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Compress model to int8
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Wenxh/fp8 on a100 v1 pr
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Add OWNERS file
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<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? Add feature and model support matrix ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI test is enough Signed-off-by: wangxiyuan <[email protected]>
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njhill
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May 10, 2025
Abstract async saving
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Oct 11, 2025
Fused moe lora cleanup
Pradyun92
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Oct 17, 2025
Issue: TPU sampler and Eagle code had two separate but related issues: 1. TPU sampler divides by zero for greedy requests (temperature=0.0) 2. Eagle code triggers mypy type errors due to missing None check Root Cause: - TPU sampler's apply_temperature() method lacks epsilon guard to prevent division by zero when temperature=0.0 (greedy sampling) - Eagle's compute_probs_and_sample_next_token() uses temperature without asserting it's not None, causing mypy type errors Impact: - TPU: Division by zero produces NaN/Inf logits, breaking speculative decoding on TPU platforms for all models using Eagle/rejection sampling - Eagle: mypy type checking failures prevent pre-commit hooks from passing Fix: 1. TPU Sampler (vllm/v1/sample/tpu/sampler.py): - Add all_random parameter to apply_temperature() method - Add epsilon guard: if not all_random: temp = torch.where(temp < _SAMPLING_EPS, 1.0, temp) - Update call site to pass sampling_metadata.all_random 2. TPU Metadata (vllm/v1/sample/tpu/metadata.py): - Add all_random property to TPUSupportedSamplingMetadata - Populate all_random from input_batch in from_input_batch() 3. Eagle (vllm/v1/spec_decode/eagle.py): - Add assert sampling_metadata.temperature is not None after all_greedy early return - Matches sampler.py pattern (line 162) for type safety Files Modified: - vllm/v1/sample/tpu/sampler.py: Epsilon guard in apply_temperature() - vllm/v1/sample/tpu/metadata.py: Added all_random property - vllm/v1/spec_decode/eagle.py: Added temperature None assertion - CLAUDE.md: Updated modification vllm-project#11 to document fixes This addresses PR vllm-project#27077 reviewer feedback and resolves mypy type errors. Signed-off-by: Pradyun Ramadorai <[email protected]>
Pradyun92
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Oct 17, 2025
Issue: TPU sampler and Eagle code had two separate but related issues: 1. TPU sampler divides by zero for greedy requests (temperature=0.0) 2. Eagle code triggers mypy type errors due to missing None check Root Cause: - TPU sampler's apply_temperature() method lacks epsilon guard to prevent division by zero when temperature=0.0 (greedy sampling) - Eagle's compute_probs_and_sample_next_token() uses temperature without asserting it's not None, causing mypy type errors Impact: - TPU: Division by zero produces NaN/Inf logits, breaking speculative decoding on TPU platforms for all models using Eagle/rejection sampling - Eagle: mypy type checking failures prevent pre-commit hooks from passing Fix: 1. TPU Sampler (vllm/v1/sample/tpu/sampler.py): - Add all_random parameter to apply_temperature() method - Add epsilon guard: if not all_random: temp = torch.where(temp < _SAMPLING_EPS, 1.0, temp) - Update call site to pass sampling_metadata.all_random 2. TPU Metadata (vllm/v1/sample/tpu/metadata.py): - Add all_random property to TPUSupportedSamplingMetadata - Populate all_random from input_batch in from_input_batch() 3. Eagle (vllm/v1/spec_decode/eagle.py): - Add assert sampling_metadata.temperature is not None after all_greedy early return - Matches sampler.py pattern (line 162) for type safety Files Modified: - vllm/v1/sample/tpu/sampler.py: Epsilon guard in apply_temperature() - vllm/v1/sample/tpu/metadata.py: Added all_random property - vllm/v1/spec_decode/eagle.py: Added temperature None assertion - CLAUDE.md: Updated modification vllm-project#11 to document fixes This addresses PR vllm-project#27077 reviewer feedback and resolves mypy type errors. Signed-off-by: Pradyun Ramadorai <[email protected]>
Pradyun92
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Oct 17, 2025
Issue: TPU sampler and Eagle code had two separate but related issues: 1. TPU sampler divides by zero for greedy requests (temperature=0.0) 2. Eagle code triggers mypy type errors due to missing None check Root Cause: - TPU sampler's apply_temperature() method lacks epsilon guard to prevent division by zero when temperature=0.0 (greedy sampling) - Eagle's compute_probs_and_sample_next_token() uses temperature without asserting it's not None, causing mypy type errors Impact: - TPU: Division by zero produces NaN/Inf logits, breaking speculative decoding on TPU platforms for all models using Eagle/rejection sampling - Eagle: mypy type checking failures prevent pre-commit hooks from passing Fix: 1. TPU Sampler (vllm/v1/sample/tpu/sampler.py): - Add all_random parameter to apply_temperature() method - Add epsilon guard: if not all_random: temp = torch.where(temp < _SAMPLING_EPS, 1.0, temp) - Update call site to pass sampling_metadata.all_random 2. TPU Metadata (vllm/v1/sample/tpu/metadata.py): - Add all_random property to TPUSupportedSamplingMetadata - Populate all_random from input_batch in from_input_batch() 3. Eagle (vllm/v1/spec_decode/eagle.py): - Add assert sampling_metadata.temperature is not None after all_greedy early return - Matches sampler.py pattern (line 162) for type safety Files Modified: - vllm/v1/sample/tpu/sampler.py: Epsilon guard in apply_temperature() - vllm/v1/sample/tpu/metadata.py: Added all_random property - vllm/v1/spec_decode/eagle.py: Added temperature None assertion - CLAUDE.md: Updated modification vllm-project#11 to document fixes This addresses PR vllm-project#27077 reviewer feedback and resolves mypy type errors. Signed-off-by: Pradyun Ramadorai <[email protected]>
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This PR implements the watermark mechanism to prevent frequent preemption.
If we admit new sequences such that the GPU KV cache becomes full, preemptions are highly likely to happen in the next few steps. Instead, we can reserve a small portion of the cache and refrain from utilizing the entire cache space when admitting new sequences. This will help us avoid the inefficiencies.