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>
221 lines
7.1 KiB
Python
221 lines
7.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import torch
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from vllm.sequence import ExecuteModelRequest
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from vllm.spec_decode.ngram_worker import NGramWorker
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from vllm.spec_decode.top1_proposer import Top1Proposer
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from .utils import create_seq_group_metadata_from_prompts, create_worker
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def test_ngram_algo_correctness_for_single_no_match():
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"""Verify our ngram algo find the right candidate in the prompt
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For the scenario cannot find any candidate in one single batch
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"""
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block_size = 32
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num_gpu_blocks = 2048 // block_size
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seed = 100
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model_name = 'JackFram/llama-68m'
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vocab_size = 32_000
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device = 'cuda:0'
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ngram_worker = create_worker(
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NGramWorker,
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model_name,
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block_size,
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num_gpu_blocks,
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seed,
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)
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proposer = Top1Proposer(
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worker=ngram_worker,
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device=device,
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vocab_size=vocab_size,
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max_proposal_len=20,
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)
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# set ngram window [1, 3], which is window=1/2/3
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ngram_worker.set_ngram_window_size(1, 3)
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prompts = [
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# shall find no candidate
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[1, 2, 3, 4, 5, 6, 7],
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]
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proposal_len = 5
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final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
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seq_group_metadata_list = create_seq_group_metadata_from_prompts(
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prompts,
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num_gpu_blocks,
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block_size,
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final_prompt_lens=final_prompt_lens)
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proposals = proposer.get_spec_proposals(
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execute_model_req=ExecuteModelRequest(
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seq_group_metadata_list=seq_group_metadata_list,
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num_lookahead_slots=proposal_len),
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seq_ids_with_bonus_token_in_last_step=None)
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assert torch.is_tensor(proposals.proposal_token_ids)
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assert torch.is_tensor(proposals.proposal_probs)
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assert proposals.proposal_token_ids.shape == torch.Size([1, proposal_len])
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assert proposals.proposal_probs.shape[:-1] == torch.Size([1, proposal_len])
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assert proposals.proposal_lens.shape == torch.Size([1])
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assert proposals.proposal_lens.tolist() == [0]
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def test_ngram_algo_correctness_for_batches_not_match_all():
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"""Verify our ngram algo find the right candidate in the prompt
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For the scenario find some candidate not full in batchs
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"""
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block_size = 32
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num_gpu_blocks = 2048 // block_size
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seed = 100
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model_name = 'JackFram/llama-68m'
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vocab_size = 32_000
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device = 'cuda:0'
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ngram_worker = create_worker(
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NGramWorker,
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model_name,
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block_size,
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num_gpu_blocks,
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seed,
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)
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proposer = Top1Proposer(
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worker=ngram_worker,
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device=device,
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vocab_size=vocab_size,
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max_proposal_len=20,
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)
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# set ngram window [1, 3], which is window=1/2/3
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ngram_worker.set_ngram_window_size(1, 3)
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prompts = [
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# shall find no candidate
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[1, 2, 3, 4, 5, 6, 7],
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# shall find candidate 12,13,14,15,16
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[11, 12, 13, 14, 15, 16, 11],
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# shall find candidate 23,24,25,26,21
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[21, 21, 22, 23, 24, 25, 26, 21, 22],
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# shall find candidate 34,35,36,37,38
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[31, 32, 31, 32, 33, 34, 35, 36, 37, 38, 31, 32, 33],
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# shall find no candidate as exceed max_proposal_len
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[
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31, 32, 31, 32, 31, 32, 31, 32, 31, 32, 31, 32, 33, 34, 35, 36, 37,
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38, 31, 32, 33
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],
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]
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proposal_len = 5
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final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
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seq_group_metadata_list = create_seq_group_metadata_from_prompts(
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prompts,
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num_gpu_blocks,
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block_size,
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final_prompt_lens=final_prompt_lens)
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for sg in seq_group_metadata_list:
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sg.is_prompt = False
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proposals = proposer.get_spec_proposals(
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execute_model_req=ExecuteModelRequest(
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seq_group_metadata_list=seq_group_metadata_list,
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num_lookahead_slots=proposal_len),
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seq_ids_with_bonus_token_in_last_step=None)
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assert torch.is_tensor(proposals.proposal_token_ids)
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assert torch.is_tensor(proposals.proposal_probs)
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assert proposals.proposal_token_ids.shape == torch.Size([5, proposal_len])
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assert proposals.proposal_probs.shape[:-1] == torch.Size([5, proposal_len])
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assert proposals.proposal_lens.shape == torch.Size([5])
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# the first sequence has no match so proposal_len should be overwritten to 0
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assert proposals.proposal_lens.tolist(
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) == [0] + [proposal_len for _ in range(3)] + [0]
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for i in range(proposal_len):
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assert proposals.proposal_token_ids[0][i] == -1
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assert proposals.proposal_token_ids[1][i] == prompts[1][i + 1]
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assert proposals.proposal_token_ids[2][i] == prompts[2][i + 3]
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assert proposals.proposal_token_ids[3][i] == prompts[3][i + 5]
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assert proposals.proposal_token_ids[4][i] == -1
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def test_ngram_algo_correctness_for_batches_match_all():
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"""Verify our ngram algo find the right candidate in the prompt
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For the scenario find candidate in all batches
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"""
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block_size = 32
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num_gpu_blocks = 2048 // block_size
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seed = 100
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model_name = 'JackFram/llama-68m'
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vocab_size = 32_000
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device = 'cuda:0'
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ngram_worker = create_worker(
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NGramWorker,
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model_name,
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block_size,
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num_gpu_blocks,
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seed,
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)
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proposer = Top1Proposer(
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worker=ngram_worker,
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device=device,
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vocab_size=vocab_size,
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max_proposal_len=20,
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)
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# set ngram window [0, 3], which is window=1/2/3
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ngram_worker.set_ngram_window_size(1, 3)
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prompts = [
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# shall find candidate 12,13,14,15,16
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[11, 12, 13, 14, 15, 16, 11],
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# shall find candidate 23,24,25,26,21
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[21, 21, 22, 23, 24, 25, 26, 21, 22],
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# shall find candidate 34,35,36,37,38
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[31, 32, 31, 32, 33, 34, 35, 36, 37, 38, 31, 32, 33],
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]
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proposal_len = 5
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final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
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seq_group_metadata_list = create_seq_group_metadata_from_prompts(
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prompts,
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num_gpu_blocks,
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block_size,
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final_prompt_lens=final_prompt_lens)
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# Normally drafter is run on decode requests only; here we check the output
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# of the ngram worker as it is the sole proposer that has no forward.
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for sg in seq_group_metadata_list:
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sg.is_prompt = False
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proposals = proposer.get_spec_proposals(
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execute_model_req=ExecuteModelRequest(
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seq_group_metadata_list=seq_group_metadata_list,
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num_lookahead_slots=proposal_len),
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seq_ids_with_bonus_token_in_last_step=None)
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assert torch.is_tensor(proposals.proposal_token_ids)
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assert torch.is_tensor(proposals.proposal_probs)
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assert proposals.proposal_token_ids.shape == torch.Size([3, proposal_len])
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assert proposals.proposal_probs.shape[:-1] == torch.Size([3, proposal_len])
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assert proposals.proposal_lens.shape == torch.Size([3])
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assert proposals.proposal_lens.tolist() == [proposal_len for _ in range(3)]
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for i in range(proposal_len):
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assert proposals.proposal_token_ids[0][i] == prompts[0][i + 1]
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assert proposals.proposal_token_ids[1][i] == prompts[1][i + 3]
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assert proposals.proposal_token_ids[2][i] == prompts[2][i + 5]
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