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>
377 lines
14 KiB
Python
377 lines
14 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import List
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import pytest
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import torch
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from vllm.distributed.parallel_state import (ensure_model_parallel_initialized,
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init_distributed_environment)
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from vllm.engine.arg_utils import EngineArgs
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
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from vllm.utils import get_open_port
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from vllm.worker.model_runner import ModelRunner
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def _create_model_runner(model: str, *args, **kwargs) -> ModelRunner:
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engine_args = EngineArgs(model, *args, **kwargs)
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engine_config = engine_args.create_engine_config()
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model_runner = ModelRunner(
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vllm_config=engine_config,
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is_driver_worker=True,
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)
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return model_runner
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@pytest.mark.parametrize("batch_size", list(range(1, 257)))
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def test_prepare_prompt(batch_size):
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model_runner = _create_model_runner(
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"facebook/opt-125m",
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max_num_batched_tokens=100000,
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max_num_seqs=100000,
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enable_chunked_prefill=False,
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)
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seq_lens: List[int] = []
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seq_group_metadata_list: List[SequenceGroupMetadata] = []
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block_tables = {0: [1]}
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for i in range(batch_size):
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# make sure all tokens fit into one block
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seq_len = i % (model_runner.block_size - 1) + 1
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seq_lens.append(seq_len)
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seq_data = SequenceData.from_seqs(range(seq_len))
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seq_group_metadata = SequenceGroupMetadata(
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request_id=f"test_{i}",
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is_prompt=True,
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seq_data={0: seq_data},
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sampling_params=SamplingParams(temperature=0),
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block_tables=block_tables,
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)
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assert seq_group_metadata.token_chunk_size == seq_data.get_len()
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seq_group_metadata_list.append(seq_group_metadata)
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expected_selected_token_indices = []
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selected_token_start_idx = 0
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for seq_len in seq_lens:
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expected_selected_token_indices.append(selected_token_start_idx +
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seq_len - 1)
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selected_token_start_idx += seq_len
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model_input = model_runner._prepare_model_input_tensors(
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seq_group_metadata_list)
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input_tokens = model_input.input_tokens
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input_positions = model_input.input_positions
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attn_metadata = model_input.attn_metadata
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return_seq_lens = model_input.seq_lens
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slot_mapping = attn_metadata.slot_mapping
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assert return_seq_lens == seq_lens
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assert len(slot_mapping) == len(input_tokens)
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# Verify input metadata is correct for prompts.
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device = model_runner.device
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assert attn_metadata.num_prefills > 0
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assert attn_metadata.num_decode_tokens == 0
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torch.testing.assert_close(
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attn_metadata.seq_lens_tensor,
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torch.tensor(seq_lens, device=device, dtype=torch.int))
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assert attn_metadata.seq_lens == seq_lens
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assert attn_metadata.max_prefill_seq_len == max(seq_lens)
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assert attn_metadata.max_decode_seq_len == 0
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# Test subquery start locs.
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start_idx = 0
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start_loc = [start_idx]
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for seq_len in seq_lens:
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start_idx += seq_len
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start_loc.append(start_idx)
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torch.testing.assert_close(
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attn_metadata.query_start_loc,
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torch.tensor(start_loc, dtype=torch.int32, device=device))
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# Test seq start locs. Note that for normal prefill it is
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# equivalent to query_start_loc.
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start_idx = 0
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seq_start_loc = [start_idx]
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for seq_len in seq_lens:
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start_idx += seq_len
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seq_start_loc.append(start_idx)
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torch.testing.assert_close(
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attn_metadata.seq_start_loc,
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torch.tensor(start_loc, dtype=torch.int32, device=device))
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torch.testing.assert_close(
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attn_metadata.context_lens_tensor,
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torch.zeros(attn_metadata.context_lens_tensor.shape[0],
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dtype=torch.int,
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device=device))
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expected = torch.tensor([[] for _ in range(len(seq_group_metadata_list))],
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dtype=torch.int32,
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device=model_runner.device)
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torch.testing.assert_close(attn_metadata.block_tables, expected)
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# Cuda graph should not be used for prerill.
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assert attn_metadata.use_cuda_graph is False
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assert len(input_tokens) == sum(seq_lens)
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assert len(input_positions) == sum(seq_lens)
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torch.testing.assert_close(input_tokens, input_positions)
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sampling_metadata = SamplingMetadata.prepare(
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seq_group_metadata_list,
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seq_lens,
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query_lens=seq_lens,
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device=model_runner.device,
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pin_memory=model_runner.pin_memory)
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assert len(input_tokens) == sum(seq_lens)
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assert len(input_positions) == sum(seq_lens)
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actual = sampling_metadata.selected_token_indices
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expected = torch.tensor(expected_selected_token_indices,
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device=actual.device,
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dtype=actual.dtype)
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torch.testing.assert_close(actual, expected)
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torch.allclose(input_tokens, input_positions)
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actual = sampling_metadata.selected_token_indices
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expected = torch.tensor(expected_selected_token_indices,
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device=actual.device,
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dtype=actual.dtype)
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torch.testing.assert_close(actual, expected)
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@pytest.mark.parametrize("batch_size", list(range(1, 257)))
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def test_prepare_decode_cuda_graph(batch_size):
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model_runner = _create_model_runner(
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"facebook/opt-125m",
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seed=0,
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dtype="float16",
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enforce_eager=False,
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max_num_batched_tokens=100000,
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max_num_seqs=100000,
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enable_chunked_prefill=False,
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)
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context_lens: List[int] = []
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seq_group_metadata_list: List[SequenceGroupMetadata] = []
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# Assume each seq group finishes prefill.
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for i in range(batch_size):
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# make sure all tokens fit into one block
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context_len = i % (model_runner.block_size - 1) + 1
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context_lens.append(context_len)
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seq_data = SequenceData.from_seqs(range(context_len))
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seq_data.update_num_computed_tokens(context_len)
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# Append one token ID since prefill is finished.
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seq_data.append_token_id(1, 0)
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seq_group_metadata = SequenceGroupMetadata(
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request_id=f"test_{i}",
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is_prompt=False,
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seq_data={0: seq_data},
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sampling_params=SamplingParams(temperature=0),
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block_tables={0: [1]},
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)
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assert seq_group_metadata.token_chunk_size == 1
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seq_group_metadata_list.append(seq_group_metadata)
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model_input = model_runner._prepare_model_input_tensors(
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seq_group_metadata_list)
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input_tokens, input_positions, attn_metadata, slot_mapping = (
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model_input.input_tokens, model_input.input_positions,
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model_input.attn_metadata, model_input.attn_metadata.slot_mapping)
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assert len(slot_mapping) == len(input_tokens)
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expected_bs = model_runner.vllm_config.pad_for_cudagraph(
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len(seq_group_metadata_list))
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# Verify input metadata is correct for prompts.
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device = model_runner.device
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assert attn_metadata.num_prefills == 0
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assert attn_metadata.num_prefill_tokens == 0
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seq_lens = [context_len + 1 for context_len in context_lens]
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# seq_lens are padded to expected_bs
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for _ in range(expected_bs - len(seq_lens)):
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seq_lens.append(1)
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assert attn_metadata.seq_lens == seq_lens
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assert attn_metadata.num_decode_tokens == len(seq_lens)
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start_idx = 0
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start_loc = [start_idx]
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for _ in context_lens:
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# decode has only 1 token for query.
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start_idx += 1
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start_loc.append(start_idx)
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torch.testing.assert_close(
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attn_metadata.query_start_loc,
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torch.tensor(start_loc, dtype=torch.int32, device=device))
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start_idx = 0
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seq_start_loc = [start_idx]
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for seq_len in seq_lens:
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start_idx += seq_len
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seq_start_loc.append(start_idx)
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torch.testing.assert_close(
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attn_metadata.seq_start_loc,
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torch.tensor(seq_start_loc, dtype=torch.int32, device=device))
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torch.testing.assert_close(
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attn_metadata.context_lens_tensor,
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torch.tensor(context_lens, dtype=torch.int, device=device))
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assert attn_metadata.max_decode_seq_len == max(seq_lens)
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torch.testing.assert_close(
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attn_metadata.seq_lens_tensor[:len(seq_lens)],
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torch.tensor(seq_lens, dtype=torch.int, device=device))
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# block table's first index corresponds to each batch, meaning in
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# decoding it is each token.
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assert attn_metadata.block_tables.shape[0] == len(input_tokens)
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# Block table's second dim correspondsd to each token's block number.
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# It is padded up to
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assert attn_metadata.block_tables.shape[1] == (
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model_runner.get_max_block_per_batch())
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assert attn_metadata.use_cuda_graph is True
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assert len(input_tokens) == expected_bs
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assert len(input_positions) == expected_bs
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torch.allclose(input_tokens, input_positions)
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# Verify Sampling
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expected_selected_token_indices = []
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for selected_token_start_idx, _ in enumerate(context_lens):
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expected_selected_token_indices.append(selected_token_start_idx)
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sampling_metadata = SamplingMetadata.prepare(
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seq_group_metadata_list,
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seq_lens,
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# query lens is all 1 for decode.
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query_lens=[1 for _ in range(len(context_lens))],
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device=model_runner.device,
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pin_memory=model_runner.pin_memory)
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actual = sampling_metadata.selected_token_indices
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expected = torch.tensor(expected_selected_token_indices,
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device=actual.device,
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dtype=actual.dtype)
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torch.testing.assert_close(actual, expected)
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def test_empty_seq_group():
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"""Verify prepare prompt and decode returns empty output."""
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model_runner = _create_model_runner(
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"facebook/opt-125m",
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seed=0,
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dtype="float16",
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enforce_eager=False,
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)
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seq_group_metadata_list: List[SequenceGroupMetadata] = []
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model_input = model_runner._prepare_model_input_tensors(
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seq_group_metadata_list)
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input_tokens, input_positions, attn_metadata = (
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model_input.input_tokens,
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model_input.input_positions,
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model_input.attn_metadata,
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)
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assert input_tokens is None
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assert input_positions is None
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assert attn_metadata is None
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model_input = model_runner._prepare_model_input_tensors(
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seq_group_metadata_list)
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(input_tokens, input_positions, attn_metadata, return_seq_lens) = (
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model_input.input_tokens,
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model_input.input_positions,
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model_input.attn_metadata,
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model_input.seq_lens,
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)
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assert input_tokens is None
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assert input_positions is None
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assert attn_metadata is None
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assert return_seq_lens is None
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@pytest.fixture
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def distributed_init():
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init_distributed_environment(
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world_size=1,
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rank=0,
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distributed_init_method=f"tcp://127.0.0.1:{get_open_port()}",
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local_rank=0)
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ensure_model_parallel_initialized(1, 1)
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@pytest.mark.parametrize("batch_size", list(range(2, 128)))
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@pytest.mark.parametrize("enforce_eager", [True, False])
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def test_hybrid_batches(batch_size, enforce_eager, distributed_init):
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model_runner = _create_model_runner(
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"facebook/opt-125m",
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seed=0,
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dtype="float16",
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enforce_eager=enforce_eager,
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max_num_batched_tokens=100000,
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max_num_seqs=100000,
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enable_chunked_prefill=True,
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)
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# Add prefill requests.
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seq_lens: List[int] = []
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seq_group_metadata_list: List[SequenceGroupMetadata] = []
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prefill_metadata_list: List[SequenceGroupMetadata] = []
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decode_metadata_list: List[SequenceGroupMetadata] = []
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block_tables = {0: [1]}
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prefill_batch_size = batch_size // 2
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decode_batch_size = batch_size - prefill_batch_size
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for i in range(prefill_batch_size):
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# make sure all tokens fit into one block
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seq_len = i % (model_runner.block_size - 1) + 1
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seq_lens.append(seq_len)
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seq_data = SequenceData.from_seqs(range(seq_len))
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seq_group_metadata = SequenceGroupMetadata(
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request_id=f"test_{i}",
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is_prompt=True,
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seq_data={0: seq_data},
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sampling_params=SamplingParams(temperature=0),
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block_tables=block_tables,
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)
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assert seq_group_metadata.token_chunk_size == seq_data.get_len()
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seq_group_metadata_list.append(seq_group_metadata)
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prefill_metadata_list.append(seq_group_metadata)
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# Add decode requests
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for i in range(prefill_batch_size, batch_size):
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# make sure all tokens fit into one block
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context_len = i % (model_runner.block_size - 1) + 1
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seq_data = SequenceData.from_seqs(range(context_len))
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seq_data.append_token_id(1, 0)
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seq_data.update_num_computed_tokens(context_len)
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seq_group_metadata = SequenceGroupMetadata(
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request_id=f"test_{i}",
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is_prompt=False,
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seq_data={0: seq_data},
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sampling_params=SamplingParams(temperature=0),
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block_tables={0: [1]},
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)
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assert seq_group_metadata.token_chunk_size == 1
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seq_group_metadata_list.append(seq_group_metadata)
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decode_metadata_list.append(seq_group_metadata)
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model_input = model_runner.prepare_model_input(seq_group_metadata_list)
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(input_tokens, input_positions, attn_metadata) = (
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model_input.input_tokens,
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model_input.input_positions,
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model_input.attn_metadata,
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)
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prefill_meta_actual = attn_metadata.prefill_metadata
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decode_meta_actual = attn_metadata.decode_metadata
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assert len(attn_metadata.slot_mapping) == len(input_tokens)
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assert len(input_positions) == len(input_tokens)
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assert attn_metadata.num_prefills == prefill_batch_size
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assert attn_metadata.num_decode_tokens == decode_batch_size
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assert attn_metadata.num_prefill_tokens == sum(seq_lens)
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# Verify attn metadata is consistent. We don't need to test individual
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# values here because they are tested above.
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attn_metadata = model_runner._prepare_model_input_tensors(
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seq_group_metadata_list).attn_metadata
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for attr_expected, attr_actual in zip(vars(attn_metadata.prefill_metadata),
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vars(prefill_meta_actual)):
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assert attr_expected[1] == attr_actual[1]
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for attr_expected, attr_actual in zip(vars(attn_metadata.decode_metadata),
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vars(decode_meta_actual)):
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assert attr_expected[1] == attr_actual[1]
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