init

tests/tokenization/test_cached_tokenizer.py

@@ -0,0 +1,22 @@
+from copy import deepcopy
+
+from transformers import AutoTokenizer
+
+from vllm.transformers_utils.tokenizer import get_cached_tokenizer
+
+
+def test_cached_tokenizer():
+    reference_tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    reference_tokenizer.add_special_tokens({"cls_token": "<CLS>"})
+    reference_tokenizer.add_special_tokens(
+        {"additional_special_tokens": ["<SEP>"]})
+    cached_tokenizer = get_cached_tokenizer(deepcopy(reference_tokenizer))
+
+    assert reference_tokenizer.encode("prompt") == cached_tokenizer.encode(
+        "prompt")
+    assert set(reference_tokenizer.all_special_ids) == set(
+        cached_tokenizer.all_special_ids)
+    assert set(reference_tokenizer.all_special_tokens) == set(
+        cached_tokenizer.all_special_tokens)
+    assert set(reference_tokenizer.all_special_tokens_extended) == set(
+        cached_tokenizer.all_special_tokens_extended)

tests/tokenization/test_detokenize.py

@@ -0,0 +1,208 @@
+from typing import Dict, List
+
+import pytest
+from transformers import AutoTokenizer
+
+from vllm.sequence import Logprob, SamplingParams, Sequence, SequenceGroup
+from vllm.transformers_utils.detokenizer import (Detokenizer,
+                                                 detokenize_incrementally)
+from vllm.transformers_utils.tokenizer_group import get_tokenizer_group
+
+TRUTH = [
+    "Hello here, this is a simple test",
+    "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be used in production environments, where inference and serving",  # noqa
+    "我很感谢你的热情"
+]
+TOKENIZERS = [
+    "facebook/opt-125m",
+    "gpt2",
+    "bigcode/tiny_starcoder_py",
+    "EleutherAI/gpt-j-6b",
+    "EleutherAI/pythia-70m",
+    "bigscience/bloom-560m",
+    "mosaicml/mpt-7b",
+    "tiiuae/falcon-7b",
+    "meta-llama/Llama-2-7b-hf",
+    "codellama/CodeLlama-7b-hf",
+]
+
+
+def _run_incremental_decode(tokenizer, all_input_ids,
+                            skip_special_tokens: bool, starting_index: int):
+    decoded_text = ""
+    offset = 0
+    token_offset = 0
+    prev_tokens = None
+    for i in range(starting_index, len(all_input_ids)):
+        new_tokens, text, offset, token_offset = detokenize_incrementally(
+            tokenizer,
+            all_input_ids[:i + 1],
+            prev_tokens,
+            offset,
+            token_offset,
+            skip_special_tokens=skip_special_tokens)
+        decoded_text += text
+        if prev_tokens is None:
+            prev_tokens = new_tokens
+        else:
+            prev_tokens += new_tokens
+    return decoded_text
+
+
+@pytest.mark.parametrize("truth", TRUTH)
+@pytest.mark.parametrize("with_prompt", [True, False])
+@pytest.mark.parametrize("tokenizer_id", TOKENIZERS)
+@pytest.mark.parametrize("skip_special_tokens", (True, False))
+def test_decode_streaming(tokenizer_id, truth, with_prompt,
+                          skip_special_tokens):
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
+    if with_prompt:
+        truth_tokens = tokenizer(truth, add_special_tokens=False)["input_ids"]
+        prompt_input_ids = truth_tokens[:len(truth) // 2]
+        generated_input_ids = truth_tokens[len(truth) // 2:]
+        all_input_ids = prompt_input_ids + generated_input_ids
+        starting_index = len(prompt_input_ids)
+        prompt = tokenizer.decode(prompt_input_ids,
+                                  skip_special_tokens=skip_special_tokens)
+        generated = truth[len(prompt):]
+    else:
+        generated = truth
+        starting_index = 0
+        all_input_ids = tokenizer(truth, add_special_tokens=False)["input_ids"]
+    if skip_special_tokens:
+        if tokenizer.bos_token_id is not None:
+            all_input_ids = [tokenizer.bos_token_id] + all_input_ids
+            starting_index += 1
+        all_input_ids = all_input_ids + [tokenizer.eos_token_id]
+
+    decoded_text = _run_incremental_decode(
+        tokenizer,
+        all_input_ids,
+        skip_special_tokens=skip_special_tokens,
+        starting_index=starting_index)
+
+    assert decoded_text == generated
+
+    decoded_text = _run_incremental_decode(
+        tokenizer, [len(tokenizer)],
+        skip_special_tokens=skip_special_tokens,
+        starting_index=starting_index)
+
+    assert decoded_text == ''
+
+
+@pytest.fixture
+def detokenizer(tokenizer_name: str) -> Detokenizer:
+    init_kwargs = dict(
+        tokenizer_id=tokenizer_name,
+        enable_lora=False,
+        max_num_seqs=100,
+        max_input_length=None,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        revision=None,
+    )
+
+    tokenizer_group = get_tokenizer_group(
+        None,
+        **init_kwargs,
+    )
+
+    return Detokenizer(tokenizer_group)
+
+
+@pytest.fixture(name="complete_sequence_token_ids")
+def create_complete_sequence_token_ids(complete_sequence: str,
+                                       tokenizer_name: str) -> List[int]:
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+    complete_sequence_token_ids = tokenizer(complete_sequence)["input_ids"]
+    return complete_sequence_token_ids
+
+
+def create_sequence(prompt_token_ids=None):
+    prompt_token_ids = prompt_token_ids or [1]
+    return Sequence(
+        seq_id=0,
+        prompt="<s>",
+        prompt_token_ids=prompt_token_ids,
+        block_size=16,
+    )
+
+
+def create_dummy_logprobs(
+        complete_sequence_token_ids: List[int]) -> List[Dict[int, Logprob]]:
+    return [{
+        token_id: Logprob(logprob=0.0),
+        token_id + 1: Logprob(logprob=0.1)
+    } for token_id in complete_sequence_token_ids]
+
+
+@pytest.mark.parametrize("complete_sequence", TRUTH)
+@pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
+@pytest.mark.parametrize("skip_special_tokens", [True, False])
+def test_decode_sequence_logprobs(complete_sequence: str,
+                                  complete_sequence_token_ids: List[int],
+                                  detokenizer: Detokenizer,
+                                  skip_special_tokens: bool):
+    """Verify Detokenizer decodes logprobs correctly."""
+    sampling_params = SamplingParams(skip_special_tokens=skip_special_tokens,
+                                     logprobs=2)
+
+    # Run sequentially.
+    seq = create_sequence()
+    dummy_logprobs = create_dummy_logprobs(complete_sequence_token_ids)
+    sequential_logprobs_text_chosen_token = []
+    sequential_logprobs_text_other_token = []
+    for new_token, logprobs in zip(complete_sequence_token_ids,
+                                   dummy_logprobs):
+        seq.append_token_id(new_token, logprobs)
+        detokenizer.decode_sequence_inplace(seq, sampling_params)
+        sequential_logprobs_text_chosen_token.append(
+            seq.output_logprobs[-1][new_token].decoded_token)
+        sequential_logprobs_text_other_token.append(
+            seq.output_logprobs[-1][new_token + 1].decoded_token)
+    sequential_result = seq.output_text
+
+    assert sequential_result == "".join(sequential_logprobs_text_chosen_token)
+    assert sequential_result != "".join(sequential_logprobs_text_other_token)
+
+    if skip_special_tokens:
+        # Text for logprobs for the chosen token should be the same as the
+        # generated text. Note that this will only be true if we skip
+        # special tokens.
+        assert sequential_result == complete_sequence
+
+
+@pytest.mark.parametrize("complete_sequence", TRUTH)
+@pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
+@pytest.mark.parametrize("skip_special_tokens", [True])
+def test_decode_prompt_logprobs(complete_sequence: str,
+                                complete_sequence_token_ids: List[int],
+                                detokenizer: Detokenizer,
+                                skip_special_tokens: bool):
+    """Verify Detokenizer decodes prompt logprobs correctly."""
+    sampling_params = SamplingParams(skip_special_tokens=skip_special_tokens,
+                                     prompt_logprobs=1)
+
+    # Run sequentially.
+    seq = create_sequence(complete_sequence_token_ids)
+    seq_group = SequenceGroup(request_id="1",
+                              seqs=[seq],
+                              sampling_params=sampling_params,
+                              arrival_time=0.0)
+    dummy_logprobs = create_dummy_logprobs(complete_sequence_token_ids)
+    detokenizer.decode_prompt_logprobs_inplace(seq_group, dummy_logprobs)
+    decoded_prompt_logprobs = dummy_logprobs
+
+    if skip_special_tokens:
+        # Text for logprobs for the chosen token should be the same as the
+        # prompt text. Note that this will only be true if we skip
+        # special tokens.
+        assert complete_sequence == "".join([
+            logprobs[token_id].decoded_token for token_id, logprobs in zip(
+                complete_sequence_token_ids, decoded_prompt_logprobs)
+        ])
+        assert complete_sequence != "".join([
+            logprobs[token_id + 1].decoded_token for token_id, logprobs in zip(
+                complete_sequence_token_ids, decoded_prompt_logprobs)
+        ])

tests/tokenization/test_tokenizer.py

@@ -0,0 +1,20 @@
+import pytest
+from transformers import PreTrainedTokenizerBase
+
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+TOKENIZER_NAMES = [
+    "facebook/opt-125m",
+    "gpt2",
+]
+
+
+@pytest.mark.parametrize("tokenizer_name", TOKENIZER_NAMES)
+def test_tokenizer_revision(tokenizer_name: str):
+    # Assume that "main" branch always exists
+    tokenizer = get_tokenizer(tokenizer_name, revision="main")
+    assert isinstance(tokenizer, PreTrainedTokenizerBase)
+
+    # Assume that "never" branch always does not exist
+    with pytest.raises(OSError, match='not a valid git identifier'):
+        get_tokenizer(tokenizer_name, revision="never")

tests/tokenization/test_tokenizer_group.py

@@ -0,0 +1,102 @@
+import asyncio
+import os
+from unittest.mock import patch
+
+import pytest
+from transformers import AutoTokenizer, PreTrainedTokenizerBase
+
+from vllm.transformers_utils.tokenizer_group import get_tokenizer_group
+from vllm.transformers_utils.tokenizer_group.ray_tokenizer_group import (
+    RayTokenizerGroupPool)
+from vllm.transformers_utils.tokenizer_group.tokenizer_group import (
+    TokenizerGroup)
+
+from ..conftest import get_tokenizer_pool_config
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("tokenizer_group_type", [None, "ray"])
+async def test_tokenizer_group(tokenizer_group_type):
+    reference_tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    tokenizer_group = get_tokenizer_group(
+        get_tokenizer_pool_config(tokenizer_group_type),
+        tokenizer_id="gpt2",
+        enable_lora=False,
+        max_num_seqs=1,
+        max_input_length=None,
+    )
+    assert reference_tokenizer.encode("prompt") == tokenizer_group.encode(
+        request_id="request_id", prompt="prompt", lora_request=None)
+    assert reference_tokenizer.encode(
+        "prompt") == await tokenizer_group.encode_async(
+            request_id="request_id", prompt="prompt", lora_request=None)
+    assert isinstance(tokenizer_group.get_lora_tokenizer(None),
+                      PreTrainedTokenizerBase)
+    assert tokenizer_group.get_lora_tokenizer(
+        None) == await tokenizer_group.get_lora_tokenizer_async(None)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("tokenizer_group_type", ["ray"])
+async def test_tokenizer_group_pool(tokenizer_group_type):
+    reference_tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    tokenizer_group_pool = get_tokenizer_group(
+        get_tokenizer_pool_config(tokenizer_group_type),
+        tokenizer_id="gpt2",
+        enable_lora=False,
+        max_num_seqs=1,
+        max_input_length=None,
+    )
+    # Send multiple requests to the tokenizer group pool
+    # (more than the pool size)
+    # and check that all requests are processed correctly.
+    num_requests = tokenizer_group_pool.pool_size * 5
+    requests = [
+        tokenizer_group_pool.encode_async(request_id=str(i),
+                                          prompt=f"prompt {i}",
+                                          lora_request=None)
+        for i in range(num_requests)
+    ]
+    results = await asyncio.gather(*requests)
+    expected_results = [
+        reference_tokenizer.encode(f"prompt {i}") for i in range(num_requests)
+    ]
+    assert results == expected_results
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("tokenizer_group_type", ["ray"])
+async def test_tokenizer_group_ray_pool_env_var_propagation(
+        tokenizer_group_type):
+    """Test that env vars from caller process are propagated to
+    tokenizer Ray actors."""
+    env_var = "MY_ENV_VAR"
+
+    class EnvVarCheckerTokenizerGroup(TokenizerGroup):
+
+        def ping(self):
+            assert os.environ.get(env_var) == "1"
+            return super().ping()
+
+    class EnvVarCheckerRayTokenizerGroupPool(RayTokenizerGroupPool):
+        _worker_cls = EnvVarCheckerTokenizerGroup
+
+    tokenizer_pool_config = get_tokenizer_pool_config(tokenizer_group_type)
+    tokenizer_pool = EnvVarCheckerRayTokenizerGroupPool.from_config(
+        tokenizer_pool_config,
+        tokenizer_id="gpt2",
+        enable_lora=False,
+        max_num_seqs=1,
+        max_input_length=None)
+    with pytest.raises(AssertionError):
+        tokenizer_pool.ping()
+
+    with patch.dict(os.environ, {env_var: "1"}):
+        tokenizer_pool_config = get_tokenizer_pool_config(tokenizer_group_type)
+        tokenizer_pool = EnvVarCheckerRayTokenizerGroupPool.from_config(
+            tokenizer_pool_config,
+            tokenizer_id="gpt2",
+            enable_lora=False,
+            max_num_seqs=1,
+            max_input_length=None)
+        tokenizer_pool.ping()