mirror of
https://github.com/vllm-project/vllm.git
synced 2025-10-20 23:03:52 +08:00
334 lines
12 KiB
Python
334 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import io
|
|
from dataclasses import dataclass
|
|
from typing import Optional
|
|
from unittest.mock import AsyncMock, MagicMock
|
|
|
|
import pybase64
|
|
import pytest
|
|
import torch
|
|
|
|
from vllm.entrypoints.renderer import CompletionRenderer, RenderConfig
|
|
from vllm.inputs.data import is_embeds_prompt
|
|
|
|
|
|
@dataclass
|
|
class MockModelConfig:
|
|
max_model_len: int = 100
|
|
encoder_config: Optional[dict] = None
|
|
|
|
|
|
class MockTokenizerResult:
|
|
|
|
def __init__(self, input_ids):
|
|
self.input_ids = input_ids
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_model_config():
|
|
return MockModelConfig()
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_tokenizer():
|
|
tokenizer = MagicMock()
|
|
return tokenizer
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_async_tokenizer():
|
|
async_tokenizer = AsyncMock()
|
|
return async_tokenizer
|
|
|
|
|
|
@pytest.fixture
|
|
def renderer(mock_model_config, mock_tokenizer):
|
|
return CompletionRenderer(model_config=mock_model_config,
|
|
tokenizer=mock_tokenizer,
|
|
async_tokenizer_pool={})
|
|
|
|
|
|
class TestRenderPrompt:
|
|
"""Test Category A: Basic Functionality Tests"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_token_input(self, renderer):
|
|
tokens = [101, 7592, 2088]
|
|
results = await renderer.render_prompt(
|
|
prompt_or_prompts=tokens, config=RenderConfig(max_length=100))
|
|
|
|
assert len(results) == 1
|
|
assert results[0]["prompt_token_ids"] == tokens
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_token_list_input(self, renderer):
|
|
token_lists = [[101, 7592, 2088], [102, 1234, 5678, 9012], [103, 4567]]
|
|
results = await renderer.render_prompt(
|
|
prompt_or_prompts=token_lists, config=RenderConfig(max_length=100))
|
|
|
|
assert len(results) == 3
|
|
assert results[0]["prompt_token_ids"] == [101, 7592, 2088]
|
|
assert results[1]["prompt_token_ids"] == [102, 1234, 5678, 9012]
|
|
assert results[2]["prompt_token_ids"] == [103, 4567]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_text_input(self, renderer, mock_async_tokenizer):
|
|
mock_async_tokenizer.return_value = MockTokenizerResult(
|
|
[101, 7592, 2088])
|
|
renderer.async_tokenizer_pool[
|
|
renderer.tokenizer] = mock_async_tokenizer
|
|
|
|
results = await renderer.render_prompt(
|
|
prompt_or_prompts="Hello world",
|
|
config=RenderConfig(max_length=100))
|
|
|
|
assert len(results) == 1
|
|
assert results[0]["prompt_token_ids"] == [101, 7592, 2088]
|
|
mock_async_tokenizer.assert_called_once()
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_text_list_input(self, renderer, mock_async_tokenizer):
|
|
mock_async_tokenizer.return_value = MockTokenizerResult(
|
|
[101, 7592, 2088])
|
|
renderer.async_tokenizer_pool[
|
|
renderer.tokenizer] = mock_async_tokenizer
|
|
|
|
text_list_input = ["Hello world", "How are you?", "Good morning"]
|
|
results = await renderer.render_prompt(
|
|
prompt_or_prompts=text_list_input,
|
|
config=RenderConfig(max_length=100))
|
|
|
|
assert len(results) == 3
|
|
for result in results:
|
|
assert result["prompt_token_ids"] == [101, 7592, 2088]
|
|
assert mock_async_tokenizer.call_count == 3
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_no_truncation(self, renderer, mock_async_tokenizer):
|
|
mock_async_tokenizer.return_value = MockTokenizerResult(
|
|
[101, 7592, 2088])
|
|
renderer.async_tokenizer_pool[
|
|
renderer.tokenizer] = mock_async_tokenizer
|
|
|
|
results = await renderer.render_prompt(
|
|
prompt_or_prompts="Hello world",
|
|
config=RenderConfig(max_length=100))
|
|
|
|
assert len(results) == 1
|
|
call_args = mock_async_tokenizer.call_args
|
|
assert "truncation" not in call_args.kwargs or call_args.kwargs[
|
|
"truncation"] is False
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_truncation_positive(self, renderer, mock_async_tokenizer):
|
|
mock_async_tokenizer.return_value = MockTokenizerResult(
|
|
[101, 7592, 2088]) # Truncated
|
|
renderer.async_tokenizer_pool[
|
|
renderer.tokenizer] = mock_async_tokenizer
|
|
|
|
results = await renderer.render_prompt(prompt_or_prompts="Hello world",
|
|
config=RenderConfig(
|
|
max_length=100,
|
|
truncate_prompt_tokens=50))
|
|
|
|
assert len(results) == 1
|
|
call_args = mock_async_tokenizer.call_args
|
|
assert call_args.kwargs["truncation"] is True
|
|
assert call_args.kwargs["max_length"] == 50
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_truncation_negative(self, renderer, mock_async_tokenizer):
|
|
# Test that negative truncation uses model's max_model_len
|
|
mock_async_tokenizer.return_value = MockTokenizerResult(
|
|
[101, 7592, 2088]) # Truncated to max_model_len
|
|
renderer.async_tokenizer_pool[
|
|
renderer.tokenizer] = mock_async_tokenizer
|
|
|
|
results = await renderer.render_prompt(prompt_or_prompts="Hello world",
|
|
config=RenderConfig(
|
|
max_length=200,
|
|
truncate_prompt_tokens=-1))
|
|
|
|
assert len(results) == 1
|
|
call_args = mock_async_tokenizer.call_args
|
|
assert call_args.kwargs["truncation"] is True
|
|
assert call_args.kwargs["max_length"] == 100 # model's max_model_len
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_token_truncation_last_elements(self, renderer):
|
|
# Test that token truncation keeps the last N elements
|
|
long_tokens = [100, 101, 102, 103, 104, 105, 106, 107, 108,
|
|
109] # 10 tokens
|
|
results = await renderer.render_prompt(prompt_or_prompts=long_tokens,
|
|
config=RenderConfig(
|
|
max_length=100,
|
|
truncate_prompt_tokens=5))
|
|
|
|
assert len(results) == 1
|
|
# Should keep the last 5 tokens: [105, 106, 107, 108, 109]
|
|
assert results[0]["prompt_token_ids"] == [105, 106, 107, 108, 109]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_max_length_exceeded(self, renderer):
|
|
long_tokens = list(range(150)) # Exceeds max_model_len=100
|
|
|
|
with pytest.raises(ValueError, match="maximum context length"):
|
|
await renderer.render_prompt(prompt_or_prompts=long_tokens,
|
|
config=RenderConfig(max_length=100))
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_no_tokenizer_for_text(self, mock_model_config):
|
|
renderer_no_tokenizer = CompletionRenderer(
|
|
model_config=mock_model_config,
|
|
tokenizer=None,
|
|
async_tokenizer_pool={})
|
|
|
|
with pytest.raises(ValueError, match="No tokenizer available"):
|
|
await renderer_no_tokenizer.render_prompt(
|
|
prompt_or_prompts="Hello world",
|
|
config=RenderConfig(max_length=100))
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_token_input_with_needs_detokenization(
|
|
self, renderer, mock_async_tokenizer):
|
|
# When needs_detokenization=True for token inputs, renderer should
|
|
# use the async tokenizer to decode and include the original text
|
|
# in the returned prompt object.
|
|
mock_async_tokenizer.decode = AsyncMock(return_value="decoded text")
|
|
renderer.async_tokenizer_pool[
|
|
renderer.tokenizer] = mock_async_tokenizer
|
|
|
|
tokens = [1, 2, 3, 4]
|
|
results = await renderer.render_prompt(
|
|
prompt_or_prompts=tokens,
|
|
config=RenderConfig(needs_detokenization=True),
|
|
)
|
|
|
|
assert len(results) == 1
|
|
assert results[0]["prompt_token_ids"] == tokens
|
|
assert results[0]["prompt"] == "decoded text"
|
|
mock_async_tokenizer.decode.assert_awaited_once()
|
|
|
|
|
|
class TestRenderEmbedPrompt:
|
|
|
|
def _create_test_embed_bytes(self, tensor: torch.Tensor) -> bytes:
|
|
"""Helper to create base64-encoded tensor bytes"""
|
|
buffer = io.BytesIO()
|
|
torch.save(tensor, buffer)
|
|
buffer.seek(0)
|
|
return pybase64.b64encode(buffer.read())
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_single_prompt_embed(self, renderer):
|
|
# Create a test tensor
|
|
test_tensor = torch.randn(10, 768, dtype=torch.float32)
|
|
embed_bytes = self._create_test_embed_bytes(test_tensor)
|
|
|
|
results = await renderer.render_prompt_and_embeds(
|
|
prompt_embeds=embed_bytes,
|
|
config=RenderConfig(cache_salt="test_salt"),
|
|
)
|
|
|
|
assert len(results) == 1
|
|
assert is_embeds_prompt(results[0])
|
|
assert torch.allclose(results[0]["prompt_embeds"], test_tensor)
|
|
assert results[0]["cache_salt"] == "test_salt"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_multiple_prompt_embeds(self, renderer):
|
|
# Create multiple test tensors
|
|
test_tensors = [
|
|
torch.randn(8, 512, dtype=torch.float32),
|
|
torch.randn(12, 512, dtype=torch.float32),
|
|
]
|
|
embed_bytes_list = [
|
|
self._create_test_embed_bytes(t) for t in test_tensors
|
|
]
|
|
|
|
results = await renderer.render_prompt_and_embeds(
|
|
prompt_embeds=embed_bytes_list,
|
|
config=RenderConfig(),
|
|
)
|
|
|
|
assert len(results) == 2
|
|
for i, result in enumerate(results):
|
|
assert is_embeds_prompt(result)
|
|
assert torch.allclose(result["prompt_embeds"], test_tensors[i])
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_prompt_embed_truncation(self, renderer):
|
|
# Create tensor with more tokens than truncation limit
|
|
test_tensor = torch.randn(20, 768, dtype=torch.float32)
|
|
embed_bytes = self._create_test_embed_bytes(test_tensor)
|
|
|
|
results = await renderer.render_prompt_and_embeds(
|
|
prompt_embeds=embed_bytes,
|
|
config=RenderConfig(truncate_prompt_tokens=10),
|
|
)
|
|
|
|
assert len(results) == 1
|
|
# Should keep last 10 tokens
|
|
expected = test_tensor[-10:]
|
|
assert torch.allclose(results[0]["prompt_embeds"], expected)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_prompt_embed_different_dtypes(self, renderer):
|
|
# Test different supported dtypes
|
|
dtypes = [torch.float32, torch.float16, torch.bfloat16]
|
|
|
|
for dtype in dtypes:
|
|
test_tensor = torch.randn(5, 256, dtype=dtype)
|
|
embed_bytes = self._create_test_embed_bytes(test_tensor)
|
|
|
|
results = await renderer.render_prompt_and_embeds(
|
|
prompt_embeds=embed_bytes,
|
|
config=RenderConfig(),
|
|
)
|
|
|
|
assert len(results) == 1
|
|
assert results[0]["prompt_embeds"].dtype == dtype
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_prompt_embed_squeeze_batch_dim(self, renderer):
|
|
# Test tensor with batch dimension gets squeezed
|
|
test_tensor = torch.randn(1, 10, 768, dtype=torch.float32)
|
|
embed_bytes = self._create_test_embed_bytes(test_tensor)
|
|
|
|
results = await renderer.render_prompt_and_embeds(
|
|
prompt_embeds=embed_bytes,
|
|
config=RenderConfig(),
|
|
)
|
|
|
|
assert len(results) == 1
|
|
# Should be squeezed to 2D
|
|
assert results[0]["prompt_embeds"].shape == (10, 768)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_both_prompts_and_embeds(self, renderer,
|
|
mock_async_tokenizer):
|
|
# Set up text tokenization
|
|
mock_async_tokenizer.return_value = MockTokenizerResult(
|
|
[101, 102, 103])
|
|
renderer.async_tokenizer_pool[
|
|
renderer.tokenizer] = mock_async_tokenizer
|
|
|
|
# Create embed
|
|
test_tensor = torch.randn(5, 256, dtype=torch.float32)
|
|
embed_bytes = self._create_test_embed_bytes(test_tensor)
|
|
|
|
results = await renderer.render_prompt_and_embeds(
|
|
prompt_or_prompts="Hello world",
|
|
prompt_embeds=embed_bytes,
|
|
config=RenderConfig(),
|
|
)
|
|
|
|
assert len(results) == 2
|
|
# First should be embed prompt
|
|
assert is_embeds_prompt(results[0])
|
|
# Second should be tokens prompt
|
|
assert "prompt_token_ids" in results[1]
|
|
assert results[1]["prompt_token_ids"] == [101, 102, 103]
|