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