Consolidate rendering parameters into RenderConfig dataclass (#24543)
Signed-off-by: sfeng33 <4florafeng@gmail.com>
This commit is contained in:
Flora Feng
GitHub
@ -10,7 +10,7 @@ import pybase64
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import pytest
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import torch
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from vllm.entrypoints.renderer import CompletionRenderer
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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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@ -56,8 +56,8 @@ class TestRenderPrompt:
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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(prompt_or_prompts=tokens,
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max_length=100)
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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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assert len(results) == 1
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assert results[0]["prompt_token_ids"] == tokens
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@ -65,8 +65,8 @@ class TestRenderPrompt:
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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(prompt_or_prompts=token_lists,
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max_length=100)
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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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assert len(results) == 3
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assert results[0]["prompt_token_ids"] == [101, 7592, 2088]
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@ -80,8 +80,9 @@ class TestRenderPrompt:
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renderer.async_tokenizer_pool[
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renderer.tokenizer] = mock_async_tokenizer
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results = await renderer.render_prompt(prompt_or_prompts="Hello world",
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max_length=100)
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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))
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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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@ -96,7 +97,8 @@ class TestRenderPrompt:
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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, max_length=100)
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prompt_or_prompts=text_list_input,
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config=RenderConfig(max_length=100))
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assert len(results) == 3
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for result in results:
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@ -110,8 +112,9 @@ class TestRenderPrompt:
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renderer.async_tokenizer_pool[
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renderer.tokenizer] = mock_async_tokenizer
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results = await renderer.render_prompt(prompt_or_prompts="Hello world",
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max_length=100)
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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))
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assert len(results) == 1
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call_args = mock_async_tokenizer.call_args
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@ -126,8 +129,9 @@ class TestRenderPrompt:
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renderer.tokenizer] = mock_async_tokenizer
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results = await renderer.render_prompt(prompt_or_prompts="Hello world",
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max_length=100,
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truncate_prompt_tokens=50)
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config=RenderConfig(
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max_length=100,
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truncate_prompt_tokens=50))
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assert len(results) == 1
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call_args = mock_async_tokenizer.call_args
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@ -143,8 +147,9 @@ class TestRenderPrompt:
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renderer.tokenizer] = mock_async_tokenizer
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results = await renderer.render_prompt(prompt_or_prompts="Hello world",
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max_length=200,
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truncate_prompt_tokens=-1)
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config=RenderConfig(
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max_length=200,
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truncate_prompt_tokens=-1))
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assert len(results) == 1
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call_args = mock_async_tokenizer.call_args
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@ -157,8 +162,9 @@ class TestRenderPrompt:
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long_tokens = [100, 101, 102, 103, 104, 105, 106, 107, 108,
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109] # 10 tokens
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results = await renderer.render_prompt(prompt_or_prompts=long_tokens,
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max_length=100,
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truncate_prompt_tokens=5)
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config=RenderConfig(
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max_length=100,
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truncate_prompt_tokens=5))
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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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@ -170,7 +176,7 @@ class TestRenderPrompt:
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with pytest.raises(ValueError, match="maximum context length"):
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await renderer.render_prompt(prompt_or_prompts=long_tokens,
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max_length=100)
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config=RenderConfig(max_length=100))
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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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@ -181,7 +187,8 @@ class TestRenderPrompt:
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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", max_length=100)
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prompt_or_prompts="Hello world",
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config=RenderConfig(max_length=100))
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@pytest.mark.asyncio
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async def test_token_input_with_needs_detokenization(
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@ -196,7 +203,7 @@ class TestRenderPrompt:
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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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needs_detokenization=True,
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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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@ -221,7 +228,9 @@ class TestRenderEmbedPrompt:
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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, cache_salt="test_salt")
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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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@ -240,7 +249,9 @@ class TestRenderEmbedPrompt:
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]
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results = await renderer.render_prompt_and_embeds(
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prompt_embeds=embed_bytes_list)
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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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@ -254,7 +265,9 @@ class TestRenderEmbedPrompt:
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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, truncate_prompt_tokens=10)
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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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@ -271,7 +284,9 @@ class TestRenderEmbedPrompt:
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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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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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@ -283,7 +298,9 @@ class TestRenderEmbedPrompt:
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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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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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@ -303,7 +320,10 @@ class TestRenderEmbedPrompt:
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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", prompt_embeds=embed_bytes)
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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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