[BugFix][Frontend] apply task instruction as system prompt in cohere v2/embed (#38362)

Signed-off-by: walterbm <walter.beller.morales@gmail.com>

tests/entrypoints/pooling/embed/test_cohere_openai_parity.py

@@ -57,16 +57,25 @@ def _openai_embed(
     return [item["embedding"] for item in resp.json()["data"]]
 
 
+def _cosine_sim(a: list[float], b: list[float]) -> float:
+    va, vb = np.array(a), np.array(b)
+    return float(np.dot(va, vb) / (np.linalg.norm(va) * np.linalg.norm(vb)))
+
+
 def test_single_text_parity(server: RemoteOpenAIServer):
-    """A single text should produce identical embeddings via both APIs."""
+    """A single text should produce equivalent embeddings via both APIs."""
     texts = ["the quick brown fox jumps over the lazy dog"]
     v2 = _cohere_embed(server, texts)
     v1 = _openai_embed(server, texts)
-    np.testing.assert_allclose(v2[0], v1[0], rtol=1e-5)
+    # Full-suite BF16 runs can introduce tiny numerical drift even when both
+    # endpoints are functionally equivalent, so compare semantic equivalence
+    # instead of exact elementwise equality.
+    cos = _cosine_sim(v2[0], v1[0])
+    assert cos > 0.9999, f"single-text parity failed, cosine={cos}"
 
 
 def test_batch_parity(server: RemoteOpenAIServer):
-    """A batch of texts should produce identical embeddings via both APIs,
+    """A batch of texts should produce equivalent embeddings via both APIs,
     in the same order."""
     texts = [
         "machine learning",
@@ -76,8 +85,18 @@ def test_batch_parity(server: RemoteOpenAIServer):
     v2 = _cohere_embed(server, texts)
     v1 = _openai_embed(server, texts)
     assert len(v2) == len(v1) == 3
+
+    similarities = np.array(
+        [[_cosine_sim(v2_emb, v1_emb) for v1_emb in v1] for v2_emb in v2]
+    )
     for i in range(3):
-        np.testing.assert_allclose(v2[i], v1[i], rtol=1e-5, err_msg=f"index {i}")
+        assert int(np.argmax(similarities[i])) == i, (
+            f"batch parity order mismatch at index {i}: "
+            f"similarities={similarities[i].tolist()}"
+        )
+        assert similarities[i, i] > 0.9999, (
+            f"batch parity failed at index {i}, cosine={similarities[i, i]}"
+        )
 
 
 def test_token_count_parity(server: RemoteOpenAIServer):

tests/entrypoints/pooling/embed/test_io_processor.py

@@ -6,8 +6,11 @@ import pytest
 
 from vllm.entrypoints.pooling.embed.io_processor import EmbedIOProcessor
 from vllm.entrypoints.pooling.embed.protocol import (
+    CohereEmbedContent,
+    CohereEmbedInput,
     CohereEmbedRequest,
 )
+from vllm.entrypoints.pooling.typing import PoolingServeContext
 
 
 class TestResolveTruncation:
@@ -206,3 +209,116 @@ class TestValidateInputType:
         handler = self._make_handler({"a": "", "b": ""})
         with pytest.raises(ValueError, match="Supported values: a, b"):
             handler._validate_input_type("z")
+
+
+class TestPreProcessCohereOnline:
+    """Unit tests for EmbedIOProcessor._pre_process_cohere_online."""
+
+    @staticmethod
+    def _make_context(**request_kwargs) -> PoolingServeContext[CohereEmbedRequest]:
+        return PoolingServeContext(
+            request=CohereEmbedRequest(model="test", **request_kwargs),
+            model_name="test",
+            request_id="embd-test",
+        )
+
+    @staticmethod
+    def _make_handler():
+        handler = object.__new__(EmbedIOProcessor)
+        handler._validate_input_type = lambda _input_type: None
+        return handler
+
+    def test_text_only_without_task_prefix_uses_completion_path(self):
+        handler = self._make_handler()
+        ctx = self._make_context(texts=["hello"])
+        calls: list[tuple[str, object]] = []
+
+        def preprocess_completion(request, prompt_input, prompt_embeds):
+            calls.append(("completion", prompt_input))
+            return ["completion"]
+
+        handler._get_task_instruction_prefix = lambda _input_type: None
+        handler._has_chat_template = lambda: False
+        handler._preprocess_completion_online = preprocess_completion
+        handler._batch_render_chat = lambda *_args, **_kwargs: (
+            pytest.fail("text-only request should not require chat rendering")
+        )
+
+        handler._pre_process_cohere_online(ctx)
+
+        assert ctx.engine_inputs == ["completion"]
+        assert calls == [("completion", ["hello"])]
+
+    def test_text_only_falls_back_to_prefixed_completion_without_template(self):
+        handler = self._make_handler()
+        ctx = self._make_context(texts=["hello"], input_type="query")
+        calls: list[tuple[str, object]] = []
+
+        def preprocess_completion(request, prompt_input, prompt_embeds):
+            calls.append(("completion", prompt_input))
+            return ["fallback"]
+
+        handler._get_task_instruction_prefix = lambda _input_type: "query: "
+        handler._has_chat_template = lambda: False
+        handler._batch_render_chat = lambda *_args, **_kwargs: (
+            pytest.fail("chat rendering should be skipped without a template")
+        )
+        handler._preprocess_completion_online = preprocess_completion
+
+        handler._pre_process_cohere_online(ctx)
+
+        assert ctx.engine_inputs == ["fallback"]
+        assert calls == [("completion", ["query: hello"])]
+
+    def test_text_only_with_template_uses_chat_path(self):
+        handler = self._make_handler()
+        ctx = self._make_context(texts=["hello"], input_type="query")
+        calls: list[tuple[str, object]] = []
+
+        def batch_render_chat(
+            request,
+            all_messages,
+            truncate_prompt_tokens,
+            truncation_side,
+        ):
+            calls.append(
+                (
+                    "chat",
+                    {
+                        "request": request,
+                        "all_messages": all_messages,
+                        "truncate_prompt_tokens": truncate_prompt_tokens,
+                        "truncation_side": truncation_side,
+                    },
+                )
+            )
+            return ["chat"]
+
+        handler._get_task_instruction_prefix = lambda _input_type: "query: "
+        handler._has_chat_template = lambda: True
+        handler._batch_render_chat = batch_render_chat
+        handler._preprocess_completion_online = lambda *_args, **_kwargs: (
+            pytest.fail("completion path should be skipped when a template exists")
+        )
+
+        handler._pre_process_cohere_online(ctx)
+
+        assert ctx.engine_inputs == ["chat"]
+        assert calls == [
+            (
+                "chat",
+                {
+                    "request": ctx.request,
+                    "all_messages": [
+                        handler._mixed_input_to_messages(
+                            CohereEmbedInput(
+                                content=[CohereEmbedContent(type="text", text="hello")]
+                            ),
+                            task_prefix="query: ",
+                        )
+                    ],
+                    "truncate_prompt_tokens": -1,
+                    "truncation_side": None,
+                },
+            )
+        ]

vllm/entrypoints/pooling/embed/io_processor.py

@@ -18,6 +18,7 @@ from vllm.entrypoints.chat_utils import (
 )
 from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
 from vllm.entrypoints.pooling.embed.protocol import (
+    CohereEmbedContent,
     CohereEmbedInput,
     CohereEmbedRequest,
     EmbeddingChatRequest,
@@ -28,6 +29,7 @@ from vllm.inputs import EngineInput, tokens_input
 from vllm.logger import init_logger
 from vllm.outputs import PoolingOutput, PoolingRequestOutput
 from vllm.renderers import merge_kwargs
+from vllm.renderers.hf import resolve_chat_template
 from vllm.utils.collection_utils import chunk_list
 from vllm.utils.mistral import is_mistral_tokenizer
 
@@ -284,13 +286,27 @@ class EmbedIOProcessor(PoolingIOProcessor):
     ) -> list[ChatCompletionMessageParam]:
         """Build chat messages from a mixed text+image input.
 
-        When *task_prefix* is given, it is prepended to each text part.
+        When *task_prefix* is given, it is used as the system prompt.
         """
+        messages: list[ChatCompletionMessageParam] = []
+        if task_prefix is not None:
+            messages.append(
+                CustomChatCompletionMessageParam(
+                    role="system",
+                    content=[
+                        ChatCompletionContentPartTextParam(
+                            type="text", text=task_prefix
+                        )
+                    ],
+                )
+            )
+
         parts: list[ChatCompletionContentPartParam] = []
         for item in inp.content:
             if item.type == "text" and item.text is not None:
-                text = task_prefix + item.text if task_prefix else item.text
-                parts.append(ChatCompletionContentPartTextParam(type="text", text=text))
+                parts.append(
+                    ChatCompletionContentPartTextParam(type="text", text=item.text)
+                )
             elif item.type == "image_url" and item.image_url is not None:
                 parts.append(
                     ChatCompletionContentPartImageParam(
@@ -298,7 +314,8 @@ class EmbedIOProcessor(PoolingIOProcessor):
                         image_url=ImageURL(url=item.image_url["url"]),
                     )
                 )
-        return [CustomChatCompletionMessageParam(role="user", content=parts)]
+        messages.append(CustomChatCompletionMessageParam(role="user", content=parts))
+        return messages
 
     @staticmethod
     def _check_cohere_max_tokens(
@@ -346,9 +363,11 @@ class EmbedIOProcessor(PoolingIOProcessor):
     def _pre_process_cohere_online(self, ctx: PoolingServeContext) -> None:
         """Convert a ``CohereEmbedRequest`` into engine prompts.
 
-        For texts, a single batched completion request path is used.
-        For images and mixed inputs, conversations are batch-rendered
-        through the chat template in one ``render_chat`` call.
+        If a model has a chat template the task instruction are rendered
+        as a system prompt. Otherwise they are just prepended to the input text.
+
+        Images and mixed inputs are always batch-rendered through the chat
+        template in one ``render_chat`` call.
         """
         request = ctx.request
         assert isinstance(request, CohereEmbedRequest)
@@ -363,42 +382,91 @@ class EmbedIOProcessor(PoolingIOProcessor):
         self._validate_input_type(input_type)
 
         if request.images is not None:
-            all_messages: list[list[ChatCompletionMessageParam]] = [
-                [
-                    CustomChatCompletionMessageParam(
-                        role="user",
-                        content=[{"type": "image_url", "image_url": {"url": uri}}],
-                    )
-                ]
+            input: list[CohereEmbedInput] = [
+                CohereEmbedInput(
+                    content=[
+                        CohereEmbedContent(type="image_url", image_url={"url": uri})
+                    ]
+                )
                 for uri in request.images
             ]
-            ctx.engine_inputs = self._batch_render_chat(
-                request, all_messages, truncate_prompt_tokens, truncation_side
-            )
-
         elif request.inputs is not None:
+            input = request.inputs
+        else:
+            texts = request.texts or []
             task_prefix = self._get_task_instruction_prefix(input_type)
+
+            if task_prefix is None:
+                ctx.engine_inputs = self._preprocess_cohere_text_completion(
+                    request,
+                    texts,
+                    truncate_prompt_tokens,
+                    truncation_side,
+                )
+                return
+
             all_messages = [
-                self._mixed_input_to_messages(inp, task_prefix=task_prefix)
-                for inp in request.inputs
+                self._mixed_input_to_messages(
+                    CohereEmbedInput(
+                        content=[CohereEmbedContent(type="text", text=text)]
+                    ),
+                    task_prefix=task_prefix,
+                )
+                for text in texts
             ]
-            ctx.engine_inputs = self._batch_render_chat(
-                request, all_messages, truncate_prompt_tokens, truncation_side
-            )
+            if self._has_chat_template():
+                ctx.engine_inputs = self._batch_render_chat(
+                    request,
+                    all_messages,
+                    truncate_prompt_tokens,
+                    truncation_side,
+                )
+            else:
+                ctx.engine_inputs = self._preprocess_cohere_text_completion(
+                    request,
+                    self._apply_task_instruction(texts, input_type),
+                    truncate_prompt_tokens,
+                    truncation_side,
+                )
+            return
 
-        else:
-            prefixed = self._apply_task_instruction(request.texts or [], input_type)
-            proxy = EmbeddingCompletionRequest(
-                model=request.model,
-                input=prefixed,
-                dimensions=request.output_dimension,
-                encoding_format="float",
-                truncate_prompt_tokens=truncate_prompt_tokens,
-                truncation_side=truncation_side,
-            )
-            ctx.engine_inputs = self._preprocess_completion_online(
-                proxy, prompt_input=proxy.input, prompt_embeds=None
+        task_prefix = self._get_task_instruction_prefix(input_type)
+        all_messages = [
+            self._mixed_input_to_messages(inp, task_prefix=task_prefix) for inp in input
+        ]
+        ctx.engine_inputs = self._batch_render_chat(
+            request, all_messages, truncate_prompt_tokens, truncation_side
+        )
+
+    def _has_chat_template(self) -> bool:
+        return (
+            resolve_chat_template(
+                self.renderer.tokenizer,
+                chat_template=self.chat_template,
+                tools=None,
+                model_config=self.model_config,
             )
+            is not None
+        )
+
+    def _preprocess_cohere_text_completion(
+        self,
+        request: CohereEmbedRequest,
+        texts: list[str],
+        truncate_prompt_tokens: int | None,
+        truncation_side: Literal["left", "right"] | None,
+    ) -> list[EngineInput]:
+        proxy = EmbeddingCompletionRequest(
+            model=request.model,
+            input=texts,
+            dimensions=request.output_dimension,
+            encoding_format="float",
+            truncate_prompt_tokens=truncate_prompt_tokens,
+            truncation_side=truncation_side,
+        )
+        return self._preprocess_completion_online(
+            proxy, prompt_input=proxy.input, prompt_embeds=None
+        )
 
     def _batch_render_chat(
         self,