add qwen3

vllm-v0.6.2/tests/encoder_decoder/test_e2e_correctness.py

@@ -0,0 +1,119 @@
+"""E2E tests to verify the correctness of the encoder-decoder framework
+
+Run `pytest tests/encoder_decoder/test_e2e_correctness.py`.
+"""
+from typing import List, Optional, Tuple
+
+import pytest
+from transformers import AutoModelForSeq2SeqLM
+
+from vllm.attention.selector import (_Backend, _cached_get_attn_backend,
+                                     global_force_attn_backend_context_manager)
+from vllm.platforms import current_platform
+from vllm.sequence import SampleLogprobs
+
+from ..conftest import DecoderPromptType
+from ..models.utils import check_logprobs_close
+
+LIST_ENC_DEC_SUPPORTED_BACKENDS = [
+    _Backend.XFORMERS, _Backend.FLASH_ATTN, None
+]
+
+
+def vllm_to_hf_output(
+    vllm_output: Tuple[List[int], str, Optional[SampleLogprobs]],
+    decoder_prompt_type: DecoderPromptType,
+):
+    """Sanitize vllm output to be comparable with hf output."""
+    output_ids, output_str, out_logprobs = vllm_output
+
+    hf_output_str = output_str + "</s>"
+    if decoder_prompt_type == DecoderPromptType.NONE:
+        hf_output_str = "<s>" + hf_output_str
+
+    return output_ids, hf_output_str, out_logprobs
+
+
+@pytest.fixture(autouse=True)
+def clear_cache():
+    """Fixture to clear backend cache before each test."""
+    _cached_get_attn_backend.cache_clear()  # Clear the cache
+    yield  # This allows the test to run
+
+
+@pytest.mark.parametrize("model", ["facebook/bart-large-cnn"])
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("attn_backend", LIST_ENC_DEC_SUPPORTED_BACKENDS)
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+@pytest.mark.parametrize("decoder_prompt_type", list(DecoderPromptType))
+@pytest.mark.parametrize("enforce_eager", [True, False])
+@pytest.mark.skipif(
+    current_platform.is_cpu(),
+    reason="CPU backend is not currently supported with encoder/decoder models"
+)
+def test_encoder_decoder_e2e(
+    hf_runner,
+    vllm_runner,
+    example_encoder_decoder_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    decoder_prompt_type: DecoderPromptType,
+    enforce_eager: bool,
+    attn_backend: _Backend,
+) -> None:
+    '''
+    End-to-End (E2E) test for the encoder-decoder framework.
+    This test evaluates the encoder-decoder functionality using the BART
+    model. We compare the outputs of the Hugging Face and vLLM
+    implementations to ensure that both implementations produce consistent
+    and correct results.
+    '''
+    with global_force_attn_backend_context_manager(attn_backend):
+        if attn_backend == _Backend.FLASH_ATTN:
+            # Flash Attention works only with bfloat16 data-type
+            dtype = 'bfloat16'
+        test_case_prompts = example_encoder_decoder_prompts[
+            decoder_prompt_type]
+
+        # Configuration settings for HF baseline
+        hf_kwargs = {
+            "top_k": None,
+            "num_beams": 1,
+            "repetition_penalty": 1.0,
+            "top_p": 1.0,
+            "length_penalty": 1.0,
+            "early_stopping": False,
+            "no_repeat_ngram_size": None,
+            "min_length": 0
+        }
+
+        with hf_runner(model, dtype=dtype,
+                       auto_cls=AutoModelForSeq2SeqLM) as hf_model:
+            hf_outputs = (
+                hf_model.generate_encoder_decoder_greedy_logprobs_limit(
+                    test_case_prompts,
+                    max_tokens,
+                    num_logprobs,
+                    **hf_kwargs,
+                ))
+        with vllm_runner(model, dtype=dtype,
+                         enforce_eager=enforce_eager) as vllm_model:
+            vllm_outputs = vllm_model.generate_encoder_decoder_greedy_logprobs(
+                test_case_prompts, max_tokens, num_logprobs)
+
+        hf_skip_tokens = (1 if decoder_prompt_type == DecoderPromptType.NONE
+                          else 0)
+
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=[
+                vllm_to_hf_output(vllm_output, decoder_prompt_type)
+                for vllm_output in vllm_outputs
+            ],
+            name_0="hf",
+            name_1="vllm",
+            num_outputs_0_skip_tokens=hf_skip_tokens,
+        )