Fix implementation divergence for BLOOM models between vLLM and HuggingFace...

Fix implementation divergence for BLOOM models between vLLM and HuggingFace when using prompt embeds (#24686)
Signed-off-by: Andrew Sansom <andrew@protopia.ai>

tests/models/language/generation/test_common.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import os
 from typing import Optional
 
 import pytest
@@ -99,9 +98,10 @@ AITER_MODEL_LIST = [
 @pytest.mark.parametrize("num_logprobs", [5])
 @pytest.mark.parametrize(
     "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
+@pytest.mark.parametrize("use_prompt_embeds", [True, False])
 def test_models(hf_runner, vllm_runner, example_prompts, model: str,
                 max_tokens: int, num_logprobs: int, use_rocm_aiter: bool,
-                monkeypatch) -> None:
+                use_prompt_embeds: bool, monkeypatch) -> None:
 
     model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
     model_info.check_available_online(on_fail="skip")
@@ -119,8 +119,6 @@ def test_models(hf_runner, vllm_runner, example_prompts, model: str,
         # in parts of the operators
         pytest.skip(f"Skipping '{model}' model test with AITER kernel.")
 
-    use_prompt_embeds = os.getenv("VLLM_USE_V1") == "0"
-
     with hf_runner(model) as hf_model:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)

vllm/model_executor/models/bloom.py

@@ -257,7 +257,7 @@ class BloomModel(nn.Module):
                                                     config.hidden_size))
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.word_embeddings_layernorm(self.word_embeddings(input_ids))
+        return self.word_embeddings(input_ids)
 
     def forward(
         self,
@@ -271,6 +271,7 @@ class BloomModel(nn.Module):
                 hidden_states = inputs_embeds
             else:
                 hidden_states = self.get_input_embeddings(input_ids)
+            hidden_states = self.word_embeddings_layernorm(hidden_states)
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]