[Bug fix ]: Triton /infer handler incompatible with batch responses (#7337)

* migrate triton to base llm http handler

* clean up triton handler.py

* use transform functions for triton

* add TritonConfig

* get openai params for triton

* use triton embedding config

* test_completion_triton_generate_api

* test_completion_triton_infer_api

* fix TritonConfig doc string

* use TritonResponseIterator

* fix triton embeddings

* docs triton chat usage

litellm/main.py

@@ -128,7 +128,6 @@ from .llms.replicate.chat.handler import completion as replicate_chat_completion
 from .llms.sagemaker.chat.handler import SagemakerChatHandler
 from .llms.sagemaker.completion.handler import SagemakerLLM
 from .llms.together_ai.completion.handler import TogetherAITextCompletion
-from .llms.triton.completion.handler import TritonChatCompletion
 from .llms.vertex_ai import vertex_ai_non_gemini
 from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
 from .llms.vertex_ai.gemini_embeddings.batch_embed_content_handler import (
@@ -194,7 +193,6 @@ azure_audio_transcriptions = AzureAudioTranscription()
 huggingface = Huggingface()
 predibase_chat_completions = PredibaseChatCompletion()
 codestral_text_completions = CodestralTextCompletion()
-triton_chat_completions = TritonChatCompletion()
 bedrock_chat_completion = BedrockLLM()
 bedrock_converse_chat_completion = BedrockConverseLLM()
 bedrock_embedding = BedrockEmbedding()
@@ -2711,24 +2709,22 @@ def completion(  # type: ignore # noqa: PLR0915
 
         elif custom_llm_provider == "triton":
             api_base = litellm.api_base or api_base
-            model_response = triton_chat_completions.completion(
-                api_base=api_base,
-                timeout=timeout,  # type: ignore
+            response = base_llm_http_handler.completion(
                 model=model,
+                stream=stream,
                 messages=messages,
+                acompletion=acompletion,
+                api_base=api_base,
                 model_response=model_response,
                 optional_params=optional_params,
-                logging_obj=logging,
-                stream=stream,
-                acompletion=acompletion,
-                client=client,
                 litellm_params=litellm_params,
+                custom_llm_provider=custom_llm_provider,
+                timeout=timeout,
+                headers=headers,
+                encoding=encoding,
+                api_key=api_key,
+                logging_obj=logging,
             )
-
-            ## RESPONSE OBJECT
-            response = model_response
-            return response
-
         elif custom_llm_provider == "cloudflare":
             api_key = (
                 api_key
@@ -3477,9 +3473,10 @@ def embedding(  # noqa: PLR0915
                 raise ValueError(
                     "api_base is required for triton. Please pass `api_base`"
                 )
-            response = triton_chat_completions.embedding(  # type: ignore
+            response = base_llm_http_handler.embedding(
                 model=model,
                 input=input,
+                custom_llm_provider=custom_llm_provider,
                 api_base=api_base,
                 api_key=api_key,
                 logging_obj=logging,