update name.

comps/llms/text-generation/lm-eval/self_hosted_hf.py

@@ -0,0 +1,79 @@
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+from typing import List
+
+import lm_eval.api.registry
+import torch
+from docarray import BaseDoc
+from GenAIEval.evaluation.lm_evaluation_harness.lm_eval.models.huggingface import HFLM, GaudiHFModelAdapter
+
+from comps import ServiceType, opea_microservices, opea_telemetry, register_microservice
+
+lm_eval.api.registry.MODEL_REGISTRY["hf"] = HFLM
+lm_eval.api.registry.MODEL_REGISTRY["gaudi-hf"] = GaudiHFModelAdapter
+
+
+class LLMCompletionDoc(BaseDoc):
+    batched_inputs: List
+    logprobs: int = 10
+    max_tokens: int = 0
+    temperature: float = 0.0
+
+
+model = os.getenv("MODEL", "")
+model_args = os.getenv("MODEL_ARGS", "")
+device = os.getenv("DEVICE", "")
+
+llm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+    model_args,
+    {
+        "batch_size": 1,  # dummy
+        "max_batch_size": None,
+        "device": device,
+    },
+)
+
+
+@register_microservice(
+    name="opea_service@self_hosted_hf",
+    service_type=ServiceType.LLM,
+    endpoint="/v1/completions",
+    host="0.0.0.0",
+    port=9006,
+)
+@opea_telemetry
+def llm_generate(input: LLMCompletionDoc):
+    global llm
+    batched_inputs = torch.tensor(input.batched_inputs, dtype=torch.long, device=llm.device)
+    with torch.no_grad():
+        # TODO, use model.generate.
+        logits = llm.model(batched_inputs).logits
+
+    logits = torch.nn.functional.log_softmax(logits, dim=-1)
+    # Check if per-token argmax is exactly equal to continuation
+    greedy_tokens = logits.argmax(dim=-1)
+    logprobs = torch.gather(logits, 2, batched_inputs[:, 1:].unsqueeze(-1)).squeeze(-1)
+
+    return {
+        "greedy_tokens": greedy_tokens.detach().cpu().tolist(),
+        "logprobs": logprobs.detach().cpu().tolist(),
+        "batched_inputs": input.batched_inputs,
+    }
+
+
+if __name__ == "__main__":
+    opea_microservices["opea_service@self_hosted_hf"].start()