Adding support for fireworks and vllm models and sample for wiki mode…

configs/eval_fireworks.py

@@ -0,0 +1,26 @@
+from mmengine.config import read_base
+from opencompass.models import Fireworks
+from opencompass.partitioners import NaivePartitioner
+from opencompass.runners.local_api import LocalAPIRunner
+from opencompass.tasks import OpenICLInferTask
+
+with read_base():
+    from .datasets.CLUE_cmnli.CLUE_cmnli_gen import cmnli_datasets
+    from .datasets.CLUE_ocnli.CLUE_ocnli_gen import ocnli_datasets
+    from .datasets.FewCLUE_ocnli_fc.FewCLUE_ocnli_fc_gen import ocnli_fc_datasets
+    from .datasets.SuperGLUE_AX_b.SuperGLUE_AX_b_gen import AX_b_datasets
+    from .datasets.SuperGLUE_AX_g.SuperGLUE_AX_g_gen import AX_g_datasets
+    from .datasets.SuperGLUE_CB.SuperGLUE_CB_gen import CB_datasets
+    from .datasets.SuperGLUE_RTE.SuperGLUE_RTE_gen import RTE_datasets
+    from .datasets.anli.anli_gen import anli_datasets
+datasets = [*CB_datasets]
+
+models = [
+    dict(abbr='mistral-7b',
+        type=Fireworks, path='accounts/fireworks/models/mistral-7b',
+        key='ENV',
+        query_per_second=1,
+        max_out_len=2048, max_seq_len=2048, batch_size=8),
+]
+
+work_dir = "outputs/api_mistral_7b/"

configs/eval_mistral.py

@@ -0,0 +1,8 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .datasets.CLUE_cmnli.CLUE_cmnli_gen import cmnli_datasets
+    from .models.mistral.hf_mistral_7b_v0_1 import models
+
+
+datasets = [*cmnli_datasets]

configs/eval_vllm_openai.py

@@ -0,0 +1,27 @@
+from mmengine.config import read_base
+from opencompass.models import VLLM_OPENAI
+from opencompass.partitioners import NaivePartitioner
+from opencompass.runners.local_api import LocalAPIRunner
+from opencompass.tasks import OpenICLInferTask
+
+with read_base():
+    from .datasets.CLUE_cmnli.CLUE_cmnli_gen import cmnli_datasets
+    # from .models.fireworks.mistral_7b import mistral_7b
+    from .datasets.CLUE_ocnli.CLUE_ocnli_gen import ocnli_datasets
+    from .datasets.FewCLUE_ocnli_fc.FewCLUE_ocnli_fc_gen import ocnli_fc_datasets
+    from .datasets.SuperGLUE_AX_b.SuperGLUE_AX_b_gen import AX_b_datasets
+    from .datasets.SuperGLUE_AX_g.SuperGLUE_AX_g_gen import AX_g_datasets
+    from .datasets.SuperGLUE_CB.SuperGLUE_CB_gen import CB_datasets
+    from .datasets.SuperGLUE_RTE.SuperGLUE_RTE_gen import RTE_datasets
+    from .datasets.anli.anli_gen import anli_datasets
+datasets = [*ocnli_datasets,*ocnli_fc_datasets]
+
+models = [
+    dict(abbr='full-finetuned',
+        type=VLLM_OPENAI, path='model',
+        key='ENV',
+        query_per_second=1,
+        max_out_len=2048, max_seq_len=2048, batch_size=8),
+]
+
+work_dir = "outputs/api_full_finetuned_ift_20k/"

configs/eval_wikisft.py

@@ -0,0 +1,27 @@
+from mmengine.config import read_base
+from opencompass.models import Fireworks
+from opencompass.partitioners import NaivePartitioner
+from opencompass.runners.local_api import LocalAPIRunner
+from opencompass.tasks import OpenICLInferTask
+
+with read_base():
+    from .datasets.CLUE_cmnli.CLUE_cmnli_gen import cmnli_datasets
+    # from .models.fireworks.mistral_7b import mistral_7b
+    from .datasets.CLUE_ocnli.CLUE_ocnli_gen import ocnli_datasets
+    from .datasets.FewCLUE_ocnli_fc.FewCLUE_ocnli_fc_gen import ocnli_fc_datasets
+    from .datasets.SuperGLUE_AX_b.SuperGLUE_AX_b_gen import AX_b_datasets
+    from .datasets.SuperGLUE_AX_g.SuperGLUE_AX_g_gen import AX_g_datasets
+    from .datasets.SuperGLUE_CB.SuperGLUE_CB_gen import CB_datasets
+    from .datasets.SuperGLUE_RTE.SuperGLUE_RTE_gen import RTE_datasets
+    from .datasets.anli.anli_gen import anli_datasets
+datasets = [*CB_datasets]
+
+models = [
+    dict(abbr='mistral_wiki_sft',
+        type=Fireworks, path='path/to/fireworks/api',
+        key='ENV',
+        query_per_second=1,
+        max_out_len=2048, max_seq_len=2048, batch_size=8),
+]
+
+work_dir = "outputs/api_wikisft/"

configs/models/fireworks/mistral_7b.py

@@ -0,0 +1,23 @@
+from opencompass.models import Fireworks
+from opencompass.partitioners import NaivePartitioner
+from opencompass.runners.local_api import LocalAPIRunner
+from opencompass.tasks import OpenICLInferTask
+
+api_meta_template = dict(
+    round=[
+            dict(role='HUMAN', api_role='HUMAN'),
+            dict(role='BOT', api_role='BOT', generate=True),
+    ],
+)
+
+mistral_7b = [
+    dict(abbr='mistral-7b',
+        type=Fireworks, path='accounts/fireworks/models/mistral-7b',
+        key='ENV',  # The key will be obtained from $FIREWORKS_API_KEY, but you can write down your key here as well
+        meta_template=api_meta_template,
+        query_per_second=1,
+        max_out_len=2048,
+        max_seq_len=4096,
+        batch_size=8),
+]
+

opencompass/models/__init__.py

@@ -36,3 +36,5 @@
 from .yayi_api import Yayi  # noqa: F401
 from .zhipuai_api import ZhiPuAI  # noqa: F401
 from .zhipuai_v2_api import ZhiPuV2AI  # noqa: F401
+from .fireworks_api import Fireworks
+from .vllm_openai_api import VLLM_OPENAI

opencompass/models/fireworks_api.py

@@ -0,0 +1,67 @@
+import requests
+from typing import List
+from .base_api import BaseAPIModel
+import fireworks.client
+import os
+import tiktoken
+from opencompass.registry import MODELS
+from typing import Dict, List, Optional, Union
+NUM_ALLOWED_TOKENS_GPT_4 = 8192
+# Load environment variables
+fireworks.client.api_key = os.getenv('FIREWORKS_API_KEY')
+class LLMError(Exception):
+    """A custom exception used to report errors in use of Large Language Model class"""
+@MODELS.register_module()
+class Fireworks(BaseAPIModel):
+    is_api: bool = True
+    def __init__(self,
+                 path: str = "accounts/fireworks/models/mistral-7b",
+                 max_seq_len: int = 2048,
+                 query_per_second: int = 1,
+                 retry: int = 2,
+                 key: Union[str, List[str]] = 'ENV',
+                 **kwargs):
+        super().__init__(path=path,
+                         max_seq_len=max_seq_len,
+                         query_per_second=query_per_second,
+                         retry=retry,
+                         **kwargs)
+        self.model_name = path
+        self.tokenizer = tiktoken.encoding_for_model("gpt-4")
+        self.retry = retry
+    def query_fireworks(
+        self,
+        prompt: str,
+        max_tokens: int = 512,
+        temperature: float = 0.01,
+    ) -> str:
+        if max_tokens == 0:
+            max_tokens = NUM_ALLOWED_TOKENS_GPT_4  # Adjust based on your model's capabilities
+        completion = fireworks.client.Completion.create(
+            model=self.model_name,
+            prompt=prompt,
+            n=1,
+            max_tokens=max_tokens,
+            temperature=temperature,
+        )
+        if len(completion.choices) != 1:
+            raise LLMError("Unexpected number of choices returned by Fireworks.")
+        return completion.choices[0].text
+    def generate(
+        self,
+        inputs,
+        max_out_len: int = 512,
+        temperature: float = 0,
+    ) -> List[str]:
+        """Generate results given a list of inputs."""
+        outputs = []
+        for input_text in inputs:
+            try:
+                output = self.query_fireworks(prompt=input_text, max_tokens=max_out_len, temperature=temperature)
+                outputs.append(output)
+            except Exception as e:
+                print(f"Failed to generate output for input: {input_text} due to {e}")
+        return outputs
+    def get_token_len(self, prompt: str) -> int:
+        """Get lengths of the tokenized string."""
+        return len(self.tokenizer.encode(prompt))

opencompass/models/vllm_openai_api.py

@@ -0,0 +1,74 @@
+from typing import List
+from .base_api import BaseAPIModel
+from openai import OpenAI
+import os
+import tiktoken
+from opencompass.registry import MODELS
+from typing import Dict, List, Optional, Union
+NUM_ALLOWED_TOKENS_GPT_4 = 8192
+class LLMError(Exception):
+    """A custom exception used to report errors in use of Large Language Model class"""
+@MODELS.register_module()
+class VLLM_OPENAI(BaseAPIModel):
+    is_api: bool = True
+    def __init__(self,
+                 path: str = "model",
+                 max_seq_len: int = 2048,
+                 query_per_second: int = 1,
+                 retry: int = 2,
+                 key: Union[str, List[str]] = 'ENV',
+                 **kwargs):
+        super().__init__(path=path,
+                         max_seq_len=max_seq_len,
+                         query_per_second=query_per_second,
+                         retry=retry,
+                         **kwargs)
+        self.model_name = path
+        self.tokenizer = tiktoken.encoding_for_model("gpt-4")
+        self.retry = retry
+        if isinstance(key, str):
+            self.keys = [os.getenv('OPENAI_API_KEY') if key == 'ENV' else key]
+        else:
+            self.keys = key
+
+    def query_vllm(
+        self,
+        prompt: str,
+        temperature: float,
+        max_tokens: int,
+    ) -> str:
+        if "OPENAI_API_KEY" not in os.environ:
+            raise ValueError(
+                "OPENAI_API_KEY environment variable must be set when using OpenAI API."
+            )
+        client = OpenAI(
+            api_key=os.getenv('OPENAI_API_KEY'),
+            base_url=os.getenv('OPENAI_API_BASE'),
+        )
+        response = client.completions.create(  # type: ignore
+            prompt=prompt,
+            model=self.model_name,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        answer: str = response.choices[0].text
+        return answer
+
+    def generate(
+        self,
+        inputs,
+        max_out_len: int = 512,
+        temperature: float = 0,
+    ) -> List[str]:
+        """Generate results given a list of inputs."""
+        outputs = []
+        for input_text in inputs:
+            try:
+                output = self.query_vllm(prompt=input_text, max_tokens=max_out_len, temperature=temperature)
+                outputs.append(output)
+            except Exception as e:
+                print(f"Failed to generate output for input: {input_text} due to {e}")
+        return outputs
+    def get_token_len(self, prompt: str) -> int:
+        """Get lengths of the tokenized string."""
+        return len(self.tokenizer.encode(prompt))