From cf04285cc16d87f8c45a6f1129d555c8ab3358b7 Mon Sep 17 00:00:00 2001 From: Aymeric Roucher <69208727+aymeric-roucher@users.noreply.github.com> Date: Thu, 9 Jan 2025 15:15:06 +0100 Subject: [PATCH] =?UTF-8?q?Enable=20smolagent=20tools=20in=20Hugging=20Cha?= =?UTF-8?q?t!=20=F0=9F=9A=80=20=20(#132)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add correct signature, api_name, and description --- examples/benchmark.ipynb | 389 +++++++++++++++++++-------------------- src/smolagents/tools.py | 10 +- 2 files changed, 200 insertions(+), 199 deletions(-) diff --git a/examples/benchmark.ipynb b/examples/benchmark.ipynb index ff3b5a2..02d7b7b 100644 --- a/examples/benchmark.ipynb +++ b/examples/benchmark.ipynb @@ -21,15 +21,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aymeric/venv/test/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", "Using the latest cached version of the dataset since m-ric/smolagentsbenchmark couldn't be found on the Hugging Face Hub\n", "Found the latest cached dataset configuration 'default' at /Users/aymeric/.cache/huggingface/datasets/m-ric___smolagentsbenchmark/default/0.0.0/0ad5fb2293ab185eece723a4ac0e4a7188f71add (last modified on Wed Jan 8 17:50:13 2025).\n" ] @@ -174,7 +172,7 @@ "[132 rows x 4 columns]" ] }, - "execution_count": 1, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -197,19 +195,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/aymeric/venv/test/lib/python3.12/site-packages/pydantic/_internal/_config.py:345: UserWarning: Valid config keys have changed in V2:\n", - "* 'fields' has been removed\n", - " warnings.warn(message, UserWarning)\n" - ] - } - ], + "outputs": [], "source": [ "import time\n", "import json\n", @@ -243,7 +231,9 @@ " return str(obj)\n", "\n", "\n", - "def answer_questions(eval_ds, file_name, agent, model_id, action_type):\n", + "def answer_questions(\n", + " eval_ds, file_name, agent, model_id, action_type, is_vanilla_llm=False\n", + "):\n", " answered_questions = []\n", " if os.path.exists(file_name):\n", " with open(file_name, \"r\") as f:\n", @@ -260,17 +250,22 @@ " if question in answered_questions:\n", " continue\n", " start_time = time.time()\n", - " answer = agent.run(question)\n", + "\n", + " if is_vanilla_llm:\n", + " llm = agent\n", + " answer = llm([{\"role\": \"user\", \"content\": question}])\n", + " token_count = llm.last_input_token_count + llm.last_output_token_count\n", + " intermediate_steps = []\n", + " else:\n", + " answer = agent.run(question)\n", + " token_count = agent.monitor.get_total_token_counts()\n", + " intermediate_steps = str(agent.logs)\n", + " # Remove memory from logs to make them more compact.\n", + " for step in agent.logs:\n", + " if isinstance(step, ActionStep):\n", + " step.agent_memory = None\n", + "\n", " end_time = time.time()\n", - " for step_log in agent.logs:\n", - " if hasattr(step_log, \"memory\"):\n", - " step_log.memory = None\n", - "\n", - " # Remove memory from logs to make them more compact.\n", - " for step in agent.logs:\n", - " if isinstance(step, ActionStep):\n", - " step.agent_memory = None\n", - "\n", " annotated_example = {\n", " \"model_id\": model_id,\n", " \"agent_action_type\": action_type,\n", @@ -278,10 +273,10 @@ " \"answer\": answer,\n", " \"true_answer\": example[\"true_answer\"],\n", " \"source\": example[\"source\"],\n", - " \"intermediate_steps\": str(agent.logs),\n", + " \"intermediate_steps\": intermediate_steps,\n", " \"start_time\": start_time,\n", " \"end_time\": end_time,\n", - " \"token_counts\": agent.monitor.get_total_token_counts(),\n", + " \"token_counts\": token_count,\n", " }\n", "\n", " with open(file_name, \"a\") as f:\n", @@ -394,7 +389,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run benchmark\n", + "## Benchmark agents\n", "\n", "### Open models" ] @@ -403,7 +398,23 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating 'meta-llama/Llama-3.3-70B-Instruct'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 132/132 [00:00<00:00, 27836.90it/s]\n", + " 16%|█▌ | 21/132 [02:18<07:35, 4.11s/it]" + ] + } + ], "source": [ "open_model_ids = [\n", " \"meta-llama/Llama-3.3-70B-Instruct\",\n", @@ -435,7 +446,15 @@ " max_steps=10,\n", " )\n", " file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n", - " answer_questions(eval_ds, file_name, agent, model_id, action_type)" + " answer_questions(eval_ds, file_name, agent, model_id, action_type)\n", + "\n", + " # Also evaluate vanilla model\n", + " action_type = \"vanilla\"\n", + " llm = HfApiModel(model_id)\n", + " file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n", + " answer_questions(\n", + " eval_ds, file_name, llm, model_id, action_type, is_vanilla_llm=True\n", + " )" ] }, { @@ -478,45 +497,22 @@ " max_steps=10,\n", " )\n", " file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n", - " answer_questions(eval_ds, file_name, agent, model_id, action_type)" + " answer_questions(eval_ds, file_name, agent, model_id, action_type)\n", + "\n", + " # Also evaluate vanilla model\n", + " action_type = \"vanilla\"\n", + " llm = LiteLLMModel(model_id)\n", + " file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n", + " answer_questions(\n", + " eval_ds, file_name, llm, model_id, action_type, is_vanilla_llm=True\n", + " )" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/Qwen_Qwen2.5-Coder-32B-Instruct-code-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/meta-llama_Llama-3.3-70B-Instruct-code-26-dec-2024.jsonl\n", - "Removed 124 lines.\n", - "output/Qwen_Qwen2.5-72B-Instruct-code-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/anthropic_claude-3-5-sonnet-latest-tool_calling-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/meta-llama_Llama-3.3-70B-Instruct-tool_calling-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/anthropic_claude-3-5-sonnet-latest-code-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/Qwen_Qwen2.5-72B-Instruct-tool_calling-26-dec-2024.jsonl\n", - "Removed 99 lines.\n", - "output/HuggingFaceTB_SmolLM2-1.7B-Instruct-code-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/gpt-4o-code-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/meta-llama_Llama-3.1-70B-Instruct-code-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/meta-llama_Llama-3.2-3B-Instruct-code-26-dec-2024.jsonl\n", - "Removed 109 lines.\n", - "output/gpt-4o-tool_calling-26-dec-2024.jsonl\n", - "Removed 109 lines.\n" - ] - } - ], + "outputs": [], "source": [ "# import glob\n", "# import json\n", @@ -553,17 +549,15 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_17219/1724525657.py:154: UserWarning:\n", - "\n", - "Answer lists have different lengths, returning False.\n", - "\n" + "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_74415/3026956094.py:163: UserWarning: Answer lists have different lengths, returning False.\n", + " warnings.warn(\n" ] } ], @@ -572,13 +566,15 @@ "import glob\n", "\n", "res = []\n", - "for f in glob.glob(\"output/*.jsonl\"):\n", - " res.append(pd.read_json(f, lines=True))\n", + "for file_path in glob.glob(\"output/*.jsonl\"):\n", + " smoldf = pd.read_json(file_path, lines=True)\n", + " smoldf[\"action_type\"] = \"vanilla\" if \"-vanilla-\" in file_path else \"code\"\n", + " res.append(smoldf)\n", "result_df = pd.concat(res)\n", "\n", "\n", "def get_correct(row):\n", - " if row[\"source\"] == \"MATH\":\n", + " if row[\"source\"] == \"MATH\": # Checks the last number in answer\n", " numbers_answer = extract_numbers(str(row[\"answer\"]))\n", " if len(numbers_answer) == 0:\n", " return False\n", @@ -589,74 +585,27 @@ "\n", "result_df[\"correct\"] = result_df.apply(get_correct, axis=1)\n", "\n", - "result_df = result_df.loc[\n", - " (result_df[\"agent_action_type\"] == \"code\")\n", - " & (\n", - " ~result_df[\"model_id\"].isin(\n", - " [\n", - " \"meta-llama/Llama-3.2-3B-Instruct\",\n", - " \"meta-llama/Llama-3.1-70B-Instruct\",\n", - " \"HuggingFaceTB/SmolLM2-1.7B-Instruct\",\n", - " ]\n", - " )\n", - " )\n", - "]\n", "result_df = (\n", - " (result_df.groupby([\"model_id\", \"source\"])[[\"correct\"]].mean() * 100)\n", + " (result_df.groupby([\"model_id\", \"source\", \"action_type\"])[[\"correct\"]].mean() * 100)\n", " .round(1)\n", " .reset_index()\n", - ")\n", - "result_df[\"type\"] = \"agent\"" + ")" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ - "vanilla_data = [\n", - " [\"gpt-4o\", \"SimpleQA\", 38.2],\n", - " [\"gpt-4o\", \"GAIA\", 9.3],\n", - " [\"Qwen/Qwen2.5-72B-Instruct\", \"SimpleQA\", 9.1],\n", - " [\"anthropic/claude-3-5-sonnet-latest\", \"SimpleQA\", 28.4],\n", - " [\"gpt-4o\", \"GSM8K\", 94.3],\n", - " [\"anthropic/claude-3-5-sonnet-latest\", \"GSM8K\", 96.4],\n", - " [\"meta-llama/Llama-3.3-70B-Instruct\", \"GSM8K\", 95.1],\n", - " [\n", - " \"meta-llama/Llama-3.3-70B-Instruct\",\n", - " \"MATH\",\n", - " 30.7,\n", - " ], # As per Open LLM Leaderboard for 3.1, score for 3.3 is too low. https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=llama-3.1\n", - " [\n", - " \"Qwen/Qwen2.5-Coder-32B-Instruct\",\n", - " \"MATH\",\n", - " 30.6,\n", - " ], # As per Open LLM Leaderboard for the base model, score for instruct too low. https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=llama-3.1\n", - "]\n", - "\n", - "df2 = pd.DataFrame(vanilla_data, columns=[\"model_id\", \"source\", \"correct\"])\n", - "df2[\"type\"] = \"vanilla\"\n", - "\n", - "combined_df = pd.concat([result_df, df2], ignore_index=True)\n", - "\n", - "pivot_df = combined_df.pivot_table(\n", + "pivot_df = result_df.pivot_table(\n", " index=[\"model_id\", \"source\"],\n", - " columns=[\"type\"],\n", + " columns=[\"action_type\"],\n", " values=\"correct\",\n", " fill_value=float(\"nan\"),\n", ").reset_index()" ] }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "pivot_df = pivot_df.loc[~pivot_df[\"source\"].isin([\"GSM8K\"])]" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -666,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -689,10 +638,10 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -701,128 +650,176 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
typeaction_typemodel_idsourceagentcodevanilla
0Qwen/Qwen2.5-72B-InstructGAIA12.5NaN28.16.2
1Qwen/Qwen2.5-72B-InstructMATH74.031.9
2Qwen/Qwen2.5-72B-InstructMATH77.5NaNSimpleQA70.010.0
3Qwen/Qwen2.5-72B-InstructSimpleQA42.59.1Qwen/Qwen2.5-Coder-32B-InstructGAIA18.83.1
4Qwen/Qwen2.5-Coder-32B-InstructGAIA28.1NaNMATH76.060.0
5Qwen/Qwen2.5-Coder-32B-InstructSimpleQA86.08.0
6Qwen/Qwen2.5-Coder-32B-InstructMATH85.030.6anthropic/claude-3-5-sonnet-latestGAIA40.63.1
7Qwen/Qwen2.5-Coder-32B-InstructSimpleQA42.5NaNanthropic/claude-3-5-sonnet-latestMATH67.050.0
8anthropic/claude-3-5-sonnet-latestSimpleQA90.034.0
9gpt-4oGAIA43.8NaN28.13.1
10anthropic/claude-3-5-sonnet-latestgpt-4oMATH85.0NaN70.040.0
11anthropic/claude-3-5-sonnet-latestgpt-4oSimpleQA47.528.488.06.0
12gpt-4ometa-llama/Llama-3.1-8B-InstructGAIA25.09.30.00.0
13meta-llama/Llama-3.1-8B-InstructMATH42.018.0
14gpt-4oMATH77.5NaNmeta-llama/Llama-3.1-8B-InstructSimpleQA54.06.0
15gpt-4oSimpleQA60.038.2meta-llama/Llama-3.2-3B-InstructGAIA3.10.0
16meta-llama/Llama-3.3-70B-InstructGAIA21.9NaNmeta-llama/Llama-3.2-3B-InstructMATH32.012.0
17meta-llama/Llama-3.2-3B-InstructSimpleQA4.00.0
18meta-llama/Llama-3.3-70B-InstructMATH82.130.7GAIA34.43.1
19meta-llama/Llama-3.3-70B-InstructMATH82.040.0
20meta-llama/Llama-3.3-70B-InstructSimpleQA30.9NaN84.012.0
\n", "" ], "text/plain": [ - "type model_id source agent vanilla\n", - "0 Qwen/Qwen2.5-72B-Instruct GAIA 12.5 NaN\n", - "2 Qwen/Qwen2.5-72B-Instruct MATH 77.5 NaN\n", - "3 Qwen/Qwen2.5-72B-Instruct SimpleQA 42.5 9.1\n", - "4 Qwen/Qwen2.5-Coder-32B-Instruct GAIA 28.1 NaN\n", - "6 Qwen/Qwen2.5-Coder-32B-Instruct MATH 85.0 30.6\n", - "7 Qwen/Qwen2.5-Coder-32B-Instruct SimpleQA 42.5 NaN\n", - "8 anthropic/claude-3-5-sonnet-latest GAIA 43.8 NaN\n", - "10 anthropic/claude-3-5-sonnet-latest MATH 85.0 NaN\n", - "11 anthropic/claude-3-5-sonnet-latest SimpleQA 47.5 28.4\n", - "12 gpt-4o GAIA 25.0 9.3\n", - "14 gpt-4o MATH 77.5 NaN\n", - "15 gpt-4o SimpleQA 60.0 38.2\n", - "16 meta-llama/Llama-3.3-70B-Instruct GAIA 21.9 NaN\n", - "18 meta-llama/Llama-3.3-70B-Instruct MATH 82.1 30.7\n", - "19 meta-llama/Llama-3.3-70B-Instruct SimpleQA 30.9 NaN" + "action_type model_id source code vanilla\n", + "0 Qwen/Qwen2.5-72B-Instruct GAIA 28.1 6.2\n", + "1 Qwen/Qwen2.5-72B-Instruct MATH 74.0 31.9\n", + "2 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meta-llama/Llama-3.3-70B-Instruct SimpleQA 84.0 12.0" ] }, "metadata": {}, @@ -835,12 +832,12 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -877,7 +874,7 @@ "for i, source in enumerate(sources):\n", " source_data = pivot_df[pivot_df[\"source\"] == source]\n", " agent_scores = [\n", - " source_data[source_data[\"model_id\"] == model][\"agent\"].values[0]\n", + " source_data[source_data[\"model_id\"] == model][\"code\"].values[0]\n", " if len(source_data[source_data[\"model_id\"] == model]) > 0\n", " else np.nan\n", " for model in models\n", diff --git a/src/smolagents/tools.py b/src/smolagents/tools.py index 7acff0d..5dae400 100644 --- a/src/smolagents/tools.py +++ b/src/smolagents/tools.py @@ -779,8 +779,10 @@ def launch_gradio_demo(tool: Tool): "number": gr.Textbox, } - def fn(*args, **kwargs): - return tool(*args, **kwargs, sanitize_inputs_outputs=True) + def tool_forward(*args, **kwargs): + return tool(*args, sanitize_inputs_outputs=True, **kwargs) + + tool_forward.__signature__ = inspect.signature(tool.forward) gradio_inputs = [] for input_name, input_details in tool.inputs.items(): @@ -794,11 +796,13 @@ def launch_gradio_demo(tool: Tool): gradio_output = output_gradio_componentclass(label="Output") gr.Interface( - fn=fn, + fn=tool_forward, inputs=gradio_inputs, outputs=gradio_output, title=tool.name, article=tool.description, + description=tool.description, + api_name=tool.name, ).launch()