forked from phoenix-oss/llama-stack-mirror
a51c8b4efc
# What does this PR do?
Cleans up how we provide sampling params. Earlier, strategy was an enum
and all params (top_p, temperature, top_k) across all strategies were
grouped. We now have a strategy union object with each strategy (greedy,
top_p, top_k) having its corresponding params.
Earlier,
```
class SamplingParams:
strategy: enum ()
top_p, temperature, top_k and other params
```
However, the `strategy` field was not being used in any providers making
it confusing to know the exact sampling behavior purely based on the
params since you could pass temperature, top_p, top_k and how the
provider would interpret those would not be clear.
Hence we introduced -- a union where the strategy and relevant params
are all clubbed together to avoid this confusion.
Have updated all providers, tests, notebooks, readme and otehr places
where sampling params was being used to use the new format.
## Test Plan
`pytest llama_stack/providers/tests/inference/groq/test_groq_utils.py`
// inference on ollama, fireworks and together
`with-proxy pytest -v -s -k "ollama"
--inference-model="meta-llama/Llama-3.1-8B-Instruct"
llama_stack/providers/tests/inference/test_text_inference.py `
// agents on fireworks
`pytest -v -s -k 'fireworks and create_agent'
--inference-model="meta-llama/Llama-3.1-8B-Instruct"
llama_stack/providers/tests/agents/test_agents.py
--safety-shield="meta-llama/Llama-Guard-3-8B"`
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Ran pre-commit to handle lint / formatting issues.
- [X] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [X] Updated relevant documentation.
- [X] Wrote necessary unit or integration tests.
---------
Co-authored-by: Hardik Shah <hjshah@fb.com>
259 lines
9.1 KiB
Python
259 lines
9.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import json
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import pandas as pd
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import streamlit as st
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from modules.api import llama_stack_api
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def select_eval_task_1():
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# Select Eval Tasks
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st.subheader("1. Choose An Eval Task")
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eval_tasks = llama_stack_api.client.eval_tasks.list()
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eval_tasks = {et.identifier: et for et in eval_tasks}
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eval_tasks_names = list(eval_tasks.keys())
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selected_eval_task = st.selectbox(
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"Choose an eval task.",
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options=eval_tasks_names,
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help="Choose an eval task. Each eval task is parameterized by a dataset, and list of scoring functions.",
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)
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with st.expander("View Eval Task"):
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st.json(eval_tasks[selected_eval_task], expanded=True)
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st.session_state["selected_eval_task"] = selected_eval_task
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st.session_state["eval_tasks"] = eval_tasks
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if st.button("Confirm", key="confirm_1"):
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st.session_state["selected_eval_task_1_next"] = True
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def define_eval_candidate_2():
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if not st.session_state.get("selected_eval_task_1_next", None):
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return
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st.subheader("2. Define Eval Candidate")
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st.info(
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"""
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Define the configurations for the evaluation candidate model or agent used for generation.
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Select "model" if you want to run generation with inference API, or "agent" if you want to run generation with agent API through specifying AgentConfig.
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"""
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)
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with st.expander("Define Eval Candidate", expanded=True):
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# Define Eval Candidate
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candidate_type = st.radio("Candidate Type", ["model", "agent"])
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available_models = llama_stack_api.client.models.list()
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available_models = [model.identifier for model in available_models]
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selected_model = st.selectbox(
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"Choose a model",
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available_models,
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index=0,
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)
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# Sampling Parameters
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st.markdown("##### Sampling Parameters")
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temperature = st.slider(
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"Temperature",
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min_value=0.0,
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max_value=1.0,
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value=0.0,
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step=0.1,
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help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable",
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)
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top_p = st.slider(
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"Top P",
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min_value=0.0,
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max_value=1.0,
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value=0.95,
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step=0.1,
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)
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max_tokens = st.slider(
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"Max Tokens",
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min_value=0,
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max_value=4096,
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value=512,
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step=1,
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help="The maximum number of tokens to generate",
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)
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repetition_penalty = st.slider(
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"Repetition Penalty",
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min_value=1.0,
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max_value=2.0,
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value=1.0,
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step=0.1,
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help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.",
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)
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if candidate_type == "model":
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if temperature > 0.0:
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strategy = {
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"type": "top_p",
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"temperature": temperature,
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"top_p": top_p,
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}
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else:
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strategy = {"type": "greedy"}
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eval_candidate = {
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"type": "model",
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"model": selected_model,
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"sampling_params": {
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"strategy": strategy,
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"max_tokens": max_tokens,
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"repetition_penalty": repetition_penalty,
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},
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}
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elif candidate_type == "agent":
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system_prompt = st.text_area(
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"System Prompt",
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value="You are a helpful AI assistant.",
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help="Initial instructions given to the AI to set its behavior and context",
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)
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tools_json = st.text_area(
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"Tools Configuration (JSON)",
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value=json.dumps(
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[
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{
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"type": "brave_search",
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"engine": "brave",
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"api_key": "ENTER_BRAVE_API_KEY_HERE",
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}
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]
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),
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help="Enter tool configurations in JSON format. Each tool should have a name, description, and parameters.",
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height=200,
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)
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try:
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tools = json.loads(tools_json)
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except json.JSONDecodeError:
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st.error("Invalid JSON format for tools configuration")
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tools = []
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eval_candidate = {
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"type": "agent",
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"config": {
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"model": selected_model,
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"instructions": system_prompt,
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"tools": tools,
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"tool_choice": "auto",
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"tool_prompt_format": "json",
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"input_shields": [],
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"output_shields": [],
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"enable_session_persistence": False,
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},
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}
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st.session_state["eval_candidate"] = eval_candidate
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if st.button("Confirm", key="confirm_2"):
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st.session_state["selected_eval_candidate_2_next"] = True
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def run_evaluation_3():
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if not st.session_state.get("selected_eval_candidate_2_next", None):
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return
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st.subheader("3. Run Evaluation")
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# Add info box to explain configurations being used
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st.info(
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"""
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Review the configurations that will be used for this evaluation run, make any necessary changes, and then click the "Run Evaluation" button.
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"""
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)
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selected_eval_task = st.session_state["selected_eval_task"]
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eval_tasks = st.session_state["eval_tasks"]
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eval_candidate = st.session_state["eval_candidate"]
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dataset_id = eval_tasks[selected_eval_task].dataset_id
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rows = llama_stack_api.client.datasetio.get_rows_paginated(
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dataset_id=dataset_id,
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rows_in_page=-1,
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)
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total_rows = len(rows.rows)
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# Add number of examples control
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num_rows = st.number_input(
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"Number of Examples to Evaluate",
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min_value=1,
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max_value=total_rows,
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value=5,
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help="Number of examples from the dataset to evaluate. ",
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)
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eval_task_config = {
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"type": "benchmark",
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"eval_candidate": eval_candidate,
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"scoring_params": {},
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}
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with st.expander("View Evaluation Task", expanded=True):
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st.json(eval_tasks[selected_eval_task], expanded=True)
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with st.expander("View Evaluation Task Configuration", expanded=True):
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st.json(eval_task_config, expanded=True)
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# Add run button and handle evaluation
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if st.button("Run Evaluation"):
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progress_text = "Running evaluation..."
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progress_bar = st.progress(0, text=progress_text)
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rows = rows.rows
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if num_rows < total_rows:
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rows = rows[:num_rows]
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# Create separate containers for progress text and results
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progress_text_container = st.empty()
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results_container = st.empty()
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output_res = {}
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for i, r in enumerate(rows):
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# Update progress
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progress = i / len(rows)
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progress_bar.progress(progress, text=progress_text)
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# Run evaluation for current row
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eval_res = llama_stack_api.client.eval.evaluate_rows(
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task_id=selected_eval_task,
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input_rows=[r],
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scoring_functions=eval_tasks[selected_eval_task].scoring_functions,
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task_config=eval_task_config,
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)
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for k in r.keys():
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if k not in output_res:
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output_res[k] = []
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output_res[k].append(r[k])
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for k in eval_res.generations[0].keys():
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if k not in output_res:
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output_res[k] = []
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output_res[k].append(eval_res.generations[0][k])
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for scoring_fn in eval_tasks[selected_eval_task].scoring_functions:
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if scoring_fn not in output_res:
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output_res[scoring_fn] = []
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output_res[scoring_fn].append(eval_res.scores[scoring_fn].score_rows[0])
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progress_text_container.write(
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f"Expand to see current processed result ({i + 1} / {len(rows)})"
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)
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results_container.json(eval_res, expanded=2)
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progress_bar.progress(1.0, text="Evaluation complete!")
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# Display results in dataframe
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if output_res:
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output_df = pd.DataFrame(output_res)
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st.subheader("Evaluation Results")
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st.dataframe(output_df)
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def native_evaluation_page():
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st.set_page_config(page_title="Evaluations (Generation + Scoring)", page_icon="🦙")
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st.title("📊 Evaluations (Generation + Scoring)")
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select_eval_task_1()
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define_eval_candidate_2()
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run_evaluation_3()
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native_evaluation_page()
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