From 255b39e7ae809b8effd83ab053d8b2b9a2dced6a Mon Sep 17 00:00:00 2001 From: StanChan03 Date: Sat, 7 Dec 2024 23:32:02 -0800 Subject: [PATCH 01/11] sem_join + sem_filter pbars --- lotus/models/lm.py | 23 +++++++++++++++++++---- lotus/sem_ops/sem_filter.py | 5 ++++- lotus/sem_ops/sem_join.py | 10 ++++++++++ 3 files changed, 33 insertions(+), 5 deletions(-) diff --git a/lotus/models/lm.py b/lotus/models/lm.py index a5fdc570..b1749f30 100644 --- a/lotus/models/lm.py +++ b/lotus/models/lm.py @@ -42,7 +42,10 @@ def __init__( self.cache = Cache(max_cache_size) def __call__( - self, messages: list[list[dict[str, str]]], safe_mode: bool = False, **kwargs: dict[str, Any] + self, + messages: list[list[dict[str, str]]], + show_pbar: bool = True, + **kwargs: dict[str, Any], ) -> LMOutput: all_kwargs = {**self.kwargs, **kwargs} @@ -59,7 +62,7 @@ def __call__( self.stats.total_usage.cache_hits += len(messages) - len(uncached_data) # Process uncached messages in batches - uncached_responses = self._process_uncached_messages(uncached_data, all_kwargs) + uncached_responses = self._process_uncached_messages(uncached_data, all_kwargs, show_pbar) # Add new responses to cache for resp, (_, hash) in zip(uncached_responses, uncached_data): @@ -74,12 +77,24 @@ def __call__( return LMOutput(outputs=outputs, logprobs=logprobs) - def _process_uncached_messages(self, uncached_data, all_kwargs): + def _process_uncached_messages(self, uncached_data, all_kwargs, show_pbar): """Processes uncached messages in batches and returns responses.""" uncached_responses = [] - for i in tqdm(range(0, len(uncached_data), self.max_batch_size), desc="Processing uncached messages"): + total_calls = len(uncached_data) + + pbar = tqdm( + total=total_calls, + desc="Processing uncached messages", + disable=not show_pbar, + bar_format="{l_bar}{bar} {n}/{total} LM calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", + ) + for i in range(0, total_calls, self.max_batch_size): batch = [msg for msg, _ in uncached_data[i : i + self.max_batch_size]] uncached_responses.extend(batch_completion(self.model, batch, drop_params=True, **all_kwargs)) + + pbar.update(len(batch)) + pbar.close() + return uncached_responses def _cache_response(self, response, hash): diff --git a/lotus/sem_ops/sem_filter.py b/lotus/sem_ops/sem_filter.py index 372df5dc..bce4078d 100644 --- a/lotus/sem_ops/sem_filter.py +++ b/lotus/sem_ops/sem_filter.py @@ -24,6 +24,7 @@ def sem_filter( strategy: str | None = None, logprobs: bool = False, safe_mode: bool = False, + show_pbar: bool = True, ) -> SemanticFilterOutput: """ Filters a list of documents based on a given user instruction using a language model. @@ -55,7 +56,7 @@ def sem_filter( estimated_total_cost = sum(model.count_tokens(input) for input in inputs) show_safe_mode(estimated_total_cost, estimated_total_calls) - lm_output: LMOutput = model(inputs, **kwargs) + lm_output: LMOutput = model(inputs, show_pbar=show_pbar, **kwargs) postprocess_output = filter_postprocess( lm_output.outputs, default=default, cot_reasoning=strategy in ["cot", "zs-cot"] @@ -235,6 +236,7 @@ def __call__( logprobs=True, strategy=helper_strategy, safe_mode=safe_mode, + show_pbar=True, ) helper_outputs, helper_logprobs = helper_output.outputs, helper_output.logprobs formatted_helper_logprobs: LogprobsForFilterCascade = ( @@ -338,6 +340,7 @@ def __call__( cot_reasoning=cot_reasoning, strategy=strategy, safe_mode=safe_mode, + show_pbar=True, ) outputs = output.outputs raw_outputs = output.raw_outputs diff --git a/lotus/sem_ops/sem_join.py b/lotus/sem_ops/sem_join.py index a9aae5f7..acf330e9 100644 --- a/lotus/sem_ops/sem_join.py +++ b/lotus/sem_ops/sem_join.py @@ -1,6 +1,7 @@ from typing import Any import pandas as pd +from tqdm import tqdm import lotus from lotus.templates import task_instructions @@ -68,6 +69,11 @@ def sem_join( print("Sem_Join:") show_safe_mode(estimated_total_cost, estimated_total_calls) + pbar = tqdm( + total=len(l1), + desc="Processing uncached messages", + bar_format="{l_bar}{bar} {n}/{total} LM Calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", + ) # for i1 in enumerate(l1): for id1, i1 in zip(ids1, left_multimodal_data): # perform llm filter @@ -81,6 +87,7 @@ def sem_join( cot_reasoning=cot_reasoning, default=default, strategy=strategy, + show_pbar=False, ) outputs = output.outputs raw_outputs = output.raw_outputs @@ -98,6 +105,9 @@ def sem_join( ] ) + pbar.update(1) + pbar.close() + lotus.logger.debug(f"outputs: {filter_outputs}") lotus.logger.debug(f"explanations: {all_explanations}") From ad789c9759d12ea0be87effb609d35f139771b75 Mon Sep 17 00:00:00 2001 From: StanChan03 Date: Sun, 8 Dec 2024 00:08:07 -0800 Subject: [PATCH 02/11] update to LM calls from filter calls --- lotus/sem_ops/sem_join.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lotus/sem_ops/sem_join.py b/lotus/sem_ops/sem_join.py index acf330e9..4c97d34f 100644 --- a/lotus/sem_ops/sem_join.py +++ b/lotus/sem_ops/sem_join.py @@ -70,7 +70,7 @@ def sem_join( show_safe_mode(estimated_total_cost, estimated_total_calls) pbar = tqdm( - total=len(l1), + total=len(l1) * len(l2), desc="Processing uncached messages", bar_format="{l_bar}{bar} {n}/{total} LM Calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", ) @@ -105,7 +105,7 @@ def sem_join( ] ) - pbar.update(1) + pbar.update(len(l1) * len(l2)) pbar.close() lotus.logger.debug(f"outputs: {filter_outputs}") From c36308971193fe485f737b7a9b42e721618326d3 Mon Sep 17 00:00:00 2001 From: StanChan03 Date: Mon, 9 Dec 2024 18:26:29 -0800 Subject: [PATCH 03/11] sem_join_cascade pbar --- examples/op_examples/Skill:right_index/index | Bin 0 -> 301101 bytes examples/op_examples/Skill:right_index/vecs | Bin 0 -> 301218 bytes lotus/sem_ops/sem_join.py | 29 ++++++++++++------- lotus/sem_ops/sem_topk.py | 14 +++++++-- 4 files changed, 31 insertions(+), 12 deletions(-) create mode 100644 examples/op_examples/Skill:right_index/index create mode 100644 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total=len(l1) * len(l2), - desc="Processing uncached messages", - bar_format="{l_bar}{bar} {n}/{total} LM Calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", - ) + if show_pbar: + pbar = tqdm( + total=len(l1) * len(l2), + desc="Processing uncached messages", + bar_format="{l_bar}{bar} {n}/{total} LM Calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", + ) # for i1 in enumerate(l1): for id1, i1 in zip(ids1, left_multimodal_data): # perform llm filter @@ -104,9 +105,9 @@ def sem_join( if output ] ) - - pbar.update(len(l1) * len(l2)) - pbar.close() + if show_pbar: + pbar.update(len(l1) * len(l2)) + pbar.close() lotus.logger.debug(f"outputs: {filter_outputs}") lotus.logger.debug(f"explanations: {all_explanations}") @@ -215,6 +216,11 @@ def sem_join_cascade( # Accept helper results with high confidence join_results = [(row["_left_id"], row["_right_id"], None) for _, row in helper_high_conf.iterrows()] + pbar = tqdm( + total=len(helper_low_conf) + len(helper_high_conf), + desc="Processing uncached messages", + bar_format="{l_bar}{bar} {n}/{total} LM calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", + ) # Send low confidence rows to large LM for unique_l1 in helper_low_conf[col1_label].unique(): unique_l1_id = helper_low_conf[helper_low_conf[col1_label] == unique_l1]["_left_id"].iloc[0] @@ -234,8 +240,10 @@ def sem_join_cascade( cot_reasoning=cot_reasoning, default=default, strategy=strategy, + show_pbar=False, ) - + pbar.update(len(helper_low_conf) + len(helper_high_conf)) + pbar.close() join_results.extend(large_join_output.join_results) lotus.logger.debug(f"outputs: {filter_outputs}") @@ -522,6 +530,7 @@ def learn_join_cascade_threshold( examples_answers=examples_answers, cot_reasoning=cot_reasoning, strategy=strategy, + show_pbar=False, ) (pos_threshold, neg_threshold), _ = learn_cascade_thresholds( diff --git a/lotus/sem_ops/sem_topk.py b/lotus/sem_ops/sem_topk.py index 461e06a1..dbc0826c 100644 --- a/lotus/sem_ops/sem_topk.py +++ b/lotus/sem_ops/sem_topk.py @@ -4,6 +4,7 @@ import numpy as np import pandas as pd +from tqdm import tqdm import lotus from lotus.templates import task_instructions @@ -57,14 +58,16 @@ def parse_ans_binary(answer: str) -> bool: def compare_batch_binary( - pairs: list[tuple[dict[str, Any], dict[str, Any]]], user_instruction: str, strategy: str | None = None + pairs: list[tuple[dict[str, Any], dict[str, Any]]], + user_instruction: str, + strategy: str | None = None, ) -> tuple[list[bool], int]: match_prompts = [] tokens = 0 for doc1, doc2 in pairs: match_prompts.append(get_match_prompt_binary(doc1, doc2, user_instruction, strategy=strategy)) tokens += lotus.settings.lm.count_tokens(match_prompts[-1]) - lm_results: LMOutput = lotus.settings.lm(match_prompts) + lm_results: LMOutput = lotus.settings.lm(match_prompts, show_pbar=False) results: list[bool] = list(map(parse_ans_binary, lm_results.outputs)) return results, tokens @@ -141,7 +144,14 @@ def llm_naive_sort( pairs.append((docs[i], docs[j])) llm_calls = len(pairs) + pbar = tqdm( + total=llm_calls, + desc="Processing uncached messages", + bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} LM calls [{elapsed}<{remaining}]", + ) comparisons, tokens = compare_batch_binary(pairs, user_instruction, strategy=strategy) + pbar.update(len(pairs)) + pbar.close() if safe_mode: show_safe_mode(tokens, llm_calls) votes = [0] * N From 7c24b9e462a6df7abda2d5208debf94be709cbb0 Mon Sep 17 00:00:00 2001 From: Sid Jha Date: Mon, 9 Dec 2024 19:29:20 -0800 Subject: [PATCH 04/11] Small updates --- examples/op_examples/Skill:right_index/index | Bin 301101 -> 0 bytes examples/op_examples/Skill:right_index/vecs | Bin 301218 -> 0 bytes lotus/models/cross_encoder_reranker.py | 2 +- lotus/models/lm.py | 8 +++--- lotus/models/sentence_transformers_rm.py | 2 +- lotus/sem_ops/sem_filter.py | 8 +++--- lotus/sem_ops/sem_join.py | 15 ++++++----- lotus/sem_ops/sem_topk.py | 25 ++++++++++++------- 8 files changed, 33 insertions(+), 27 deletions(-) delete mode 100644 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z&V`7xUe2b6O>uARe4(SVNql0*v_CWiog-G0x9AM6a%ZHq`?6?mI|bLCvV(J3#^@_` zxEntWqGfU@{V#Q)31LHV`WX}b+oWtyF1+~<&*v{zi|W{-N0GsTKu3-D8d2&>Lk zlC^^c`lWjC>-}%&b;3E=(KP}WHQSL}of%&Yn1#(valEkOuQ2PTF}zq+<;=Y&V`sew zpV=m@Wh+jCTwy)j>+OwJv({1l*RNt)mnEp_GaU=pNL=uEsaJ693=OQ`43aBU9QCCU zf*NMf?yc7RZ{csz?apYHsWrr#9*tE0^MUwjY%RG{G;wqa7ejCh7xN|X# zUey@m++8xb}M*oVJZ2$8Sm)sCSDBbSC1!p2Hv`T*`rA ZRkiWBXfdln4YL$w{{Jg;Mdp0#{{bD(ml^;7 diff --git a/lotus/models/cross_encoder_reranker.py b/lotus/models/cross_encoder_reranker.py index 65827ce2..a177ef22 100644 --- a/lotus/models/cross_encoder_reranker.py +++ b/lotus/models/cross_encoder_reranker.py @@ -23,6 +23,6 @@ def __init__( self.model = CrossEncoder(model, device=device) # type: ignore # CrossEncoder has wrong type stubs def __call__(self, query: str, docs: list[str], K: int) -> RerankerOutput: - results = self.model.rank(query, docs, top_k=K, batch_size=self.max_batch_size) + results = self.model.rank(query, docs, top_k=K, batch_size=self.max_batch_size, show_progress_bar=False) indices = [int(result["corpus_id"]) for result in results] return RerankerOutput(indices=indices) diff --git a/lotus/models/lm.py b/lotus/models/lm.py index b1749f30..71843e06 100644 --- a/lotus/models/lm.py +++ b/lotus/models/lm.py @@ -44,7 +44,7 @@ def __init__( def __call__( self, messages: list[list[dict[str, str]]], - show_pbar: bool = True, + show_progress_bar: bool = True, **kwargs: dict[str, Any], ) -> LMOutput: all_kwargs = {**self.kwargs, **kwargs} @@ -62,7 +62,7 @@ def __call__( self.stats.total_usage.cache_hits += len(messages) - len(uncached_data) # Process uncached messages in batches - uncached_responses = self._process_uncached_messages(uncached_data, all_kwargs, show_pbar) + uncached_responses = self._process_uncached_messages(uncached_data, all_kwargs, show_progress_bar) # Add new responses to cache for resp, (_, hash) in zip(uncached_responses, uncached_data): @@ -77,7 +77,7 @@ def __call__( return LMOutput(outputs=outputs, logprobs=logprobs) - def _process_uncached_messages(self, uncached_data, all_kwargs, show_pbar): + def _process_uncached_messages(self, uncached_data, all_kwargs, show_progress_bar): """Processes uncached messages in batches and returns responses.""" uncached_responses = [] total_calls = len(uncached_data) @@ -85,7 +85,7 @@ def _process_uncached_messages(self, uncached_data, all_kwargs, show_pbar): pbar = tqdm( total=total_calls, desc="Processing uncached messages", - disable=not show_pbar, + disable=not show_progress_bar, bar_format="{l_bar}{bar} {n}/{total} LM calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", ) for i in range(0, total_calls, self.max_batch_size): diff --git a/lotus/models/sentence_transformers_rm.py b/lotus/models/sentence_transformers_rm.py index 1f86ce7e..e4e55633 100644 --- a/lotus/models/sentence_transformers_rm.py +++ b/lotus/models/sentence_transformers_rm.py @@ -31,7 +31,7 @@ def _embed(self, docs: pd.Series | list) -> NDArray[np.float64]: batch = docs[i : i + self.max_batch_size] _batch = convert_to_base_data(batch) torch_embeddings = self.transformer.encode( - _batch, convert_to_tensor=True, normalize_embeddings=self.normalize_embeddings + _batch, convert_to_tensor=True, normalize_embeddings=self.normalize_embeddings, show_progress_bar=False ) assert isinstance(torch_embeddings, torch.Tensor) cpu_embeddings = torch_embeddings.cpu().numpy() diff --git a/lotus/sem_ops/sem_filter.py b/lotus/sem_ops/sem_filter.py index bce4078d..e0977917 100644 --- a/lotus/sem_ops/sem_filter.py +++ b/lotus/sem_ops/sem_filter.py @@ -24,7 +24,7 @@ def sem_filter( strategy: str | None = None, logprobs: bool = False, safe_mode: bool = False, - show_pbar: bool = True, + show_progress_bar: bool = True, ) -> SemanticFilterOutput: """ Filters a list of documents based on a given user instruction using a language model. @@ -56,7 +56,7 @@ def sem_filter( estimated_total_cost = sum(model.count_tokens(input) for input in inputs) show_safe_mode(estimated_total_cost, estimated_total_calls) - lm_output: LMOutput = model(inputs, show_pbar=show_pbar, **kwargs) + lm_output: LMOutput = model(inputs, show_progress_bar=show_progress_bar, **kwargs) postprocess_output = filter_postprocess( lm_output.outputs, default=default, cot_reasoning=strategy in ["cot", "zs-cot"] @@ -236,7 +236,7 @@ def __call__( logprobs=True, strategy=helper_strategy, safe_mode=safe_mode, - show_pbar=True, + show_progress_bar=True, ) helper_outputs, helper_logprobs = helper_output.outputs, helper_output.logprobs formatted_helper_logprobs: LogprobsForFilterCascade = ( @@ -340,7 +340,7 @@ def __call__( cot_reasoning=cot_reasoning, strategy=strategy, safe_mode=safe_mode, - show_pbar=True, + show_progress_bar=True, ) outputs = output.outputs raw_outputs = output.raw_outputs diff --git a/lotus/sem_ops/sem_join.py b/lotus/sem_ops/sem_join.py index fb09fadf..763feaaa 100644 --- a/lotus/sem_ops/sem_join.py +++ b/lotus/sem_ops/sem_join.py @@ -27,7 +27,7 @@ def sem_join( default: bool = True, strategy: str | None = None, safe_mode: bool = False, - show_pbar: bool = True, + show_progress_bar: bool = True, ) -> SemanticJoinOutput: """ Joins two series using a model. @@ -69,7 +69,7 @@ def sem_join( estimated_total_cost = estimated_tokens_per_call * estimated_total_calls print("Sem_Join:") show_safe_mode(estimated_total_cost, estimated_total_calls) - if show_pbar: + if show_progress_bar: pbar = tqdm( total=len(l1) * len(l2), desc="Processing uncached messages", @@ -88,7 +88,7 @@ def sem_join( cot_reasoning=cot_reasoning, default=default, strategy=strategy, - show_pbar=False, + show_progress_bar=False, ) outputs = output.outputs raw_outputs = output.raw_outputs @@ -105,7 +105,7 @@ def sem_join( if output ] ) - if show_pbar: + if show_progress_bar: pbar.update(len(l1) * len(l2)) pbar.close() @@ -217,7 +217,7 @@ def sem_join_cascade( join_results = [(row["_left_id"], row["_right_id"], None) for _, row in helper_high_conf.iterrows()] pbar = tqdm( - total=len(helper_low_conf) + len(helper_high_conf), + total=num_large, desc="Processing uncached messages", bar_format="{l_bar}{bar} {n}/{total} LM calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", ) @@ -240,9 +240,9 @@ def sem_join_cascade( cot_reasoning=cot_reasoning, default=default, strategy=strategy, - show_pbar=False, + show_progress_bar=False, ) - pbar.update(len(helper_low_conf) + len(helper_high_conf)) + pbar.update(num_large) pbar.close() join_results.extend(large_join_output.join_results) @@ -530,7 +530,6 @@ def learn_join_cascade_threshold( examples_answers=examples_answers, cot_reasoning=cot_reasoning, strategy=strategy, - show_pbar=False, ) (pos_threshold, neg_threshold), _ = learn_cascade_thresholds( diff --git a/lotus/sem_ops/sem_topk.py b/lotus/sem_ops/sem_topk.py index dbc0826c..19ae8750 100644 --- a/lotus/sem_ops/sem_topk.py +++ b/lotus/sem_ops/sem_topk.py @@ -67,7 +67,7 @@ def compare_batch_binary( for doc1, doc2 in pairs: match_prompts.append(get_match_prompt_binary(doc1, doc2, user_instruction, strategy=strategy)) tokens += lotus.settings.lm.count_tokens(match_prompts[-1]) - lm_results: LMOutput = lotus.settings.lm(match_prompts, show_pbar=False) + lm_results: LMOutput = lotus.settings.lm(match_prompts, show_progress_bar=False) results: list[bool] = list(map(parse_ans_binary, lm_results.outputs)) return results, tokens @@ -257,14 +257,21 @@ def quicksort_recursive(indexes: list[int], low: int, high: int, K: int) -> None if high <= low: return - if low < high: - pi = partition(indexes, low, high, K) - left_size = pi - low - if left_size + 1 >= K: - quicksort_recursive(indexes, low, pi - 1, K) - else: - quicksort_recursive(indexes, low, pi - 1, left_size) - quicksort_recursive(indexes, pi + 1, high, K - left_size - 1) + num_comparisons = high - low + pbar = tqdm( + total=num_comparisons, + desc="Processing uncached messages", + bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} LM calls [{elapsed}<{remaining}]", + ) + pi = partition(indexes, low, high, K) + pbar.update(num_comparisons) + pbar.close() + left_size = pi - low + if left_size + 1 >= K: + quicksort_recursive(indexes, low, pi - 1, K) + else: + quicksort_recursive(indexes, low, pi - 1, left_size) + quicksort_recursive(indexes, pi + 1, high, K - left_size - 1) indexes = list(range(len(docs))) quicksort_recursive(indexes, 0, len(indexes) - 1, K) From b4ad1b530495fd1e6daab788936a040472325413 Mon Sep 17 00:00:00 2001 From: Sid Jha Date: Mon, 9 Dec 2024 21:22:23 -0800 Subject: [PATCH 05/11] Small updates --- examples/op_examples/agg.py | 5 ++--- lotus/models/lm.py | 9 ++++++--- lotus/sem_ops/sem_agg.py | 7 +++++-- lotus/sem_ops/sem_extract.py | 5 ++++- lotus/sem_ops/sem_filter.py | 10 +++++++++- lotus/sem_ops/sem_join.py | 10 +++++++--- lotus/sem_ops/sem_map.py | 5 ++++- lotus/sem_ops/sem_topk.py | 6 +++--- 8 files changed, 40 insertions(+), 17 deletions(-) diff --git a/examples/op_examples/agg.py b/examples/op_examples/agg.py index 206e3cc8..f04f84a8 100644 --- a/examples/op_examples/agg.py +++ b/examples/op_examples/agg.py @@ -1,12 +1,11 @@ import pandas as pd import lotus -from lotus.models import LM, SentenceTransformersRM +from lotus.models import LM lm = LM(model="gpt-4o-mini") -rm = SentenceTransformersRM(model="intfloat/e5-base-v2") -lotus.settings.configure(lm=lm, rm=rm) +lotus.settings.configure(lm=lm) data = { "Course Name": [ "Probability and Random Processes", diff --git a/lotus/models/lm.py b/lotus/models/lm.py index 71843e06..1cf8b4e4 100644 --- a/lotus/models/lm.py +++ b/lotus/models/lm.py @@ -45,6 +45,7 @@ def __call__( self, messages: list[list[dict[str, str]]], show_progress_bar: bool = True, + progress_bar_desc: str = "Processing uncached messages", **kwargs: dict[str, Any], ) -> LMOutput: all_kwargs = {**self.kwargs, **kwargs} @@ -62,7 +63,9 @@ def __call__( self.stats.total_usage.cache_hits += len(messages) - len(uncached_data) # Process uncached messages in batches - uncached_responses = self._process_uncached_messages(uncached_data, all_kwargs, show_progress_bar) + uncached_responses = self._process_uncached_messages( + uncached_data, all_kwargs, show_progress_bar, progress_bar_desc + ) # Add new responses to cache for resp, (_, hash) in zip(uncached_responses, uncached_data): @@ -77,14 +80,14 @@ def __call__( return LMOutput(outputs=outputs, logprobs=logprobs) - def _process_uncached_messages(self, uncached_data, all_kwargs, show_progress_bar): + def _process_uncached_messages(self, uncached_data, all_kwargs, show_progress_bar, progress_bar_desc): """Processes uncached messages in batches and returns responses.""" uncached_responses = [] total_calls = len(uncached_data) pbar = tqdm( total=total_calls, - desc="Processing uncached messages", + desc=progress_bar_desc, disable=not show_progress_bar, bar_format="{l_bar}{bar} {n}/{total} LM calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", ) diff --git a/lotus/sem_ops/sem_agg.py b/lotus/sem_ops/sem_agg.py index b8c9a278..721eb729 100644 --- a/lotus/sem_ops/sem_agg.py +++ b/lotus/sem_ops/sem_agg.py @@ -13,6 +13,7 @@ def sem_agg( user_instruction: str, partition_ids: list[int], safe_mode: bool = False, + progress_bar_desc: str = "Aggregating", ) -> SemanticAggOutput: """ Aggregates multiple documents into a single answer using a model. @@ -115,7 +116,7 @@ def doc_formatter(tree_level: int, doc: str, ctr: int) -> str: batch.append([{"role": "user", "content": prompt}]) new_partition_ids.append(cur_partition_id) - lm_output: LMOutput = model(batch) + lm_output: LMOutput = model(batch, progress_bar_desc=progress_bar_desc) summaries = lm_output.outputs partition_ids = new_partition_ids @@ -149,6 +150,7 @@ def __call__( suffix: str = "_output", group_by: list[str] | None = None, safe_mode: bool = False, + progress_bar_desc: str = "Aggregating", ) -> pd.DataFrame: """ Applies semantic aggregation over a dataframe. @@ -178,7 +180,7 @@ def __call__( grouped = self._obj.groupby(group_by) new_df = pd.DataFrame() for name, group in grouped: - res = group.sem_agg(user_instruction, all_cols, suffix, None) + res = group.sem_agg(user_instruction, all_cols, suffix, None, progress_bar_desc=progress_bar_desc) new_df = pd.concat([new_df, res]) return new_df @@ -200,6 +202,7 @@ def __call__( formatted_usr_instr, partition_ids, safe_mode=safe_mode, + progress_bar_desc=progress_bar_desc, ) # package answer in a dataframe diff --git a/lotus/sem_ops/sem_extract.py b/lotus/sem_ops/sem_extract.py index 93c4c9ba..56ff60be 100644 --- a/lotus/sem_ops/sem_extract.py +++ b/lotus/sem_ops/sem_extract.py @@ -18,6 +18,7 @@ def sem_extract( extract_quotes: bool = False, postprocessor: Callable[[list[str]], SemanticExtractPostprocessOutput] = extract_postprocess, safe_mode: bool = False, + progress_bar_desc: str = "Extracting", ) -> SemanticExtractOutput: """ Extracts attributes and values from a list of documents using a model. @@ -48,7 +49,7 @@ def sem_extract( show_safe_mode(estimated_cost, estimated_LM_calls) # call model - lm_output: LMOutput = model(inputs, response_format={"type": "json_object"}) + lm_output: LMOutput = model(inputs, response_format={"type": "json_object"}, progress_bar_desc=progress_bar_desc) # post process results postprocess_output = postprocessor(lm_output.outputs) @@ -79,6 +80,7 @@ def __call__( postprocessor: Callable[[list[str]], SemanticExtractPostprocessOutput] = extract_postprocess, return_raw_outputs: bool = False, safe_mode: bool = False, + progress_bar_desc: str = "Extracting", ) -> pd.DataFrame: """ Extracts the attributes and values of a dataframe. @@ -108,6 +110,7 @@ def __call__( extract_quotes=extract_quotes, postprocessor=postprocessor, safe_mode=safe_mode, + progress_bar_desc=progress_bar_desc, ) new_df = self._obj.copy() diff --git a/lotus/sem_ops/sem_filter.py b/lotus/sem_ops/sem_filter.py index e0977917..dc604bd9 100644 --- a/lotus/sem_ops/sem_filter.py +++ b/lotus/sem_ops/sem_filter.py @@ -25,6 +25,7 @@ def sem_filter( logprobs: bool = False, safe_mode: bool = False, show_progress_bar: bool = True, + progress_bar_desc: str = "Filtering", ) -> SemanticFilterOutput: """ Filters a list of documents based on a given user instruction using a language model. @@ -56,7 +57,9 @@ def sem_filter( estimated_total_cost = sum(model.count_tokens(input) for input in inputs) show_safe_mode(estimated_total_cost, estimated_total_calls) - lm_output: LMOutput = model(inputs, show_progress_bar=show_progress_bar, **kwargs) + lm_output: LMOutput = model( + inputs, show_progress_bar=show_progress_bar, progress_bar_desc=progress_bar_desc, **kwargs + ) postprocess_output = filter_postprocess( lm_output.outputs, default=default, cot_reasoning=strategy in ["cot", "zs-cot"] @@ -101,6 +104,7 @@ def learn_filter_cascade_thresholds( cot_reasoning=cot_reasoning, strategy=strategy, safe_mode=False, + progress_bar_desc="Large model filter for threshold learning", ).outputs best_combination, _ = learn_cascade_thresholds( @@ -151,6 +155,7 @@ def __call__( failure_probability: float | None = None, return_stats: bool = False, safe_mode: bool = False, + progress_bar_desc: str = "Filtering", ) -> pd.DataFrame | tuple[pd.DataFrame, dict[str, Any]]: """ Applies semantic filter over a dataframe. @@ -237,6 +242,7 @@ def __call__( strategy=helper_strategy, safe_mode=safe_mode, show_progress_bar=True, + progress_bar_desc="Small model filter", ) helper_outputs, helper_logprobs = helper_output.outputs, helper_output.logprobs formatted_helper_logprobs: LogprobsForFilterCascade = ( @@ -319,6 +325,7 @@ def __call__( cot_reasoning=cot_reasoning, strategy=strategy, safe_mode=safe_mode, + progress_bar_desc="Large model filter", ) for idx, large_idx in enumerate(low_conf_idxs): @@ -341,6 +348,7 @@ def __call__( strategy=strategy, safe_mode=safe_mode, show_progress_bar=True, + progress_bar_desc=progress_bar_desc, ) outputs = output.outputs raw_outputs = output.raw_outputs diff --git a/lotus/sem_ops/sem_join.py b/lotus/sem_ops/sem_join.py index 763feaaa..b763a038 100644 --- a/lotus/sem_ops/sem_join.py +++ b/lotus/sem_ops/sem_join.py @@ -28,6 +28,7 @@ def sem_join( strategy: str | None = None, safe_mode: bool = False, show_progress_bar: bool = True, + progress_bar_desc: str = "Join comparisons", ) -> SemanticJoinOutput: """ Joins two series using a model. @@ -72,7 +73,7 @@ def sem_join( if show_progress_bar: pbar = tqdm( total=len(l1) * len(l2), - desc="Processing uncached messages", + desc=progress_bar_desc, bar_format="{l_bar}{bar} {n}/{total} LM Calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", ) # for i1 in enumerate(l1): @@ -218,7 +219,7 @@ def sem_join_cascade( pbar = tqdm( total=num_large, - desc="Processing uncached messages", + desc="Large model join comparisons", bar_format="{l_bar}{bar} {n}/{total} LM calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", ) # Send low confidence rows to large LM @@ -343,7 +344,7 @@ def map_l1_to_l2( mapped_col1_name = f"_{col1_label}" # Map l1 to l2 - out = l1_df.sem_map(inst, suffix=mapped_col1_name, examples=map_examples) + out = l1_df.sem_map(inst, suffix=mapped_col1_name, examples=map_examples, progress_bar_desc="Mapping examples") out = out.rename(columns={real_left_on: col1_label}) return out, mapped_col1_name @@ -530,6 +531,7 @@ def learn_join_cascade_threshold( examples_answers=examples_answers, cot_reasoning=cot_reasoning, strategy=strategy, + progress_bar_desc="Large model filter for threshold learning", ) (pos_threshold, neg_threshold), _ = learn_cascade_thresholds( @@ -577,6 +579,7 @@ def __call__( cascade_args: SemJoinCascadeArgs | None = None, return_stats: bool = False, safe_mode: bool = False, + progress_bar_desc: str = "Join comparisons", ) -> pd.DataFrame: """ Applies semantic join over a dataframe. @@ -704,6 +707,7 @@ def __call__( default=default, strategy=strategy, safe_mode=safe_mode, + progress_bar_desc=progress_bar_desc, ) join_results = output.join_results all_raw_outputs = output.all_raw_outputs diff --git a/lotus/sem_ops/sem_map.py b/lotus/sem_ops/sem_map.py index 0526e221..b2f2d4e0 100644 --- a/lotus/sem_ops/sem_map.py +++ b/lotus/sem_ops/sem_map.py @@ -20,6 +20,7 @@ def sem_map( cot_reasoning: list[str] | None = None, strategy: str | None = None, safe_mode: bool = False, + progress_bar_desc: str = "Mapping", ) -> SemanticMapOutput: """ Maps a list of documents to a list of outputs using a model. @@ -53,7 +54,7 @@ def sem_map( show_safe_mode(estimated_cost, estimated_LM_calls) # call model - lm_output: LMOutput = model(inputs) + lm_output: LMOutput = model(inputs, progress_bar_desc=progress_bar_desc) # post process results postprocess_output = postprocessor(lm_output.outputs, strategy in ["cot", "zs-cot"]) @@ -89,6 +90,7 @@ def __call__( examples: pd.DataFrame | None = None, strategy: str | None = None, safe_mode: bool = False, + progress_bar_desc: str = "Mapping", ) -> pd.DataFrame: """ Applies semantic map over a dataframe. @@ -137,6 +139,7 @@ def __call__( cot_reasoning=cot_reasoning, strategy=strategy, safe_mode=safe_mode, + progress_bar_desc=progress_bar_desc, ) new_df = self._obj.copy() diff --git a/lotus/sem_ops/sem_topk.py b/lotus/sem_ops/sem_topk.py index 19ae8750..9482a1de 100644 --- a/lotus/sem_ops/sem_topk.py +++ b/lotus/sem_ops/sem_topk.py @@ -146,7 +146,7 @@ def llm_naive_sort( llm_calls = len(pairs) pbar = tqdm( total=llm_calls, - desc="Processing uncached messages", + desc="All-pairs comparisons", bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} LM calls [{elapsed}<{remaining}]", ) comparisons, tokens = compare_batch_binary(pairs, user_instruction, strategy=strategy) @@ -260,7 +260,7 @@ def quicksort_recursive(indexes: list[int], low: int, high: int, K: int) -> None num_comparisons = high - low pbar = tqdm( total=num_comparisons, - desc="Processing uncached messages", + desc="Quicksort comparisons", bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} LM calls [{elapsed}<{remaining}]", ) pi = partition(indexes, low, high, K) @@ -295,7 +295,7 @@ def __lt__(self, other: "HeapDoc") -> bool: prompt = get_match_prompt_binary(self.doc, other.doc, self.user_instruction, strategy=self.strategy) HeapDoc.num_calls += 1 HeapDoc.total_tokens += lotus.settings.lm.count_tokens(prompt) - result: LMOutput = lotus.settings.lm([prompt]) + result: LMOutput = lotus.settings.lm([prompt], progress_bar_desc="Heap comparisons") return parse_ans_binary(result.outputs[0]) From 0f886eb9ecccac57f29940d476c8355fc29c41c8 Mon Sep 17 00:00:00 2001 From: liana313 Date: Mon, 9 Dec 2024 21:39:36 -0800 Subject: [PATCH 06/11] change description of bars --- lotus/sem_ops/sem_filter.py | 6 +++--- lotus/sem_ops/sem_join.py | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/lotus/sem_ops/sem_filter.py b/lotus/sem_ops/sem_filter.py index dc604bd9..81f4f2d1 100644 --- a/lotus/sem_ops/sem_filter.py +++ b/lotus/sem_ops/sem_filter.py @@ -104,7 +104,7 @@ def learn_filter_cascade_thresholds( cot_reasoning=cot_reasoning, strategy=strategy, safe_mode=False, - progress_bar_desc="Large model filter for threshold learning", + progress_bar_desc="Running oracle for threshold learning", ).outputs best_combination, _ = learn_cascade_thresholds( @@ -242,7 +242,7 @@ def __call__( strategy=helper_strategy, safe_mode=safe_mode, show_progress_bar=True, - progress_bar_desc="Small model filter", + progress_bar_desc="Running helper LM", ) helper_outputs, helper_logprobs = helper_output.outputs, helper_output.logprobs formatted_helper_logprobs: LogprobsForFilterCascade = ( @@ -325,7 +325,7 @@ def __call__( cot_reasoning=cot_reasoning, strategy=strategy, safe_mode=safe_mode, - progress_bar_desc="Large model filter", + progress_bar_desc="Running predicate evals with oracle LM", ) for idx, large_idx in enumerate(low_conf_idxs): diff --git a/lotus/sem_ops/sem_join.py b/lotus/sem_ops/sem_join.py index b763a038..67f2bdf0 100644 --- a/lotus/sem_ops/sem_join.py +++ b/lotus/sem_ops/sem_join.py @@ -143,7 +143,7 @@ def sem_join_cascade( safe_mode: bool = False, ) -> SemanticJoinOutput: """ - Joins two series using a cascade helper model and a large model. + Joins two series using a cascade helper model and a oracle model. Args: l1 (pd.Series): The first series. @@ -175,7 +175,7 @@ def sem_join_cascade( join_resolved_by_helper_model: total number of join records resolved by the helper model join_helper_positive: number of high confidence positive results from the helper model join_helper_negative: number of high confidence negative results from the helper model - join_resolved_by_large_model: total number of joins resolved by the large model + join_resolved_by_large_model: total number of joins resolved by the oracle model optimized_join_cost: number of LM calls from finding optimal join plan total_LM_calls: the total number of LM calls from join cascade, ie: optimized_join_cost + join_resolved_by_helper_model """ @@ -219,7 +219,7 @@ def sem_join_cascade( pbar = tqdm( total=num_large, - desc="Large model join comparisons", + desc="Running predicate evals with oracle model", bar_format="{l_bar}{bar} {n}/{total} LM calls [{elapsed}<{remaining}, {rate_fmt}{postfix}]", ) # Send low confidence rows to large LM @@ -531,7 +531,7 @@ def learn_join_cascade_threshold( examples_answers=examples_answers, cot_reasoning=cot_reasoning, strategy=strategy, - progress_bar_desc="Large model filter for threshold learning", + progress_bar_desc="Running oracle for threshold learning", ) (pos_threshold, neg_threshold), _ = learn_cascade_thresholds( From 833511fc84be822859c3b04283af5f214ea0b6b3 Mon Sep 17 00:00:00 2001 From: liana313 Date: Mon, 9 Dec 2024 22:03:00 -0800 Subject: [PATCH 07/11] fix to filter and join to be consistent api --- examples/op_examples/filter_cascade.py | 14 +++++------ examples/op_examples/join_cascade.py | 4 ++-- lotus/sem_ops/sem_filter.py | 33 ++++++++++++-------------- lotus/sem_ops/sem_join.py | 6 ++--- lotus/types.py | 2 +- 5 files changed, 27 insertions(+), 32 deletions(-) diff --git a/examples/op_examples/filter_cascade.py b/examples/op_examples/filter_cascade.py index 583fd78b..da00aacc 100644 --- a/examples/op_examples/filter_cascade.py +++ b/examples/op_examples/filter_cascade.py @@ -2,6 +2,8 @@ import lotus from lotus.models import LM +from lotus.types import CascadeArgs + gpt_35_turbo = LM("gpt-3.5-turbo") gpt_4o = LM("gpt-4o") @@ -116,13 +118,9 @@ } df = pd.DataFrame(data) user_instruction = "{Course Name} requires a lot of math" -df, stats = df.sem_filter( - user_instruction=user_instruction, - learn_cascade_threshold_sample_percentage=0.5, - recall_target=0.9, - precision_target=0.9, - failure_probability=0.2, - return_stats=True, -) + +cascade_args = CascadeArgs(recall_target=0.9, precision_target=0.9, sampling_percentage=0.5, failure_probability=0.2) + +df, stats = df.sem_filter(user_instruction=user_instruction, cascade_args=cascade_args, return_stats=True) print(df) print(stats) diff --git a/examples/op_examples/join_cascade.py b/examples/op_examples/join_cascade.py index f351dccf..05e3e562 100644 --- a/examples/op_examples/join_cascade.py +++ b/examples/op_examples/join_cascade.py @@ -2,7 +2,7 @@ import lotus from lotus.models import LM, SentenceTransformersRM -from lotus.types import SemJoinCascadeArgs +from lotus.types import CascadeArgs lm = LM(model="gpt-4o-mini") rm = SentenceTransformersRM(model="intfloat/e5-base-v2") @@ -124,7 +124,7 @@ df2 = pd.DataFrame(data2) join_instruction = "By taking {Course Name:left} I will learn {Skill:right}" -cascade_args = SemJoinCascadeArgs(recall_target=0.7, precision_target=0.7) +cascade_args = CascadeArgs(recall_target=0.7, precision_target=0.7) res, stats = df1.sem_join(df2, join_instruction, cascade_args=cascade_args, return_stats=True) diff --git a/lotus/sem_ops/sem_filter.py b/lotus/sem_ops/sem_filter.py index 81f4f2d1..d56f454b 100644 --- a/lotus/sem_ops/sem_filter.py +++ b/lotus/sem_ops/sem_filter.py @@ -6,7 +6,7 @@ import lotus from lotus.templates import task_instructions -from lotus.types import LMOutput, LogprobsForFilterCascade, SemanticFilterOutput +from lotus.types import LMOutput, LogprobsForFilterCascade, SemanticFilterOutput, CascadeArgs from lotus.utils import show_safe_mode from .cascade_utils import calibrate_llm_logprobs, importance_sampling, learn_cascade_thresholds @@ -148,11 +148,7 @@ def __call__( examples: pd.DataFrame | None = None, helper_examples: pd.DataFrame | None = None, strategy: str | None = None, - helper_strategy: str | None = None, - learn_cascade_threshold_sample_percentage: int | None = None, - recall_target: float | None = None, - precision_target: float | None = None, - failure_probability: float | None = None, + cascade_args: CascadeArgs | None = None, return_stats: bool = False, safe_mode: bool = False, progress_bar_desc: str = "Filtering", @@ -168,11 +164,11 @@ def __call__( examples (pd.DataFrame | None): The examples dataframe. Defaults to None. helper_examples (pd.DataFrame | None): The helper examples dataframe. Defaults to None. strategy (str | None): The reasoning strategy. Defaults to None. - helper_strategy (str | None): The reasoning strategy for helper. Defaults to None. - learn_cascade_threshold_sample_size (Optional[int]): The percentage of samples from which to learn thresholds when cascading. - recall_target (float | None): The specified recall target when cascading. - precision_target (float | None): The specified precision target when cascading. - failure_probability (float | None): The specified failure probability for precision/recall targets when cascading. + cascade_args (CascadeArgs | None): The arguments for join cascade. Defaults to None. + recall_target (float | None): The target recall. Defaults to None. + precision_target (float | None): The target precision when cascading. Defaults to None. + sampling_percentage (float): The percentage of the data to sample when cascading. Defaults to 0.1. + failure_probability (float): The failure probability when cascading. Defaults to 0.2. return_stats (bool): Whether to return statistics. Defaults to False. Returns: @@ -182,6 +178,7 @@ def __call__( lotus.logger.debug(user_instruction) col_li = lotus.nl_expression.parse_cols(user_instruction) lotus.logger.debug(col_li) + helper_strategy = strategy # check that column exists for column in col_li: @@ -205,7 +202,7 @@ def __call__( cot_reasoning = examples["Reasoning"].tolist() pos_cascade_threshold, neg_cascade_threshold = None, None - if learn_cascade_threshold_sample_percentage is not None: + if cascade_args is not None: # Get few-shot examples for small LM helper_examples_multimodal_data = None helper_examples_answers = None @@ -218,12 +215,12 @@ def __call__( if helper_strategy == "cot": helper_cot_reasoning = examples["Reasoning"].tolist() - if learn_cascade_threshold_sample_percentage and lotus.settings.helper_lm: + if cascade_args and lotus.settings.helper_lm: if helper_strategy == "cot": lotus.logger.error("CoT not supported for helper models in cascades.") raise Exception - if recall_target is None or precision_target is None or failure_probability is None: + if cascade_args.recall_target is None or cascade_args.precision_target is None or cascade_args.failure_probability is None: lotus.logger.error( "Recall target, precision target, and confidence need to be specified for learned thresholds." ) @@ -251,7 +248,7 @@ def __call__( helper_true_probs = calibrate_llm_logprobs(formatted_helper_logprobs.true_probs) sample_indices, correction_factors = importance_sampling( - helper_true_probs, learn_cascade_threshold_sample_percentage + helper_true_probs, cascade_args.sampling_percentage ) sample_df = self._obj.loc[sample_indices] sample_multimodal_data = task_instructions.df2multimodal_info(sample_df, col_li) @@ -263,9 +260,9 @@ def __call__( lm=lotus.settings.lm, formatted_usr_instr=formatted_usr_instr, default=default, - recall_target=recall_target, - precision_target=precision_target, - delta=failure_probability / 2, + recall_target=cascade_args.recall_target, + precision_target=cascade_args.precision_target, + delta=cascade_args.failure_probability / 2, helper_true_probs=sample_helper_true_probs, sample_correction_factors=sample_correction_factors, examples_multimodal_data=examples_multimodal_data, diff --git a/lotus/sem_ops/sem_join.py b/lotus/sem_ops/sem_join.py index 67f2bdf0..8829dc5f 100644 --- a/lotus/sem_ops/sem_join.py +++ b/lotus/sem_ops/sem_join.py @@ -5,7 +5,7 @@ import lotus from lotus.templates import task_instructions -from lotus.types import SemanticJoinOutput, SemJoinCascadeArgs +from lotus.types import SemanticJoinOutput, CascadeArgs from lotus.utils import show_safe_mode from .cascade_utils import calibrate_sem_sim_join, importance_sampling, learn_cascade_thresholds @@ -576,7 +576,7 @@ def __call__( examples: pd.DataFrame | None = None, strategy: str | None = None, default: bool = True, - cascade_args: SemJoinCascadeArgs | None = None, + cascade_args: CascadeArgs | None = None, return_stats: bool = False, safe_mode: bool = False, progress_bar_desc: str = "Join comparisons", @@ -593,7 +593,7 @@ def __call__( examples (pd.DataFrame | None): The examples dataframe. Defaults to None. strategy (str | None): The reasoning strategy. Defaults to None. default (bool): The default value for the join in case of parsing errors. Defaults to True. - cascade_args (SemJoinCascadeArgs | None): The arguments for join cascade. Defaults to None. + cascade_args (CascadeArgs | None): The arguments for join cascade. Defaults to None. recall_target (float | None): The target recall. Defaults to None. precision_target (float | None): The target precision when cascading. Defaults to None. sampling_percentage (float): The percentage of the data to sample when cascading. Defaults to 0.1. diff --git a/lotus/types.py b/lotus/types.py index 12678ffd..f49391a9 100644 --- a/lotus/types.py +++ b/lotus/types.py @@ -87,7 +87,7 @@ class SemanticJoinOutput(StatsMixin): all_explanations: list[str | None] -class SemJoinCascadeArgs(BaseModel): +class CascadeArgs(BaseModel): recall_target: float | None = None precision_target: float | None = None sampling_percentage: float = 0.1 From ca191167603e92307888e6bf1d369f71d4c406de Mon Sep 17 00:00:00 2001 From: Sid Jha Date: Mon, 9 Dec 2024 22:07:24 -0800 Subject: [PATCH 08/11] Small updates --- .github/tests/lm_tests.py | 38 ++++++++++++++++++++++---------------- 1 file changed, 22 insertions(+), 16 deletions(-) diff --git a/.github/tests/lm_tests.py b/.github/tests/lm_tests.py index 2c772b86..c0f6c531 100644 --- a/.github/tests/lm_tests.py +++ b/.github/tests/lm_tests.py @@ -6,7 +6,7 @@ import lotus from lotus.models import LM, SentenceTransformersRM -from lotus.types import SemJoinCascadeArgs +from lotus.types import CascadeArgs ################################################################################ # Setup @@ -286,10 +286,12 @@ def test_filter_cascade(setup_models): def test_join_cascade(setup_models): models = setup_models rm = SentenceTransformersRM(model="intfloat/e5-base-v2") - lotus.settings.configure(lm=models["gpt-4o-mini"], - rm=rm, - min_join_cascade_size=10, # for smaller testings - cascade_IS_random_seed=42) + lotus.settings.configure( + lm=models["gpt-4o-mini"], + rm=rm, + min_join_cascade_size=10, # for smaller testings + cascade_IS_random_seed=42, + ) data1 = { "School": [ @@ -308,37 +310,41 @@ def test_join_cascade(setup_models): "Yale University", "Cornell University", "University of Pennsylvania", - ]} + ] + } data2 = {"School Type": ["Public School", "Private School"]} df1 = pd.DataFrame(data1) df2 = pd.DataFrame(data2) join_instruction = "{School} is a {School Type}" - expected_pairs = [("University of California, Berkeley", "Public School"), ("Stanford University", "Private School")] + expected_pairs = [ + ("University of California, Berkeley", "Public School"), + ("Stanford University", "Private School"), + ] # Cascade join joined_df, stats = df1.sem_join( - df2, join_instruction, - cascade_args=SemJoinCascadeArgs(recall_target=0.7, precision_target=0.7), - return_stats=True) + df2, join_instruction, cascade_args=CascadeArgs(recall_target=0.7, precision_target=0.7), return_stats=True + ) for pair in expected_pairs: school, school_type = pair - exists = ((joined_df['School'] == school) & (joined_df['School Type'] == school_type)).any() + exists = ((joined_df["School"] == school) & (joined_df["School Type"] == school_type)).any() assert exists, f"Expected pair {pair} does not exist in the dataframe!" assert stats["join_resolved_by_helper_model"] > 0, stats # All joins resolved by the large model joined_df, stats = df1.sem_join( - df2, join_instruction, - cascade_args=SemJoinCascadeArgs(recall_target=1.0, precision_target=1.0), - return_stats=True) + df2, join_instruction, cascade_args=CascadeArgs(recall_target=1.0, precision_target=1.0), return_stats=True + ) for pair in expected_pairs: school, school_type = pair - exists = ((joined_df['School'] == school) & (joined_df['School Type'] == school_type)).any() + exists = ((joined_df["School"] == school) & (joined_df["School Type"] == school_type)).any() assert exists, f"Expected pair {pair} does not exist in the dataframe!" - assert stats["join_resolved_by_large_model"] > stats["join_resolved_by_helper_model"], stats # helper negative still can still meet the precision target + assert ( + stats["join_resolved_by_large_model"] > stats["join_resolved_by_helper_model"] + ), stats # helper negative still can still meet the precision target assert stats["join_helper_positive"] == 0, stats From 052594485bbbc979002825d9591b4c7502f69881 Mon Sep 17 00:00:00 2001 From: Sid Jha Date: Mon, 9 Dec 2024 22:09:32 -0800 Subject: [PATCH 09/11] Small updates --- lotus/sem_ops/cascade_utils.py | 18 ++++++++++++------ lotus/sem_ops/sem_filter.py | 10 +++++++--- lotus/sem_ops/sem_join.py | 2 +- lotus/settings.py | 6 ++++-- lotus/types.py | 1 + 5 files changed, 25 insertions(+), 12 deletions(-) diff --git a/lotus/sem_ops/cascade_utils.py b/lotus/sem_ops/cascade_utils.py index 30995e93..a9cf2712 100644 --- a/lotus/sem_ops/cascade_utils.py +++ b/lotus/sem_ops/cascade_utils.py @@ -28,7 +28,7 @@ def importance_sampling( sample_size = int(sample_percentage * len(proxy_scores)) sample_indices = np.random.choice(indices, sample_size, p=sample_w) - correction_factors = (1/len(proxy_scores)) / w + correction_factors = (1 / len(proxy_scores)) / w return sample_indices, correction_factors @@ -65,8 +65,14 @@ def recall(pos_threshold: float, neg_threshold: float, sorted_pairs: list[tuple[ sent_to_oracle = [x for x in sorted_pairs if x[0] < pos_threshold and x[0] > neg_threshold] total_correct = sum(pair[1] * pair[2] for pair in sorted_pairs) recall = ( - sum(1 for x in helper_accepted if x[0] >= pos_threshold and x[1]) + sum(x[1] * x[2] for x in sent_to_oracle) - ) / total_correct if total_correct > 0 else 0.0 + ( + sum(1 for x in helper_accepted if x[0] >= pos_threshold and x[1]) + + sum(x[1] * x[2] for x in sent_to_oracle) + ) + / total_correct + if total_correct > 0 + else 0.0 + ) return recall def precision(pos_threshold: float, neg_threshold: float, sorted_pairs: list[tuple[float, bool, float]]) -> float: @@ -80,8 +86,7 @@ def precision(pos_threshold: float, neg_threshold: float, sorted_pairs: list[tup def calculate_tau_neg(sorted_pairs: list[tuple[float, bool, float]], tau_pos: float, recall_target: float) -> float: return max( - (x[0] for x in sorted_pairs[::-1] if recall(tau_pos, x[0], sorted_pairs) >= recall_target), - default=0 + (x[0] for x in sorted_pairs[::-1] if recall(tau_pos, x[0], sorted_pairs) >= recall_target), default=0 ) # Pair helper model probabilities with helper correctness and oracle answer @@ -135,6 +140,7 @@ def calculate_tau_neg(sorted_pairs: list[tuple[float, bool, float]], tau_pos: fl return best_combination, oracle_calls + def calibrate_sem_sim_join(true_score: list[float]) -> list[float]: true_score = list(np.clip(true_score, 0, 1)) - return true_score \ No newline at end of file + return true_score diff --git a/lotus/sem_ops/sem_filter.py b/lotus/sem_ops/sem_filter.py index d56f454b..50dc8784 100644 --- a/lotus/sem_ops/sem_filter.py +++ b/lotus/sem_ops/sem_filter.py @@ -6,7 +6,7 @@ import lotus from lotus.templates import task_instructions -from lotus.types import LMOutput, LogprobsForFilterCascade, SemanticFilterOutput, CascadeArgs +from lotus.types import CascadeArgs, LMOutput, LogprobsForFilterCascade, SemanticFilterOutput from lotus.utils import show_safe_mode from .cascade_utils import calibrate_llm_logprobs, importance_sampling, learn_cascade_thresholds @@ -178,7 +178,7 @@ def __call__( lotus.logger.debug(user_instruction) col_li = lotus.nl_expression.parse_cols(user_instruction) lotus.logger.debug(col_li) - helper_strategy = strategy + helper_strategy = strategy # check that column exists for column in col_li: @@ -220,7 +220,11 @@ def __call__( lotus.logger.error("CoT not supported for helper models in cascades.") raise Exception - if cascade_args.recall_target is None or cascade_args.precision_target is None or cascade_args.failure_probability is None: + if ( + cascade_args.recall_target is None + or cascade_args.precision_target is None + or cascade_args.failure_probability is None + ): lotus.logger.error( "Recall target, precision target, and confidence need to be specified for learned thresholds." ) diff --git a/lotus/sem_ops/sem_join.py b/lotus/sem_ops/sem_join.py index 8829dc5f..eaa5bd53 100644 --- a/lotus/sem_ops/sem_join.py +++ b/lotus/sem_ops/sem_join.py @@ -5,7 +5,7 @@ import lotus from lotus.templates import task_instructions -from lotus.types import SemanticJoinOutput, CascadeArgs +from lotus.types import CascadeArgs, SemanticJoinOutput from lotus.utils import show_safe_mode from .cascade_utils import calibrate_sem_sim_join, importance_sampling, learn_cascade_thresholds diff --git a/lotus/settings.py b/lotus/settings.py index 632046bb..d3e5135b 100644 --- a/lotus/settings.py +++ b/lotus/settings.py @@ -116,5 +116,7 @@ def __repr__(self) -> str: # set defaults settings = Settings() settings.configure(enable_cache=False) -settings.configure(cascade_IS_weight=0.5, cascade_num_calibration_quantiles=50) # Filter cascade settings -settings.configure(min_join_cascade_size=100, cascade_IS_max_sample_range=250, cascade_IS_random_seed=None) # Join cascade settings +settings.configure(cascade_IS_weight=0.5, cascade_num_calibration_quantiles=50) # Filter cascade settings +settings.configure( + min_join_cascade_size=100, cascade_IS_max_sample_range=250, cascade_IS_random_seed=None +) # Join cascade settings diff --git a/lotus/types.py b/lotus/types.py index f49391a9..d91bd72b 100644 --- a/lotus/types.py +++ b/lotus/types.py @@ -99,6 +99,7 @@ class CascadeArgs(BaseModel): class Config: arbitrary_types_allowed = True + class SemanticTopKOutput(StatsMixin): indexes: list[int] From bde84a81b6b44a29fcf0e13fd84fd9f5af175463 Mon Sep 17 00:00:00 2001 From: Sid Jha Date: Mon, 9 Dec 2024 22:11:27 -0800 Subject: [PATCH 10/11] Small updates --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 796088bc..494ded8c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -29,7 +29,7 @@ jobs: pip install ruff==0.7.2 - name: Run ruff - run: ruff check . + run: ruff check lotus/ mypy: name: Type Check From 878847fd36702aa8797afb5c3ea11e687cd822b0 Mon Sep 17 00:00:00 2001 From: Sid Jha Date: Mon, 9 Dec 2024 22:16:51 -0800 Subject: [PATCH 11/11] Small updates --- .github/tests/lm_tests.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/tests/lm_tests.py b/.github/tests/lm_tests.py index c0f6c531..f477ebd6 100644 --- a/.github/tests/lm_tests.py +++ b/.github/tests/lm_tests.py @@ -270,10 +270,12 @@ def test_filter_cascade(setup_models): # All filters resolved by the helper model filtered_df, stats = df.sem_filter( user_instruction=user_instruction, - learn_cascade_threshold_sample_percentage=0.5, - recall_target=0.9, - precision_target=0.9, - failure_probability=0.2, + cascade_args=CascadeArgs( + learn_cascade_threshold_sample_percentage=0.5, + recall_target=0.9, + precision_target=0.9, + failure_probability=0.2, + ), return_stats=True, )

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