feat: add warnings in s2s for bad inputs

dataquality/core/finish.py

@@ -40,8 +40,9 @@ def finish(
         via dq.metrics.get_data_embeddings(). Default True if a GPU is
         available, else default False.
     :param data_embs_col: Optional text col on which to compute data embeddings.
-        If not set, we default to 'text' which corresponds to the input text
-        Can also be set to `target` or `generated_output`
+        If not set, we default to 'text' which corresponds to the input text.
+        Can also be set to `target`, `generated_output` or any other column that is
+        logged as metadata.
     """
     a.log_function("dq/finish")
     if create_data_embs is None:

dataquality/integrations/seq2seq/core.py

@@ -121,8 +121,8 @@ def watch(
     """Seq2seq only. Log model generations for your run
 
     Iterates over a given dataset and logs the generations for each sample.
-    `model` must be an instance of transformers PreTrainedModel and have a `generate`
-    method.
+    To generate outputs, a model that is an instance of transformers PreTrainedModel
+     must be given and it must have a `generate` method.
 
     Unlike other watch functions, in this one we are just registering the model
     and generation config and not attaching any hooks to the model. We call it 'watch'
@@ -135,25 +135,24 @@ def watch(
         max_target_tokens=max_target_tokens,
     )
 
-    if model:
-        assert isinstance(
-            model, PreTrainedModel
-        ), "model must be an instance of transformers PreTrainedModel"
-        assert (
-            model.can_generate()
-        ), "model must contain a `generate` method for seq2seq"
-
-    if model_type == Seq2SeqModelType.decoder_only and not response_template:
-        raise GalileoException(
-            "You must specify a `response_template` when using Decoder-Only models."
-            " This is necessary to internally isolate the target response tokens."
-        )
-
-    if model_type == Seq2SeqModelType.encoder_decoder and response_template:
+    if model_type == Seq2SeqModelType.decoder_only:
+        if response_template is None:
+            raise GalileoException(
+                "You must specify a `response_template` when using Decoder-Only models."
+                " This is necessary to internally isolate the target response tokens."
+            )
+        elif not isinstance(response_template, list) or not all(
+            isinstance(token, int) for token in response_template
+        ):
+            raise GalileoException(
+                "The response template must already be tokenized and be a list of ints."
+            )
+    elif model_type == Seq2SeqModelType.encoder_decoder and response_template:
         warn(
             "The argument response_template is only used when working with "
             "DecoderOnly models. This value will be ignored."
         )
+
     seq2seq_logger_config.response_template = response_template
     seq2seq_logger_config.model = model
     seq2seq_logger_config.generation_config = generation_config
@@ -170,4 +169,13 @@ def watch(
 
         generation_splits_set.add(Split[split])
 
+    # A model of the correct type is required if we need to generate
+    if generation_splits:
+        assert isinstance(
+            model, PreTrainedModel
+        ), "model must be an instance of transformers PreTrainedModel"
+        assert (
+            model.can_generate()
+        ), "model must contain a `generate` method for seq2seq"
+
     seq2seq_logger_config.generation_splits = generation_splits_set

dataquality/schemas/seq2seq.py

@@ -72,22 +72,21 @@ class AlignedTokenData:
     token_label_offsets: List[List[Tuple[int, int]]]
     token_label_positions: List[List[Set[int]]]
 
-    def append(self, aligned_token_data: "AlignedTokenData") -> None:
+    def append(self, data: "AlignedTokenData") -> None:
         """Append offsets and positions for a *single* sample
 
-        Assumes that `sample_aligned_token_data` holds alignment info for
+        Assumes that `data` holds alignment info for
         a *single* data sample. As such, when appending to `token_label_offsets`
         and `token_label_positions` we remove the "batch" dimensions respectively.
             e.g.
-            >> sample_aligned_token_data.token_label_offsets[0]
+            >> data.token_label_offsets[0]
         """
         assert (
-            len(aligned_token_data.token_label_offsets) == 1
-            and len(aligned_token_data.token_label_positions) == 1
+            len(data.token_label_offsets) == 1 and len(data.token_label_positions) == 1
         )
 
-        self.token_label_offsets.append(aligned_token_data.token_label_offsets[0])
-        self.token_label_positions.append(aligned_token_data.token_label_positions[0])
+        self.token_label_offsets.append(data.token_label_offsets[0])
+        self.token_label_positions.append(data.token_label_positions[0])
 
 
 @dataclass

dataquality/utils/vaex.py

@@ -1,4 +1,5 @@
 import os
+import warnings
 from typing import Dict, List, Union
 
 import numpy as np
@@ -9,7 +10,7 @@
 
 from dataquality import config
 from dataquality.clients.objectstore import ObjectStore
-from dataquality.exceptions import GalileoException
+from dataquality.exceptions import GalileoException, GalileoWarning
 from dataquality.loggers.base_logger import BaseLoggerAttributes
 from dataquality.schemas.split import Split
 from dataquality.utils.cuda import (
@@ -185,12 +186,34 @@ def add_umap_pca_to_df(df: DataFrame, data_embs: bool = False) -> DataFrame:
 def create_data_embs_df(df: DataFrame, text_col: str, lazy: bool = True) -> DataFrame:
     """Runs sentence transformer on raw text to get off the shelf data embeddings
 
-    text_col can be passed in as "input" or "target" for Seq2Seq tasks
+    text_col can be passed in as "input" or "target" for Seq2Seq tasks.
+    text_col can also be any given metadata text column.
 
     :param df: The dataframe to get data embeddings for. Must have the text_col
     :param text_col: The column to use for calculating data embeddings
     :param lazy: If true, we lazily apply the model to encode the text
     """
+    # If the specified column doesn't exist, fall back on default columns
+    col_to_encode = text_col
+    if col_to_encode not in df.get_column_names():
+        col_to_encode = ""
+        for col in ["text", "input"]:
+            if col in df.get_column_names():
+                warnings.warn(
+                    f"Column `{text_col}` not found, `{col}` will be used for data "
+                    "embeddings",
+                    GalileoWarning,
+                )
+                col_to_encode = col
+                break
+
+    # If user specified col and default cols don't exist, raise error
+    if not col_to_encode:
+        raise GalileoException(
+            f"The specified column create_data_embs={text_col} in dq.finish does not "
+            "exist in the dataframe. Re-run dq.finish with an existing column name."
+        )
+
     # This import takes up to 25 seconds, so we don't want to eagerly import it
     import transformers
     from sentence_transformers import SentenceTransformer
@@ -208,15 +231,15 @@ def apply_sentence_transformer(text: pa.array) -> np.ndarray:
         )
 
     if lazy:
-        df_copy["emb"] = df_copy[text_col].apply_sentence_transformer()
+        df_copy["emb"] = df_copy[col_to_encode].apply_sentence_transformer()
         df_copy = df_copy[["id", "emb"]]
     else:
         import torch
 
         # Downcasts to float16 where possible, speeds up processing by 10 it/sec
         with torch.autocast("cuda"):
             df_copy["emb"] = data_model.encode(
-                df_copy[text_col].tolist(), show_progress_bar=True
+                df_copy[col_to_encode].tolist(), show_progress_bar=True
             ).astype(np.float32)
 
     return df_copy

tests/integrations/seq2seq/test_core.py

@@ -5,6 +5,7 @@
 from tokenizers.models import BPE
 from transformers import PreTrainedTokenizerFast
 
+from dataquality.exceptions import GalileoException
 from dataquality.integrations.seq2seq.core import set_tokenizer, watch
 from dataquality.schemas.seq2seq import Seq2SeqModelType
 from tests.conftest import TestSessionVariables, tokenizer_T5, tokenizer_T5_not_auto
@@ -56,10 +57,9 @@ def test_set_tokenizer_other(
     )
     with pytest.raises(ValueError) as e:
         set_tokenizer(tokenizer_T5_not_auto, "encoder_decoder")
-        assert str(e.value) == (
-            "The tokenizer must be an instance of PreTrainedTokenizerFast "
-            "or Tokenizer"
-        )
+    assert str(e.value) == (
+        "The tokenizer must be an instance of PreTrainedTokenizerFast " "or Tokenizer"
+    )
 
 
 def test_watch_invalid_task_type(
@@ -71,22 +71,52 @@ def test_watch_invalid_task_type(
     set_test_config(task_type="text_classification")
     with pytest.raises(AssertionError) as e:
         watch(tokenizer_T5, "encoder_decoder")
-        assert str(e.value) == (
-            "This method is only supported for seq2seq tasks. "
-            "Make sure to set the task type with dq.init()"
-        )
+    assert str(e.value) == (
+        "This method is only supported for seq2seq tasks. "
+        "Make sure to set the task type with dq.init()"
+    )
 
 
 def test_watch_invalid_model_type(
     set_test_config: Callable,
     cleanup_after_use: Callable,
     test_session_vars: TestSessionVariables,
 ) -> None:
-    """Test that we can't watch without a tokenizer"""
+    """Test that we can't watch without an appropriate model_type"""
     set_test_config(task_type="seq2seq")
     with pytest.raises(ValueError) as e:
         watch(tokenizer_T5, "invalid_model_type")
-        assert str(e.value) == (
-            f"model_type must be one of {Seq2SeqModelType.members()}, "
-            "got invalid_model_type"
-        )
+    assert str(e.value) == (
+        f"model_type must be one of {Seq2SeqModelType.members()}, "
+        "got invalid_model_type"
+    )
+
+
+def test_watch_response_template_required(
+    set_test_config: Callable,
+    cleanup_after_use: Callable,
+    test_session_vars: TestSessionVariables,
+) -> None:
+    """Test that we need response template to be passed for decoder-only"""
+    set_test_config(task_type="seq2seq")
+    with pytest.raises(GalileoException) as e:
+        watch(tokenizer_T5, "decoder_only")
+    assert str(e.value) == (
+        "You must specify a `response_template` when using Decoder-Only models."
+        " This is necessary to internally isolate the target response tokens."
+    )
+
+
+def test_watch_response_template_not_tokenized(
+    set_test_config: Callable,
+    cleanup_after_use: Callable,
+    test_session_vars: TestSessionVariables,
+) -> None:
+    """Test that we need response template to be tokenized (for decoder-only)"""
+    set_test_config(task_type="seq2seq")
+    with pytest.raises(GalileoException) as e:
+        watch(tokenizer_T5, "decoder_only", response_template="###\nRESPONSE:\n")
+    assert (
+        str(e.value)
+        == "The response template must already be tokenized and be a list of ints."
+    )

tests/loggers/test_seq2seq.py

@@ -12,6 +12,7 @@
 from transformers import GenerationConfig, T5ForConditionalGeneration
 
 import dataquality as dq
+from dataquality.exceptions import GalileoWarning
 from dataquality.integrations.seq2seq.core import set_tokenizer, watch
 from dataquality.loggers.data_logger.base_data_logger import DataSet
 from dataquality.loggers.data_logger.seq2seq.seq2seq_base import Seq2SeqDataLogger
@@ -28,7 +29,7 @@
     generate_sample_output,
 )
 from dataquality.utils.thread_pool import ThreadPoolManager
-from dataquality.utils.vaex import GALILEO_DATA_EMBS_ENCODER
+from dataquality.utils.vaex import GALILEO_DATA_EMBS_ENCODER, create_data_embs_df
 from tests.conftest import (
     LOCAL_MODEL_PATH,
     TestSessionVariables,
@@ -340,7 +341,6 @@ def test_tokenize_input_provide_maxlength(
     set_test_config: Callable,
     cleanup_after_use: Generator,
 ) -> None:
-    # TODO comment!
     """
     Test that as we generate output and the user provided the max_input_tokens argument,
     the input is tokenized correctly to the length set by max_input_tokens.
@@ -558,3 +558,94 @@ def test_create_and_upload_data_embs(
     assert data_embs.emb.values.ndim == 2
     # mini BERT model spits out 32 dims
     assert data_embs.emb.values.shape == (10, 32)
+
+
+def test_create_data_embs_df_bad_text_col_name(
+    cleanup_after_use: Callable,
+    set_test_config: Callable,
+    test_session_vars: TestSessionVariables,
+) -> None:
+    """Test data embeddings flow works with bad col name
+
+    When the wrong column name is passed, test that the flow still works
+    using the default value of "input".
+    """
+    set_test_config(task_type=TaskType.seq2seq)
+    # Use the local mini bert model
+    os.environ[GALILEO_DATA_EMBS_ENCODER] = LOCAL_MODEL_PATH
+
+    watch(tokenizer_T5, "encoder_decoder", generation_splits=[])
+
+    input_1, input_2 = "dog dog dog done - tricked you", "bird"
+    target_1, target_2 = "cat cat cat cat cat done", "cat"
+    ds = Dataset.from_dict(
+        {
+            "id": [0, 1],
+            "inpinp": [input_1, input_2],
+            "tongtong": [target_1, target_2],
+        }
+    )
+    data_logger = Seq2SeqDataLogger()
+    data_logger.log_dataset(ds, text="inpinp", label="tongtong", split="training")
+
+    df = vaex.open(f"{data_logger.input_data_path}/**/data*.arrow")
+
+    # Check that no exception is thrown and that data embs are created
+    assert "text" not in df.get_column_names()
+    with pytest.warns(GalileoWarning) as gw:
+        data_embs = create_data_embs_df(df, text_col="text")
+
+    warning_msg = "Column `text` not found, `input` will be used for data embeddings"
+    assert str(list(gw)[0].message) == warning_msg
+
+    assert len(data_embs) == 2
+    assert data_embs.get_column_names() == ["id", "emb"]
+    assert isinstance(data_embs.emb.values, np.ndarray)
+    assert data_embs.emb.values.ndim == 2
+    # mini BERT model spits out 32 dims
+    assert data_embs.emb.values.shape == (2, 32)
+
+
+def test_create_data_embs_df_custom_column(
+    cleanup_after_use: Callable,
+    set_test_config: Callable,
+    test_session_vars: TestSessionVariables,
+) -> None:
+    """Test that data embeddings work with a column specified by the user"""
+    set_test_config(task_type=TaskType.seq2seq)
+    # Use the local mini bert model
+    os.environ[GALILEO_DATA_EMBS_ENCODER] = LOCAL_MODEL_PATH
+
+    watch(tokenizer_T5, "encoder_decoder", generation_splits=[])
+
+    input_1, input_2 = "dog dog dog done - tricked you", "bird"
+    target_1, target_2 = "cat cat cat cat cat done", "cat"
+    other = "just some text"
+    ds = Dataset.from_dict(
+        {
+            "id": [0, 1],
+            "inpinp": [input_1, input_2],
+            "tartar": [target_1, target_2],
+            "other": [other, other],
+        }
+    )
+    data_logger = Seq2SeqDataLogger()
+    data_logger.log_dataset(
+        ds, text="inpinp", label="tartar", split="training", meta=["other"]
+    )
+
+    df = vaex.open(f"{data_logger.input_data_path}/**/data*.arrow")
+
+    # Check that no exception is thrown and that data embs are created
+    assert "text" not in df.get_column_names()
+    with pytest.warns(None):
+        data_embs = create_data_embs_df(df, text_col="other")
+
+    assert len(data_embs) == 2
+    assert data_embs.get_column_names() == ["id", "emb"]
+    np_emb = data_embs.emb.values
+    assert isinstance(np_emb, np.ndarray)
+    # mini BERT model spits out 32 dims
+    assert np_emb.shape == (2, 32)
+    # Check that the two embeddings are the same, i.e., we used the column "other"
+    assert np.isclose(np_emb[0], np_emb[1]).all()