Merge remote-tracking branch 'upstream/branch-24.10' into gpu_cpu_met…

…rics

java/src/main/java/ai/rapids/cudf/ColumnVector.java

@@ -218,7 +218,13 @@ static long initViewHandle(DType type, int numRows, int nullCount,
         od, vd, nullCount, numRows, childHandles);
   }
 
-  static ColumnVector fromViewWithContiguousAllocation(long columnViewAddress, DeviceMemoryBuffer buffer) {
+  /**
+   * Creates a ColumnVector from a native column_view using a contiguous device allocation.
+   *
+   * @param columnViewAddress address of the native column_view
+   * @param buffer device buffer containing the data referenced by the column view
+   */
+  public static ColumnVector fromViewWithContiguousAllocation(long columnViewAddress, DeviceMemoryBuffer buffer) {
     return new ColumnVector(columnViewAddress, buffer);
   }
 

java/src/main/java/ai/rapids/cudf/ParquetWriterOptions.java

@@ -1,6 +1,6 @@
 /*
  *
- *  Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *  Copyright (c) 2019-2024, NVIDIA CORPORATION.
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");
  *  you may not use this file except in compliance with the License.
@@ -24,9 +24,13 @@
  */
 public final class ParquetWriterOptions extends CompressionMetadataWriterOptions {
   private final StatisticsFrequency statsGranularity;
+  private int rowGroupSizeRows;
+  private long rowGroupSizeBytes;
 
   private ParquetWriterOptions(Builder builder) {
     super(builder);
+    this.rowGroupSizeRows = builder.rowGroupSizeRows;
+    this.rowGroupSizeBytes = builder.rowGroupSizeBytes;
     this.statsGranularity = builder.statsGranularity;
   }
 
@@ -51,18 +55,38 @@ public static Builder builder() {
     return new Builder();
   }
 
+  public int getRowGroupSizeRows() {
+    return rowGroupSizeRows;
+  }
+
+  public long getRowGroupSizeBytes() {
+    return rowGroupSizeBytes;
+  }
+
   public StatisticsFrequency getStatisticsFrequency() {
     return statsGranularity;
   }
 
   public static class Builder extends CompressionMetadataWriterOptions.Builder
         <Builder, ParquetWriterOptions> {
+    private int rowGroupSizeRows = 1000000; //Max of 1 million rows per row group
+    private long rowGroupSizeBytes = 128 * 1024 * 1024; //Max of 128MB per row group
     private StatisticsFrequency statsGranularity = StatisticsFrequency.ROWGROUP;
 
     public Builder() {
       super();
     }
 
+    public Builder withRowGroupSizeRows(int rowGroupSizeRows) {
+      this.rowGroupSizeRows = rowGroupSizeRows;
+      return this;
+    }
+
+    public Builder withRowGroupSizeBytes(long rowGroupSizeBytes) {
+      this.rowGroupSizeBytes = rowGroupSizeBytes;
+      return this;
+    }
+
     public Builder withStatisticsFrequency(StatisticsFrequency statsGranularity) {
       this.statsGranularity = statsGranularity;
       return this;

java/src/main/java/ai/rapids/cudf/Table.java

@@ -332,20 +332,22 @@ private static native long[] readAvroFromDataSource(String[] filterColumnNames,
 
   /**
    * Setup everything to write parquet formatted data to a file.
-   * @param columnNames     names that correspond to the table columns
-   * @param numChildren     Children of the top level
-   * @param flatNumChildren flattened list of children per column
-   * @param nullable        true if the column can have nulls else false
-   * @param metadataKeys    Metadata key names to place in the Parquet file
-   * @param metadataValues  Metadata values corresponding to metadataKeys
-   * @param compression     native compression codec ID
-   * @param statsFreq       native statistics frequency ID
-   * @param isInt96         true if timestamp type is int96
-   * @param precisions      precision list containing all the precisions of the decimal types in
-   *                        the columns
-   * @param isMapValues     true if a column is a map
-   * @param isBinaryValues  true if a column is a binary
-   * @param filename        local output path
+   * @param columnNames       names that correspond to the table columns
+   * @param numChildren       Children of the top level
+   * @param flatNumChildren   flattened list of children per column
+   * @param nullable          true if the column can have nulls else false
+   * @param metadataKeys      Metadata key names to place in the Parquet file
+   * @param metadataValues    Metadata values corresponding to metadataKeys
+   * @param compression       native compression codec ID
+   * @param rowGroupSizeRows  max #rows in a row group
+   * @param rowGroupSizeBytes max #bytes in a row group
+   * @param statsFreq         native statistics frequency ID
+   * @param isInt96           true if timestamp type is int96
+   * @param precisions        precision list containing all the precisions of the decimal types in
+   *                          the columns
+   * @param isMapValues       true if a column is a map
+   * @param isBinaryValues    true if a column is a binary
+   * @param filename          local output path
    * @return a handle that is used in later calls to writeParquetChunk and writeParquetEnd.
    */
   private static native long writeParquetFileBegin(String[] columnNames,
@@ -355,6 +357,8 @@ private static native long writeParquetFileBegin(String[] columnNames,
                                                    String[] metadataKeys,
                                                    String[] metadataValues,
                                                    int compression,
+                                                   int rowGroupSizeRows,
+                                                   long rowGroupSizeBytes,
                                                    int statsFreq,
                                                    boolean[] isInt96,
                                                    int[] precisions,
@@ -366,20 +370,22 @@ private static native long writeParquetFileBegin(String[] columnNames,
 
   /**
    * Setup everything to write parquet formatted data to a buffer.
-   * @param columnNames     names that correspond to the table columns
-   * @param numChildren     Children of the top level
-   * @param flatNumChildren flattened list of children per column
-   * @param nullable        true if the column can have nulls else false
-   * @param metadataKeys    Metadata key names to place in the Parquet file
-   * @param metadataValues  Metadata values corresponding to metadataKeys
-   * @param compression     native compression codec ID
-   * @param statsFreq       native statistics frequency ID
-   * @param isInt96         true if timestamp type is int96
-   * @param precisions      precision list containing all the precisions of the decimal types in
-   *                        the columns
-   * @param isMapValues     true if a column is a map
-   * @param isBinaryValues  true if a column is a binary
-   * @param consumer        consumer of host buffers produced.
+   * @param columnNames       names that correspond to the table columns
+   * @param numChildren       Children of the top level
+   * @param flatNumChildren   flattened list of children per column
+   * @param nullable          true if the column can have nulls else false
+   * @param metadataKeys      Metadata key names to place in the Parquet file
+   * @param metadataValues    Metadata values corresponding to metadataKeys
+   * @param compression       native compression codec ID
+   * @param rowGroupSizeRows  max #rows in a row group
+   * @param rowGroupSizeBytes max #bytes in a row group
+   * @param statsFreq         native statistics frequency ID
+   * @param isInt96           true if timestamp type is int96
+   * @param precisions        precision list containing all the precisions of the decimal types in
+   *                          the columns
+   * @param isMapValues       true if a column is a map
+   * @param isBinaryValues    true if a column is a binary
+   * @param consumer          consumer of host buffers produced.
    * @return a handle that is used in later calls to writeParquetChunk and writeParquetEnd.
    */
   private static native long writeParquetBufferBegin(String[] columnNames,
@@ -389,6 +395,8 @@ private static native long writeParquetBufferBegin(String[] columnNames,
                                                      String[] metadataKeys,
                                                      String[] metadataValues,
                                                      int compression,
+                                                     int rowGroupSizeRows,
+                                                     long rowGroupSizeBytes,
                                                      int statsFreq,
                                                      boolean[] isInt96,
                                                      int[] precisions,
@@ -1820,6 +1828,8 @@ private ParquetTableWriter(ParquetWriterOptions options, File outputFile) {
           options.getMetadataKeys(),
           options.getMetadataValues(),
           options.getCompressionType().nativeId,
+          options.getRowGroupSizeRows(),
+          options.getRowGroupSizeBytes(),
           options.getStatisticsFrequency().nativeId,
           options.getFlatIsTimeTypeInt96(),
           options.getFlatPrecision(),
@@ -1840,6 +1850,8 @@ private ParquetTableWriter(ParquetWriterOptions options, HostBufferConsumer cons
           options.getMetadataKeys(),
           options.getMetadataValues(),
           options.getCompressionType().nativeId,
+          options.getRowGroupSizeRows(),
+          options.getRowGroupSizeBytes(),
           options.getStatisticsFrequency().nativeId,
           options.getFlatIsTimeTypeInt96(),
           options.getFlatPrecision(),

java/src/main/native/src/TableJni.cpp

@@ -2150,6 +2150,8 @@ Java_ai_rapids_cudf_Table_writeParquetBufferBegin(JNIEnv* env,
                                                   jobjectArray j_metadata_keys,
                                                   jobjectArray j_metadata_values,
                                                   jint j_compression,
+                                                  jint j_row_group_size_rows,
+                                                  jlong j_row_group_size_bytes,
                                                   jint j_stats_freq,
                                                   jbooleanArray j_isInt96,
                                                   jintArray j_precisions,
@@ -2205,6 +2207,8 @@ Java_ai_rapids_cudf_Table_writeParquetBufferBegin(JNIEnv* env,
       chunked_parquet_writer_options::builder(sink)
         .metadata(std::move(metadata))
         .compression(static_cast<compression_type>(j_compression))
+        .row_group_size_rows(j_row_group_size_rows)
+        .row_group_size_bytes(j_row_group_size_bytes)
         .stats_level(static_cast<statistics_freq>(j_stats_freq))
         .key_value_metadata({kv_metadata})
         .compression_statistics(stats)
@@ -2227,6 +2231,8 @@ Java_ai_rapids_cudf_Table_writeParquetFileBegin(JNIEnv* env,
                                                 jobjectArray j_metadata_keys,
                                                 jobjectArray j_metadata_values,
                                                 jint j_compression,
+                                                jint j_row_group_size_rows,
+                                                jlong j_row_group_size_bytes,
                                                 jint j_stats_freq,
                                                 jbooleanArray j_isInt96,
                                                 jintArray j_precisions,
@@ -2280,6 +2286,8 @@ Java_ai_rapids_cudf_Table_writeParquetFileBegin(JNIEnv* env,
       chunked_parquet_writer_options::builder(sink)
         .metadata(std::move(metadata))
         .compression(static_cast<compression_type>(j_compression))
+        .row_group_size_rows(j_row_group_size_rows)
+        .row_group_size_bytes(j_row_group_size_bytes)
         .stats_level(static_cast<statistics_freq>(j_stats_freq))
         .key_value_metadata({kv_metadata})
         .compression_statistics(stats)

java/src/test/java/ai/rapids/cudf/TableTest.java

@@ -9122,7 +9122,11 @@ void testParquetWriteToBufferChunked() {
     columns.add(Columns.STRUCT.name);
     WriteUtils.buildWriterOptions(optBuilder, columns);
     ParquetWriterOptions options = optBuilder.build();
-    ParquetWriterOptions optionsNoCompress = optBuilder.withCompressionType(CompressionType.NONE).build();
+    ParquetWriterOptions optionsNoCompress =
+      optBuilder.withCompressionType(CompressionType.NONE)
+      .withRowGroupSizeRows(10000)
+      .withRowGroupSizeBytes(10000)
+      .build();
     try (Table table0 = getExpectedFileTable(columns);
          MyBufferConsumer consumer = new MyBufferConsumer()) {
       try (TableWriter writer = Table.writeParquetChunked(options, consumer)) {
@@ -9208,6 +9212,8 @@ void testParquetWriteToFileUncompressedNoStats() throws IOException {
           .withDecimalColumn("_c7", 4)
           .withDecimalColumn("_c8", 6)
           .withCompressionType(CompressionType.NONE)
+          .withRowGroupSizeRows(10000)
+          .withRowGroupSizeBytes(10000)
           .withStatisticsFrequency(ParquetWriterOptions.StatisticsFrequency.NONE)
           .build();
       try (TableWriter writer = Table.writeParquetChunked(options, tempFile.getAbsoluteFile())) {

python/cudf/cudf/core/reshape.py

@@ -738,7 +738,8 @@ def get_dummies(
     sparse : boolean, optional
         Right now this is NON-FUNCTIONAL argument in rapids.
     drop_first : boolean, optional
-        Right now this is NON-FUNCTIONAL argument in rapids.
+        Whether to get k-1 dummies out of k categorical levels by removing the
+        first level.
     columns : sequence of str, optional
         Names of columns to encode. If not provided, will attempt to encode all
         columns. Note this is different from pandas default behavior, which
@@ -806,9 +807,6 @@ def get_dummies(
     if sparse:
         raise NotImplementedError("sparse is not supported yet")
 
-    if drop_first:
-        raise NotImplementedError("drop_first is not supported yet")
-
     if isinstance(data, cudf.DataFrame):
         encode_fallback_dtypes = ["object", "category"]
 
@@ -862,6 +860,7 @@ def get_dummies(
                     prefix=prefix_map.get(name, prefix),
                     prefix_sep=prefix_sep_map.get(name, prefix_sep),
                     dtype=dtype,
+                    drop_first=drop_first,
                 )
                 result_data.update(col_enc_data)
             return cudf.DataFrame._from_data(result_data, index=data.index)
@@ -874,6 +873,7 @@ def get_dummies(
             prefix=prefix,
             prefix_sep=prefix_sep,
             dtype=dtype,
+            drop_first=drop_first,
         )
         return cudf.DataFrame._from_data(data, index=ser.index)
 
@@ -1256,6 +1256,7 @@ def _one_hot_encode_column(
     prefix: str | None,
     prefix_sep: str | None,
     dtype: Dtype | None,
+    drop_first: bool,
 ) -> dict[str, ColumnBase]:
     """Encode a single column with one hot encoding. The return dictionary
     contains pairs of (category, encodings). The keys may be prefixed with
@@ -1276,6 +1277,8 @@ def _one_hot_encode_column(
         )
     data = one_hot_encode(column, categories)
 
+    if drop_first and len(data):
+        data.pop(next(iter(data)))
     if prefix is not None and prefix_sep is not None:
         data = {f"{prefix}{prefix_sep}{col}": enc for col, enc in data.items()}
     if dtype:

python/cudf/cudf/tests/test_onehot.py

@@ -161,3 +161,20 @@ def test_get_dummies_cats_deprecated():
     df = cudf.DataFrame(range(3))
     with pytest.warns(FutureWarning):
         cudf.get_dummies(df, cats={0: [0, 1, 2]})
+
+
+def test_get_dummies_drop_first_series():
+    result = cudf.get_dummies(cudf.Series(list("abcaa")), drop_first=True)
+    expected = pd.get_dummies(pd.Series(list("abcaa")), drop_first=True)
+    assert_eq(result, expected)
+
+
+def test_get_dummies_drop_first_dataframe():
+    result = cudf.get_dummies(
+        cudf.DataFrame({"A": list("abcaa"), "B": list("bcaab")}),
+        drop_first=True,
+    )
+    expected = pd.get_dummies(
+        pd.DataFrame({"A": list("abcaa"), "B": list("bcaab")}), drop_first=True
+    )
+    assert_eq(result, expected)

python/dask_cudf/dask_cudf/expr/_collection.py

@@ -49,8 +49,24 @@ def to_dask_dataframe(self, **kwargs):
 
         return self.to_backend("pandas", **kwargs)
 
+    def _prepare_cov_corr(self, min_periods, numeric_only):
+        # Upstream version of this method sets min_periods
+        # to 2 by default (which is not supported by cudf)
+        # TODO: Remove when cudf supports both min_periods
+        # and numeric_only
+        # See: https://github.com/rapidsai/cudf/issues/12626
+        # See: https://github.com/rapidsai/cudf/issues/9009
+        self._meta.cov(min_periods=min_periods)
+
+        frame = self
+        if numeric_only:
+            numerics = self._meta._get_numeric_data()
+            if len(numerics.columns) != len(self.columns):
+                frame = frame[list(numerics.columns)]
+        return frame, min_periods
+
     # var can be removed if cudf#15179 is addressed.
-    # See: https://github.com/rapidsai/cudf/issues/15179
+    # See: https://github.com/rapidsai/cudf/issues/14935
     def var(
         self,
         axis=0,

python/dask_cudf/dask_cudf/tests/test_core.py

@@ -1007,3 +1007,20 @@ def test_to_backend_simplify():
         df2 = df.to_backend("cudf")[["y"]].simplify()
         df3 = df[["y"]].to_backend("cudf").to_backend("cudf").simplify()
         assert df2._name == df3._name
+
+
+@pytest.mark.parametrize("numeric_only", [True, False])
+@pytest.mark.parametrize("op", ["corr", "cov"])
+def test_cov_corr(op, numeric_only):
+    df = cudf.DataFrame.from_dict(
+        {
+            "x": np.random.randint(0, 5, size=10),
+            "y": np.random.normal(size=10),
+        }
+    )
+    ddf = dd.from_pandas(df, npartitions=2)
+    res = getattr(ddf, op)(numeric_only=numeric_only)
+    # Use to_pandas until cudf supports numeric_only
+    # (See: https://github.com/rapidsai/cudf/issues/12626)
+    expect = getattr(df.to_pandas(), op)(numeric_only=numeric_only)
+    dd.assert_eq(res, expect)