Merge remote-tracking branch 'upstream/branch-24.06' into skip-dgx-tc…

…p-test-ucx-1.16

ci/release/update-version.sh

@@ -56,3 +56,8 @@ for FILE in .github/workflows/*.yaml; do
   sed_runner "/shared-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}"
 done
 sed_runner "s/RAPIDS_VERSION_NUMBER=\".*/RAPIDS_VERSION_NUMBER=\"${NEXT_SHORT_TAG}\"/g" ci/build_docs.sh
+
+# Docs referencing source code
+find docs/source/ -type f -name *.rst -print0 | while IFS= read -r -d '' filename; do
+    sed_runner "s|/branch-[^/]*/|/branch-${NEXT_SHORT_TAG}/|g" "${filename}"
+done

ci/test_python.sh

@@ -35,11 +35,16 @@ rapids-logger "Check GPU usage"
 nvidia-smi
 
 EXITCODE=0
-trap "EXITCODE=1" ERR
+set_exit_code() {
+    EXITCODE=$?
+    rapids-logger "Test failed with error ${EXITCODE}"
+}
+trap set_exit_code ERR
 set +e
 
-rapids-logger "pytest dask-cuda"
+rapids-logger "pytest dask-cuda (dask-expr)"
 pushd dask_cuda
+DASK_DATAFRAME__QUERY_PLANNING=True \
 DASK_CUDA_TEST_SINGLE_GPU=1 \
 DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \
 UCXPY_IFNAME=eth0 \
@@ -58,18 +63,56 @@ timeout 60m pytest \
   tests -k "not ucxx"
 popd
 
-rapids-logger "Run local benchmark"
+rapids-logger "pytest explicit-comms (legacy dd)"
+pushd dask_cuda
+DASK_DATAFRAME__QUERY_PLANNING=False \
+DASK_CUDA_TEST_SINGLE_GPU=1 \
+DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \
+UCXPY_IFNAME=eth0 \
+UCX_WARN_UNUSED_ENV_VARS=n \
+UCX_MEMTYPE_CACHE=n \
+timeout 30m pytest \
+  -vv \
+  --durations=0 \
+  --capture=no \
+  --cache-clear \
+  --junitxml="${RAPIDS_TESTS_DIR}/junit-dask-cuda-legacy.xml" \
+  --cov-config=../pyproject.toml \
+  --cov=dask_cuda \
+  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/dask-cuda-coverage-legacy.xml" \
+  --cov-report=term \
+  tests/test_explicit_comms.py -k "not ucxx"
+popd
+
+rapids-logger "Run local benchmark (dask-expr)"
+DASK_DATAFRAME__QUERY_PLANNING=True \
+python dask_cuda/benchmarks/local_cudf_shuffle.py \
+  --partition-size="1 KiB" \
+  -d 0 \
+  --runs 1 \
+  --backend dask
+
+DASK_DATAFRAME__QUERY_PLANNING=True \
+python dask_cuda/benchmarks/local_cudf_shuffle.py \
+  --partition-size="1 KiB" \
+  -d 0 \
+  --runs 1 \
+  --backend explicit-comms
+
+rapids-logger "Run local benchmark (legacy dd)"
+DASK_DATAFRAME__QUERY_PLANNING=False \
 python dask_cuda/benchmarks/local_cudf_shuffle.py \
   --partition-size="1 KiB" \
   -d 0 \
   --runs 1 \
   --backend dask
 
+DASK_DATAFRAME__QUERY_PLANNING=False \
 python dask_cuda/benchmarks/local_cudf_shuffle.py \
   --partition-size="1 KiB" \
   -d 0 \
   --runs 1 \
   --backend explicit-comms
 
-rapids-logger "Test script exiting with value: $EXITCODE"
+rapids-logger "Test script exiting with latest error code: $EXITCODE"
 exit ${EXITCODE}

dask_cuda/__init__.py

@@ -20,6 +20,18 @@
 from .proxify_device_objects import proxify_decorator, unproxify_decorator
 
 
+if dask.config.get("dataframe.query-planning", None) is not False and dask.config.get(
+    "explicit-comms", False
+):
+    raise NotImplementedError(
+        "The 'explicit-comms' config is not yet supported when "
+        "query-planning is enabled in dask. Please use the shuffle "
+        "API directly, or use the legacy dask-dataframe API "
+        "(set the 'dataframe.query-planning' config to `False`"
+        "before importing `dask.dataframe`).",
+    )
+
+
 # Monkey patching Dask to make use of explicit-comms when `DASK_EXPLICIT_COMMS=True`
 dask.dataframe.shuffle.rearrange_by_column = get_rearrange_by_column_wrapper(
     dask.dataframe.shuffle.rearrange_by_column

dask_cuda/benchmarks/local_cudf_merge.py

@@ -7,8 +7,7 @@
 import pandas as pd
 
 import dask
-from dask.base import tokenize
-from dask.dataframe.core import new_dd_object
+import dask.dataframe as dd
 from dask.distributed import performance_report, wait
 from dask.utils import format_bytes, parse_bytes
 
@@ -25,12 +24,20 @@
 # <https://gist.github.com/rjzamora/0ffc35c19b5180ab04bbf7c793c45955>
 
 
-def generate_chunk(i_chunk, local_size, num_chunks, chunk_type, frac_match, gpu):
+# Set default shuffle method to "tasks"
+if dask.config.get("dataframe.shuffle.method", None) is None:
+    dask.config.set({"dataframe.shuffle.method": "tasks"})
+
+
+def generate_chunk(input):
+    i_chunk, local_size, num_chunks, chunk_type, frac_match, gpu = input
+
     # Setting a seed that triggers max amount of comm in the two-GPU case.
     if gpu:
         import cupy as xp
 
         import cudf as xdf
+        import dask_cudf  # noqa: F401
     else:
         import numpy as xp
         import pandas as xdf
@@ -105,25 +112,25 @@ def get_random_ddf(chunk_size, num_chunks, frac_match, chunk_type, args):
 
     parts = [chunk_size for _ in range(num_chunks)]
     device_type = True if args.type == "gpu" else False
-    meta = generate_chunk(0, 4, 1, chunk_type, None, device_type)
+    meta = generate_chunk((0, 4, 1, chunk_type, None, device_type))
     divisions = [None] * (len(parts) + 1)
 
-    name = "generate-data-" + tokenize(chunk_size, num_chunks, frac_match, chunk_type)
-
-    graph = {
-        (name, i): (
-            generate_chunk,
-            i,
-            part,
-            len(parts),
-            chunk_type,
-            frac_match,
-            device_type,
-        )
-        for i, part in enumerate(parts)
-    }
-
-    ddf = new_dd_object(graph, name, meta, divisions)
+    ddf = dd.from_map(
+        generate_chunk,
+        [
+            (
+                i,
+                part,
+                len(parts),
+                chunk_type,
+                frac_match,
+                device_type,
+            )
+            for i, part in enumerate(parts)
+        ],
+        meta=meta,
+        divisions=divisions,
+    )
 
     if chunk_type == "build":
         if not args.no_shuffle:

dask_cuda/benchmarks/local_cudf_shuffle.py

@@ -8,8 +8,6 @@
 
 import dask
 import dask.dataframe
-from dask.dataframe.core import new_dd_object
-from dask.dataframe.shuffle import shuffle
 from dask.distributed import Client, performance_report, wait
 from dask.utils import format_bytes, parse_bytes
 
@@ -33,7 +31,7 @@
 
 
 def shuffle_dask(df, args):
-    result = shuffle(df, index="data", shuffle="tasks", ignore_index=args.ignore_index)
+    result = df.shuffle("data", shuffle_method="tasks", ignore_index=args.ignore_index)
     if args.backend == "dask-noop":
         result = as_noop(result)
     t1 = perf_counter()
@@ -94,18 +92,24 @@ def create_data(
         )
 
     # Create partition based to the specified partition distribution
-    dsk = {}
+    futures = []
     for i, part_size in enumerate(dist):
         for _ in range(part_size):
             # We use `client.submit` to control placement of the partition.
-            dsk[(name, len(dsk))] = client.submit(
-                create_df, chunksize, args.type, workers=[workers[i]], pure=False
+            futures.append(
+                client.submit(
+                    create_df, chunksize, args.type, workers=[workers[i]], pure=False
+                )
             )
-    wait(dsk.values())
+    wait(futures)
 
     df_meta = create_df(0, args.type)
-    divs = [None] * (len(dsk) + 1)
-    ret = new_dd_object(dsk, name, df_meta, divs).persist()
+    divs = [None] * (len(futures) + 1)
+    ret = dask.dataframe.from_delayed(
+        futures,
+        meta=df_meta,
+        divisions=divs,
+    ).persist()
     wait(ret)
 
     data_processed = args.in_parts * args.partition_size
@@ -254,7 +258,9 @@ def parse_args():
     ]
 
     return parse_benchmark_args(
-        description="Distributed shuffle (dask/cudf) benchmark", args_list=special_args
+        description="Distributed shuffle (dask/cudf) benchmark",
+        args_list=special_args,
+        check_explicit_comms=False,
     )
 
 

dask_cuda/benchmarks/utils.py

@@ -11,6 +11,7 @@
 import numpy as np
 import pandas as pd
 
+from dask import config
 from dask.distributed import Client, SSHCluster
 from dask.utils import format_bytes, format_time, parse_bytes
 from distributed.comm.addressing import get_address_host
@@ -47,7 +48,11 @@ def as_noop(dsk):
         raise RuntimeError("Requested noop computation but dask-noop not installed.")
 
 
-def parse_benchmark_args(description="Generic dask-cuda Benchmark", args_list=[]):
+def parse_benchmark_args(
+    description="Generic dask-cuda Benchmark",
+    args_list=[],
+    check_explicit_comms=True,
+):
     parser = argparse.ArgumentParser(description=description)
     worker_args = parser.add_argument_group(description="Worker configuration")
     worker_args.add_argument(
@@ -317,6 +322,24 @@ def parse_benchmark_args(description="Generic dask-cuda Benchmark", args_list=[]
     if args.multi_node and len(args.hosts.split(",")) < 2:
         raise ValueError("--multi-node requires at least 2 hosts")
 
+    # Raise error early if "explicit-comms" is not allowed
+    if (
+        check_explicit_comms
+        and args.backend == "explicit-comms"
+        and config.get(
+            "dataframe.query-planning",
+            None,
+        )
+        is not False
+    ):
+        raise NotImplementedError(
+            "The 'explicit-comms' config is not yet supported when "
+            "query-planning is enabled in dask. Please use the legacy "
+            "dask-dataframe API by setting the following environment "
+            "variable before executing:",
+            "    DASK_DATAFRAME__QUERY_PLANNING=False",
+        )
+
     return args
 
 

dask_cuda/explicit_comms/dataframe/shuffle.py

@@ -11,10 +11,12 @@
 import dask
 import dask.config
 import dask.dataframe
+import dask.dataframe as dd
 import dask.utils
 import distributed.worker
 from dask.base import tokenize
-from dask.dataframe.core import DataFrame, Series, _concat as dd_concat, new_dd_object
+from dask.dataframe import DataFrame, Series
+from dask.dataframe.core import _concat as dd_concat
 from dask.dataframe.shuffle import group_split_dispatch, hash_object_dispatch
 from distributed import wait
 from distributed.protocol import nested_deserialize, to_serialize
@@ -468,18 +470,19 @@ def shuffle(
         npartitions = df.npartitions
 
     # Step (a):
-    df = df.persist()  # Make sure optimizations are apply on the existing graph
+    df = df.persist()  # Make sure optimizations are applied on the existing graph
     wait([df])  # Make sure all keys has been materialized on workers
+    persisted_keys = [f.key for f in c.client.futures_of(df)]
     name = (
         "explicit-comms-shuffle-"
-        f"{tokenize(df, column_names, npartitions, ignore_index)}"
+        f"{tokenize(df, column_names, npartitions, ignore_index, batchsize)}"
     )
     df_meta: DataFrame = df._meta
 
     # Stage all keys of `df` on the workers and cancel them, which makes it possible
     # for the shuffle to free memory as the partitions of `df` are consumed.
     # See CommsContext.stage_keys() for a description of staging.
-    rank_to_inkeys = c.stage_keys(name=name, keys=df.__dask_keys__())
+    rank_to_inkeys = c.stage_keys(name=name, keys=persisted_keys)
     c.client.cancel(df)
 
     # Get batchsize
@@ -526,23 +529,26 @@ def shuffle(
     # TODO: can we do this without using `submit()` to avoid the overhead
     #       of creating a Future for each dataframe partition?
 
-    dsk = {}
+    futures = []
     for rank in ranks:
         for part_id in rank_to_out_part_ids[rank]:
-            dsk[(name, part_id)] = c.client.submit(
-                getitem,
-                shuffle_result[rank],
-                part_id,
-                workers=[c.worker_addresses[rank]],
+            futures.append(
+                c.client.submit(
+                    getitem,
+                    shuffle_result[rank],
+                    part_id,
+                    workers=[c.worker_addresses[rank]],
+                )
             )
 
     # Create a distributed Dataframe from all the pieces
-    divs = [None] * (len(dsk) + 1)
-    ret = new_dd_object(dsk, name, df_meta, divs).persist()
+    divs = [None] * (len(futures) + 1)
+    kwargs = {"meta": df_meta, "divisions": divs, "prefix": "explicit-comms-shuffle"}
+    ret = dd.from_delayed(futures, **kwargs).persist()
     wait([ret])
 
     # Release all temporary dataframes
-    for fut in [*shuffle_result.values(), *dsk.values()]:
+    for fut in [*shuffle_result.values(), *futures]:
         fut.release()
     return ret