TransferQueue/tests/test_controller.py at dev · TransferQueue/TransferQueue · GitHub

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import logging

import sys

from pathlib import Path

import pytest

import ray

import torch

parent_dir = Path(__file__).resolve().parent.parent

sys.path.append(str(parent_dir))

# Set up logging

logging.basicConfig(level=logging.INFO)

logger = logging.getLogger(__name__)

from transfer_queue import TransferQueueController # noqa: E402

from transfer_queue.utils.utils import ProductionStatus # noqa: E402

@pytest.fixture(scope="function")

def ray_setup():

if ray.is_initialized():

ray.shutdown()

ray.init(

ignore_reinit_error=True,

runtime_env={"env_vars": {"RAY_DEBUG": "1", "RAY_DEDUP_LOGS": "0"}},

log_to_driver=True,

)

yield

if ray.is_initialized():

ray.shutdown()

logger.info("Ray has been shut down completely after test")

class TestTransferQueueController:

def test_controller_with_single_partition(self, ray_setup):

gbs = 8

num_n_samples = 4

tq_controller = TransferQueueController.remote()

# Test get metadata in insert mode

partition_id = "train_0"

data_fields = ["prompt_ids", "attention_mask"]

metadata = ray.get(

tq_controller.get_metadata.remote(

data_fields=data_fields,

batch_size=gbs * num_n_samples,

partition_id=partition_id,

mode="insert",

)

assert metadata.global_indexes == list(range(gbs * num_n_samples))

assert metadata.samples[0].partition_id == "train_0"

assert sum([int(sample.fields.get("prompt_ids").production_status) for sample in metadata.samples]) == int(

ProductionStatus.NOT_PRODUCED

)

assert sum([int(sample.fields.get("attention_mask").production_status) for sample in metadata.samples]) == int(

ProductionStatus.NOT_PRODUCED

)

partition_index_range = ray.get(tq_controller.get_partition_index_range.remote(partition_id))

assert partition_index_range == set(range(gbs * num_n_samples))

print("✓ Initial get metadata correct")

# Test update production status

dtypes = {k: {"prompt_ids": "torch.int64", "attention_mask": "torch.bool"} for k in metadata.global_indexes}

shapes = {k: {"prompt_ids": (32,), "attention_mask": (32,)} for k in metadata.global_indexes}

success = ray.get(

tq_controller.update_production_status.remote(

partition_id=partition_id,

global_indexes=metadata.global_indexes,

field_names=metadata.field_names,

dtypes=dtypes,

shapes=shapes,

)

assert success

partition = ray.get(tq_controller.get_partition_snapshot.remote(partition_id))

assert partition.production_status is not None

assert partition.production_status.size(0) == gbs * num_n_samples

# Test for get production status

production_status = ray.get(

tq_controller.get_production_status.remote(

partition_id=partition_id,

data_fields=data_fields,

)

assert production_status

# Total fields should match the number of fields we added

assert partition.total_fields_num == len(data_fields)

# Allocated fields should be at least the number of actual fields

assert partition.allocated_fields_num >= partition.total_fields_num

# Check production status for the fields we added

assert torch.equal(

sum(partition.production_status[:, : len(data_fields)]),

torch.Tensor([gbs * num_n_samples, gbs * num_n_samples]),

)

# Any additional allocated fields should be zero (unused)

if partition.allocated_fields_num > len(data_fields):

assert torch.equal(

sum(partition.production_status[:, len(data_fields) :]),

torch.zeros(1 * (partition.allocated_fields_num - len(data_fields))),

)

print(f"✓ Updated production status for partition {partition_id}")

# Test for get consumption status

consumption_status = ray.get(

tq_controller.get_consumption_status.remote(

partition_id=partition_id,

task_name="generate_sequences",

)

assert torch.equal(consumption_status, torch.zeros(gbs * num_n_samples))

# Test get metadate in fetch mode

gen_meta = ray.get(

tq_controller.get_metadata.remote(

data_fields=["prompt_ids"],

batch_size=gbs * num_n_samples,

partition_id=partition_id,

mode="fetch",

task_name="generate_sequences",

)

assert gen_meta.global_indexes == list(range(gbs * num_n_samples))

assert gen_meta.samples[0].partition_id == "train_0"

assert gen_meta.field_names == ["prompt_ids"]

partition = ray.get(tq_controller.get_partition_snapshot.remote(partition_id))

assert torch.equal(partition.consumption_status["generate_sequences"], torch.ones(gbs * num_n_samples))

print("✓ Get metadata in fetch mode correct")

# Test for get consumption status

consumption_status = ray.get(

tq_controller.get_consumption_status.remote(

partition_id=partition_id,

task_name="generate_sequences",

)

assert torch.equal(consumption_status, torch.ones(gbs * num_n_samples))

# Test get clear meta

clear_meta = ray.get(

tq_controller.get_metadata.remote(

data_fields=[],

partition_id=partition_id,

mode="insert",

)

assert clear_meta.global_indexes == list(range(gbs * num_n_samples))

assert [sample.fields for sample in clear_meta.samples] == [{}] * (gbs * num_n_samples)

print("✓ Clear metadata correct")

# Test clear_partition

ray.get(tq_controller.clear_partition.remote(partition_id))

partition = ray.get(tq_controller.get_partition_snapshot.remote(partition_id))

partition_index_range = ray.get(tq_controller.get_partition_index_range.remote(partition_id))

assert partition_index_range == set()

assert partition is None

print("✓ Clear partition correct")

def test_controller_with_multi_partitions(self, ray_setup):

gbs_1 = 8

num_n_samples_1 = 4

partition_id_1 = "train_0"

gbs_2 = 16

num_n_samples_2 = 1

partition_id_2 = "val_0"

gbs_3 = 32

num_n_samples_3 = 2

partition_id_3 = "train_1"

tq_controller = TransferQueueController.remote()

# Test get metadata in insert mode

data_fields = ["prompt_ids", "attention_mask"]

metadata = ray.get(

tq_controller.get_metadata.remote(

data_fields=data_fields,

batch_size=gbs_1 * num_n_samples_1,

partition_id=partition_id_1,

mode="insert",

)

# Test update production status

dtypes = {k: {"prompt_ids": "torch.int64", "attention_mask": "torch.bool"} for k in metadata.global_indexes}

shapes = {k: {"prompt_ids": (32,), "attention_mask": (32,)} for k in metadata.global_indexes}

success = ray.get(

tq_controller.update_production_status.remote(

partition_id=partition_id_1,

global_indexes=metadata.global_indexes,

field_names=metadata.field_names,

dtypes=dtypes,

shapes=shapes,

)

assert success

# Test get metadate in fetch mode

gen_meta = ray.get(

tq_controller.get_metadata.remote(

data_fields=["prompt_ids"],

batch_size=gbs_1 * num_n_samples_1,

partition_id=partition_id_1,

mode="fetch",

task_name="generate_sequences",

)

assert gen_meta

# Test get clear meta

clear_meta = ray.get(

tq_controller.get_metadata.remote(

data_fields=[],

partition_id=partition_id_1,

mode="insert",

)

assert clear_meta

# =========================partition 2=============================#

data_fields = ["prompt_ids", "attention_mask"]

val_metadata = ray.get(

tq_controller.get_metadata.remote(

data_fields=data_fields,

batch_size=gbs_2 * num_n_samples_2,

partition_id=partition_id_2,

mode="insert",

)

part1_index_range = gbs_1 * num_n_samples_1

part2_index_range = gbs_2 * num_n_samples_2

assert val_metadata.global_indexes == list(range(part1_index_range, part2_index_range + part1_index_range))

assert val_metadata.samples[0].partition_id == "val_0"

assert sum([int(sample.fields.get("prompt_ids").production_status) for sample in val_metadata.samples]) == int(

ProductionStatus.NOT_PRODUCED

)

assert sum(

[int(sample.fields.get("attention_mask").production_status) for sample in val_metadata.samples]

) == int(ProductionStatus.NOT_PRODUCED)

partition_index_range = ray.get(tq_controller.get_partition_index_range.remote(partition_id_2))

assert partition_index_range == set(range(part1_index_range, part2_index_range + part1_index_range))

# Update production status

dtypes = {k: {"prompt_ids": "torch.int64", "attention_mask": "torch.bool"} for k in val_metadata.global_indexes}

shapes = {k: {"prompt_ids": (32,), "attention_mask": (32,)} for k in val_metadata.global_indexes}

success = ray.get(

tq_controller.update_production_status.remote(

partition_id=partition_id_2,

global_indexes=val_metadata.global_indexes,

field_names=val_metadata.field_names,

dtypes=dtypes,

shapes=shapes,

)

assert success

# Clear partition 1

partition_index_range_1 = ray.get(tq_controller.get_partition_index_range.remote(partition_id_1))

assert partition_index_range_1

ray.get(tq_controller.clear_partition.remote(partition_id_1))

partition_1_after_clear = ray.get(tq_controller.get_partition_snapshot.remote(partition_id_1))

partition_index_range_1_after_clear = ray.get(tq_controller.get_partition_index_range.remote(partition_id_1))

assert not partition_index_range_1_after_clear

assert partition_1_after_clear is None

assert partition_index_range_1_after_clear == set()

partition_2 = ray.get(tq_controller.get_partition_snapshot.remote(partition_id_2))

partition_index_range_2 = ray.get(tq_controller.get_partition_index_range.remote(partition_id_2))

assert partition_index_range_2 == set([32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47])

assert torch.all(

partition_2.production_status[list(partition_index_range_2), : len(val_metadata.field_names)] == 1

)

print("✓ Only clear partition 1 correct")

# =========================partition 3=============================#

metadata_2 = ray.get(

tq_controller.get_metadata.remote(

data_fields=data_fields,

batch_size=gbs_3 * num_n_samples_3,

partition_id=partition_id_3,

mode="insert",

)

assert metadata_2.global_indexes == list(range(32)) + list(range(48, 80))

assert metadata_2.samples[0].partition_id == "train_1"

assert sum([int(sample.fields.get("prompt_ids").production_status) for sample in metadata_2.samples]) == int(

ProductionStatus.NOT_PRODUCED

)

assert sum(

[int(sample.fields.get("attention_mask").production_status) for sample in metadata_2.samples]

) == int(ProductionStatus.NOT_PRODUCED)

partition_index_range = ray.get(tq_controller.get_partition_index_range.remote(partition_id_3))

assert partition_index_range == set(list(range(32)) + list(range(48, 80)))

print("✓ Correctly assign partition_3")

def test_controller_clear_meta(self, ray_setup):

"""Test clear_meta functionality for individual samples"""

gbs = 4

num_n_samples = 2

partition_id = "test_clear_meta"

tq_controller = TransferQueueController.remote()

# Create metadata in insert mode

data_fields = ["prompt_ids", "attention_mask"]

metadata = ray.get(

tq_controller.get_metadata.remote(

data_fields=data_fields,

batch_size=gbs * num_n_samples,

partition_id=partition_id,

mode="insert",

)

assert metadata.global_indexes == list(range(gbs * num_n_samples))

# Update production status

dtypes = {k: {"prompt_ids": "torch.int64", "attention_mask": "torch.bool"} for k in metadata.global_indexes}

shapes = {k: {"prompt_ids": (32,), "attention_mask": (32,)} for k in metadata.global_indexes}

success = ray.get(

tq_controller.update_production_status.remote(

partition_id=partition_id,

global_indexes=metadata.global_indexes,

field_names=metadata.field_names,

dtypes=dtypes,

shapes=shapes,

)

assert success

# Get partition snapshot before clear

partition_before = ray.get(tq_controller.get_partition_snapshot.remote(partition_id))

assert partition_before is not None

assert len(partition_before.global_indexes) == gbs * num_n_samples

assert set(partition_before.global_indexes) == set(range(gbs * num_n_samples))

# Test clear_meta - clear first 4 samples (indexes 0-3)

global_indexes_to_clear = [0, 1, 2, 3, 6]

partition_ids_to_clear = [partition_id] * len(global_indexes_to_clear)

ray.get(

tq_controller.clear_meta.remote(

global_indexes=global_indexes_to_clear,

partition_ids=partition_ids_to_clear,

)

# Check that only the cleared samples are affected

partition_after = ray.get(tq_controller.get_partition_snapshot.remote(partition_id))

assert partition_after is not None

# Verify production status is cleared for the specified indexes

assert set(partition_after.global_indexes) == set([4, 5, 7])

print("✓ Clear meta correct")

TransferQueue/tests/test_controller.py at dev · TransferQueue/TransferQueue · GitHub

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