diff --git a/nemo_curator/stages/text/embedders/base.py b/nemo_curator/stages/text/embedders/base.py index 5d5d89fed5..5fde0ea901 100644 --- a/nemo_curator/stages/text/embedders/base.py +++ b/nemo_curator/stages/text/embedders/base.py @@ -18,6 +18,8 @@ import pandas as pd import torch import torch.nn.functional as F # noqa: N812 +from loguru import logger +from sentence_transformers import SentenceTransformer from transformers import AutoModel from nemo_curator.backends.base import WorkerMetadata @@ -98,9 +100,49 @@ def _get_last_token(self, model_output: torch.Tensor, attention_mask: torch.Tens return F.normalize(last_token_embeddings, dim=1) +class SentenceTransformerEmbeddingModelStage(EmbeddingModelStage): + def __init__( # noqa: PLR0913 + self, + model_identifier: str, + embedding_field: str = "embeddings", + hf_token: str | None = None, + model_inference_batch_size: int = 1024, + has_seq_order: bool = True, + padding_side: Literal["left", "right"] = "right", + autocast: bool = True, + ): + super().__init__( + model_identifier=model_identifier, + hf_token=hf_token, + model_inference_batch_size=model_inference_batch_size, + has_seq_order=has_seq_order, + padding_side=padding_side, + autocast=autocast, + ) + # Override unpack_inference_batch to False as SentenceTransformer expects a dictionary input + self.unpack_inference_batch = False + self.embedding_field = embedding_field + + def outputs(self) -> tuple[list[str], list[str]]: + return ["data"], [self.embedding_field] + + def setup(self, _: WorkerMetadata | None = None) -> None: + """Load the model for inference.""" + self.model = SentenceTransformer(self.model_identifier, local_files_only=True) + self.model.eval().to("cuda") + + def process_model_output( + self, + outputs: torch.Tensor, + model_input_batch: dict[str, torch.Tensor] | None = None, # noqa: ARG002 + ) -> torch.Tensor: + return outputs["sentence_embedding"].cpu() + + @dataclass(kw_only=True) class EmbeddingCreatorStage(CompositeStage[DocumentBatch, DocumentBatch]): model_identifier: str = "sentence-transformers/all-MiniLM-L6-v2" + use_sentence_transformer: bool = True text_field: str = "text" embedding_field: str = "embeddings" max_chars: int | None = None @@ -115,6 +157,16 @@ class EmbeddingCreatorStage(CompositeStage[DocumentBatch, DocumentBatch]): def __post_init__(self) -> None: super().__init__() + model_class = SentenceTransformerEmbeddingModelStage if self.use_sentence_transformer else EmbeddingModelStage + + if self.use_sentence_transformer: + logger.warning("Using SentenceTransformer for embedding model ignoring embedding_pooling") + model_additional_kwargs = {} + else: + model_additional_kwargs = { + "pooling": self.embedding_pooling, + } + self.stages = [ TokenizerStage( model_identifier=self.model_identifier, @@ -125,15 +177,15 @@ def __post_init__(self) -> None: padding_side=self.padding_side, sort_by_length=self.sort_by_length, ), - EmbeddingModelStage( + model_class( model_identifier=self.model_identifier, embedding_field=self.embedding_field, - pooling=self.embedding_pooling, hf_token=self.hf_token, model_inference_batch_size=self.model_inference_batch_size, has_seq_order=self.sort_by_length, padding_side=self.padding_side, autocast=self.autocast, + **model_additional_kwargs, ), ] diff --git a/nemo_curator/stages/text/embedders/vllm.py b/nemo_curator/stages/text/embedders/vllm.py new file mode 100644 index 0000000000..402e679cfc --- /dev/null +++ b/nemo_curator/stages/text/embedders/vllm.py @@ -0,0 +1,138 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# 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 time +from typing import TYPE_CHECKING, Any + +from huggingface_hub import snapshot_download +from vllm import LLM + +from nemo_curator.backends.base import NodeInfo, WorkerMetadata +from nemo_curator.stages.base import ProcessingStage +from nemo_curator.stages.resources import Resources +from nemo_curator.stages.text.models.utils import format_name_with_suffix +from nemo_curator.tasks import DocumentBatch + +if TYPE_CHECKING: + from transformers import AutoTokenizer + + +class VLLMEmbeddingModelStage(ProcessingStage[DocumentBatch, DocumentBatch]): + def __init__( # noqa: PLR0913 + self, + model_identifier: str, + vllm_init_kwargs: dict[str, Any] | None = None, + text_field: str = "text", + pretokenize: bool = False, + embedding_field: str = "embeddings", + cache_dir: str | None = None, + hf_token: str | None = None, + verbose: bool = False, + ): + self.model_identifier = model_identifier + self.vllm_init_kwargs = vllm_init_kwargs or {} + + self.text_field = text_field + self.pretokenize = pretokenize + self.embedding_field = embedding_field + + self.cache_dir = cache_dir + self.hf_token = hf_token + + self.verbose = verbose + # after setup + self.model: None | LLM = None + self.tokenizer: None | AutoTokenizer = None + # stage setup + self.resources = Resources( + cpus=1, + gpus=1, + ) + self.name = format_name_with_suffix(model_identifier, suffix="_vllm") + + def inputs(self) -> tuple[list[str], list[str]]: + return ["data"], [self.text_field] + + def outputs(self) -> tuple[list[str], list[str]]: + return ["data"], [self.text_field, self.embedding_field] + + def _initialize_vllm(self) -> None: + vllm_init_kwargs = self.vllm_init_kwargs.copy() + # set defaults here + if "enforce_eager" not in vllm_init_kwargs: + vllm_init_kwargs["enforce_eager"] = False + if "runner" not in vllm_init_kwargs: + vllm_init_kwargs["runner"] = "pooling" + if "model_impl" not in vllm_init_kwargs: + # TODO: Once transformers is bumpted to 5.0 then we should also support transformers + vllm_init_kwargs["model_impl"] = "vllm" + + # Reduce verbosity when not in verbose mode + if not self.verbose and "disable_log_stats" not in vllm_init_kwargs: + vllm_init_kwargs["disable_log_stats"] = True + + self.model = LLM(model=self.model_identifier, **vllm_init_kwargs) + + def setup_on_node(self, node_info: NodeInfo | None = None, worker_metadata: WorkerMetadata | None = None) -> None: # noqa: ARG002 + if not self.verbose: + from huggingface_hub.utils import disable_progress_bars + + disable_progress_bars() + + snapshot_download(self.model_identifier, cache_dir=self.cache_dir, token=self.hf_token, local_files_only=False) + self._initialize_vllm() + + def setup(self, worker_metadata: WorkerMetadata | None = None) -> None: # noqa: ARG002 + if self.model is None: + self._initialize_vllm() + if self.pretokenize: + from transformers import AutoTokenizer + + self.tokenizer = AutoTokenizer.from_pretrained(self.model_identifier) + + def process(self, batch: DocumentBatch) -> DocumentBatch: + df = batch.to_pandas() + input_data = df[self.text_field].tolist() + metrics = {} + + if self.pretokenize: + from vllm.inputs import TokensPrompt + + if self.tokenizer is None: + msg = ( + "Tokenizer is not initialized. Please call setup() before processing or set pretokenize to False." + ) + raise ValueError(msg) + + t0 = time.perf_counter() + max_model_len = self.model.model_config.max_model_len + tokenized_data = self.tokenizer.batch_encode_plus(input_data, truncation=True, max_length=max_model_len) + input_data = [TokensPrompt(prompt_token_ids=ids) for ids in tokenized_data.input_ids] + metrics["tokenization_time"] = time.perf_counter() - t0 + + t0 = time.perf_counter() + vllm_output = self.model.embed(input_data, truncate_prompt_tokens=-1, use_tqdm=self.verbose) + metrics["vllm_embedding_time"] = time.perf_counter() - t0 + + df[self.embedding_field] = [e.outputs.embedding for e in vllm_output] + + self._log_metrics(metrics) + + return DocumentBatch( + task_id=batch.task_id, + dataset_name=batch.dataset_name, + data=df, + _metadata=batch._metadata, + _stage_perf=batch._stage_perf, + ) diff --git a/pyproject.toml b/pyproject.toml index 15a407601a..f86f339eb1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -68,11 +68,13 @@ dependencies = [ [project.optional-dependencies] cuda12 = ["gpustat", "nvidia-ml-py"] +vllm = ["vllm>=0.13; (platform_machine == 'x86_64' and platform_system != 'Darwin')"] # Installs CPU + GPU text curation modules deduplication_cuda12 = [ "cudf-cu12==25.10.*", "cuml-cu12==25.10.*", + "scikit-learn<1.8.0", # cuml 25.10.0 is incompatible with scikit-learn 1.8.0 "pylibcugraph-cu12==25.10.*", "pylibraft-cu12==25.10.*", "raft-dask-cu12==25.10.*", @@ -124,6 +126,8 @@ text_cuda12 = [ "nemo_curator[cuda12]", "nemo_curator[deduplication_cuda12]", "nemo_curator[text_cpu]", + "nemo_curator[vllm]", + "sentence-transformers" ] # Video Curation Dependencies @@ -138,13 +142,13 @@ video_cpu = [ video_cuda12 = [ "nemo_curator[video_cpu]", "nemo_curator[cuda12]", + "nemo_curator[vllm]", "cvcuda_cu12", "flash-attn<=2.8.3; (platform_machine == 'x86_64' and platform_system != 'Darwin')", "pycuda", "PyNvVideoCodec==2.0.2; (platform_machine == 'x86_64' and platform_system != 'Darwin')", - "torch<=2.9.0", + "torch<=2.9.1", "torchaudio", - "vllm==0.11.1; (platform_machine == 'x86_64' and platform_system != 'Darwin')", ] # All dependencies @@ -156,7 +160,7 @@ all = [ ] [dependency-groups] -build = ["setuptools", "torch<=2.9.0"] +build = ["setuptools", "torch<=2.9.1"] dev = ["jupyter"] linting = ["pre-commit", "ruff==0.11.4"] test = [ @@ -166,7 +170,7 @@ test = [ "pytest-asyncio", "pytest-cov", "pytest-loguru", - "scikit-learn", + "scikit-learn<1.8.0", # cuml 25.10.0 is incompatible with scikit-learn 1.8.0 "s3fs", # added for testing cloud fs ] diff --git a/tests/backends/test_integration.py b/tests/backends/test_integration.py index 59fe58e4bb..e2c4464513 100644 --- a/tests/backends/test_integration.py +++ b/tests/backends/test_integration.py @@ -202,13 +202,18 @@ def test_ray_data_execution_plan(self): execution_plan_stages = [stage.strip() for stage in stages] # Tasks can get fused with Actors, but Actors can't get fused with Tasks or Actors # StreamingRepartition should never get fused + + if ray.__version__ >= "2.53.0": + streaming_repartition = "StreamingRepartition[num_rows_per_block=1]" + else: + streaming_repartition = "StreamingRepartition" expected_stages = [ "InputDataBuffer[Input]", "TaskPoolMapOperator[MapBatches(FilePartitioningStageTask)]", - "TaskPoolMapOperator[StreamingRepartition]", + f"TaskPoolMapOperator[{streaming_repartition}]", "ActorPoolMapOperator[MapBatches(JsonlReaderStageTask)->MapBatches(AddLengthStageActor)]", "ActorPoolMapOperator[MapBatches(SplitIntoRowsStageActor)]", - "TaskPoolMapOperator[StreamingRepartition]", + f"TaskPoolMapOperator[{streaming_repartition}]", "ActorPoolMapOperator[MapBatches(AddLengthStageActor)]", "ActorPoolMapOperator[MapBatches(StageWithSetupActor)]", "TaskPoolMapOperator[MapBatches(JsonlWriterTask)]", diff --git a/tests/stages/text/embedders/test_base.py b/tests/stages/text/embedders/test_base.py index bf787f70a3..dad61f3e84 100644 --- a/tests/stages/text/embedders/test_base.py +++ b/tests/stages/text/embedders/test_base.py @@ -217,6 +217,7 @@ def sample_data(self) -> DocumentBatch: def test_embedding_creator_stage_initialization_and_decomposition(self) -> None: """Test initialization, decomposition, and parameter passing to decomposed stages.""" # Test with custom parameters including hf_token and unk_token + # Note: use_sentence_transformer=False is required to test EmbeddingModelStage with custom pooling stage = EmbeddingCreatorStage( model_identifier="test-model", text_field="content", @@ -228,6 +229,7 @@ def test_embedding_creator_stage_initialization_and_decomposition(self) -> None: model_inference_batch_size=128, sort_by_length=False, hf_token="test-token", # noqa:S106 + use_sentence_transformer=False, ) # Test decomposition and stage types @@ -295,15 +297,22 @@ def test_embedding_creator_stage_process_integration(self) -> None: @pytest.mark.parametrize("pooling_strategy", ["mean_pooling", "last_token"]) @pytest.mark.parametrize("autocast", [True, False]) + @pytest.mark.parametrize("use_sentence_transformer", [True, False]) + @pytest.mark.gpu def test_embedding_creator_stage_with_reference_embeddings( - self, pooling_strategy: str, sample_data: DocumentBatch, autocast: bool + self, pooling_strategy: str, sample_data: DocumentBatch, autocast: bool, use_sentence_transformer: bool ) -> None: """Test embeddings match reference implementation (requires GPU and model download).""" + if use_sentence_transformer and pooling_strategy != "mean_pooling": + pytest.skip( + "Ignoring last_token strategy for sentence transformer as behavior for miniLM is mean pooling " + ) stage = EmbeddingCreatorStage( model_identifier="sentence-transformers/all-MiniLM-L6-v2", embedding_pooling=pooling_strategy, model_inference_batch_size=32, autocast=autocast, + use_sentence_transformer=use_sentence_transformer, ) # Decompose and setup stages diff --git a/tests/stages/text/embedders/test_vllm.py b/tests/stages/text/embedders/test_vllm.py new file mode 100644 index 0000000000..50506fbdc7 --- /dev/null +++ b/tests/stages/text/embedders/test_vllm.py @@ -0,0 +1,120 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# 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. + +# ruff: noqa: E402 +import pytest + +pytest.importorskip("vllm", reason="VLLMEmbeddingModelStage tests require vllm") + +import numpy as np +import pandas as pd +import torch +from sentence_transformers import SentenceTransformer + +from nemo_curator.stages.text.embedders.vllm import VLLMEmbeddingModelStage +from nemo_curator.tasks import DocumentBatch + +# Test model that works with both VLLM and SentenceTransformer +TEST_MODEL = "sentence-transformers/all-MiniLM-L6-v2" + + +@pytest.fixture(scope="module") +def reference_model() -> SentenceTransformer: + """Load SentenceTransformer model once for the module.""" + return SentenceTransformer(TEST_MODEL).to("cuda") + + +@pytest.fixture +def sample_data() -> DocumentBatch: + """Create sample text data for testing.""" + texts = ["Hello world", "This is a test", "Machine learning is great"] + data = pd.DataFrame({"text": texts}) + return DocumentBatch(task_id="test_batch", dataset_name="test_dataset", data=data) + + +@pytest.mark.gpu +class TestVLLMEmbeddingModelStage: + """Test VLLMEmbeddingModelStage initialization and processing.""" + + def test_default_initialization(self) -> None: + """Test initialization with default parameters.""" + stage = VLLMEmbeddingModelStage(model_identifier=TEST_MODEL) + + assert stage.model_identifier == TEST_MODEL + assert stage.text_field == "text" + assert stage.embedding_field == "embeddings" + assert stage.pretokenize is False + assert stage.verbose is False + assert stage.model is None + assert stage.tokenizer is None + + assert stage.inputs() == (["data"], ["text"]) + assert stage.outputs() == (["data"], ["text", "embeddings"]) + + def test_custom_initialization(self) -> None: + """Test initialization with custom parameters.""" + stage = VLLMEmbeddingModelStage( + model_identifier=TEST_MODEL, + text_field="content", + embedding_field="emb", + pretokenize=True, + cache_dir="/tmp/cache", # noqa: S108 + hf_token="test-token", # noqa: S106 + verbose=True, + ) + + assert stage.model_identifier == TEST_MODEL + assert stage.text_field == "content" + assert stage.embedding_field == "emb" + assert stage.pretokenize is True + assert stage.cache_dir == "/tmp/cache" # noqa: S108 + assert stage.hf_token == "test-token" # noqa: S105 + assert stage.verbose is True + + assert stage.inputs() == (["data"], ["content"]) + assert stage.outputs() == (["data"], ["content", "emb"]) + + assert stage.resources.gpus == 1 + assert stage.resources.cpus == 1 + + @pytest.mark.parametrize("pretokenize", [True, False]) + def test_vllm_produces_valid_embeddings( + self, sample_data: DocumentBatch, pretokenize: bool, reference_model: SentenceTransformer + ) -> None: + """Test that VLLM produces embeddings matching SentenceTransformer reference.""" + vllm_stage = VLLMEmbeddingModelStage( + model_identifier=TEST_MODEL, + pretokenize=pretokenize, + verbose=False, + ) + try: + vllm_stage.setup_on_node() + except Exception: # noqa: BLE001 + pytest.skip("Skipping test due to model download failure") + vllm_stage.setup() + result = vllm_stage.process(sample_data) + + assert isinstance(result, DocumentBatch) + result_df = result.to_pandas() + assert "embeddings" in result_df.columns + assert len(result_df) == 3 + + reference_embeddings = reference_model.encode(sample_data.to_pandas()["text"].tolist()) + vllm_embeddings = np.array(result_df["embeddings"].tolist()) + + vllm_embeddings_torch = torch.tensor(vllm_embeddings) + reference_embeddings_torch = torch.tensor(reference_embeddings) + + cosine_sim = torch.nn.functional.cosine_similarity(vllm_embeddings_torch, reference_embeddings_torch, dim=1) + assert torch.allclose(cosine_sim, torch.ones_like(cosine_sim), atol=1e-5) diff --git a/uv.lock b/uv.lock index 3dcc317476..4fd53753a8 100644 --- a/uv.lock +++ b/uv.lock @@ -57,7 +57,7 @@ wheels = [ [[package]] name = "accelerate" -version = "1.11.0" +version = "1.12.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -66,17 +66,17 @@ dependencies = [ { name = "psutil" }, { name = "pyyaml" }, { name = "safetensors" }, - { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" }, + { name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 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= "scikit-learn" }, + { name = "sentence-transformers" }, { name = "sentencepiece" }, { name = "trafilatura" }, + { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" }, { name = "warcio" }, ] video-cpu = [ @@ -4191,7 +4316,7 @@ video-cpu = [ { name = "easydict" }, { name = "einops" }, { name = "opencv-python" }, - { name = "torchvision", version = "0.23.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" }, + { name = "torchvision", version = "0.24.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" }, { name = "torchvision", version = "0.24.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" }, ] video-cuda12 = [ @@ -4205,19 +4330,22 @@ video-cuda12 = [ { name = "opencv-python" }, { name = "pycuda" }, { name = 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