Add GradCache + MNRL: Go beyond GPU-memory limit for MNRL

sentence_transformers/losses/CachedMultipleNegativesRankingLoss.py

@@ -0,0 +1,210 @@
+from __future__ import annotations
+from contextlib import nullcontext
+from functools import partial
+import torch
+from torch import nn, Tensor
+from torch.utils.checkpoint import get_device_states, set_device_states
+from typing import Iterable, Dict, Iterator, List, Optional, Tuple
+from sentence_transformers import SentenceTransformer
+from sentence_transformers import util
+import tqdm
+
+
+class RandContext:
+    """
+    Random-state context manager class. Reference: https://github.com/luyug/GradCache.
+
+    This class will back up the pytorch's random state during initialization. Then when the context is activated,
+    the class will set up the random state with the backed-up one.
+    """
+
+    def __init__(self, *tensors):
+        self.fwd_cpu_state = torch.get_rng_state()
+        self.fwd_gpu_devices, self.fwd_gpu_states = get_device_states(*tensors)
+
+    def __enter__(self):
+        self._fork = torch.random.fork_rng(devices=self.fwd_gpu_devices, enabled=True)
+        self._fork.__enter__()
+        torch.set_rng_state(self.fwd_cpu_state)
+        set_device_states(self.fwd_gpu_devices, self.fwd_gpu_states)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self._fork.__exit__(exc_type, exc_val, exc_tb)
+        self._fork = None
+
+
+def _backward_hook(
+    grad_output: Tensor,
+    sentence_features: Iterable[Dict[str, Tensor]],
+    loss_obj: CachedMultipleNegativesRankingLoss,
+):
+    """A backward hook to backpropagate the cached gradients mini-batch by mini-batch."""
+    assert loss_obj.cache is not None
+    assert loss_obj.random_states is not None
+    with torch.enable_grad():
+        for sentence_feature, grad, random_states in zip(sentence_features, loss_obj.cache, loss_obj.random_states):
+            for (reps_mb, _), grad_mb in zip(
+                loss_obj.embed_minibatch_iter(
+                    sentence_feature=sentence_feature,
+                    with_grad=True,
+                    copy_random_state=False,
+                    random_states=random_states,
+                ),
+                grad,
+            ):
+                surrogate = torch.dot(reps_mb.flatten(), grad_mb.flatten()) * grad_output
+                surrogate.backward()
+
+
+class CachedMultipleNegativesRankingLoss(nn.Module):
+    """
+    Boosted version of MultipleNegativesRankingLoss (https://arxiv.org/pdf/1705.00652.pdf) by GradCache (https://arxiv.org/pdf/2101.06983.pdf).
+
+    Constrastive learning (here our MNRL loss) with in-batch negatives is usually hard to work with large batch sizes due to (GPU) memory limitation.
+    Even with batch-scaling methods like gradient-scaling, it cannot work either. This is because the in-batch negatives make the data points within
+    the same batch non-independent and thus the batch cannot be broke down into mini-batches. GradCache is a smart way to solve this problem.
+    It achieves the goal by dividing the computation into two stages of embedding and loss calculation, which both can be scaled by mini-batches.
+    As a result, memory of constant size (e.g. that works with batch size = 32) can now process much larger batches (e.g. 65536).
+
+    In detail:
+        (1) It first does a quick embedding step without gradients/computation graphs to get all the embeddings;
+        (2) Calculate the loss, backward up to the embeddings and cache the gradients wrt. to the embeddings;
+        (3) A 2nd embedding step with gradients/computation graphs and connect the cached gradients into the backward chain.
+
+    Notes: All steps are done with mini-batches. In the original implementation of GradCache, (2) is not done in mini-batches and
+    requires a lot memory when batch size large. One drawback is about the speed. GradCache will sacrifice around 20% computation time according to the paper.
+
+    Example:
+
+        from sentence_transformers import SentenceTransformer
+        train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=1024)  # Here we can try much larger batch sizes!
+        train_loss = losses.CachedMultipleNegativesRankingLoss(model=model, mini_batch_size: int = 32)
+    """
+
+    def __init__(
+        self,
+        model: SentenceTransformer,
+        scale: float = 20.0,
+        similarity_fct: callable[[Tensor, Tensor], Tensor] = util.cos_sim,
+        mini_batch_size: int = 32,
+        show_progress_bar: bool = False,
+    ):
+        """
+        :param model: SentenceTransformer model
+        :param scale: Output of similarity function is multiplied by scale value
+        :param similarity_fct: similarity function between sentence embeddings. By default, cos_sim. Can also be set to dot product (and then set scale to 1)
+        """
+        super(CachedMultipleNegativesRankingLoss, self).__init__()
+        self.model = model
+        self.scale = scale
+        self.similarity_fct = similarity_fct
+        self.cross_entropy_loss = nn.CrossEntropyLoss()
+        self.mini_batch_size = mini_batch_size
+        self.cache: Optional[List[List[Tensor]]] = None
+        self.random_states: Optional[List[List[RandContext]]] = None
+        self.show_progress_bar = show_progress_bar
+
+    def embed_minibatch(
+        self,
+        sentence_feature: Dict[str, Tensor],
+        begin: int,
+        end: int,
+        with_grad: bool,
+        copy_random_state: bool,
+        random_state: Optional[RandContext] = None,
+    ) -> Tuple[Tensor, Optional[RandContext]]:
+        """Do forward pass on a minibatch of the input features and return corresponding embeddings."""
+        grad_context = nullcontext if with_grad else torch.no_grad
+        random_state_context = nullcontext() if random_state is None else random_state
+        sentence_feature_minibatch = {k: v[begin:end] for k, v in sentence_feature.items()}
+        with random_state_context:
+            with grad_context():
+                random_state = RandContext(*sentence_feature_minibatch.values()) if copy_random_state else None
+                reps = self.model(sentence_feature_minibatch)["sentence_embedding"]  # (mbsz, hdim)
+        return reps, random_state
+
+    def embed_minibatch_iter(
+        self,
+        sentence_feature: Dict[str, Tensor],
+        with_grad: bool,
+        copy_random_state: bool,
+        random_states: Optional[List[RandContext]] = None,
+    ) -> Iterator[Tuple[Tensor, Optional[RandContext]]]:
+        """Do forward pass on all the minibatches of the input features and yield corresponding embeddings."""
+        input_ids: Tensor = sentence_feature["input_ids"]
+        bsz, _ = input_ids.shape
+        for i, b in enumerate(
+            tqdm.trange(
+                0,
+                bsz,
+                self.mini_batch_size,
+                desc="Embed mini-batches",
+                disable=not self.show_progress_bar,
+            )
+        ):
+            e = b + self.mini_batch_size
+            reps, random_state = self.embed_minibatch(
+                sentence_feature=sentence_feature,
+                begin=b,
+                end=e,
+                with_grad=with_grad,
+                copy_random_state=copy_random_state,
+                random_state=None if random_states is None else random_states[i],
+            )
+            yield reps, random_state  # reps: (mbsz, hdim)
+
+    def calculate_loss_and_cache_gradients(self, reps: List[List[Tensor]]) -> Tensor:
+        """Calculate the cross-entropy loss and cache the gradients wrt. the embeddings."""
+        embeddings_a = torch.cat(reps[0])  # (bsz, hdim)
+        embeddings_b = torch.cat([torch.cat(r) for r in reps[1:]])  # ((1 + nneg) * bsz, hdim)
+
+        batch_size = len(embeddings_a)
+        labels = torch.tensor(
+            range(batch_size), dtype=torch.long, device=embeddings_a.device
+        )  # (bsz, (1 + nneg) * bsz)  Example a[i] should match with b[i]
+        losses: List[torch.Tensor] = []
+        for b in tqdm.trange(
+            0,
+            batch_size,
+            self.mini_batch_size,
+            desc="Preparing caches",
+            disable=not self.show_progress_bar,
+        ):
+            e = b + self.mini_batch_size
+            scores: Tensor = self.similarity_fct(embeddings_a[b:e], embeddings_b) * self.scale
+            loss_mbatch: torch.Tensor = self.cross_entropy_loss(scores, labels[b:e]) * len(scores) / batch_size
+            loss_mbatch.backward()
+            losses.append(loss_mbatch.detach())
+
+        loss = sum(losses).requires_grad_()
+
+        self.cache = [[r.grad for r in rs] for rs in reps]  # e.g. 3 * bsz/mbsz * (mbsz, hdim)
+
+        return loss
+
+    def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor) -> Tensor:
+        # Step (1): A quick embedding step without gradients/computation graphs to get all the embeddings
+        reps = []
+        self.random_states = []  # Copy random states to guarantee exact reproduction of the embeddings during the second forward pass, i.e. step (3)
+        for sentence_feature in sentence_features:
+            reps_mbs = []
+            random_state_mbs = []
+            for reps_mb, random_state in self.embed_minibatch_iter(
+                sentence_feature=sentence_feature,
+                with_grad=False,
+                copy_random_state=True,
+            ):
+                reps_mbs.append(reps_mb.detach().requires_grad_())
+                random_state_mbs.append(random_state)
+            reps.append(reps_mbs)
+            self.random_states.append(random_state_mbs)
+
+        # Step (2): Calculate the loss, backward up to the embeddings and cache the gradients wrt. to the embeddings
+        loss = self.calculate_loss_and_cache_gradients(reps)
+
+        # Step (3): A 2nd embedding step with gradients/computation graphs and connect the cached gradients into the backward chain
+        loss.register_hook(partial(_backward_hook, sentence_features=sentence_features, loss_obj=self))
+        return loss
+
+    def get_config_dict(self):
+        return {"scale": self.scale, "similarity_fct": self.similarity_fct.__name__}

sentence_transformers/losses/__init__.py

@@ -5,6 +5,7 @@
 from .TripletLoss import TripletDistanceMetric, TripletLoss
 from .MarginMSELoss import MarginMSELoss
 from .MSELoss import MSELoss
+from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
 from .ContrastiveLoss import SiameseDistanceMetric, ContrastiveLoss
 from .ContrastiveTensionLoss import (
     ContrastiveTensionLoss,
@@ -32,6 +33,7 @@
     "MSELoss",
     "ContrastiveLoss",
     "SiameseDistanceMetric",
+    "CachedMultipleNegativesRankingLoss",
     "ContrastiveTensionLoss",
     "ContrastiveTensionLossInBatchNegatives",
     "ContrastiveTensionDataLoader",

tests/test_cmnrl.py

@@ -0,0 +1,148 @@
+from contextlib import nullcontext
+from typing import List
+import pytest
+from sentence_transformers import SentenceTransformer, InputExample, losses
+import tqdm
+from transformers import set_seed
+import torch
+from torch.optim import Adam
+
+
+@pytest.mark.parametrize(
+    ["train_samples_mnrl", "train_samples_cmnrl", "same_grad", "scaler", "precision"],
+    [
+        (
+            [
+                InputExample(texts=[q, p, n])
+                for q, p, n in zip(
+                    ["aaa", "bbb", "ccc", "ddd", "eee"],
+                    ["aas", "bbs", "ccs", "dds", "ees"],
+                    ["xxx", "yyy", "zzz", "kkk", "fff"],
+                )
+            ],
+            [
+                InputExample(texts=[q, p, n])
+                for q, p, n in zip(
+                    ["aaa", "bbb", "ccc", "ddd", "eee"],
+                    ["aas", "bbs", "ccs", "dds", "ees"],
+                    ["xxx", "yyy", "zzz", "kkk", "fff"],
+                )
+            ],
+            True,
+            1.0,
+            1e-6,
+        ),
+        (
+            [
+                InputExample(texts=[q, p, n])
+                for q, p, n in zip(
+                    ["adsa", "czx", "dsada"],
+                    ["b", "fas", "xcz"],
+                    ["c", "yyy", "asdas"],
+                )
+            ],
+            [
+                InputExample(texts=[q, p, n])
+                for q, p, n in zip(
+                    ["aaa", "bbb", "ccc", "ddd", "eee"],
+                    ["aas", "bbs", "ccs", "dds", "ees"],
+                    ["xxx", "yyy", "zzz", "kkk", "fff"],
+                )
+            ],
+            False,
+            1.0,
+            1e-6,
+        ),
+        (
+            [
+                InputExample(texts=[q, p, n])
+                for q, p, n in zip(
+                    ["aaa", "bbb", "ccc", "ddd", "eee"],
+                    ["aas", "bbs", "ccs", "dds", "ees"],
+                    ["xxx", "yyy", "zzz", "kkk", "fff"],
+                )
+            ],
+            [
+                InputExample(texts=[q, p, n])
+                for q, p, n in zip(
+                    ["aaa", "bbb", "ccc", "ddd", "eee"],
+                    ["aas", "bbs", "ccs", "dds", "ees"],
+                    ["xxx", "yyy", "zzz", "kkk", "fff"],
+                )
+            ],
+            True,
+            1000.0,
+            1e-3,
+        ),
+    ],
+)
+def test_cmnrl_same_grad(
+    train_samples_mnrl: List[InputExample],
+    train_samples_cmnrl: List[InputExample],
+    same_grad: bool,
+    scaler: float,
+    precision: float,
+):
+    # Given:
+    sbert = SentenceTransformer("distilbert-base-uncased")
+    sbert.to("cpu")
+    optimizer = Adam(sbert.parameters())
+    # train_samples_mnrl
+    # train_samples_cmnrl
+    # same_grad
+    # scaler  # This simulates AMP scenarios
+    # precision
+
+    # When:
+    # First run with MNRL
+    set_seed(42)
+    optimizer.zero_grad()
+    loss_mnrl = losses.MultipleNegativesRankingLoss(sbert)
+    loss_mnrl_value: torch.Tensor = loss_mnrl.forward(*sbert.smart_batching_collate(train_samples_mnrl)) * scaler
+    loss_mnrl_value.backward()
+    grad_expected = {name: p.grad.clone() for name, p in loss_mnrl.named_parameters() if p.grad is not None}
+
+    # Then run with this cached version:
+    set_seed(42)
+    optimizer.zero_grad()
+    loss_cmnrl = losses.CachedMultipleNegativesRankingLoss(sbert, mini_batch_size=2)
+    loss_cmnrl_value = loss_cmnrl.forward(*sbert.smart_batching_collate(train_samples_cmnrl)) * scaler
+    loss_cmnrl_value.backward()
+    grad = {name: p.grad.clone() for name, p in loss_cmnrl.named_parameters() if p.grad is not None}
+
+    # Then:
+    if same_grad:
+        assert pytest.approx(loss_mnrl_value.item()) == loss_cmnrl_value.item()
+    else:
+        assert pytest.approx(loss_mnrl_value.item()) != loss_cmnrl_value.item()
+
+    nclose = 0
+    for name in tqdm.tqdm(grad_expected):
+        nclose += torch.allclose(grad[name], grad_expected[name], precision, precision)
+
+    if same_grad:
+        assert nclose == len(grad_expected)
+    else:
+        assert nclose != len(grad_expected)
+
+
+@pytest.mark.parametrize("use_rand_context", [True, False])
+def test_rand_context_working(use_rand_context: bool):
+    # Given:
+    from sentence_transformers.losses.CachedMultipleNegativesRankingLoss import (
+        RandContext,
+    )
+
+    a = torch.Tensor(1)
+    b = torch.Tensor(1)
+    random_state = RandContext(a, b) if use_rand_context else nullcontext()
+    expected = torch.rand(1000)
+    precision = 1e-6
+
+    # When:
+    with random_state:
+        # Then:
+        if use_rand_context:
+            assert torch.allclose(torch.rand(1000), expected, precision, precision)
+        else:
+            assert not torch.allclose(torch.rand(1000), expected, precision, precision)