[JoaoMarcos44]

Fixes #9207.

Description

ContrastiveLoss(temperature=0.01) returns NaN loss and gradients for ordinary finite float32 embeddings. With two identical sample pairs, every anchor has three equally similar nonself candidates and the exact loss is ln(3). This PR evaluates the same NT-Xent objective using positive-shifted logits and log-space reduction.

import torch
from monai.losses import ContrastiveLoss

x = torch.ones((2, 4), requires_grad=True)
loss = ContrastiveLoss(temperature=0.01)(x, x)
loss.backward()
print(loss.item(), torch.isfinite(x.grad).all().item())
# Upstream: nan False
# Fixed:    1.0986123085021973 True

Root cause and correction

The original implementation exponentiates similarity / temperature and then applies a zero-diagonal mask. A float32 self-similarity of 1 at temperature 0.01 overflows at exp(100); masking afterward creates 0 * inf = NaN. The positive exponential and probability ratio can also overflow or underflow.

The corrected evaluation is:

shifted_logits = (similarity - positive_similarity) / temperature
loss_per_anchor = logsumexp(shifted_logits, excluding self comparisons)

The positive remains in its denominator with shifted logit zero. Shifting before temperature scaling avoids subtracting large independently rounded quantities after logsumexp. Float16/bfloat16 embeddings are promoted to float32 before cosine similarity, loss arithmetic, and accumulation, preserving upstream's float32 loss output for these dtypes. Float64 inputs retain float64 computation, including construction of the temperature scalar. Signatures, pairing, shape validation, batch inference, cosine epsilon semantics, and averaging over both views remain intact. No loss clamping, epsilon adjustment, or dependency is added.

Validation

Final source: 49542ec865f11a7b54a44e16fc367633d1f77041. Validated on Windows, Python 3.14.1, PyTorch 2.12.1+cpu, NumPy 2.3.5:

Check	Executed result
Original reproduction on unmodified dev at bbebca6f17cabf98ea5fb8ec5776eeacd10aa60d	11 expected failures among the original 25 focused cases
Final focused suite	39 passed, no skips (32 new cases plus seven existing cases)
Changed loss-module line coverage	93%, 27 of 29 executable lines; existing legacy-warning and mismatched-shape error lines not exercised
Ruff, Black, isort on both changed Python files	All passed
Test dependencies, caches, coverage database, temporary files	Confined to disposable sandboxes and removed afterward
Two independent final adversarial code reviews	No blocking findings after the precision corrections

The final suite covers analytic identical/antipodal/singleton expectations, a batch-1280 float16 accumulation regression, float16/bfloat16/float32/float64 inputs, independent float64 cross-entropy loss and nonzero-gradient references, CPU autocast, and finite-difference gradcheck. Reference similarities use normalized matrix multiplication rather than the production cosine-similarity path. Cancellation probes also check selected very small temperatures, including a float64 scalar that would underflow if constructed in float32.

python -m unittest tests.losses.test_contrastive_loss -v

Tests ran with numerical-library threads limited to one. Source/test SHA-256 values were checked against the validated files before the final commit. Coverage used a sandbox-local configuration with pytrace and a source-file filter; the initial module-discovery coverage setup failed while importing PyTorch and is not counted as successful validation.

Adversarial findings addressed

• Lost promotion: an intermediate implementation returned inf in float16 for batch size 1280 at temperature 0.1, while upstream returned a finite float32 value around 27.1537. Explicit float32 arithmetic restores the accumulation range and the test checks the analytic mean.
• Cancellation: evaluating logsumexp(logits) - positive_logit still lost precision, including loss 1.0 or 0.0 instead of ln(3) for selected small-temperature cases. Shifting similarities before scaling and reduction corrects those cases and the nonzero-gradient regressions.
• Temperature dtype: constructing a Python temperature in default float32 could turn a representable float64 temperature into zero. The scalar now uses the computation dtype.

These findings were reproduced before their corresponding corrections; the new regression cases failed before passing on the final implementation.

Validation limits

GPU, distributed execution, full integration, end-to-end training, performance measurements, and documentation builds were not executed locally. The existing test named test_with_cuda used its CPU fallback.

Related precision-control PR

Dependent follow-up: PR #9210, tracked by issue #9209, adds opt-in compute_dtype control for the cosine-backward roundoff limitation described below. This PR (#9208) provides its log-space forward-stability prerequisite. Review and merge #9208 before integrating #9210; both PRs are open for review.

Extreme-temperature backward precision

Finite gradients do not guarantee accurate gradients at arbitrarily small temperatures. An investigation comprising 1,363 diagnostic configurations and seven explicit control checks isolated roundoff in cosine-similarity backward and its amplification by temperature scaling. The following results are specific to the CPU setup above:

Diagnostic at temperature 1e-8	Maximum absolute gradient residual (mathematical expectation: zero)
Identical float32 three-feature vectors	1.0
Same inputs, casting only the already-computed loss to float64	1.0
Same inputs, passed as float64 to ContrastiveLoss	0.0
24 additional identical-vector cases, float32 inputs	2.0
Same 24 cases, passing inputs as float64	3.725290298461914e-9

The residual is reproducible without MONAI: on this setup, differentiating 1e6 * F.cosine_similarity(x, y, dim=0) for identical three-feature float32 all-one vectors produces a gradient residual of 0.03125 rather than zero. Replacing the loss reduction with cross-entropy did not remove the observed error. Normalizing once per vector improved some cases but left residuals in others; substituting F.normalize also changed gradients near the cosine norm epsilon. Those prototypes do not justify changing the default cosine implementation.

When higher backward precision is needed, convert the embeddings before evaluating the loss:

loss = ContrastiveLoss(temperature=1e-8)(embeddings_a.double(), embeddings_b.double())
loss.backward()

This retains autograd connectivity to the original embeddings. loss_fn(embeddings_a, embeddings_b).double() is too late to improve the similarity/backward calculation. The higher-precision option has memory/computation costs that were not benchmarked here; no automatic float64 promotion or arbitrary temperature threshold is introduced.

The diagnostics are exploratory configurations, not 1,363 independent unit tests; the focused suite remains 39 tests. Float64 reduces error in the evaluated cases but does not recover information already lost when generating lower-precision embeddings, expand the representable range of gradients stored in lower-precision inputs, or guarantee accuracy for every temperature. The error need not grow monotonically because binary rounding can cancel exactly in some configurations. This PR does not claim universal gradient accuracy, validate invalid temperatures, or alter cosine normalization semantics.

Infographic

The native-generated image describes the architecture, final computation, 39-test result, coverage, and numerical limitations. It is hosted in a separate documentation branch so this code diff contains only implementation and regression tests. No benchmark or training result is implied.

Duplicate check

Searched open and closed issues/PRs for ContrastiveLoss, contrastive nan, contrastive temperature, contrastive overflow, contrastive underflow, contrastive stability, NT-Xent, and logsumexp. Existing items concern batch size (#5070/#5093), warning behavior (#3872/#3873), reduction (#3460/#3464), initial implementation (#3286), and redundant normalization (#6550); none addresses this reproduced numerical failure. Forward-stability work stays in this PR and issue #9207; explicit backward precision control is tracked separately in #9209 and dependent PR #9210.

Types of changes

• Non-breaking correction; public signatures and mathematical objective preserved.
• Regression tests added and executed.
• Inline documentation describes computation/output precision, return value, legacy argument, and shape errors.
• Full integration tests executed locally.
• Quick/distributed suite executed locally.
• Documentation built locally with make html.

AI assistance disclosure: investigation, implementation, tests, documentation, and independent adversarial reviews were prepared with OpenAI Codex. The infographic was generated and edited with native OpenAI image generation. The submitting author remains responsible under MONAI's tool-assisted contribution policy. All code commits carry DCO sign-offs and AI assistance trailers.

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📝 Walkthrough

ContrastiveLoss now computes temperature-scaled, positive-shifted logits with self-similarity masking and logsumexp. Float16 and bfloat16 inputs use float32 computation. Documentation describes validation errors, output dtype, and low-temperature precision guidance. Tests cover low temperatures, reduced precision, autocast, batch reduction, gradients, gradcheck, analytical cases, single-pair inputs, and cross-entropy reference results.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Severity of issue fixed: Medium

Merge Risk: ⚪ Minimal · up to 49542

No actionable merge-blocking risk is identified for the contrastive-loss change; normal validation remains appropriate.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 16.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 12 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly and concisely describes the main change: improving ContrastiveLoss stability at small temperatures.
Linked Issues check		Issue #9207 requires finite small-temperature loss and gradients, singleton zero loss, positive-shifted log-space evaluation, low-precision promotion, computation-dtype temperature handling, and regre…
Out of Scope Changes check		The reported diff changes only monai/losses/contrastive.py and tests/losses/test_contrastive_loss.py. The production changes and inline documentation support issue #9207. The added tests verify th…
Description check		The pull request description is complete and follows the repository template. It includes the issue reference, change summary, technical rationale, validation results, test coverage, known validation …

Full details: Linked Issues check

Explanation

Issue #9207 requires finite small-temperature loss and gradients, singleton zero loss, positive-shifted log-space evaluation, low-precision promotion, computation-dtype temperature handling, and regression tests. The reported changes implement these requirements in monai/losses/contrastive.py. The focused suite reports 39 passing tests covering analytic values, singleton inputs, low-precision accumulation, CPU autocast, gradcheck, and independent float64 references. Public signatures and stated objective behavior remain unchanged.

Full details: Out of Scope Changes check

Explanation

The reported diff changes only monai/losses/contrastive.py and tests/losses/test_contrastive_loss.py. The production changes and inline documentation support issue #9207. The added tests verify the required numerical behavior. No unrelated production behavior, dependency, public API, or external documentation change is reported.

Full details: Description check

Explanation

The pull request description is complete and follows the repository template. It includes the issue reference, change summary, technical rationale, validation results, test coverage, known validation limits, documentation details, and change-type selections. It also clearly identifies unexecuted integration, quick, distributed, GPU, and documentation-build checks.

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Actionable comments posted: 1

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• 🪄 Fix comments: Fix CodeRabbit comments on this PR

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63-63: 📐 Maintainability & Code Quality | 🔵 Trivial | 💤 Low value

Document all nine new test methods.

The Python instruction requires Google-style docstrings for every definition. Add docstrings to the five listed methods and the four adjacent methods at lines 122–173. Document each parameter and the behavior each test verifies.

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Review comment at @tests/losses/test_contrastive_loss.py at line 63:
Update test_reduced_precision_loss_and_gradients and the four adjacent test
methods in the same test class to include Google-style docstrings; document
every parameter and the behavior each test verifies, covering all nine new test
methods without changing their test logic.

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- Line 63: Update test_reduced_precision_loss_and_gradients and the four
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