Add `NVTE_BACKWARD_OVERRIDE=high_precision|dequantized` by zianglih · Pull Request #2644 · NVIDIA/TransformerEngine

zianglih · 2026-02-03T00:48:37Z

Description

~~Add an NVTE_KEEP_BACKWARD_UNQUANTIZED env var for quantized fprop + high precision wgrad & dgrad.~~

~~Add NVTE_BACKWARD_MODE=default|unquant|dequant env var~~

Add NVTE_BACKWARD_OVERRIDE=high_precision|dequantized env var:

Not set: existing default quantization behavior
high_precision: quantized fprop + high precision wgrad & dgrad using unquantized activation and weight
dequantized: quantized fpop + high precision wgrad & dgrad using activation and weight dequantized directly from fprop quantized value

The movitivation for this dequantized design is RL. Unlike pre-training which only needs to preserve coarse optimization direction and convergence, RL gradients are noisy and useful updates are small and delicate. If gradient quantization and chain rule violation are present, noise dominates the true and fragile update signal and model will collapse. This dequantized design avoids gradient quantization and effectively preserves chain rule.

Type of change

Documentation change (change only to the documentation, either a fix or a new content)
Bug fix (non-breaking change which fixes an issue)
New feature (non-breaking change which adds functionality)
Breaking change (fix or feature that would cause existing functionality to not work as expected)
Infra/Build change
Code refactoring

Changes

Please list the changes introduced in this PR:

Change A
Change B

Checklist:

I have read and followed the contributing guidelines
The functionality is complete
I have commented my code, particularly in hard-to-understand areas
I have made corresponding changes to the documentation
My changes generate no new warnings
I have added tests that prove my fix is effective or that my feature works
New and existing unit tests pass locally with my changes

greptile-apps · 2026-02-03T00:52:31Z

Greptile Summary

This PR adds NVTE_BACKWARD_OVERRIDE=high_precision|dequantized support, enabling high-precision backward passes (wgrad & dgrad) in combination with quantized fprop. The high_precision mode saves original unquantized activations/weights; dequantized mode saves quantized fprop tensors and dequantizes them before backward GEMMs to avoid gradient quantization errors during RL fine-tuning.

LayerNormMLP and DelayedScaling recipe intentionally reject NVTE_BACKWARD_OVERRIDE with clear assertion messages and guidance to use LayerNormLinear + Linear as an alternative. All other previously flagged concerns (duplicate recipe fields, recipe None crash, unnecessary saved tensors) appear resolved in this revision.

Confidence Score: 5/5

Safe to merge — all previously flagged blocking issues are resolved; remaining findings are P2 style suggestions.

All previously raised P0/P1 concerns (duplicate recipe fields, recipe None crash, LayerNormMLP assertion message quality, unnecessary saved tensors, DelayedScaling interaction) are addressed in this revision. The feature is guarded by explicit assertions with clear error messages for unsupported combinations. The only new findings are a defensive getattr suggestion in fuser.py and a minor asymmetry in empty-tensor guards for MXFP8 storage, both P2. Comprehensive test coverage was added.

transformer_engine/pytorch/ops/fuser.py (minor: direct attribute access on recipe.backward_override), transformer_engine/pytorch/tensor/storage/mxfp8_tensor_storage.py (minor: asymmetric empty-tensor guard in dequantize vs _FromMXFP8Func.forward)

Important Files Changed

Filename	Overview
transformer_engine/common/recipe/init.py	Adds `backward_override` field to all recipe dataclasses. DelayedScaling asserts `backward_override is None` with a clear error message. Previously flagged duplicate fields in Float8CurrentScaling are gone.
transformer_engine/pytorch/module/linear.py	Adds `backward_override` detection; sets `save_original_input=True` for `high_precision` mode and saves unquantized/quantized operands accordingly. Properly asserts against Float8Quantizer (DelayedScaling) when `save_original_input` is true.
transformer_engine/pytorch/ops/basic/basic_linear.py	Functional forward sets `columnwise=False` for quantized operands when `backward_override` is not None. `op_forward` chooses between saving original (high_precision) or quantized (dequantized) tensors. Backward pass dispatches dequantization accordingly.
transformer_engine/pytorch/module/layernorm_linear.py	Correctly implements both `high_precision` and `dequantized` modes. Disables `optimize_for_gemm` for MXFP8/NVFP4 dequantized mode. `save_for_backward` order matches test expectations.
transformer_engine/pytorch/module/layernorm_mlp.py	Explicitly asserts `backward_override is None` with a clear error message directing users to LayerNormLinear + Linear. Intentional documented limitation.
transformer_engine/pytorch/module/grouped_linear.py	Adds `backward_override` support; when set, disables FP8/UB/debug context in backward. Correctly handles both override modes in wgrad/dgrad GEMMs.
transformer_engine/pytorch/ops/fuser.py	Adds `backward_override` to the fusion cache key so fused op graphs are rebuilt when the override changes. Direct attribute access `recipe.backward_override` could AttributeError for custom Recipe subclasses not defining the field.
transformer_engine/pytorch/ops/basic/quantize.py	Disables backward quantization when `recipe.backward_override` is not None. `get_fp8_recipe()` is guarded by the `fp8_enabled` check and always returns a recipe, so no None crash.
transformer_engine/pytorch/tensor/storage/mxfp8_tensor_storage.py	Adds empty-tensor early returns in both `_FromMXFP8Func.forward` (rowwise and columnwise) and `MXFP8TensorStorage.dequantize` (rowwise only). Asymmetry is functionally safe but inconsistent.
transformer_engine/pytorch/tensor/storage/nvfp4_tensor_storage.py	Adds empty-tensor early returns consistent with MXFP8 pattern. Functionally safe for the dequantized backward use case.
transformer_engine/pytorch/tensor/storage/float8_blockwise_tensor_storage.py	Adds early return for empty rowwise tensor in `dequantize`, preventing errors when empty-token grouped-linear chunks are encountered in dequantized mode.
tests/pytorch/test_backward_override.py	New comprehensive test file covering both override modes across all recipe types, module types, shapes, and fused op patterns. Layout invariant checks guard against hidden requantization during backward.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[Forward Pass - quantized fprop] --> B{NVTE_BACKWARD_OVERRIDE}
    B -->|None| C[Default: save rowwise+columnwise quantized tensors]
    B -->|high_precision| D[Save original unquantized input and weight]
    B -->|dequantized| E[Save rowwise-only quantized tensors]
    C --> F[Backward: quantized dgrad and wgrad GEMMs]
    D --> G[Backward: high-precision dgrad and wgrad using original fp16/bf16/fp32 operands]
    E --> H[Backward: dequantize saved tensors then high-precision GEMMs]
    subgraph Supported
        L[Linear]
        M[LayerNormLinear]
        N[GroupedLinear]
        O[ops.Linear / fused ops]
    end
    subgraph Unsupported - assertion error with clear message
        P[LayerNormMLP]
        Q[DelayedScaling recipe]
    end

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zianglih · 2026-02-03T08:31:14Z

I'll work on potential unit test breakage.

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zhongbozhu · 2026-02-03T22:30:02Z

            # Note: dgrad GEMM requires row-wise usage, wgrad GEMM
            # requires column-wise usage
-            if ctx.grad_output_quantizer is not None:
+            if ctx.grad_output_quantizer is not None and use_fp8_bwd:


this line seems redundant since you already skip the quantization step in base.py grad_output_preprocess?

zhongbozhu · 2026-02-03T22:30:27Z

                not ctx.use_bias
                and not ctx.requires_wgrad
                and ctx.grad_output_quantizer is not None
+                and use_fp8_bwd


same comment as above

zhongbozhu · 2026-02-03T22:39:21Z

+        recipe = cls.get_fp8_recipe()
+        if recipe is not None and recipe.delayed():
+            # Ignore NVTE_KEEP_BACKWARD_UNQUANTIZED when delayed scaling is used
+            return False


Maybe it's better to assert an error for delayed scaling? Okay with both.

I agree. If the user specifies an unsupported combination, I think it's better to fail loudly than to secretly disobey their instructions.

zhongbozhu · 2026-02-03T23:41:16Z

            # Note: dgrad GEMM requires row-wise usage, wgrad GEMM
            # requires column-wise usage
-            if ctx.grad_output_quantizer is not None:
+            if ctx.grad_output_quantizer is not None and use_fp8_bwd:


this seems redundant too if we skip quant in grad_output_preprocess

Signed-off-by: Ziang Li <ziangli@umich.edu>

…zed` Signed-off-by: Ziang Li <ziangli@umich.edu>

Signed-off-by: Ziang Li <ziangli@umich.edu>

zianglih · 2026-03-16T07:03:43Z

using "dequantized" in bwd still does not preserve the chain rule 100%, as the quantization in fwd and bwd happens along different dims

@victordion I think you are describing the default TE 1d recipe or requantized behavior.

victordion · 2026-03-16T17:05:22Z

using "dequantized" in bwd still does not preserve the chain rule 100%, as the quantization in fwd and bwd happens along different dims

@victordion I think you are describing the default TE 1d recipe or requantized behavior.

Right. My mistake. My mental model assumed there is requantize happening. Thanks for responding!

zianglih · 2026-03-20T18:24:54Z

Regarding the env var design, since this feature is mainly used by RL, there has to be a way for the user to directly override the bwd behavior in RL framework instead of plumbing all the way through Megatron.

ksivaman · 2026-03-23T22:14:25Z

/te-ci L0 L1

zianglih · 2026-03-24T17:56:12Z

All pytorch ci passed.

Some failed jax tests are due to FileExistsError: [Errno 17] File exists: '/logs' .

zhongbozhu · 2026-03-30T17:36:16Z

/te-ci L0 L1

zhongbozhu

LGTM, pending CI

Unblocking

zhongbozhu · 2026-04-02T17:26:20Z

/te-ci L0 L1

Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

ptrendx · 2026-04-03T16:42:37Z

/te-ci L0 L1

zianglih · 2026-04-07T02:09:13Z

Failed JAX ci is unrelated to this PR:

B200:

../../tests/jax/test_permutation.py::TestHighLevelPermutationAPI::test_sort_chunks_by_index[dtype_float32-8-4096-1280] FAILED

L40:

../../tests/jax/test_permutation.py::TestHighLevelPermutationAPI::test_sort_chunks_by_index[dtype_float32-8-4096-1280] FAILED

ptrendx · 2026-04-07T16:31:06Z

Merged, thank you for your contribution @zianglih!

* Add NVTE_KEEP_BACKWARD_UNQUANTIZED Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Disable ub and clean up Signed-off-by: Ziang Li <ziangli@umich.edu> * Drop fuser changes Signed-off-by: Ziang Li <ziangli@umich.edu> * Replace use_quantized_bwd with use_fp8_bwd Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Ignore keep_backward_unquantized if delayed scaling Signed-off-by: Ziang Li <ziangli@umich.edu> * Refactor ignoring NVTE_KEEP_BACKWARD_UNQUANTIZED when delayed scaling is used Signed-off-by: Ziang Li <ziangli@umich.edu> * Add back missing ctx.debug Signed-off-by: Ziang Li <ziangli@umich.edu> * Refactor changes under fused Signed-off-by: Ziang Li <ziangli@umich.edu> * Clean up Signed-off-by: Ziang Li <ziangli@umich.edu> * Refactor high-precision overwrite if keep_backward_unquantized Signed-off-by: Ziang Li <ziangli@umich.edu> * Clean up Signed-off-by: Ziang Li <ziangli@umich.edu> * Drop redundant fp8_recipe_bwd Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Drop redundant ub changes Signed-off-by: Ziang Li <ziangli@umich.edu> * Drop more redundant ub changes Signed-off-by: Ziang Li <ziangli@umich.edu> * Drop redundant delayed scaling changes Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Drop unneeded backwards_needs_fc1_input Signed-off-by: Ziang Li <ziangli@umich.edu> * Drop and disallow LayerNormMLP implementation Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Move interface changes to recipe Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Move ub overrides to fwd Signed-off-by: Ziang Li <ziangli@umich.edu> * Remove duplication Signed-off-by: Ziang Li <ziangli@umich.edu> * Simplify use_fp8_bwd logic in bwd Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Set grad quantizers to none if keep bwd unquantized Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Drop delayed scaling change Signed-off-by: Ziang Li <ziangli@umich.edu> * Simplify env var logic Signed-off-by: Ziang Li <ziangli@umich.edu> * Move validation check to recipe Signed-off-by: Ziang Li <ziangli@umich.edu> * Simplify effective_enabled Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix inverted assertion logic Signed-off-by: Ziang Li <ziangli@umich.edu> * Simplify changes under ops Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Simplify ctx.keep_backward_unquantized Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix missing attribute Signed-off-by: Ziang Li <ziangli@umich.edu> * Add unit tests Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix bias errors in unit test Signed-off-by: Ziang Li <ziangli@umich.edu> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add more shapes to unit test Signed-off-by: Ziang Li <ziangli@umich.edu> * Refator interface to `NVTE_BACKWARD_MODE=default|unquant|dequant` Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix override and clean up Signed-off-by: Ziang Li <ziangli@umich.edu> * Clean up unit test Signed-off-by: Ziang Li <ziangli@umich.edu> * Clean up unit test Signed-off-by: Ziang Li <ziangli@umich.edu> * Override `ctx.reduce_and_update_bwd_fp8_tensors = False` Signed-off-by: Ziang Li <ziangli@umich.edu> * Expand unit test Signed-off-by: Ziang Li <ziangli@umich.edu> * Add `test_backward_mode_memory_peak_report` Signed-off-by: Ziang Li <ziangli@umich.edu> * Expand test coverage and fix Signed-off-by: Ziang Li <ziangli@umich.edu> * Use `numel()` Signed-off-by: Ziang Li <ziangli@umich.edu> * Refactor unit test Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix grouped linear to override `*_quantizers` instead of `*_quantizer` Signed-off-by: Ziang Li <ziangli@umich.edu> * Only save input/weight when `*_requires_grad` on unquant mode Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix Blackwell debug ci Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix sm89 and sm90 tests Signed-off-by: Ziang Li <ziangli@umich.edu> * Fix unquant mode memory saving Signed-off-by: Ziang Li <ziangli@umich.edu> * Refactor interface to `NVTE_BACKWARD_OVERRIDE=high_precision|dequantized` Signed-off-by: Ziang Li <ziangli@umich.edu> * Rename unit test Signed-off-by: Ziang Li <ziangli@umich.edu> * Simplify env var parsing Signed-off-by: Ziang Li <ziangli@umich.edu> --------- Signed-off-by: Ziang Li <ziangli@umich.edu> Signed-off-by: Przemek Tredak <ptredak@nvidia.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> Co-authored-by: Przemek Tredak <ptredak@nvidia.com>

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Fix unquant mode memory saving

9f475a1

Signed-off-by: Ziang Li <ziangli@umich.edu>

ziang-and force-pushed the keep-bwd branch from 77e1c58 to 9f475a1 Compare March 14, 2026 19:34

Refactor interface to `NVTE_BACKWARD_OVERRIDE=high_precision|dequanti…

4164907

…zed` Signed-off-by: Ziang Li <ziangli@umich.edu>

zianglih changed the title ~~Add NVTE_BACKWARD_MODE=default|unquant|dequant~~ Add NVTE_BACKWARD_OVERRIDE=high_precision|dequantized Mar 14, 2026

zianglih added 2 commits March 14, 2026 15:09

Rename unit test

00893bb

Signed-off-by: Ziang Li <ziangli@umich.edu>

Simplify env var parsing

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ziang-and force-pushed the keep-bwd branch from e6b00ba to 433880d Compare March 14, 2026 22:24

zianglih requested review from ksivaman and zhongbozhu March 17, 2026 05:42

Merge branch 'main' into keep-bwd

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Merge branch 'main' into keep-bwd

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Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

ptrendx approved these changes Apr 7, 2026

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ptrendx merged commit fdf9fb1 into NVIDIA:main Apr 7, 2026
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YigongQin mentioned this pull request Apr 21, 2026

NVFP4 per-token recipe #2913

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zianglih mentioned this pull request May 9, 2026

Implement 4over6 NVFP4 recipe #2972

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negvet mentioned this pull request Jun 10, 2026

[Pytorch][Common] Hybrid quantization #2817

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negvet mentioned this pull request Jun 23, 2026

[PyTorch] Preserve fprop operands for dequantized backward override #3141

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Checklist:

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Greptile Summary

Confidence Score: 5/5

Important Files Changed

Flowchart

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