Improve QAT nvfp4 numerics by andrewor14 · Pull Request #3050 · pytorch/ao

andrewor14 · 2025-09-23T20:32:07Z

Stack from ghstack (oldest at bottom):

-> Improve QAT nvfp4 numerics #3050

Summary: Similar to #2986,
this commit improves the prepare vs convert SQNR of NVFP4 QAT
from 12 to inf. This is achieved by refactoring NVFP4 QAT to
mimic the PTQ numerics exactly, using a new linear class to
incorporate both the quantization and mm logic.

Unit tests:

python test/quantization/test_qat.py -k test_qat_nvfp4
python test/quantization/test_qat.py -k test_quantize_api_nvfp4

End-to-end tests:
Fine-tuning Llama3.2-3B with and without this PR in axolotl:

fine-tune for 1 epoch on yahma/alpaca-cleaned
batch size 512, learning rate 2e-5, no gradient accumulation

Wikitext:

With this PR, QAT nvfp4 quantized model achieved 15% lower
perplexity than the quantized baseline
Without this PR, QAT nvfp4 quantized model was about the
same as the quantized baseline

==> Llama3.2-3B_baseline_bs512/eval_float.log <==
|        |       |none  |     0|word_perplexity|↓  |9.418|±  |   N/A|

==> Llama3.2-3B_baseline_bs512/eval_quantized.log <==
|        |       |none  |     0|word_perplexity|↓  |10.3681|±  |   N/A|

# QAT with this PR (quantized)
==> Llama3.2-3B_qat_bs512/eval_quantized.log <==
|        |       |none  |     0|word_perplexity|↓  |10.2281|±  |   N/A|

**Summary:** This commit adds an option for the existing `NVFP4InferenceConfig` to dynamically compute an appropriate fp32 per tensor scale to support the two level scaling according to the NVFP4 specification: https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/. While two level scaling is supported in `NVFP4Tensor`, today there is no config API for users to call this. The existing `NVFP4InferenceConfig` only supports single level scaling because including an explicit `per_tensor_scale` field would make serialization tricky. In the future, we should add an end-to-end calibration flow so users can compute an appropriate per tensor scale for the activations first, and then pass this to `NVFP4Tensor` as a static scale, similar to the proposal in #2572. **Test Plan:** ``` pytest test/prototype/mx_formats/test_inference_workflow.py -k test_inference_workflow_nvfp4 pytest test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` Also did a quick benchmark before and after: ``` import copy import time import torch from torchao.quantization import quantize_ from torchao.prototype.mx_formats import NVFP4InferenceConfig m_mx1 = torch.nn.Linear(64, 256, bias=True, dtype=torch.bfloat16, device="cuda") m_mx2 = copy.deepcopy(m_mx1) config1 = NVFP4InferenceConfig(use_dynamic_per_tensor_scale=False) config2 = NVFP4InferenceConfig(use_dynamic_per_tensor_scale=True) quantize_(m_mx1, config=config1) quantize_(m_mx2, config=config2) m_mx1 = torch.compile(m_mx1, fullgraph=True, backend="aot_eager") m_mx2 = torch.compile(m_mx2, fullgraph=True, backend="aot_eager") start = time.time() for _ in range(1000): m_mx1(torch.randn(128, 64, device="cuda", dtype=torch.bfloat16)) print("No per_tensor_scale = ", time.time() - start, "seconds") start = time.time() for _ in range(1000): m_mx2(torch.randn(128, 64, device="cuda", dtype=torch.bfloat16)) print("With per_tensor_scale = ", time.time() - start, "seconds") ``` On a single B200: ``` No per_tensor_scale = 1.2855589389801025 seconds With per_tensor_scale = 1.3009123802185059 seconds ``` [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. Details TBD. **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. [ghstack-poisoned]

pytorch-bot · 2025-09-23T20:32:10Z

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/3050

📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

✅ No Failures

As of commit fa4d9ee with merge base 5cbbd73 ():
💚 Looks good so far! There are no failures yet. 💚

This comment was automatically generated by Dr. CI and updates every 15 minutes.

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. Details TBD. **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: 04f6bce Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: 04f6bce Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: 04f6bce Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: 47019f4 Pull Request resolved: #3050

drisspg · 2025-09-24T03:29:23Z

        self._test_quantize_api_against_ptq(
            NVFP4InferenceConfig(use_dynamic_per_tensor_scale=use_per_tensor_scale),
-            target_prepare_sqnr=12,
+            target_prepare_sqnr=target_prepare_sqnr,


can you explain this I would expect that per-tensor scaling would have less error and thus a higher sqnr? but also I dont know what prepare sqnr means?

Update: they're both inf now. The per-tensor case wasn't inf before because we weren't simulating _addmm_nvfp4_dispatch in QAT, but instead just called F.linear on NVFP4Tensor.to_nvfp4().to_dtype(), which doesn't give the same numerics

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: d8f7eff Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked`, but in `torch.int32` instead of `torch.uint8` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: d0120f0 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to 36 with `use_per_tensor_scale`, and 12 to inf without. This is achieved by mimicking the PTQ flow more closely, in particular, in descending order of significance: 1. Simulate `f4_unpacked_to_f32` and `f32_to_f4_unpacked` 2. Do not cast intermediate fake quantized values to original dtype, e.g. bf16 which loses some fidelity from fp32 3. Fake round blockwise scales to float8 **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: d0120f0 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimick the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: ecbff90 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimick the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimick the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Test Plan:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` End-to-end tests TBD. ghstack-source-id: a707a59 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> unsloth_model_lora_qat_int4_output/lm_eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` ghstack-source-id: a707a59 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> unsloth_model_lora_qat_int4_output/lm_eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> unsloth_model_lora_qat_int4_output/lm_eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` ghstack-source-id: 633bc65 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` ghstack-source-id: 633bc65 Pull Request resolved: #3050

jerryzh168 · 2025-09-26T18:12:07Z

@@ -444,12 +444,16 @@ def _infer_fake_quantize_configs(
    elif isinstance(base_config, NVFP4InferenceConfig):
        if NVFP4MMConfig.DYNAMIC:


nit: if this is a boolean, might be good to say NVFP4MMConfig.is_dynamic I think, although probably not relevant to this PR

ok, can fix separately since this is a PTQ config

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` ghstack-source-id: 633bc65 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` ghstack-source-id: 77f47b7 Pull Request resolved: #3050

vkuzo

lg for B200, I think there should be a TODO somewhere to in the future add a path for non-B200s if they want to at least emulate nvfp4 numerics

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` ghstack-source-id: 77f47b7 Pull Request resolved: #3050

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` [ghstack-poisoned]

**Summary:** Similar to #2986, this commit improves the prepare vs convert SQNR of NVFP4 QAT from 12 to inf. This is achieved by refactoring NVFP4 QAT to mimic the PTQ numerics exactly, using a new linear class to incorporate both the quantization and mm logic. **Unit tests:** ``` python test/quantization/test_qat.py -k test_qat_nvfp4 python test/quantization/test_qat.py -k test_quantize_api_nvfp4 ``` **End-to-end tests:** Fine-tuning Llama3.2-3B with and without this PR in axolotl: - fine-tune for 1 epoch on yahma/alpaca-cleaned - batch size 512, learning rate 2e-5, no gradient accumulation Wikitext: - With this PR, QAT nvfp4 quantized model achieved 15% lower perplexity than the quantized baseline - Without this PR, QAT nvfp4 quantized model was about the same as the quantized baseline ``` ==> Llama3.2-3B_baseline_bs512/eval_float.log <== | | |none | 0|word_perplexity|↓ |9.418|± | N/A| ==> Llama3.2-3B_baseline_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.3681|± | N/A| # QAT with this PR (quantized) ==> Llama3.2-3B_qat_bs512/eval_quantized.log <== | | |none | 0|word_perplexity|↓ |10.2281|± | N/A| ``` ghstack-source-id: bb1356c Pull Request resolved: #3050

andrewor14 added 2 commits September 23, 2025 13:32

andrewor14 mentioned this pull request Sep 23, 2025

Support NVFP4 dynamic per tensor scale #3049

Merged

meta-cla Bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 23, 2025

andrewor14 added the topic: improvement Use this tag if this PR is an improvement (doesn't fit into any of the other categories) label Sep 23, 2025

andrewor14 added 2 commits September 23, 2025 14:48

andrewor14 requested review from drisspg, jerryzh168 and vkuzo September 23, 2025 21:49

andrewor14 added 2 commits September 23, 2025 15:01

andrewor14 added 2 commits September 23, 2025 15:09

drisspg reviewed Sep 24, 2025

View reviewed changes

Comment thread torchao/prototype/custom_fp_utils.py Outdated

drisspg reviewed Sep 24, 2025

View reviewed changes

Comment thread torchao/prototype/custom_fp_utils.py Outdated

vkuzo requested changes Sep 24, 2025

View reviewed changes

Comment thread torchao/prototype/custom_fp_utils.py Outdated

Comment thread torchao/prototype/custom_fp_utils.py Outdated

andrewor14 added 2 commits September 24, 2025 13:07

andrewor14 added 2 commits September 24, 2025 15:34

andrewor14 added 2 commits September 26, 2025 07:57

andrewor14 added 2 commits September 26, 2025 09:01

andrewor14 requested review from drisspg and vkuzo September 26, 2025 16:04

andrewor14 added 2 commits September 26, 2025 09:06

jerryzh168 reviewed Sep 26, 2025

View reviewed changes

vkuzo reviewed Sep 26, 2025

View reviewed changes

Comment thread torchao/prototype/qat/nvfp4.py Outdated

jerryzh168 reviewed Sep 26, 2025

View reviewed changes

Comment thread torchao/quantization/qat/linear.py Outdated

andrewor14 added 2 commits September 26, 2025 13:17

vkuzo approved these changes Sep 26, 2025

View reviewed changes

andrewor14 mentioned this pull request Sep 30, 2025

Extend NVFP4 QAT to support emulation #3102

Open

andrewor14 added 2 commits September 30, 2025 07:31

andrewor14 changed the base branch from gh/andrewor14/26/base to main September 30, 2025 15:29

andrewor14 merged commit d407246 into main Sep 30, 2025
34 checks passed

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Improve QAT nvfp4 numerics#3050

Improve QAT nvfp4 numerics#3050
andrewor14 merged 32 commits into
mainfrom
gh/andrewor14/26/head

andrewor14 commented Sep 23, 2025 •

edited

Loading

Uh oh!

pytorch-bot Bot commented Sep 23, 2025 •

edited

Loading

Uh oh!

drisspg Sep 24, 2025

Uh oh!

andrewor14 Sep 26, 2025

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

jerryzh168 Sep 26, 2025

Uh oh!

andrewor14 Sep 26, 2025

Uh oh!

Uh oh!

Uh oh!

vkuzo left a comment

Uh oh!

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

4 participants

		@@ -444,12 +444,16 @@ def _infer_fake_quantize_configs(
		elif isinstance(base_config, NVFP4InferenceConfig):
		if NVFP4MMConfig.DYNAMIC:

Conversation

andrewor14 commented Sep 23, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

pytorch-bot Bot commented Sep 23, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/3050

✅ No Failures

Uh oh!

drisspg Sep 24, 2025

Choose a reason for hiding this comment

Uh oh!

andrewor14 Sep 26, 2025

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

jerryzh168 Sep 26, 2025

Choose a reason for hiding this comment

Uh oh!

andrewor14 Sep 26, 2025

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Uh oh!

vkuzo left a comment

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

4 participants

andrewor14 commented Sep 23, 2025 •

edited

Loading

pytorch-bot Bot commented Sep 23, 2025 •

edited

Loading