From b5a6aa0038ea0ff2d02d9e24e830be368ef51d2e Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:08:46 +0300 Subject: [PATCH 01/61] Release 1.19 only: QA changes to examples --- examples/audio-classification/requirements.txt | 7 ++++--- examples/contrastive-image-text/requirements.txt | 2 +- examples/contrastive-image-text/run_bridgetower.py | 2 +- examples/contrastive-image-text/run_clip.py | 2 +- examples/image-classification/requirements.txt | 8 ++++---- examples/language-modeling/requirements.txt | 6 +++--- examples/question-answering/requirements.txt | 4 ++-- .../nli/requirements.txt | 2 +- .../paraphrases/requirements.txt | 2 +- .../sts/requirements.txt | 2 +- examples/speech-recognition/requirements.txt | 8 ++++---- examples/stable-diffusion/requirements.txt | 3 ++- examples/stable-diffusion/training/requirements.txt | 2 +- examples/summarization/requirements.txt | 12 ++++++------ examples/text-classification/requirements.txt | 8 ++++---- examples/text-generation/requirements.txt | 4 ++-- examples/text-generation/requirements_evaluation.txt | 5 +++++ examples/text-generation/requirements_lm_eval.txt | 12 +++++++++--- examples/text-to-speech/requirements.txt | 4 ++-- examples/translation/requirements.txt | 10 +++++----- examples/trl/requirements.txt | 5 +++-- 21 files changed, 62 insertions(+), 48 deletions(-) create mode 100644 examples/text-generation/requirements_evaluation.txt diff --git a/examples/audio-classification/requirements.txt b/examples/audio-classification/requirements.txt index bae36f7451..a0595c9971 100644 --- a/examples/audio-classification/requirements.txt +++ b/examples/audio-classification/requirements.txt @@ -1,4 +1,5 @@ -datasets>=1.14.0 -evaluate +datasets >= 3.0.2 +evaluate == 0.4.3 numba==0.60.0 -librosa +librosa == 0.10.2.post1 + diff --git a/examples/contrastive-image-text/requirements.txt b/examples/contrastive-image-text/requirements.txt index 877a4cc85f..d1fff8c979 100644 --- a/examples/contrastive-image-text/requirements.txt +++ b/examples/contrastive-image-text/requirements.txt @@ -1 +1 @@ -datasets>=1.8.0 +datasets >= 1.8.0, <= 2.19.2 diff --git a/examples/contrastive-image-text/run_bridgetower.py b/examples/contrastive-image-text/run_bridgetower.py index 0b98b7a0b2..3ad397cdf1 100644 --- a/examples/contrastive-image-text/run_bridgetower.py +++ b/examples/contrastive-image-text/run_bridgetower.py @@ -101,7 +101,7 @@ class ModelArguments: }, ) trust_remote_code: bool = field( - default=False, + default=True, metadata={ "help": ( "Whether to trust the execution of code from datasets/models defined on the Hub." diff --git a/examples/contrastive-image-text/run_clip.py b/examples/contrastive-image-text/run_clip.py index 2e928ec3d6..4ea0ab5f25 100644 --- a/examples/contrastive-image-text/run_clip.py +++ b/examples/contrastive-image-text/run_clip.py @@ -104,7 +104,7 @@ class ModelArguments: }, ) trust_remote_code: bool = field( - default=False, + default=True, metadata={ "help": ( "Whether to trust the execution of code from datasets/models defined on the Hub." diff --git a/examples/image-classification/requirements.txt b/examples/image-classification/requirements.txt index 4cbf42532d..2336488f93 100644 --- a/examples/image-classification/requirements.txt +++ b/examples/image-classification/requirements.txt @@ -1,6 +1,6 @@ -torch>=1.5.0 -torchvision>=0.6.0 -datasets>=2.14.0 -evaluate +torch >= 1.5.0 +torchvision >= 0.6.0 +datasets >= 2.14.0, <= 2.19.2 +evaluate == 0.4.3 scikit-learn == 1.5.2 timm>=0.9.16 diff --git a/examples/language-modeling/requirements.txt b/examples/language-modeling/requirements.txt index aa223dd7f6..a2558a3836 100644 --- a/examples/language-modeling/requirements.txt +++ b/examples/language-modeling/requirements.txt @@ -1,6 +1,6 @@ -datasets >= 2.14.0 +datasets >= 2.14.0, <= 2.19.2 sentencepiece != 0.1.92 -protobuf -evaluate +protobuf == 3.20.3 +evaluate == 0.4.3 scikit-learn == 1.5.2 peft == 0.12.0 diff --git a/examples/question-answering/requirements.txt b/examples/question-answering/requirements.txt index 09d7e4bc77..8a7942f2ad 100644 --- a/examples/question-answering/requirements.txt +++ b/examples/question-answering/requirements.txt @@ -1,3 +1,3 @@ -datasets >= 2.4.0 +datasets >= 2.4.0, <= 2.19.2 torch >= 1.3.0 -evaluate +evaluate == 0.4.3 diff --git a/examples/sentence-transformers-training/nli/requirements.txt b/examples/sentence-transformers-training/nli/requirements.txt index 680dc8a2bb..1b97e4c3d7 100644 --- a/examples/sentence-transformers-training/nli/requirements.txt +++ b/examples/sentence-transformers-training/nli/requirements.txt @@ -1,2 +1,2 @@ -datasets +datasets <= 2.19.2 peft diff --git a/examples/sentence-transformers-training/paraphrases/requirements.txt b/examples/sentence-transformers-training/paraphrases/requirements.txt index aee11b288a..b776a8dd19 100644 --- a/examples/sentence-transformers-training/paraphrases/requirements.txt +++ b/examples/sentence-transformers-training/paraphrases/requirements.txt @@ -1 +1 @@ -datasets +datasets <= 2.19.2 diff --git a/examples/sentence-transformers-training/sts/requirements.txt b/examples/sentence-transformers-training/sts/requirements.txt index 680dc8a2bb..1b97e4c3d7 100644 --- a/examples/sentence-transformers-training/sts/requirements.txt +++ b/examples/sentence-transformers-training/sts/requirements.txt @@ -1,2 +1,2 @@ -datasets +datasets <= 2.19.2 peft diff --git a/examples/speech-recognition/requirements.txt b/examples/speech-recognition/requirements.txt index 67aeeaaa30..f5c8404aa4 100644 --- a/examples/speech-recognition/requirements.txt +++ b/examples/speech-recognition/requirements.txt @@ -1,5 +1,5 @@ -datasets >= 1.18.0 +datasets >= 1.18.0, <= 2.19.2 numba==0.60.0 -librosa -jiwer -evaluate +librosa == 0.10.2.post1 +jiwer == 3.0.4 +evaluate == 0.4.3 diff --git a/examples/stable-diffusion/requirements.txt b/examples/stable-diffusion/requirements.txt index ed24d8c1b7..860ea4edf8 100644 --- a/examples/stable-diffusion/requirements.txt +++ b/examples/stable-diffusion/requirements.txt @@ -1,3 +1,4 @@ -opencv-python +opencv-python == 4.10.0.82 compel sentencepiece +numpy==1.26.4 \ No newline at end of file diff --git a/examples/stable-diffusion/training/requirements.txt b/examples/stable-diffusion/training/requirements.txt index 5795525415..b8a17c887f 100644 --- a/examples/stable-diffusion/training/requirements.txt +++ b/examples/stable-diffusion/training/requirements.txt @@ -1,6 +1,6 @@ compel datasets -imagesize +imagesize == 1.4.1 opencv-python peft==0.10.0 sentencepiece diff --git a/examples/summarization/requirements.txt b/examples/summarization/requirements.txt index 7f9dc2a9c4..8cbb65a9b4 100644 --- a/examples/summarization/requirements.txt +++ b/examples/summarization/requirements.txt @@ -1,8 +1,8 @@ -datasets >= 2.4.0 +datasets >= 2.4.0, <= 2.19.2 sentencepiece != 0.1.92 -protobuf -rouge-score -nltk -py7zr +protobuf == 3.20.3 +rouge-score == 0.1.2 +nltk == 3.8.1 +py7zr == 0.21.0 torch >= 1.3 -evaluate +evaluate == 0.4.3 diff --git a/examples/text-classification/requirements.txt b/examples/text-classification/requirements.txt index 7ce7d0ba42..48617690d1 100644 --- a/examples/text-classification/requirements.txt +++ b/examples/text-classification/requirements.txt @@ -1,7 +1,7 @@ -datasets >= 2.4.0 +datasets >= 2.4.0, <= 2.19.2 sentencepiece != 0.1.92 -scipy +scipy == 1.13.1 scikit-learn == 1.5.2 -protobuf +protobuf == 3.20.3 torch >= 1.3 -evaluate +evaluate == 0.4.3 diff --git a/examples/text-generation/requirements.txt b/examples/text-generation/requirements.txt index 44aebd041a..ca126840c6 100644 --- a/examples/text-generation/requirements.txt +++ b/examples/text-generation/requirements.txt @@ -1,5 +1,5 @@ -datasets -peft +datasets>=3.0.2 +peft == 0.11.1 sentencepiece tiktoken blobfile \ No newline at end of file diff --git a/examples/text-generation/requirements_evaluation.txt b/examples/text-generation/requirements_evaluation.txt new file mode 100644 index 0000000000..596d3f8463 --- /dev/null +++ b/examples/text-generation/requirements_evaluation.txt @@ -0,0 +1,5 @@ +evaluate == 0.4.3 +rouge_score == 0.1.2 +pandas <= 2.2.2 +sentencepiece +nltk==3.8.1 diff --git a/examples/text-generation/requirements_lm_eval.txt b/examples/text-generation/requirements_lm_eval.txt index 9d1bff173f..9f59cceddc 100644 --- a/examples/text-generation/requirements_lm_eval.txt +++ b/examples/text-generation/requirements_lm_eval.txt @@ -1,5 +1,11 @@ -lm-eval==0.4.7 -datasets==2.21.0 +lm-eval==0.4.8 +datasets>=3.0.2 +evaluate == 0.4.3 +rouge_score == 0.1.2 +accelerate +pandas <= 2.2.2 +sentencepiece <= 0.2.0 +langdetect <= 1.0.9 +immutabledict <= 4.2.1 tiktoken blobfile -sentencepiece \ No newline at end of file diff --git a/examples/text-to-speech/requirements.txt b/examples/text-to-speech/requirements.txt index 01d3da67aa..38672ead37 100644 --- a/examples/text-to-speech/requirements.txt +++ b/examples/text-to-speech/requirements.txt @@ -1,3 +1,3 @@ -datasets -soundfile +datasets == 2.19.2 +soundfile == 0.12.1 sentencepiece diff --git a/examples/translation/requirements.txt b/examples/translation/requirements.txt index ff9ede1567..7faa5906d0 100644 --- a/examples/translation/requirements.txt +++ b/examples/translation/requirements.txt @@ -1,7 +1,7 @@ -datasets >= 2.4.0 +datasets >= 2.4.0, <= 2.19.2 sentencepiece != 0.1.92 -protobuf -sacrebleu >= 1.4.12 -py7zr +protobuf == 3.20.3 +sacrebleu >= 1.4.12, <= 2.4.2 +py7zr == 0.21.0 torch >= 1.3 -evaluate +evaluate == 0.4.3 diff --git a/examples/trl/requirements.txt b/examples/trl/requirements.txt index 3a9be36241..6b2ebca6cb 100644 --- a/examples/trl/requirements.txt +++ b/examples/trl/requirements.txt @@ -1,6 +1,7 @@ trl == 0.9.6 peft == 0.12.0 datasets == 2.19.2 -tyro -evaluate +wandb == 0.17.1 +tyro == 0.8.4 +evaluate == 0.4.3 scikit-learn == 1.5.2 From 4f7b3ccfaacf5632846198d3fafbb617760cf075 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz <105052242+astachowiczhabana@users.noreply.github.com> Date: Wed, 25 Jun 2025 11:25:31 +0200 Subject: [PATCH 02/61] Upgrade to lm_eval==4.8.0 (#2082) --- examples/text-generation/run_lm_eval.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/text-generation/run_lm_eval.py b/examples/text-generation/run_lm_eval.py index 00002c3d21..9c40fac922 100644 --- a/examples/text-generation/run_lm_eval.py +++ b/examples/text-generation/run_lm_eval.py @@ -19,6 +19,7 @@ import argparse import json +import logging import multiprocessing as mp import os from typing import Literal, Optional @@ -40,7 +41,7 @@ os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") -logger = utils.eval_logger +logger = logging.getLogger(__name__) # This hack is a workaround to limitations of lm_eval which always allocates # mp.Pool with max cpu count which explodes on multinode scenarios and for hpu From 6f1cfce56560323fa1f6d127a5b1b117fda4d2b1 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:04:06 +0300 Subject: [PATCH 03/61] Add support for setting --junitxml output via JUNITXML_DIR environment variable (#2084) --- conftest.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/conftest.py b/conftest.py index b05956913c..0ef302b270 100644 --- a/conftest.py +++ b/conftest.py @@ -3,6 +3,7 @@ import operator import os import sys +import time from pathlib import Path import pytest @@ -113,6 +114,14 @@ def token(request): def pytest_configure(config): + junitxml_path = config.getoption("junitxml", None) + junitxml_global_dir = os.getenv("JUNITXML_DIR", None) + + if not junitxml_path and junitxml_global_dir: + timestamp = time.strftime("%Y%m%d%H%M%S") + os.makedirs(junitxml_global_dir, exist_ok=True) + config.option.xmlpath = os.path.join(junitxml_global_dir, f"result_{timestamp}.xml") + # Bitsandbytes installation for {test_bnb_qlora.py test_bnb_inference.py} tests # This change will be reverted shortly bnb_tests = any("bnb" in name for name in config.known_args_namespace.file_or_dir) From 67c662ef7b6359303b65ca76d67671671ec4003e Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:06:47 +0300 Subject: [PATCH 04/61] Bitsandbytes installation for qlora tests (#1951) --- conftest.py | 2 +- setup.py | 1 + .../fixture/tests/test_bnb_qlora.json | 42 ++++++++++++++++- tests/test_bnb_qlora.py | 47 +++++++++++++------ 4 files changed, 75 insertions(+), 17 deletions(-) diff --git a/conftest.py b/conftest.py index 0ef302b270..e6dc2b57ff 100644 --- a/conftest.py +++ b/conftest.py @@ -135,7 +135,7 @@ def pytest_configure(config): "-m", "pip", "install", - "git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@multi-backend-refactor", + "git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@main", ] ) name = "" diff --git a/setup.py b/setup.py index 25df5c0542..448965a2c7 100644 --- a/setup.py +++ b/setup.py @@ -52,6 +52,7 @@ "torchsde", "timm", "peft", + "bitsandbytes", "tiktoken", "blobfile", ] diff --git a/tests/baselines/fixture/tests/test_bnb_qlora.json b/tests/baselines/fixture/tests/test_bnb_qlora.json index 6e448772de..2075261672 100644 --- a/tests/baselines/fixture/tests/test_bnb_qlora.json +++ b/tests/baselines/fixture/tests/test_bnb_qlora.json @@ -1,10 +1,50 @@ { - "tests/test_bnb_qlora.py::test_nf4_quantization_finetuning": { + "tests/test_bnb_qlora.py::test_nf4_quantization_finetuning[True-meta-llama/Llama-3.2-1B-8-8]": { "gaudi2": { "eval_loss": 1.225 }, "gaudi3": { "eval_loss": 1.225 } + }, + "tests/test_bnb_qlora.py::test_nf4_quantization_finetuning[False-meta-llama/Llama-3.2-1B-8-8]": { + "gaudi2": { + "eval_loss": 1.225 + }, + "gaudi3": { + "eval_loss": 1.225 + } + }, + "tests/test_bnb_qlora.py::test_nf4_quantization_finetuning[True-meta-llama/Llama-3.1-8B-4-4]": { + "gaudi2": { + "eval_loss": 1.044 + }, + "gaudi3": { + "eval_loss": 1.044 + } + }, + "tests/test_bnb_qlora.py::test_nf4_quantization_finetuning[False-meta-llama/Llama-3.1-8B-4-4]": { + "gaudi2": { + "eval_loss": 1.044 + }, + "gaudi3": { + "eval_loss": 1.044 + } + }, + "tests/test_bnb_qlora.py::test_nf4_quantization_finetuning[True-meta-llama/Llama-3.1-70B-1-1]": { + "gaudi2": { + "eval_loss": 0.961 + }, + "gaudi3": { + "eval_loss": 0.961 + } + }, + "tests/test_bnb_qlora.py::test_nf4_quantization_finetuning[False-meta-llama/Llama-3.1-70B-1-1]": { + "gaudi2": { + "eval_loss": 0.961 + }, + "gaudi3": { + "eval_loss": 0.961 + } } } \ No newline at end of file diff --git a/tests/test_bnb_qlora.py b/tests/test_bnb_qlora.py index 610645939d..3ca1da4348 100644 --- a/tests/test_bnb_qlora.py +++ b/tests/test_bnb_qlora.py @@ -26,9 +26,6 @@ from .utils import OH_DEVICE_CONTEXT -MODEL_ID = "meta-llama/Llama-3.2-1B" - - def print_model_size(model): """ Prints the model size in GB. @@ -59,12 +56,12 @@ def get_data(tokenizer, dataset_name, max_seq_length=1024): data = dataset.map( lambda example: tokenizer(example["text"], max_length=max_seq_length, padding="max_length"), batched=True ) - split_data = data["train"].train_test_split(test_size=0.1, seed=42) + split_data = data["train"].train_test_split(test_size=0.01, seed=42) return split_data -def get_model(token: str): +def get_model(token: str, model_id: str): nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", @@ -72,25 +69,42 @@ def get_model(token: str): ) model = AutoModelForCausalLM.from_pretrained( - MODEL_ID, quantization_config=nf4_config, device_map={"": "hpu"}, torch_dtype=torch.bfloat16, token=token.value + model_id, quantization_config=nf4_config, device_map={"": "hpu"}, torch_dtype=torch.bfloat16, token=token.value ) return model +modeldata = [ + ("meta-llama/Llama-3.2-1B", 8, 8), + ("meta-llama/Llama-3.1-8B", 4, 4), + ("meta-llama/Llama-3.1-70B", 1, 1), +] + + @pytest.mark.skipif("gaudi1" == OH_DEVICE_CONTEXT, reason="execution not supported on gaudi1") -def test_nf4_quantization_finetuning(token: str, baseline): +@pytest.mark.parametrize("model_id, train_bs, eval_bs", modeldata) +@pytest.mark.parametrize("compile_on", [True, False]) +def test_nf4_quantization_finetuning( + token: str, baseline, model_id: str, train_bs: int, eval_bs: int, compile_on: bool +): os.environ["PT_HPU_LAZY_MODE"] = "0" from optimum.habana.transformers import modeling_utils modeling_utils.adapt_transformers_to_gaudi() - tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=token.value, padding_side="right") + tokenizer = AutoTokenizer.from_pretrained(model_id, token=token.value, padding_side="right") # needed for llama tokenizer tokenizer.pad_token = tokenizer.eos_token - model = get_model(token) + model = get_model(token, model_id) model.gradient_checkpointing_enable() + model.generation_config.use_flash_attention = True + model.generation_config.flash_attention_recompute = False + model.generation_config.flash_attention_causal_mask = True + model.generation_config.attn_softmax_bf16 = True + model.generation_config.use_fused_rope = True + is_large_model = "70B" in model_id print_model_size(model) model = prepare_model_for_kbit_training(model) @@ -118,9 +132,9 @@ def test_nf4_quantization_finetuning(token: str, baseline): ) training_args = GaudiTrainingArguments( - eval_strategy="steps", - per_device_train_batch_size=8, - per_device_eval_batch_size=8, + eval_strategy="no", + per_device_train_batch_size=train_bs, + per_device_eval_batch_size=eval_bs, gradient_accumulation_steps=2, max_steps=50, eval_steps=10, @@ -131,9 +145,12 @@ def test_nf4_quantization_finetuning(token: str, baseline): lr_scheduler_type="linear", use_habana=True, use_lazy_mode=False, - pipelining_fwd_bwd=True, adjust_throughput=True, - throughput_warmup_steps=2, + throughput_warmup_steps=3, + torch_compile=compile_on, + torch_compile_backend="hpu_backend" if compile_on else None, + gradient_checkpointing=is_large_model, + use_regional_compilation=compile_on and is_large_model, ) trainer = GaudiTrainer( @@ -149,7 +166,7 @@ def test_nf4_quantization_finetuning(token: str, baseline): trainer.train() baseline.assertRef( - compare=lambda actual, ref: abs(actual - ref) < 5e2, + compare=lambda actual, ref: abs(actual - ref) < 5e-2, context=[OH_DEVICE_CONTEXT], eval_loss=trainer.evaluate()["eval_loss"], ) From 76f6bd774f7cee4148bce0e4c341bec91e5adf4b Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:14:18 +0300 Subject: [PATCH 05/61] Temporarily revert SD quant files to fix promotion (#2069) --- .../measure/fp8_hooks_maxabs.json | 18871 ++++++++++++++++ .../quantization/measure/fp8_hooks_maxabs.npz | Bin 0 -> 263025 bytes .../quantization/measure_config.json | 6 + .../quantization/quant_config.json | 7 + .../maxabs_quant_dynamic_quantization.json | 13 + ...maxabs_quant_dynamic_quantization_pts.json | 13 + .../quantization_config/maxabs_quant_qdq.json | 9 + 7 files changed, 18919 insertions(+) create mode 100644 examples/stable-diffusion/quantization/measure/fp8_hooks_maxabs.json create mode 100644 examples/stable-diffusion/quantization/measure/fp8_hooks_maxabs.npz create mode 100755 examples/stable-diffusion/quantization/measure_config.json create mode 100755 examples/stable-diffusion/quantization/quant_config.json create mode 100644 examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization.json create mode 100644 examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization_pts.json create mode 100644 examples/text-generation/quantization_config/maxabs_quant_qdq.json diff --git a/examples/stable-diffusion/quantization/measure/fp8_hooks_maxabs.json b/examples/stable-diffusion/quantization/measure/fp8_hooks_maxabs.json new file mode 100644 index 0000000000..91a74c633c --- /dev/null +++ b/examples/stable-diffusion/quantization/measure/fp8_hooks_maxabs.json @@ -0,0 +1,18871 @@ +{ + "GlobalRank": null, + "LocalRank": null, + 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0000000000..04576eeb46 --- /dev/null +++ b/examples/stable-diffusion/quantization/measure_config.json @@ -0,0 +1,6 @@ +{ + "method": "HOOKS", + "mode": "MEASURE", + "observer": "maxabs", + "dump_stats_path": "./quantization/measure/fp8" +} diff --git a/examples/stable-diffusion/quantization/quant_config.json b/examples/stable-diffusion/quantization/quant_config.json new file mode 100755 index 0000000000..b372905d7f --- /dev/null +++ b/examples/stable-diffusion/quantization/quant_config.json @@ -0,0 +1,7 @@ +{ + "method": "HOOKS", + "mode": "QUANTIZE", + "observer": "maxabs", + "scale_method": "maxabs_hw", + "dump_stats_path": "./quantization/measure/fp8" +} \ No newline at end of file diff --git a/examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization.json b/examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization.json new file mode 100644 index 0000000000..62d9150d63 --- /dev/null +++ b/examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization.json @@ -0,0 +1,13 @@ +{ + "mode": "QUANTIZE", + "scale_method": "ACT_MAXABS_PCS_POW2_WEIGHT_MAXABS_PTS_POW2_HW", + "scale_format": "CONST", + "allowlist": { + "types": [], + "names": [ + "mlp" + ] + }, + "dynamic_quantization": "True", + "dump_stats_path": "./hqt_output/measure" +} diff --git a/examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization_pts.json b/examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization_pts.json new file mode 100644 index 0000000000..69f41da153 --- /dev/null +++ b/examples/text-generation/quantization_config/maxabs_quant_dynamic_quantization_pts.json @@ -0,0 +1,13 @@ +{ + "mode": "QUANTIZE", + "scale_method": "maxabs_pow2", + "scale_format": "CONST", + "allowlist": { + "types": [], + "names": [ + "mlp" + ] + }, + "dynamic_quantization": "True", + "dump_stats_path": "./hqt_output/measure" +} \ No newline at end of file diff --git a/examples/text-generation/quantization_config/maxabs_quant_qdq.json b/examples/text-generation/quantization_config/maxabs_quant_qdq.json new file mode 100644 index 0000000000..7b87c0d8d8 --- /dev/null +++ b/examples/text-generation/quantization_config/maxabs_quant_qdq.json @@ -0,0 +1,9 @@ +{ + "method": "HOOKS", + "mode": "QUANTIZE", + "observer": "maxabs", + "scale_method": "maxabs_hw", + "scale_format": "SCALAR", + "dump_stats_path": "./hqt_output/measure", + "use_qdq": "True" +} \ No newline at end of file From 0955a80a6ead00616f9f5690305f8015a7c3b78c Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:24:02 +0300 Subject: [PATCH 06/61] Update readme files for explicit lazy mode (#1921) --- examples/stable-diffusion/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md index f9753fe246..b4d4e46557 100644 --- a/examples/stable-diffusion/README.md +++ b/examples/stable-diffusion/README.md @@ -84,7 +84,7 @@ Stable Diffusion 3 was introduced by Stability AI [here](https://stability.ai/ne It uses Diffusion Transformer instead of UNet for denoising, which yields improved image quality. ```bash -PT_HPU_LAZY_MODE=1 \ +PT_HPU_LAZY_MODE=1 PT_HPU_MAX_COMPOUND_OP_SIZE=1 \ python text_to_image_generation.py \ --model_name_or_path stabilityai/stable-diffusion-3-medium-diffusers \ --prompts "Sailing ship painting by Van Gogh" \ @@ -480,4 +480,4 @@ PT_HPU_LAZY_MODE=1 python text_to_video_generation.py \ - **Batch Size Limitation**: Due to a known issue, batch sizes for some Stable Diffusion models need to be reduced. This issue is expected to be resolved in a future release. -- **Image-to-Video ControlNet**: The Image-to-Video ControlNet command is currently not supported on Gaudi3. +- **Image-to-Video ControlNet**: The Image-to-Video ControlNet command is currently not supported on Gaudi3. \ No newline at end of file From b017b2bd7ee7d48916301fe66910b3b1c354e8ff Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:28:32 +0300 Subject: [PATCH 07/61] Integrated NF4 inference tests to text-generation (#2058) --- examples/text-generation/run_generation.py | 5 ++ examples/text-generation/utils.py | 16 +++++ .../fixture/tests/test_bnb_inference.json | 5 -- .../tests/test_text_generation_example.json | 12 ++++ tests/test_bnb_inference.py | 66 ------------------- tests/test_text_generation_example.py | 38 +++++++++++ 6 files changed, 71 insertions(+), 71 deletions(-) delete mode 100644 tests/baselines/fixture/tests/test_bnb_inference.json delete mode 100644 tests/test_bnb_inference.py diff --git a/examples/text-generation/run_generation.py b/examples/text-generation/run_generation.py index ceda678419..3510d4bc01 100755 --- a/examples/text-generation/run_generation.py +++ b/examples/text-generation/run_generation.py @@ -395,6 +395,11 @@ def setup_parser(parser): action="store_true", help="Load an AutoAWQ quantized checkpoint using AutoAWQ.", ) + quant_parser_group.add_argument( + "--quantize_with_bnb", + action="store_true", + help="Quantize model to NF4 using BnB and then use NF4 weights for text-generation", + ) quant_parser_group.add_argument( "--disk_offload", action="store_true", diff --git a/examples/text-generation/utils.py b/examples/text-generation/utils.py index 2a6454be75..1284ebaa0c 100644 --- a/examples/text-generation/utils.py +++ b/examples/text-generation/utils.py @@ -290,6 +290,22 @@ def setup_model(args, model_dtype, model_kwargs, logger): model = AutoModelForCausalLM.from_pretrained( args.model_name_or_path, torch_dtype=model_dtype, quantization_config=quantization_config, **model_kwargs ) + elif args.quantize_with_bnb: + from transformers import BitsAndBytesConfig + + nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.bfloat16, + ) + model = AutoModelForCausalLM.from_pretrained( + args.model_name_or_path, + quantization_config=nf4_config, + device_map={"": "hpu"}, + torch_dtype=model_dtype, + **model_kwargs, + ) elif args.load_quantized_model_with_inc: # TODO: This will be removed in v1.20 Synapse release # Override neural_compressor split_rank_state_dict for loading neural_magic models on multi-cards. diff --git a/tests/baselines/fixture/tests/test_bnb_inference.json b/tests/baselines/fixture/tests/test_bnb_inference.json deleted file mode 100644 index 2056f34d94..0000000000 --- a/tests/baselines/fixture/tests/test_bnb_inference.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "tests/test_bnb_inference.py::test_nf4_quantization_inference": { - "output": "Hello my name is Kelsey and I am a 16 year old girl who loves to draw and paint. I have" - } -} \ No newline at end of file diff --git a/tests/baselines/fixture/tests/test_text_generation_example.json b/tests/baselines/fixture/tests/test_text_generation_example.json index bfd51703fe..6e8bb0887e 100644 --- a/tests/baselines/fixture/tests/test_text_generation_example.json +++ b/tests/baselines/fixture/tests/test_text_generation_example.json @@ -682,5 +682,17 @@ "output": "DeepSpeed is a machine learning framework that enables the training of large-scale models with reduced computational resources. It achieves this by using a technique called model parallelism, which allows the model to be split across multiple GPUs. This makes it possible to train models that are too large to fit on a single GPU.\n## What is DeepSpeed?\nDeepSpeed is a machine learning framework that enables the training of large-scale models with reduced computational resources. It achieves this by using a technique called model parallelism, which allows the model to be split across multiple GPUs", "throughput": 94.70370546821054 } + }, + "tests/test_text_generation_example.py::test_text_generation_bnb[unsloth/Meta-Llama-3.1-70B-bnb-4bit-1-20-False-True]": { + "gaudi2": { + "output": "DeepSpeed is a machine learning framework that makes distributed training easy, efficient, and flexible. DeepSpeed can train BERT-Large on", + "throughput": 0.7572952 + } + }, + "tests/test_text_generation_example.py::test_text_generation_bnb[meta-llama/Llama-3.1-70B-1-20-True-True]": { + "gaudi2": { + "output": "DeepSpeed is a machine learning framework that makes distributed training easy, efficient, and effective. It is a deep learning optimization library that makes", + "throughput": 0.7583387 + } } } \ No newline at end of file diff --git a/tests/test_bnb_inference.py b/tests/test_bnb_inference.py deleted file mode 100644 index 8898e3bd32..0000000000 --- a/tests/test_bnb_inference.py +++ /dev/null @@ -1,66 +0,0 @@ -# coding=utf-8 -# Copyright 2022 the HuggingFace Inc. team. -# -# 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 copy -import os - -import pytest -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig - -from .utils import OH_DEVICE_CONTEXT - - -MODEL_ID = "meta-llama/Llama-3.2-1B" - - -def get_model(token: str): - nf4_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=torch.bfloat16, - ) - - model = AutoModelForCausalLM.from_pretrained( - MODEL_ID, quantization_config=nf4_config, device_map={"": "hpu"}, torch_dtype=torch.bfloat16, token=token.value - ) - - return model - - -@pytest.mark.skipif("gaudi1" == OH_DEVICE_CONTEXT, reason="execution not supported on gaudi1") -def test_nf4_quantization_inference(token: str, baseline): - os.environ["PT_HPU_LAZY_MODE"] = "0" - from optimum.habana.transformers import modeling_utils - - modeling_utils.adapt_transformers_to_gaudi() - - tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=token.value) - - model = get_model(token) - - generation_config = copy.deepcopy(model.generation_config) - generation_config.max_new_tokens = 20 - generation_config.use_cache = True - generation_config.use_flash_attention = True - - input_text = "Hello my name is" - inputs = tokenizer(input_text, return_tensors="pt").to(device="hpu") - - torch.manual_seed(42) - outputs = model.generate(**inputs, generation_config=generation_config, lazy_mode=False) - decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True) - - baseline.assertEqual(output=decoded_output) diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index 6537ebbb23..7295c4e320 100644 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -92,6 +92,10 @@ "load_quantized_model_with_autoawq": [ ("TheBloke/Llama-2-7b-Chat-AWQ", 1, 10, False, 128, 2048), ], + "run_model_with_bnb": [ + ("unsloth/Meta-Llama-3.1-70B-bnb-4bit", 1, 20, False, True), + ("meta-llama/Llama-3.1-70B", 1, 20, True, True), + ], "deepspeed": [ pytest.param("bigscience/bloomz", 8, 1, marks=pytest.mark.x8), # pytest.param("meta-llama/Llama-2-70b-hf", 8, 1, marks=pytest.mark.x8), @@ -143,6 +147,7 @@ "fp8": [], "load_quantized_model_with_autogptq": [], "load_quantized_model_with_autoawq": [], + "run_model_with_bnb": [], "deepspeed": [ ("bigscience/bloomz-7b1", 8, 1), ], @@ -168,6 +173,7 @@ def _test_text_generation( fp8: bool = False, load_quantized_model_with_autogptq: bool = False, load_quantized_model_with_autoawq: bool = False, + quantize_with_bnb: bool = False, max_input_tokens: int = 0, max_output_tokens: int = 100, parallel_strategy: str = None, @@ -175,6 +181,7 @@ def _test_text_generation( num_beams: int = 1, num_return_sequences: int = 1, check_output: bool = False, + regional_compile: bool = False, ): command = ["python3"] path_to_example_dir = Path(__file__).resolve().parent.parent / "examples" @@ -239,6 +246,8 @@ def _test_text_generation( if parallel_strategy == "tp": command += ["--use_flash_attention"] command += ["--flash_attention_recompute"] + if regional_compile: + command += ["--regional_compile"] env_variables["PT_ENABLE_INT64_SUPPORT"] = "1" env_variables["PT_HPU_LAZY_MODE"] = "0" else: @@ -306,6 +315,8 @@ def _test_text_generation( command += ["--load_quantized_model_with_autogptq"] if load_quantized_model_with_autoawq: command += ["--load_quantized_model_with_autoawq"] + if quantize_with_bnb: + command += ["--quantize_with_bnb"] if parallel_strategy is not None: command += [ f"--parallel_strategy={parallel_strategy}", @@ -498,6 +509,33 @@ def test_text_generation_awq( ) +@pytest.mark.skipif(condition=bool("gaudi1" == OH_DEVICE_CONTEXT), reason=f"Skipping test for {OH_DEVICE_CONTEXT}") +@pytest.mark.parametrize( + "model_name, world_size, output_len, quantize_with_bnb, check_output", + MODELS_TO_TEST["run_model_with_bnb"], +) +def test_text_generation_bnb( + model_name: str, + world_size: int, + output_len: int, + quantize_with_bnb: bool, + check_output: bool, + baseline, + token, +): + _test_text_generation( + model_name, + baseline, + token, + world_size=world_size, + torch_compile=True, + regional_compile=True, + quantize_with_bnb=quantize_with_bnb, + max_output_tokens=output_len, + check_output=check_output, + ) + + @pytest.mark.parametrize("model_name, world_size, batch_size", MODELS_TO_TEST["deepspeed"]) def test_text_generation_deepspeed(model_name: str, world_size: int, batch_size: int, baseline, token): _test_text_generation(model_name, baseline, token, deepspeed=True, world_size=world_size, batch_size=batch_size) From 54aded1706328edbb29a9e3d79904eb30797ebc1 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:42:01 +0300 Subject: [PATCH 08/61] Remove bitsandbytes monkey-patching (II) (#2114) --- optimum/habana/quantizers/bitsandbytes.py | 265 ------------------ optimum/habana/transformers/modeling_utils.py | 15 - 2 files changed, 280 deletions(-) delete mode 100644 optimum/habana/quantizers/bitsandbytes.py diff --git a/optimum/habana/quantizers/bitsandbytes.py b/optimum/habana/quantizers/bitsandbytes.py deleted file mode 100644 index ee56b55d53..0000000000 --- a/optimum/habana/quantizers/bitsandbytes.py +++ /dev/null @@ -1,265 +0,0 @@ -from functools import lru_cache -from typing import Any, Dict, List, Optional - -from transformers.modeling_utils import PreTrainedModel -from transformers.pytorch_utils import Conv1D -from transformers.quantizers.quantizers_utils import get_module_from_name -from transformers.utils import ( - ACCELERATE_MIN_VERSION, - get_available_devices, - is_accelerate_available, - is_bitsandbytes_multi_backend_available, - is_ipex_available, - is_torch_available, - logging, -) -from transformers.utils.import_utils import _is_package_available - - -if is_torch_available(): - import torch - -_bitsandbytes_available = _is_package_available("bitsandbytes") -logger = logging.get_logger(__name__) - - -def gaudi_bitsandbytesconfig_post_init(self): - r""" - Safety checker that arguments are correct - also replaces some NoneType arguments with their default values. - Copied from https://github.com/huggingface/transformers/blob/53fad641cfdb5105e2470bcf3ef17ea8e25cc300/src/transformers/utils/quantization_config.py#L430 - Only difference is removed check on bitsandbytes version - """ - if not isinstance(self.load_in_4bit, bool): - raise TypeError("load_in_4bit must be a boolean") - - if not isinstance(self.load_in_8bit, bool): - raise TypeError("load_in_8bit must be a boolean") - - if not isinstance(self.llm_int8_threshold, float): - raise TypeError("llm_int8_threshold must be a float") - - if self.llm_int8_skip_modules is not None and not isinstance(self.llm_int8_skip_modules, list): - raise TypeError("llm_int8_skip_modules must be a list of strings") - if not isinstance(self.llm_int8_enable_fp32_cpu_offload, bool): - raise TypeError("llm_int8_enable_fp32_cpu_offload must be a boolean") - - if not isinstance(self.llm_int8_has_fp16_weight, bool): - raise TypeError("llm_int8_has_fp16_weight must be a boolean") - - if self.bnb_4bit_compute_dtype is not None and not isinstance(self.bnb_4bit_compute_dtype, torch.dtype): - raise TypeError("bnb_4bit_compute_dtype must be torch.dtype") - - if not isinstance(self.bnb_4bit_quant_type, str): - raise TypeError("bnb_4bit_quant_type must be a string") - - if not isinstance(self.bnb_4bit_use_double_quant, bool): - raise TypeError("bnb_4bit_use_double_quant must be a boolean") - - -@lru_cache() -def gaudi_is_bitsandbytes_available(): - """ - Copied from https://github.com/huggingface/transformers/blob/5523e38b553ff6c46b04d2376870fcd842feeecc/src/transformers/utils/import_utils.py#L871 - Only difference is that CUDA related checks are removed. - """ - if not is_torch_available() or not _bitsandbytes_available: - return False - - # Newer versions of `bitsandbytes` can be imported on systems without CUDA. - return True - - -def gaudi_validate_bnb_backend_availability(raise_exception=False): - """ - Validates if the available devices are supported by bitsandbytes, optionally raising an exception if not. - Copied from https://github.com/huggingface/transformers/blob/5523e38b553ff6c46b04d2376870fcd842feeecc/src/transformers/integrations/bitsandbytes.py#L545 - Only difference is that CUDA related functions calls are deleted. - """ - if is_bitsandbytes_multi_backend_available(): - return _gaudi_validate_bnb_multi_backend_availability(raise_exception) - - -def _gaudi_validate_bnb_multi_backend_availability(raise_exception): - """ - Copied https://github.com/huggingface/transformers/blob/5523e38b553ff6c46b04d2376870fcd842feeecc/src/transformers/integrations/bitsandbytes.py#L484 - Only difference is addition of check for HPU - """ - import bitsandbytes as bnb - - bnb_supported_devices = getattr(bnb, "supported_torch_devices", set()) - available_devices = get_available_devices() - - if "hpu" in bnb_supported_devices: - logger.debug("Multi-backend validation successful.") - return True - - if available_devices == {"cpu"} and not is_ipex_available(): - from importlib.util import find_spec - - if find_spec("intel_extension_for_pytorch"): - logger.warning( - "You have Intel IPEX installed but if you're intending to use it for CPU, it might not have the right version. Be sure to double check that your PyTorch and IPEX installs are compatible." - ) - - available_devices.discard("cpu") # Only Intel CPU is supported by BNB at the moment - - if not available_devices.intersection(bnb_supported_devices): - if raise_exception: - bnb_supported_devices_with_info = set( # noqa: C401 - '"cpu" (needs an Intel CPU and intel_extension_for_pytorch installed and compatible with the PyTorch version)' - if device == "cpu" - else device - for device in bnb_supported_devices - ) - err_msg = ( - f"None of the available devices `available_devices = {available_devices or None}` are supported by the bitsandbytes version you have installed: `bnb_supported_devices = {bnb_supported_devices_with_info}`. " - "Please check the docs to see if the backend you intend to use is available and how to install it: https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend" - ) - - logger.error(err_msg) - raise RuntimeError(err_msg) - - logger.warning("No supported devices found for bitsandbytes multi-backend.") - return False - - logger.debug("Multi-backend validation successful.") - return True - - -def gaudi_validate_environment(self, *args, **kwargs): - """ - Copied from https://github.com/huggingface/transformers/blob/5523e38b553ff6c46b04d2376870fcd842feeecc/src/transformers/quantizers/quantizer_bnb_4bit.py#L68 - Only difference is deletion of bitsandbytes version checks - """ - if not is_accelerate_available(): - raise ImportError( - f"Using `bitsandbytes` 4-bit quantization requires Accelerate: `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`" - ) - if not gaudi_is_bitsandbytes_available(): - raise ImportError( - "Using `bitsandbytes` 4-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`" - ) - - bnb_multibackend_is_enabled = is_bitsandbytes_multi_backend_available() - gaudi_validate_bnb_backend_availability(raise_exception=True) - - if kwargs.get("from_tf", False) or kwargs.get("from_flax", False): - raise ValueError( - "Converting into 4-bit or 8-bit weights from tf/flax weights is currently not supported, please make" - " sure the weights are in PyTorch format." - ) - - device_map = kwargs.get("device_map", None) - if ( - device_map is not None - and isinstance(device_map, dict) - and not self.quantization_config.llm_int8_enable_fp32_cpu_offload - ): - device_map_without_lm_head = { - key: device_map[key] for key in device_map.keys() if key not in self.modules_to_not_convert - } - if set(device_map.values()) == {"cpu"} and bnb_multibackend_is_enabled: - pass - elif "cpu" in device_map_without_lm_head.values() or "disk" in device_map_without_lm_head.values(): - raise ValueError( - "Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the " - "quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules " - "in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to " - "`from_pretrained`. Check " - "https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu " - "for more details. " - ) - - -def gaudi_create_quantized_param( - self, - model: "PreTrainedModel", - param_value: "torch.Tensor", - param_name: str, - target_device: "torch.device", - state_dict: Dict[str, Any], - unexpected_keys: Optional[List[str]] = None, -): - """ - Copied from https://github.com/huggingface/transformers/blob/62c60a30181a65e1a3a7f19c3055a240a6a21335/src/transformers/quantizers/quantizer_bnb_4bit.py#L138 - only diiference is addition of HPU device - """ - import bitsandbytes as bnb - - module, tensor_name = get_module_from_name(model, param_name) - - if tensor_name not in module._parameters: - raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.") - - old_value = getattr(module, tensor_name) - - if tensor_name == "bias": - if param_value is None: - new_value = old_value.to(target_device) - else: - new_value = param_value.to(target_device) - - new_value = torch.nn.Parameter(new_value, requires_grad=old_value.requires_grad) - module._parameters[tensor_name] = new_value - return - - if not isinstance(module._parameters[tensor_name], bnb.nn.Params4bit): - raise ValueError("this function only loads `Linear4bit components`") - if ( - old_value.device == torch.device("meta") - and target_device not in ["meta", torch.device("meta")] - and param_value is None - ): - raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {target_device}.") - - # construct `new_value` for the module._parameters[tensor_name]: - if self.pre_quantized: - # 4bit loading. Collecting components for restoring quantized weight - # This can be expanded to make a universal call for any quantized weight loading - - if not self.is_serializable: - raise ValueError( - "Detected int4 weights but the version of bitsandbytes is not compatible with int4 serialization. " - "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`." - ) - - if (param_name + ".quant_state.bitsandbytes__fp4" not in state_dict) and ( - param_name + ".quant_state.bitsandbytes__nf4" not in state_dict - ): - raise ValueError( - f"Supplied state dict for {param_name} does not contain `bitsandbytes__*` and possibly other `quantized_stats` components." - ) - - quantized_stats = {} - for k, v in state_dict.items(): - if param_name + "." in k: - quantized_stats[k] = v - if unexpected_keys is not None and k in unexpected_keys: - unexpected_keys.remove(k) - - param_kwargs = {} - if self.is_bnb_supports_quant_storage_module: - param_kwargs["module"] = module - - new_value = bnb.nn.Params4bit.from_prequantized( - data=param_value, - quantized_stats=quantized_stats, - requires_grad=False, - device=target_device, - **param_kwargs, - ) - else: - if target_device == "hpu": - new_value = param_value.to("hpu") - else: - new_value = param_value.to("cpu") - - # Support models using `Conv1D` in place of `nn.Linear` (e.g. openai-community/gpt2) by transposing the weight matrix prior to quantization. - # Since weights are saved in the correct "orientation", we skip transposing when loading. - if issubclass(module.source_cls, Conv1D): - new_value = new_value.T - - kwargs = old_value.__dict__ - new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device) - - module._parameters[tensor_name] = new_value diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py index b799e8ee45..2b1c19b1fa 100644 --- a/optimum/habana/transformers/modeling_utils.py +++ b/optimum/habana/transformers/modeling_utils.py @@ -18,13 +18,6 @@ import transformers import transformers.utils.fx -from ..quantizers.bitsandbytes import ( - gaudi_bitsandbytesconfig_post_init, - gaudi_create_quantized_param, - gaudi_is_bitsandbytes_available, - gaudi_validate_bnb_backend_availability, - gaudi_validate_environment, -) from .generation import ( GaudiGenerationConfig, GaudiGenerationMixin, @@ -321,14 +314,6 @@ def adapt_transformers_to_gaudi(): for Gaudi. """ - transformers.utils.quantization_config.BitsAndBytesConfig.post_init = gaudi_bitsandbytesconfig_post_init - transformers.utils.import_utils.is_bitsandbytes_available = gaudi_is_bitsandbytes_available - transformers.utils.is_bitsandbytes_available = gaudi_is_bitsandbytes_available - transformers.quantizers.quantizer_bnb_4bit.is_bitsandbytes_available = gaudi_is_bitsandbytes_available - transformers.integrations.bitsandbytes.validate_bnb_backend_availability = gaudi_validate_bnb_backend_availability - transformers.quantizers.quantizer_bnb_4bit.Bnb4BitHfQuantizer.validate_environment = gaudi_validate_environment - transformers.quantizers.quantizer_bnb_4bit.Bnb4BitHfQuantizer.create_quantized_param = gaudi_create_quantized_param - # models that support symbolic tracing should be added to this list models_with_tracing_support = [] From c03dbeac652463dea32f9a819fdf91c4ee3507c8 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:45:54 +0300 Subject: [PATCH 09/61] Add groups to slow_tests_image_to_text_example (#2008) --- Makefile | 5 ++++- tests/test_image_to_text_example.py | 4 ++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 8de3d66c05..b6c62d5b40 100644 --- a/Makefile +++ b/Makefile @@ -145,7 +145,10 @@ slow_tests_image_to_text_example: test_installs python -m pytest tests/test_image_to_text_example.py -v -s --token $(TOKEN) slow_tests_image_to_text_example_1x: test_installs - python -m pytest tests/test_image_to_text_example.py -m "not x8" -v -s --token $(TOKEN) + python -m pytest tests/test_image_to_text_example.py -m "(not x2) and (not x8)" -v -s --token $(TOKEN) + +slow_tests_image_to_text_example_2x: test_installs + python -m pytest tests/test_image_to_text_example.py -m x2 -v -s --token $(TOKEN) slow_tests_image_to_text_example_8x: test_installs python -m pytest tests/test_image_to_text_example.py -m x8 -v -s --token $(TOKEN) diff --git a/tests/test_image_to_text_example.py b/tests/test_image_to_text_example.py index de805dffd2..f58e097d33 100644 --- a/tests/test_image_to_text_example.py +++ b/tests/test_image_to_text_example.py @@ -32,8 +32,8 @@ "fp8": [ # ("llava-hf/llava-1.5-7b-hf", 1), # ("llava-hf/llava-1.5-13b-hf", 1), - ("llava-hf/llava-v1.6-mistral-7b-hf", 1), - ("llava-hf/llava-v1.6-vicuna-7b-hf", 1), + pytest.param("llava-hf/llava-v1.6-mistral-7b-hf", 1, marks=pytest.mark.x2), + pytest.param("llava-hf/llava-v1.6-vicuna-7b-hf", 1, marks=pytest.mark.x2), pytest.param("llava-hf/llava-v1.6-vicuna-13b-hf", 1, marks=pytest.mark.x8), ], } From 61be32b45b44c8216d8064414ad0490a84632cae Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 18:09:06 +0300 Subject: [PATCH 10/61] Lm eval accuracy regression fix (#2105) --- examples/text-generation/run_lm_eval.py | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/examples/text-generation/run_lm_eval.py b/examples/text-generation/run_lm_eval.py index 9c40fac922..92ad6632e5 100644 --- a/examples/text-generation/run_lm_eval.py +++ b/examples/text-generation/run_lm_eval.py @@ -22,12 +22,13 @@ import logging import multiprocessing as mp import os -from typing import Literal, Optional +from typing import List, Literal, Optional import psutil import torch import torch.nn.functional as F from lm_eval import evaluator, utils +from lm_eval.api.instance import Instance from lm_eval.models.huggingface import HFLM, TemplateLM from lm_eval.models.utils import stop_sequences_criteria @@ -206,12 +207,21 @@ def warm_up(self) -> None: def eot_token_id(self) -> int: return self._model.config.eos_token_id + @property + def max_length(self) -> int: + # Legacy + return self._max_length if self._max_length else self.buckets[-2] + @property def device(self): # We need to do padding ourselves, otherwise we'll end up with recompilations # Returning 'cpu' to keep tensors on CPU in lm_eval code return "cpu" + @max_length.setter + def max_length(self, value: int) -> None: + self._max_length = value + def find_bucket(self, length: int) -> list[int]: return [b for b in self.buckets if b >= length][0] @@ -229,8 +239,20 @@ def _model_call(self, inps: torch.Tensor) -> torch.Tensor: if self.options.static_shapes and padding_length > 0: logits = logits[:, :-padding_length, :] logits = logits.to(torch.float32) + return logits + def generate_until(self, requests: List[Instance], disable_tqdm: bool = False) -> List[str]: + """ + Override to change only max_length property + """ + legacy_max_length = self.max_length + self.max_length = super().max_length + # Call the parent class's implementation for the unchanged parts + res = super().generate_until(requests, disable_tqdm) + self.max_length = legacy_max_length + return res + def _model_generate(self, context, max_length, stop, **generation_kwargs): """ Patched method From 2b6651382b35d7640a5d8430264c1a50d750be91 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 18:10:18 +0300 Subject: [PATCH 11/61] Skip unnecessary padding in text generation task (#2055) --- optimum/habana/transformers/generation/utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/optimum/habana/transformers/generation/utils.py b/optimum/habana/transformers/generation/utils.py index e92b07d6c9..8b7e906020 100755 --- a/optimum/habana/transformers/generation/utils.py +++ b/optimum/habana/transformers/generation/utils.py @@ -2474,6 +2474,7 @@ def _contrastive_search( do_padding = ( key_to_check is not None and outputs.past_key_values[0][0].shape[2] == model_inputs[key_to_check].shape[1] + and generation_config.max_new_tokens > 1 ) if do_padding: @@ -2838,6 +2839,7 @@ def _sample( do_padding = ( key_to_check is not None and outputs.past_key_values[0][0].shape[2] == model_inputs[key_to_check].shape[1] + and generation_config.max_new_tokens > 1 ) if do_padding: From 1cf20329262b6add9b4fa4541d5f8860f99156a8 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz Date: Mon, 7 Jul 2025 14:35:34 +0300 Subject: [PATCH 12/61] Reduce index_copy to fp8 in llama2 - QDQ flow #2065 --- optimum/habana/transformers/models/llama/modeling_llama.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/optimum/habana/transformers/models/llama/modeling_llama.py b/optimum/habana/transformers/models/llama/modeling_llama.py index 75d8097cc8..4b4b903bfd 100755 --- a/optimum/habana/transformers/models/llama/modeling_llama.py +++ b/optimum/habana/transformers/models/llama/modeling_llama.py @@ -522,7 +522,7 @@ def get_k_proj_weight_dtype(self): if hasattr(self.k_proj, "qweight"): return self.k_proj.scales.dtype elif hasattr(self.k_proj, "use_qdq") and self.k_proj.use_qdq: - return self.k_proj.dequant_weights.hp_dtype + return self.k_proj.weight.dtype elif isinstance(self.k_cache, KVCache) and "float8" in str(self.k_proj.weight.dtype): return self.k_proj.hp_dtype return self.k_proj.weight.dtype @@ -1000,7 +1000,6 @@ def forward( valid_sequence_lengths=sub_valid_sequence_lengths[i], cache_idx=cache_idx, num_virtual_tokens=num_virtual_tokens, - **kwargs, ) self.self_attn.attention_all_reduce(split_hidden_states[i]) if output_attentions: @@ -1044,7 +1043,6 @@ def forward( valid_sequence_lengths=valid_sequence_lengths, cache_idx=cache_idx, num_virtual_tokens=num_virtual_tokens, - **kwargs, ) self.self_attn.attention_all_reduce(hidden_states) hidden_states, residual = self.post_attn_pre_mlp(hidden_states, residual) From 62b45d77e2a2e150957b46adacb4c14bb0fd34d8 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz <105052242+astachowiczhabana@users.noreply.github.com> Date: Wed, 9 Jul 2025 15:09:26 +0200 Subject: [PATCH 13/61] Unify SetTrueOrFalseOrNone and StoreTrueFalseAction (#2119) * Unify SetTrueOrFalseOrNone and StoreTrueFalseAction * Fix style --- examples/text-generation/run_generation.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/examples/text-generation/run_generation.py b/examples/text-generation/run_generation.py index 3510d4bc01..b4c38574ab 100755 --- a/examples/text-generation/run_generation.py +++ b/examples/text-generation/run_generation.py @@ -280,17 +280,26 @@ def setup_parser(parser): ) parser.add_argument( "--use_flash_attention", - action="store_true", + nargs="?", + const=True, + default=False, + action=SetTrueOrFalseOrNone, help="Whether to enable Habana Flash Attention, provided that the model supports it.", ) parser.add_argument( "--flash_attention_recompute", - action="store_true", + nargs="?", + const=True, + default=False, + action=SetTrueOrFalseOrNone, help="Whether to enable Habana Flash Attention in recompute mode on first token generation. This gives an opportunity of splitting graph internally which helps reduce memory consumption.", ) parser.add_argument( "--flash_attention_causal_mask", - action="store_true", + nargs="?", + const=True, + default=False, + action=SetTrueOrFalseOrNone, help="Whether to enable Habana Flash Attention in causal mode on first token generation.", ) parser.add_argument( From a5500eabf7dbcf3c97087c34a017038f9bfa2371 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz <105052242+astachowiczhabana@users.noreply.github.com> Date: Fri, 11 Jul 2025 12:13:20 +0200 Subject: [PATCH 14/61] Fix profiler (#2134) --- examples/text-generation/run_generation.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) mode change 100755 => 100644 examples/text-generation/run_generation.py diff --git a/examples/text-generation/run_generation.py b/examples/text-generation/run_generation.py old mode 100755 new mode 100644 index b4c38574ab..190eb8e54f --- a/examples/text-generation/run_generation.py +++ b/examples/text-generation/run_generation.py @@ -560,6 +560,7 @@ def get_input(ds, batch_size): def generate(input_tokens, size=None, reduce_recompile=False, disable_profiling=False): """Generates sequences from the input sentences and returns them.""" + profiler = disabled_profiler if disable_profiling else per_token_profiler timer = HabanaGenerationTime() timer.start() @@ -579,6 +580,7 @@ def generate(input_tokens, size=None, reduce_recompile=False, disable_profiling= lazy_mode=use_lazy_mode, hpu_graphs=args.use_hpu_graphs, ignore_eos=args.ignore_eos, + profiler=profiler, ).cpu() outputs = outputs.tolist() for i in range(len(outputs)): @@ -626,6 +628,7 @@ def rounder(x): # Benchmark over n_iterations iterations N = len(input_sentences) + per_sequence_profiler.start() if dyn_prompt_lens is None: for i in range(args.n_iterations): results = [] @@ -635,6 +638,7 @@ def rounder(x): results.extend(generated) print(f"Generating batch {b}/{N}") b += 1 + per_sequence_profiler.step() else: repeated_prompt_len = cycle(dyn_prompt_lens) for i in range(args.n_iterations): @@ -644,8 +648,10 @@ def rounder(x): for sentence in input_sentences: generated = generate(sentence, prompt_len, args.reduce_recompile) results.extend(generated) + per_sequence_profiler.step() timer.step() duration = timer.last_duration + per_sequence_profiler.stop() total_new_tokens_generated = args.n_iterations * args.batch_size * args.max_new_tokens throughput = total_new_tokens_generated / duration @@ -871,24 +877,24 @@ def rounder(x): if dyn_prompt_lens is None: for i in range(args.n_iterations): generated, first_token_time, rest_token_time, e2e_latency = generate(None, args.reduce_recompile) + per_sequence_profiler.step() first_token_latencies.append(first_token_time) rest_token_latencies.append(rest_token_time) e2e_latencies.append(e2e_latency) - per_sequence_profiler.step() else: repeated_prompt_len = cycle(dyn_prompt_lens) for i in range(args.n_iterations): prompt_len = next(repeated_prompt_len) print("Generating for shape,", prompt_len) generated, first_token_time, rest_token_time, e2e_latency = generate(prompt_len, args.reduce_recompile) + per_sequence_profiler.step() first_token_latencies.append(first_token_time) rest_token_latencies.append(rest_token_time) e2e_latencies.append(e2e_latency) - per_sequence_profiler.step() timer.step() + per_sequence_profiler.stop() logger.info("Finished running generate") duration = timer.last_duration - per_sequence_profiler.stop() total_new_tokens_generated = args.n_iterations * args.batch_size * args.max_new_tokens throughput = total_new_tokens_generated / duration # Calculate average latencies @@ -1045,7 +1051,7 @@ def generate_dataset(batch, disable_profiling=False): timer.start() for i, batch in enumerate(dataloader): timer.step() - generate_dataset(batch) + generate_dataset(batch, disable_profiling=True) timer.step() duration = timer.last_duration # The first three iterations take longer because of graph compilation @@ -1054,15 +1060,14 @@ def generate_dataset(batch, disable_profiling=False): torch_hpu.synchronize() timer.step() compilation_duration = timer.last_duration + total_new_tokens_generated = 0 duration = 0 separator = "-" * 50 logger.info("Running generate dataset...") - timer = HabanaGenerationTime() timer.start() per_sequence_profiler.start() - for i, batch in enumerate(dataloader): timer.step() prompt, outputs = generate_dataset(batch) @@ -1079,8 +1084,8 @@ def generate_dataset(batch, disable_profiling=False): if args.run_partial_dataset and args.n_iterations == i + 1: break per_sequence_profiler.step() - timer.step() per_sequence_profiler.stop() + timer.step() throughput = total_new_tokens_generated / duration # Print Stats From ccf00fbfe3801a29b6545b2c8a1141408446dc70 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz <105052242+astachowiczhabana@users.noreply.github.com> Date: Fri, 11 Jul 2025 12:13:36 +0200 Subject: [PATCH 15/61] Fix missing openorca dataset (#2133) --- examples/text-generation/run_generation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/text-generation/run_generation.py b/examples/text-generation/run_generation.py index 190eb8e54f..b5d6403562 100644 --- a/examples/text-generation/run_generation.py +++ b/examples/text-generation/run_generation.py @@ -530,7 +530,7 @@ def main(): per_sequence_profiler = disabled_profiler per_token_profiler = active_profiler - if args.dataset_name == "mlcommons": + if args.dataset_name == "openorca" or args.dataset_name == "mlcommons": # Benchmark over the prompts below def get_ds(args): ds = pd.read_pickle(args.mlcommons_dataset) From ca2f99432257779622b2df7595d5bd081d001955 Mon Sep 17 00:00:00 2001 From: Adam Stachowicz <105052242+astachowiczhabana@users.noreply.github.com> Date: Fri, 11 Jul 2025 17:59:48 +0200 Subject: [PATCH 16/61] Fix utils package #2141 (#2142) Fix utils package A previous PR [1] introduced a new package `optimum.habana.utils` which created a conflict with the `optimum/habana/utils.py` file. This commit adds the missing `utils/__init__.py` file and moves the `utils.py` file into the `utils/` module (renamed as `misc.py`). [1] https://github.com/huggingface/optimum-habana/pull/1926 Co-authored-by: Piotr Bielak --- optimum/habana/utils/__init__.py | 1 + optimum/habana/{utils.py => utils/misc.py} | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 optimum/habana/utils/__init__.py rename optimum/habana/{utils.py => utils/misc.py} (99%) diff --git a/optimum/habana/utils/__init__.py b/optimum/habana/utils/__init__.py new file mode 100644 index 0000000000..5bade17cdf --- /dev/null +++ b/optimum/habana/utils/__init__.py @@ -0,0 +1 @@ +from .misc import * # NOTE(pbielak): mimic previous imports from `utils.py` diff --git a/optimum/habana/utils.py b/optimum/habana/utils/misc.py similarity index 99% rename from optimum/habana/utils.py rename to optimum/habana/utils/misc.py index 74f42f909b..4239fabebc 100755 --- a/optimum/habana/utils.py +++ b/optimum/habana/utils/misc.py @@ -26,7 +26,7 @@ from optimum.utils import logging -from .version import __version__ +from ..version import __version__ logger = logging.get_logger(__name__) From 5cc564e0561f96f623b87493232ebf35f5068105 Mon Sep 17 00:00:00 2001 From: Yaser Afshar Date: Fri, 11 Jul 2025 10:52:40 -0700 Subject: [PATCH 17/61] Sync/videollava (#2129) * Align VideoLlavaProcessor with Transformers v4.51.3 The GaudiVideoLlavaProcessor has been removed from optimum-habana as its functionality is now fully aligned with the upstream Transformers implementation. No custom logic is required, and maintaining a separate class is redundant. * Update GaudiVideoLlavaForConditionalGeneration This update aligns the GaudiVideoLlavaForConditionalGeneration implementation with the v4.51.3 transformers changes while retaining `token_idx` argument for compatibility with Gaudi optimizations. --- examples/video-comprehension/run_example.py | 4 +- optimum/habana/transformers/modeling_utils.py | 2 - .../habana/transformers/models/__init__.py | 2 +- .../models/video_llava/__init__.py | 1 - .../video_llava/modeling_video_llava.py | 398 +++--------------- .../video_llava/processing_video_llava.py | 108 ----- 6 files changed, 64 insertions(+), 451 deletions(-) delete mode 100644 optimum/habana/transformers/models/video_llava/processing_video_llava.py diff --git a/examples/video-comprehension/run_example.py b/examples/video-comprehension/run_example.py index b53679fb0b..5868bea3e8 100644 --- a/examples/video-comprehension/run_example.py +++ b/examples/video-comprehension/run_example.py @@ -24,10 +24,10 @@ import numpy as np import torch from huggingface_hub import hf_hub_download +from transformers import VideoLlavaProcessor from optimum.habana.transformers.modeling_utils import ( GaudiVideoLlavaForConditionalGeneration, - GaudiVideoLlavaProcessor, adapt_transformers_to_gaudi, ) @@ -168,7 +168,7 @@ def main(): model = wrap_in_hpu_graph(model) - processor = GaudiVideoLlavaProcessor.from_pretrained(args.model_name_or_path) + processor = VideoLlavaProcessor.from_pretrained(args.model_name_or_path) processor.tokenizer.padding_side = "left" inputs = processor(text=prompts, videos=video_clips, return_tensors="pt") inputs = inputs.to(device) diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py index 2b1c19b1fa..8704f5499f 100644 --- a/optimum/habana/transformers/modeling_utils.py +++ b/optimum/habana/transformers/modeling_utils.py @@ -183,7 +183,6 @@ GaudiStarcoder2ForCausalLM, GaudiStarcoder2Model, GaudiVideoLlavaForConditionalGeneration, - GaudiVideoLlavaProcessor, GaudiVisionSdpaAttention, GaudiWav2Vec2SdpaAttention, GaudiWhisperDecoder, @@ -757,7 +756,6 @@ def adapt_transformers_to_gaudi(): transformers.models.video_llava.modeling_video_llava.VideoLlavaForConditionalGeneration = ( GaudiVideoLlavaForConditionalGeneration ) - transformers.models.video_llava.processing_video_llava.VideoLlavaProcessor = GaudiVideoLlavaProcessor # Optimization for Whisper on Gaudi transformers.models.whisper.modeling_whisper.WhisperSdpaAttention = GaudiWhisperSdpaAttention diff --git a/optimum/habana/transformers/models/__init__.py b/optimum/habana/transformers/models/__init__.py index 13370570ca..1f96aa15c3 100644 --- a/optimum/habana/transformers/models/__init__.py +++ b/optimum/habana/transformers/models/__init__.py @@ -339,7 +339,7 @@ gaudi_T5Stack_forward, ) from .table_transformer import gaudi_table_transformer_conv_encoder_forward -from .video_llava import GaudiVideoLlavaForConditionalGeneration, GaudiVideoLlavaProcessor +from .video_llava import GaudiVideoLlavaForConditionalGeneration from .vision_encoder_decoder import ( gaudi_VisionEncoderDecoderModel_prepare_inputs_for_generation, ) diff --git a/optimum/habana/transformers/models/video_llava/__init__.py b/optimum/habana/transformers/models/video_llava/__init__.py index 5c5f894d56..57831502dc 100644 --- a/optimum/habana/transformers/models/video_llava/__init__.py +++ b/optimum/habana/transformers/models/video_llava/__init__.py @@ -1,2 +1 @@ from .modeling_video_llava import GaudiVideoLlavaForConditionalGeneration -from .processing_video_llava import GaudiVideoLlavaProcessor diff --git a/optimum/habana/transformers/models/video_llava/modeling_video_llava.py b/optimum/habana/transformers/models/video_llava/modeling_video_llava.py index 6670b23375..ec9a10f053 100644 --- a/optimum/habana/transformers/models/video_llava/modeling_video_llava.py +++ b/optimum/habana/transformers/models/video_llava/modeling_video_llava.py @@ -18,148 +18,17 @@ import torch from torch import nn -from transformers.modeling_outputs import BaseModelOutputWithPooling from transformers.models.video_llava.modeling_video_llava import ( VideoLlavaCausalLMOutputWithPast, - VideoLlavaConfig, VideoLlavaForConditionalGeneration, ) -from transformers.utils import logging +from transformers.utils import is_torchdynamo_compiling, logging logger = logging.get_logger(__name__) class GaudiVideoLlavaForConditionalGeneration(VideoLlavaForConditionalGeneration): - def __init__(self, config: VideoLlavaConfig): - super().__init__(config) - self.feature_offset = 0 - - def _merge_input_ids_with_visual_features( - self, visual_features, inputs_embeds, input_ids, attention_mask, labels, token_idx, num_frames=1 - ): - r""" - Copied from VideoLlavaForConditionalGeneration._merge_input_ids_with_visual_features: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/video_llava/modeling_video_llava.py - The only differences are: - - add new args token_idx - - add self.feature_offset param - """ - num_images, num_image_patches, embed_dim = visual_features.shape - batch_size, sequence_length = input_ids.shape - last_token_idx = token_idx + self.feature_offset - left_padding = not torch.sum(input_ids[:, last_token_idx - 1] == torch.tensor(self.pad_token_id)) - special_vision_token = self.config.video_token_index if num_frames > 1 else self.config.image_token_index - - # 1. Create a mask to know where special image tokens are - special_image_token_mask = input_ids == special_vision_token - num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1) - # Compute the maximum embed dimension - max_seq_len = (num_special_image_tokens.max() * (num_image_patches * num_frames - 1)) + sequence_length - self.feature_offset = self.feature_offset + max_seq_len - sequence_length - batch_indices, non_image_indices = torch.where(input_ids != special_vision_token) - - # 2. Compute the positions where text should be written - # Calculate new positions for text tokens in merged image-text sequence. - # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens. - # `torch.cumsum` computes how each image token shifts subsequent text token positions. - # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one. - new_token_positions = ( - torch.cumsum((special_image_token_mask * (num_image_patches * num_frames - 1) + 1), dim=-1) - 1 - ) - nb_image_pad = max_seq_len - 1 - new_token_positions[:, -1] - if left_padding: - new_token_positions += nb_image_pad[:, None] # offset for left padding - text_to_overwrite = new_token_positions[batch_indices, non_image_indices] - - # 3. Create the full embedding, already padded to the maximum position - # expand input ids so that the second "merge" with videos does not fail - final_embedding = torch.zeros( - batch_size, max_seq_len, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device - ) - final_attention_mask = torch.zeros( - batch_size, max_seq_len, dtype=attention_mask.dtype, device=inputs_embeds.device - ) - final_input_ids = torch.full( - (batch_size, max_seq_len), self.pad_token_id, dtype=input_ids.dtype, device=inputs_embeds.device - ) - # In case the Vision model or the Language model has been offloaded to CPU, we need to manually - # set the corresponding tensors into their correct target device. - target_device = inputs_embeds.device - batch_indices, non_image_indices, text_to_overwrite = ( - batch_indices.to(target_device), - non_image_indices.to(target_device), - text_to_overwrite.to(target_device), - ) - attention_mask = attention_mask.to(target_device) - - # 4. Fill the embeddings based on the mask. If we have ["hey" "", "how", "are"] - # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features - final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices] - final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices] - final_input_ids[batch_indices, text_to_overwrite] = input_ids[batch_indices, non_image_indices] - if labels is not None: - final_labels = torch.full( - (batch_size, max_seq_len), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device - ) - final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices] - else: - final_labels = None - - # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling - image_to_overwrite = torch.full((batch_size, max_seq_len), True, dtype=torch.bool, device=inputs_embeds.device) - image_to_overwrite[batch_indices, text_to_overwrite] = False - image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device) - - if image_to_overwrite.sum() != visual_features.shape[:-1].numel(): - visual_type = "videos" if num_frames == 8 else "images" - num_images //= num_frames - raise ValueError( - f"The input provided to the model are wrong. The number of {visual_type} tokens is {torch.sum(special_image_token_mask)} while" - f" the number of {visual_type} given to the model is {num_images}. This prevents correct indexing and breaks batch generation." - ) - - final_embedding[image_to_overwrite] = visual_features.contiguous().reshape(-1, embed_dim).to(target_device) - final_attention_mask |= image_to_overwrite - position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1) - - return final_embedding, final_attention_mask, final_labels, position_ids, final_input_ids - - def _get_vision_features( - self, - pixel_values_images: Optional[torch.FloatTensor] = None, - pixel_values_videos: Optional[torch.FloatTensor] = None, - vision_feature_layer: Optional[int] = None, - vision_feature_select_strategy: Optional[str] = None, - ) -> Union[Tuple, BaseModelOutputWithPooling]: - if pixel_values_images is None and pixel_values_videos is None: - raise ValueError("You have to specify `pixel_values_images` or `pixel_values_videos`") - - # videos do not need to select features and it's always "full" (as it is done in the orig implementation) - if pixel_values_videos is not None: - batch_size_vid, num_frames, channels, height, width = pixel_values_videos.shape - - pixel_values = pixel_values_videos.reshape(batch_size_vid * num_frames, channels, height, width) - video_outputs = self.video_tower(pixel_values, output_hidden_states=True) - video_outputs = video_outputs.hidden_states[vision_feature_layer].squeeze(1) - else: - video_outputs = None - num_frames = 0 - - if pixel_values_images is not None: - image_outputs = self.image_tower(pixel_values_images, output_hidden_states=True) - image_outputs = image_outputs.hidden_states[vision_feature_layer].squeeze(1) - - if vision_feature_select_strategy == "default": - image_outputs = image_outputs[:, 1:] - elif vision_feature_select_strategy == "full": - image_outputs = image_outputs - else: - raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}") - else: - image_outputs = None - - return image_outputs, video_outputs, num_frames - def forward( self, input_ids: Optional[torch.LongTensor] = None, @@ -179,18 +48,12 @@ def forward( cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, token_idx: Optional[torch.Tensor] = None, - **kwargs, + **lm_kwargs, ) -> Union[Tuple, VideoLlavaCausalLMOutputWithPast]: r""" - Copied from VideoLlavaForConditionalGeneration.forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/video_llava/modeling_video_llava.py - The only differences are: + Copied from VideoLlavaForConditionalGeneration.forward: https://github.com/huggingface/transformers/blob/v4.51.3/src/transformers/models/video_llava/modeling_video_llava.py#L365 + The only difference is: - add new args token_idx - - add new args attn_softmax_bf16 - - add new args reuse_cache - - add new args use_flash_attention - - add new args flash_attention_recompute - - add new args flash_attention_causal_mask - - add new args flash_attention_fast_softmax """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions @@ -198,6 +61,59 @@ def forward( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict + vision_feature_layer = ( + vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer + ) + vision_feature_select_strategy = ( + vision_feature_select_strategy + if vision_feature_select_strategy is not None + else self.config.vision_feature_select_strategy + ) + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if (pixel_values_images is not None or pixel_values_videos is not None) and inputs_embeds is not None: + raise ValueError( + "You cannot specify both `pixel_values_images`/`pixel_values_videos` and `inputs_embeds` at the same " + "time, and must specify either one" + ) + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + if pixel_values_images is not None: + image_features = self.get_image_features( + pixel_values_images, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + ) + special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1) + special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device) + if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel(): + n_image_tokens = (input_ids == self.config.image_token_index).sum() + n_image_features = image_features.shape[0] * image_features.shape[1] + raise ValueError( + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" + ) + image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + if pixel_values_videos is not None: + video_features, num_frames = self.get_video_features( + pixel_values_videos=pixel_values_videos, vision_feature_layer=vision_feature_layer + ) + + special_image_mask = (input_ids == self.config.video_token_index).unsqueeze(-1) + special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device) + if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != video_features.numel(): + n_video_tokens = (input_ids == self.config.video_token_index).sum() + n_video_features = video_features.shape[0] * video_features.shape[1] + raise ValueError( + f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" + ) + video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, video_features) outputs = self.language_model( attention_mask=attention_mask, @@ -209,14 +125,12 @@ def forward( output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, - logits_to_keep=0, + logits_to_keep=logits_to_keep, token_idx=token_idx, - **kwargs, + **lm_kwargs, ) logits = outputs[0] - if logits.shape[1] > 1: - logits = logits[:, self.feature_offset :, :] loss = None if labels is not None: @@ -246,196 +160,6 @@ def forward( past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, - image_hidden_states=kwargs.get("image_features", None) if pixel_values_images is not None else None, - video_hidden_states=kwargs.get("video_features", None) if pixel_values_videos is not None else None, - ) - - def prepare_inputs_for_generation( - self, - input_ids, - past_key_values=None, - inputs_embeds=None, - pixel_values_images=None, - pixel_values_videos=None, - attention_mask=None, - cache_position=None, - logits_to_keep=None, - **kwargs, - ): - token_idx = kwargs.get("token_idx", None) - if token_idx is None: - return super().prepare_inputs_for_generation( - input_ids=input_ids, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - pixel_values_images=pixel_values_images, - pixel_values_videos=pixel_values_videos, - attention_mask=attention_mask, - cache_position=cache_position, - logits_to_keep=logits_to_keep, - **kwargs, - ) - # Else, we need to update token_idx when merging features from videos/images with input embeddings - labels = kwargs.get("labels", None) - if (input_ids is None) ^ (inputs_embeds is not None): - raise ValueError( - "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" - ) - - if (pixel_values_images is not None or pixel_values_videos is not None) and inputs_embeds is not None: - raise ValueError( - "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one" - ) - - legacy_processing = False - inputs_not_expanded = False - if input_ids is not None: - img_token_not_enough = (input_ids == self.config.image_token_index).sum( - 1 - ).max() < self.config.image_seq_length - video_token_not_enough = (input_ids == self.config.video_token_index).sum( - 1 - ).max() < self.config.video_seq_length - # if the number of image/video tokens is more than image embeddings seq length, then prob we expanded it in processing - # not very reliable, but we don't expect one to actually pass 500+ images for one prompt - inputs_not_expanded = (img_token_not_enough and pixel_values_images is not None) or ( - video_token_not_enough and pixel_values_videos is not None - ) - model_inputs = self.language_model.prepare_inputs_for_generation( - input_ids, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - cache_position=cache_position, - logits_to_keep=logits_to_keep, - **kwargs, - ) - position_ids = model_inputs["position_ids"] - cache_position = model_inputs["cache_position"] - attention_mask = model_inputs["attention_mask"] - inputs_embeds = model_inputs.get("inputs_embeds", None) - input_ids = model_inputs.get("input_ids", None) - - if inputs_embeds is None: - inputs_embeds = self.get_input_embeddings()(input_ids) - pixels_present = input_ids.shape[-1] == 1 and ( - pixel_values_images is not None or pixel_values_videos is not None - ) - legacy_processing = inputs_not_expanded or pixels_present - - vision_feature_layer = kwargs.get("vision_feature_layer", None) - vision_feature_layer = ( - vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer - ) - vision_feature_select_strategy = kwargs.get("vision_feature_select_strategy", None) - vision_feature_select_strategy = ( - vision_feature_select_strategy - if vision_feature_select_strategy is not None - else self.config.vision_feature_select_strategy - ) - if pixel_values_images is not None or pixel_values_videos is not None: - image_outputs, video_outputs, num_frames = self._get_vision_features( - pixel_values_images=pixel_values_images, - pixel_values_videos=pixel_values_videos, - vision_feature_layer=vision_feature_layer, - vision_feature_select_strategy=vision_feature_select_strategy, - ) - - image_features = video_features = None - if image_outputs is not None: - image_features = self.multi_modal_projector(image_outputs) - if video_outputs is not None: - video_features = self.multi_modal_projector(video_outputs) - - if legacy_processing: - logger.warning_once( - "Expanding inputs for image tokens in Video-LLaVa should be done in processing. " - "Please add `patch_size` and `vision_feature_select_strategy` to the model's processing config or set directly " - "with `processor.patch_size = {{patch_size}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. " - "Using processors without these attributes in the config is deprecated and will throw an error in v4.47." - ) - if input_ids.shape[1] != 1: - self.feature_offset = 0 - for features, frames in ((image_features, 1), (video_features, num_frames)): - if features is not None: - ( - inputs_embeds, - attention_mask, - labels, - position_ids, - input_ids, - ) = self._merge_input_ids_with_visual_features( - features, - inputs_embeds, - input_ids, - attention_mask, - labels, - token_idx, - num_frames=frames, - ) - cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device) - else: - # Retrieve the first layer to inspect the logits and mask out the hidden states - # that are set to 0 - first_layer_past_key_value = past_key_values[0][0][:, :, :, 0] - - # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941 - batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0) - - target_length = input_ids.shape[1] - past_length = first_layer_past_key_value.shape[-1] - - extended_attention_mask = torch.ones( - (attention_mask.shape[0], past_length), - dtype=attention_mask.dtype, - device=attention_mask.device, - ) - - # Filter out only the tokens that can be un-attended, this can happen - # if one uses Llava + Fused modules where the cache on the - # first iteration is already big enough, or if one passes custom cache - valid_indices = non_attended_tokens < extended_attention_mask.size(-1) - new_batch_index = batch_index[valid_indices] - new_non_attended_tokens = non_attended_tokens[valid_indices] - - # Zero-out the places where we don't need to attend - extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0 - new_token_idx = token_idx + self.feature_offset - extended_attention_mask[:, new_token_idx - 1 + target_length :] = 0 - attention_mask = extended_attention_mask.clone() - position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1 - cache_position = new_token_idx - - # TODO: @raushan retain only the new behavior after v4.47 - else: - if image_outputs is not None: - special_image_mask = ( - (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds) - ) - image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) - - if video_outputs is not None: - special_image_mask = ( - (input_ids == self.config.video_token_index).unsqueeze(-1).expand_as(inputs_embeds) - ) - video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, video_features) - - model_inputs.update( - { - "position_ids": position_ids, - "cache_position": cache_position, - "attention_mask": attention_mask, - "token_idx": token_idx + self.feature_offset, - "inputs_embeds": inputs_embeds, - } + image_hidden_states=image_features if pixel_values_images is not None else None, + video_hidden_states=video_features if pixel_values_videos is not None else None, ) - if legacy_processing or (cache_position is not None and cache_position[0]) == 0: - # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore - # Otherwise we need pixel values to be passed to model - model_inputs["pixel_values_images"] = pixel_values_images - model_inputs["pixel_values_videos"] = pixel_values_videos - model_inputs["image_features"] = image_features - model_inputs["video_features"] = video_features - return model_inputs diff --git a/optimum/habana/transformers/models/video_llava/processing_video_llava.py b/optimum/habana/transformers/models/video_llava/processing_video_llava.py deleted file mode 100644 index 9ab480220c..0000000000 --- a/optimum/habana/transformers/models/video_llava/processing_video_llava.py +++ /dev/null @@ -1,108 +0,0 @@ -from typing import List, Optional, Union - -from transformers.image_processing_utils import BatchFeature -from transformers.image_utils import ImageInput, get_image_size, to_numpy_array -from transformers.models.video_llava.processing_video_llava import VideoLlavaProcessor -from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy -from transformers.utils import TensorType - -from optimum.utils import logging - - -logger = logging.get_logger(__name__) - - -class GaudiVideoLlavaProcessor(VideoLlavaProcessor): - attributes = ["image_processor", "tokenizer"] - valid_kwargs = ["chat_template", "patch_size", "vision_feature_select_strategy", "image_token", "video_token"] - image_processor_class = "VideoLlavaImageProcessor" - tokenizer_class = "AutoTokenizer" - - def __init__( - self, - image_processor=None, - tokenizer=None, - patch_size=None, - vision_feature_select_strategy=None, - image_token="", # set the default and let users change if they have peculiar special tokens in rare cases - video_token="