Enable TorchScript export, loading and inference for causal lm models (…

…#283)

* add original example

* add jit model for generation

* enable generation

* add requirements and README

* enable caching

* move modeling to ipex subpackage

* fix import

* rename model class

* Update code and move modeling.py to utils folder. (#290)

* Update code and move modeling.py to utils folder.

* move modeling.py to generation folder

* Fixed typo

* Update code for support instance of fx model to TracedModelForCausalLM

* Update code

* Fixed typo

* Update readme

* Add generation for bloom models (#295)

* add bloom generation

* Fix generation

* refactorization

* set back input ids to prepare_inputs

* Add tests

* fix dates

* remove unecessary list comprehension

* remove unecessary list comprehension

* rename class

* fix dependencies

* fix dependency

---------

Co-authored-by: Cheng, Penghui <[email protected]>

.github/workflows/test_generation.yml

@@ -0,0 +1,37 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+name: Intel Generation Utils - Test
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [3.8, 3.9]
+        os: [ubuntu-latest]
+
+    runs-on: ${{ matrix.os }}
+    steps:
+    - uses: actions/checkout@v2
+    - name: Setup Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install optimum[exporters]
+        pip install .[tests]
+    - name: Test with Pytest
+      run: |
+        pytest tests/generation/

examples/neural_compressor/text-generation/README.md

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+<!---
+Copyright 2023 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+## Language generation
+
+Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-generation/run_generation.py).
+
+The original generation task only supported the PyTorch eager model. By calling the `TorchScriptModelForCausalLM` class, we can now support a TorchScript model for generation tasks.
+
+Example usage:
+
+```bash
+python run_generation.py \
+    --model_type=gpt2 \
+    --model_name_or_path=gpt2 \
+    --jit
+```

examples/neural_compressor/text-generation/requirements.txt

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+sentencepiece != 0.1.92
+protobuf
+torch >= 2.0.0

examples/neural_compressor/text-generation/run_generation.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
+"""
+
+
+import argparse
+import logging
+
+import numpy as np
+import torch
+from transformers import (
+    CTRLLMHeadModel,
+    CTRLTokenizer,
+    GPT2LMHeadModel,
+    GPT2Tokenizer,
+    OpenAIGPTLMHeadModel,
+    OpenAIGPTTokenizer,
+    TransfoXLLMHeadModel,
+    TransfoXLTokenizer,
+    XLMTokenizer,
+    XLMWithLMHeadModel,
+    XLNetLMHeadModel,
+    XLNetTokenizer,
+)
+
+from optimum.intel.generation.modeling import TorchScriptModelForCausalLM
+
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop
+
+MODEL_CLASSES = {
+    "gpt2": (GPT2LMHeadModel, GPT2Tokenizer),
+    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
+    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
+    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
+    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
+    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
+}
+
+# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
+# in https://github.com/rusiaaman/XLNet-gen#methodology
+# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
+PREFIX = """In 1991, the remains of Russian Tsar Nicholas II and his family
+(except for Alexei and Maria) are discovered.
+The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
+remainder of the story. 1883 Western Siberia,
+a young Grigori Rasputin is asked by his father and a group of men to perform magic.
+Rasputin has a vision and denounces one of the men as a horse thief. Although his
+father initially slaps him for making such an accusation, Rasputin watches as the
+man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
+the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
+with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
+
+
+def set_seed(args):
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+
+#
+# Functions to prepare models' input
+#
+
+
+def prepare_ctrl_input(args, _, tokenizer, prompt_text):
+    if args.temperature > 0.7:
+        logger.info("CTRL typically works better with lower temperatures (and lower top_k).")
+
+    encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False)
+    if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
+        logger.info("WARNING! You are not starting your generation from a control code so you won't get good results")
+    return prompt_text
+
+
+def prepare_xlm_input(args, model, tokenizer, prompt_text):
+    # kwargs = {"language": None, "mask_token_id": None}
+
+    # Set the language
+    use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb
+    if hasattr(model.config, "lang2id") and use_lang_emb:
+        available_languages = model.config.lang2id.keys()
+        if args.xlm_language in available_languages:
+            language = args.xlm_language
+        else:
+            language = None
+            while language not in available_languages:
+                language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ")
+
+        model.config.lang_id = model.config.lang2id[language]
+        # kwargs["language"] = tokenizer.lang2id[language]
+
+    # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
+    # XLM masked-language modeling (MLM) models need masked token
+    # is_xlm_mlm = "mlm" in args.model_name_or_path
+    # if is_xlm_mlm:
+    #     kwargs["mask_token_id"] = tokenizer.mask_token_id
+
+    return prompt_text
+
+
+def prepare_xlnet_input(args, _, tokenizer, prompt_text):
+    prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
+    prompt_text = prefix + prompt_text
+    return prompt_text
+
+
+def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
+    prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
+    prompt_text = prefix + prompt_text
+    return prompt_text
+
+
+PREPROCESSING_FUNCTIONS = {
+    "ctrl": prepare_ctrl_input,
+    "xlm": prepare_xlm_input,
+    "xlnet": prepare_xlnet_input,
+    "transfo-xl": prepare_transfoxl_input,
+}
+
+
+def adjust_length_to_model(length, max_sequence_length):
+    if length < 0 and max_sequence_length > 0:
+        length = max_sequence_length
+    elif 0 < max_sequence_length < length:
+        length = max_sequence_length  # No generation bigger than model size
+    elif length < 0:
+        length = MAX_LENGTH  # avoid infinite loop
+    return length
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_type",
+        default=None,
+        type=str,
+        required=True,
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+    )
+
+    parser.add_argument("--prompt", type=str, default="")
+    parser.add_argument("--length", type=int, default=20)
+    parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped")
+
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=1.0,
+        help="temperature of 1.0 has no effect, lower tend toward greedy sampling",
+    )
+    parser.add_argument(
+        "--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2"
+    )
+    parser.add_argument("--k", type=int, default=0)
+    parser.add_argument("--p", type=float, default=0.9)
+
+    parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.")
+    parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.")
+    parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")
+
+    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
+    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
+    parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.")
+    parser.add_argument(
+        "--fp16",
+        action="store_true",
+        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
+    )
+    parser.add_argument("--jit", action="store_true", help="Whether or not to use jit trace to accelerate inference")
+
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        help="Output directory where to save the resulting model",
+    )
+    args = parser.parse_args()
+
+    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+    args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
+
+    logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fp16}")
+
+    set_seed(args)
+
+    # Initialize the model and tokenizer
+    try:
+        args.model_type = args.model_type.lower()
+        model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    except KeyError:
+        raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)")
+
+    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
+
+    if args.jit:
+        model = TorchScriptModelForCausalLM.from_pretrained(args.model_name_or_path, export=True)
+    else:
+        model = model_class.from_pretrained(args.model_name_or_path)
+
+    if args.output_dir is not None and args.jit:
+        model.save_pretrained(args.output_dir)
+        tokenizer.save_pretrained(args.output_dir)
+
+    model.to(args.device)
+
+    args.length = adjust_length_to_model(
+        args.length,
+        max_sequence_length=model.config.max_position_embeddings
+        if hasattr(model.config, "max_position_embeddings")
+        else 0,
+    )
+    logger.info(args)
+
+    prompt_text = args.prompt if args.prompt else input("Model prompt >>> ")
+
+    # Different models need different input formatting and/or extra arguments
+    requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
+    if requires_preprocessing:
+        prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
+        preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
+
+        if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
+            tokenizer_kwargs = {"add_space_before_punct_symbol": True}
+        else:
+            tokenizer_kwargs = {}
+
+        encoded_prompt = tokenizer.encode(
+            preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
+        )
+    else:
+        prefix = args.prefix if args.prefix else args.padding_text
+        encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt")
+    encoded_prompt = encoded_prompt.to(args.device)
+
+    if encoded_prompt.size()[-1] == 0:
+        input_ids = None
+    else:
+        input_ids = encoded_prompt
+
+    output_sequences = model.generate(
+        input_ids=input_ids,
+        max_length=args.length + len(encoded_prompt[0]),
+        temperature=args.temperature,
+        top_k=args.k,
+        top_p=args.p,
+        repetition_penalty=args.repetition_penalty,
+        do_sample=True,
+        num_return_sequences=args.num_return_sequences,
+    )
+
+    # Remove the batch dimension when returning multiple sequences
+    if len(output_sequences.shape) > 2:
+        output_sequences.squeeze_()
+
+    generated_sequences = []
+
+    for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
+        print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
+        generated_sequence = generated_sequence.tolist()
+
+        # Decode text
+        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+
+        # Remove all text after the stop token
+        text = text[: text.find(args.stop_token) if args.stop_token else None]
+
+        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
+        total_sequence = (
+            prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
+        )
+
+        generated_sequences.append(total_sequence)
+        print(total_sequence)
+
+    return generated_sequences
+
+
+if __name__ == "__main__":
+    main()