Create app.py

app.py

@@ -0,0 +1,244 @@
+import os
+
+import argparse
+import logging
+
+import numpy as np
+import torch
+import datetime
+
+from transformers import (
+    CTRLLMHeadModel,
+    CTRLTokenizer,
+    GPT2LMHeadModel,
+    GPT2Tokenizer,
+    OpenAIGPTLMHeadModel,
+    OpenAIGPTTokenizer,
+    TransfoXLLMHeadModel,
+    TransfoXLTokenizer,
+    XLMTokenizer,
+    XLMWithLMHeadModel,
+    XLNetLMHeadModel,
+    XLNetTokenizer,
+)
+
+
+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),
+}
+
+def set_seed(args):
+    rd = np.random.randint(100000)
+    print('seed =', rd)
+    np.random.seed(rd)
+    torch.manual_seed(rd)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(rd)
+
+#
+# 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):
+    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
+    return prompt_text
+
+
+def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
+    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + 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="</s>", help="Token at which lyrics 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("--padding_text", type=str, default="", help="Padding lyrics for Transfo-XL and XLNet.")
+    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.")
+    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()
+
+    # 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)
+    model = model_class.from_pretrained(args.model_name_or_path)
+    model.to(args.device)
+
+    args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings)
+    logger.info(args)
+    generated_sequences = []
+    prompt_text = ""
+    while prompt_text != "stop":
+        set_seed(args)
+        while not len(prompt_text):
+            prompt_text = args.prompt if args.prompt else input("Context >>> ")
+
+        # 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)
+            encoded_prompt = tokenizer.encode(
+                preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", add_space_before_punct_symbol=True
+            )
+        else:
+            encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
+        encoded_prompt = encoded_prompt.to(args.device)
+
+        output_sequences = model.generate(
+            input_ids=encoded_prompt,
+            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_()
+
+        now = datetime.datetime.now()
+        date_time = now.strftime('%Y%m%d_%H%M%S%f')
+
+        for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
+            print("ruGPT:".format(generated_sequence_idx + 1))
+            generated_sequence = generated_sequence.tolist()
+
+            # Decode lyrics
+            text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+
+            # Remove all lyrics 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 lyrics 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)
+            # os.system('clear')
+            print(total_sequence)
+
+            fileName = '{}/{}.txt'.format('./songs', date_time)
+            with open(fileName, 'w') as f:
+                f.write(total_sequence)
+
+        prompt_text = ""
+        if args.prompt:
+            break
+
+    return generated_sequences
+
+
+if __name__ == "__main__":
+    main()