Spaces:
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Running
generate with contrastive search
Browse filesSigned-off-by: peter szemraj <[email protected]>
- app.py +33 -26
- requirements.txt +1 -1
app.py
CHANGED
@@ -17,12 +17,11 @@ use_gpu = torch.cuda.is_available()
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def generate_text(
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prompt: str,
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gen_length=64,
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-
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no_repeat_ngram_size=2,
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length_penalty=1.0,
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num_beam_groups=1,
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# perma params (not set by user)
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repetition_penalty=3.5,
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abs_max_length=512,
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verbose=False,
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):
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@@ -53,15 +52,13 @@ def generate_text(
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logging.info(f"Input too long {input_len} > {abs_max_length}, may cause errors")
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result = generator(
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prompt,
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-
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min_length=input_len + 4,
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-
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-
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repetition_penalty=repetition_penalty,
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no_repeat_ngram_size=no_repeat_ngram_size,
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length_penalty=length_penalty,
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do_sample=False,
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early_stopping=True,
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) # generate
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response = result[0]["generated_text"]
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rt = time.perf_counter() - st
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@@ -118,18 +115,19 @@ def get_parser():
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)
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parser.add_argument(
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"-
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"--
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type=
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default=
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help="
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)
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parser.add_argument(
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"
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type=int,
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default=
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help="
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)
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return parser
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@@ -146,11 +144,18 @@ available_models = [
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]
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if __name__ == "__main__":
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logging.info("\n\n\nStarting new instance of app.py")
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args = get_parser().parse_args()
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logging.info(f"received args:\t{args}")
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model_tag = args.model
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verbose = args.verbose
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logging.info(f"Loading model: {model_tag}, use GPU = {use_gpu}")
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generator = pipeline(
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"text-generation",
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@@ -228,16 +233,18 @@ if __name__ == "__main__":
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value=2,
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)
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with gr.Row():
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-
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choices=[2, 4, 8],
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label="Number of Beams",
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value=
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)
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-
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-
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-
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-
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)
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length_penalty = gr.Slider(
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minimum=0.5,
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@@ -269,10 +276,10 @@ if __name__ == "__main__":
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inputs=[
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prompt_text,
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num_gen_tokens,
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-
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no_repeat_ngram_size,
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length_penalty,
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num_beam_groups,
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],
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outputs=[email_mailto_button, generated_email],
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)
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def generate_text(
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prompt: str,
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gen_length=64,
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penalty_alpha=0.6,
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top_k=6,
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no_repeat_ngram_size=2,
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length_penalty=1.0,
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# perma params (not set by user)
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abs_max_length=512,
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verbose=False,
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):
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logging.info(f"Input too long {input_len} > {abs_max_length}, may cause errors")
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result = generator(
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prompt,
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max_new_tokens=gen_length,
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max_length=None, # in case of default max_length
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min_length=input_len + 4,
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penalty_alpha=penalty_alpha,
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top_k=top_k,
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no_repeat_ngram_size=no_repeat_ngram_size,
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length_penalty=length_penalty,
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) # generate
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response = result[0]["generated_text"]
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rt = time.perf_counter() - st
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)
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parser.add_argument(
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"-a",
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"--penalty_alpha",
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type=float,
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default=0.6,
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help="The penalty alpha for the text generation pipeline (contrastive search) - default 0.6",
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)
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parser.add_argument(
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"-k",
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"--top_k",
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type=int,
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default=6,
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help="The top k for the text generation pipeline (contrastive search) - default 6",
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)
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return parser
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]
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if __name__ == "__main__":
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+
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logging.info("\n\n\nStarting new instance of app.py")
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args = get_parser().parse_args()
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logging.info(f"received args:\t{args}")
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model_tag = args.model
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verbose = args.verbose
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top_k = args.top_k
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alpha = args.penalty_alpha
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assert top_k > 0, "top_k must be greater than 0"
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assert alpha >= 0.0 and alpha <= 1.0, "penalty_alpha must be between 0 and 1"
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logging.info(f"Loading model: {model_tag}, use GPU = {use_gpu}")
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generator = pipeline(
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"text-generation",
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value=2,
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)
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with gr.Row():
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contrastive_top_k = gr.Radio(
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choices=[2, 4, 6, 8],
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label="Number of Beams",
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value=top_k,
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)
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penalty_alpha = gr.Slider(
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label="Penalty Alpha",
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value=alpha,
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maximum=1.0,
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minimum=0.0,
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step=0.1,
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)
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length_penalty = gr.Slider(
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minimum=0.5,
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inputs=[
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prompt_text,
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num_gen_tokens,
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penalty_alpha,
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contrastive_top_k,
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no_repeat_ngram_size,
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length_penalty,
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],
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outputs=[email_mailto_button, generated_email],
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)
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requirements.txt
CHANGED
@@ -1,3 +1,3 @@
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1 |
gradio
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torch
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-
transformers
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gradio
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torch
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transformers>=4.24.0
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