mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
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fe080eaee6
This commit introduces web application for SHARK using gradio platform. This adds web visualization of `Resnet50` and `Albert_Maskfill` models as a start. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com> Signed-off-by: Gaurav Shukla <gaurav@nod-labs.com>
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
from transformers import AutoModelForMaskedLM, AutoTokenizer
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import torch
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from shark.shark_inference import SharkInference
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from shark.shark_importer import SharkImporter
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import numpy as np
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MAX_SEQUENCE_LENGTH = 512
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BATCH_SIZE = 1
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class AlbertModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.model = AutoModelForMaskedLM.from_pretrained("albert-base-v2")
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self.model.eval()
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def forward(self, input_ids, attention_mask):
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return self.model(
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input_ids=input_ids, attention_mask=attention_mask
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).logits
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################################## Preprocessing inputs and model ############
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tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
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def preprocess_data(text):
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# Preparing Data
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encoded_inputs = tokenizer(
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text,
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padding="max_length",
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truncation=True,
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max_length=MAX_SEQUENCE_LENGTH,
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return_tensors="pt",
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)
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inputs = (encoded_inputs["input_ids"], encoded_inputs["attention_mask"])
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return inputs
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def top5_possibilities(text, inputs, token_logits):
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mask_id = torch.where(
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inputs[0] == tokenizer.mask_token_id
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)[1]
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mask_token_logits = token_logits[0, mask_id, :]
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percentage = torch.nn.functional.softmax(mask_token_logits, dim=1)[0]
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top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
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top5 = {}
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for token in top_5_tokens:
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label = text.replace(tokenizer.mask_token, tokenizer.decode(token))
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top5[label] = percentage[token].item()
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return top5
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##############################################################################
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def albert_maskfill_inf(masked_text):
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inputs = preprocess_data(masked_text)
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mlir_importer = SharkImporter(
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AlbertModule(),
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inputs,
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frontend="torch",
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)
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minilm_mlir, func_name = mlir_importer.import_mlir(
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is_dynamic=False, tracing_required=True
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)
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shark_module = SharkInference(
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minilm_mlir, func_name, mlir_dialect="linalg"
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)
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shark_module.compile()
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token_logits = torch.tensor(shark_module.forward(inputs))
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return top5_possibilities(masked_text, inputs, token_logits)
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