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MVP Inconsistent output shape
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| ''' | |
| #Conda environment file (env.yml). Install with `conda env create -f env.yml` | |
| channels: | |
| - nvidia | |
| - rapidsai | |
| - anaconda | |
| - conda-forge | |
| dependencies: | |
| - python=3.8 | |
| - pip | |
| - nomkl | |
| - pylint | |
| - pandas==1.2.* | |
| - pillow==8.2.0 | |
| - python-confluent-kafka==1.6.* | |
| - ipykernel==6.2.* | |
| - psutil==5.8.* | |
| - google-cloud-sdk==342.0.* | |
| - tqdm==4.61.* | |
| - prometheus_client==0.10.* | |
| - fire==0.3.* | |
| - pyspark==3.1.2 | |
| - cudf==21.08.03 | |
| - nvtabular==0.8.0 | |
| - cudatoolkit==11.0.* | |
| - pip: | |
| - crcmod==1.7 | |
| - joblib==0.11 | |
| - annoy==1.16.3 | |
| - records==0.5.3 | |
| - psycopg2-binary==2.8.6 | |
| - pynvml | |
| - fastcore | |
| - transformers4rec==0.1.4 | |
| - https://download.pytorch.org/whl/cu113/torch-1.10.0%2Bcu113-cp38-cp38-linux_x86_64.whl | |
| - torchmetrics | |
| name: transformers4rec | |
| ''' | |
| ''' | |
| ./schema/mvp_schema.pbtxt | |
| feature { | |
| name: "item_id-list_seq" | |
| type: INT | |
| value_count { | |
| min: 2 | |
| max: 20 | |
| } | |
| int_domain { | |
| name: "item_id/list" | |
| min: 0 | |
| max: 161452 | |
| is_categorical: true | |
| } | |
| annotation { | |
| tag: "categorical" | |
| tag: "list" | |
| tag: "item_id" | |
| tag: "item" | |
| } | |
| } | |
| ''' | |
| from merlin_standard_lib import Schema | |
| from transformers4rec import torch as tr | |
| import torch | |
| SESSIONS_MAX_LENGTH = 20 | |
| schema = Schema().from_proto_text('./schema/mvp_schema.pbtxt').select_by_name(['item_id-list_seq']) | |
| d_model = 320 | |
| input_module = tr.TabularSequenceFeatures.from_schema( | |
| schema, | |
| max_sequence_length=SESSIONS_MAX_LENGTH, | |
| aggregation="concat", | |
| d_output=d_model, | |
| masking="mlm", | |
| ) | |
| prediction_task = tr.NextItemPredictionTask(hf_format=True, weight_tying=True) | |
| model_config = tr.XLNetConfig.build(d_model=d_model, n_head=8, n_layer=2, total_seq_length=SESSIONS_MAX_LENGTH) | |
| model = model_config.to_torch_model(input_module, prediction_task) | |
| items = torch.as_tensor([5, 7, 8]).unsqueeze(0) | |
| item_tensor = {'item_id-list_seq': items} | |
| for x in range(10): | |
| ans = model(item_tensor) | |
| print(ans['labels'].size()) | |
| ''' | |
| torch.Size([1]) | |
| torch.Size([1]) | |
| torch.Size([1]) | |
| torch.Size([1]) | |
| torch.Size([2]) | |
| torch.Size([2]) | |
| torch.Size([1]) | |
| torch.Size([1]) | |
| torch.Size([1]) | |
| torch.Size([2]) | |
| ''' |
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