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minor update to the repo to ensure running in all machines#14

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MaxHao56 wants to merge 21 commits into
liamhebert:mainfrom
MaxHao56:max20250926
Draft

minor update to the repo to ensure running in all machines#14
MaxHao56 wants to merge 21 commits into
liamhebert:mainfrom
MaxHao56:max20250926

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@MaxHao56

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  1. Graph fusion layer with attention mask = None
  2. sweeper = basics
  3. dataset bool

Optional Changes on the side. YAML file in the configs for giga pretrain needs to have match to your directory in you local or cluster machine

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Few minor comments on root_dir, which when resolved are good to submit :)

Comment thread src/configs/dataset/giga_pretrain.yaml Outdated
Comment thread src/configs/dataset/hateful_discussions.yaml Outdated
@MaxHao56 MaxHao56 requested a review from liamhebert November 11, 2025 05:25
@MaxHao56 MaxHao56 marked this pull request as draft November 13, 2025 06:45

@liamhebert liamhebert left a comment

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Still need to review EmbeddingGenerator, more soon.

Comment thread logs/train/runs/2025-10-24_04-31-01/.hydra/config.yaml Outdated
Comment thread src/checkpt.py
Comment thread src/debug_local.sh Outdated
Comment thread src/run_graphormer_hateful_discussions.sh Outdated
Comment thread src/test_data.embeddings.npy Outdated
Comment thread src/test_embedding.py Outdated
Comment thread src/test_embedding.py Outdated
Comment thread src/test_embedding.py
"""Flatten a discussion tree into tensors for model input."""
dut.compute_relative_distance(tree)

flat = {

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We are missing a few parameters here. Namely, is_root and distances. These should be already attributes of node (I think).

MDT-2/src/tasks/dataset.py

Lines 860 to 867 in 5f50b4a

if is_root:
parent_id = node["id"]
if node["id"] not in result["id"]:
node["images"] = node["images"][0] if node["images"] else None
result["images"].append(node["images"])
result["distances"].append(node["distances"])

Comment thread src/test_embedding.py
"out_degree": out_degree,
"attn_bias": torch.zeros((n, n)),
"distance": torch.zeros((n, n, 2)),
"distance_index": torch.zeros((n, n), dtype=torch.int16),

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This doesn't feel right... can you double check whether there should be a value here?

Comment thread src/test_embedding.py
"input_ids": tokenized_text["input_ids"],
"attention_mask": tokenized_text["attention_mask"],
"token_type_ids": tokenized_text.get(
"token_type_ids", torch.zeros_like(tokenized_text["input_ids"])

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likewise, we should be careful here

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2 participants