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llamipa3/adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:956bbad58e3c7d8101baa809d11f3aa025ddf5d36925d0c78eea43d656ad2b37
+size 109069176

llamipa3/llamipa3_adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "/tmpdir/thompson/Meta-Llama-3-8B/",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

parser_generate.py

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+import torch
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from datasets import load_dataset
+from tqdm import tqdm
+
+device_map = "auto"
+model = AutoModelForCausalLM.from_pretrained(
+    "/path/to/meta-llama3-8b/",
+    return_dict=True,
+    torch_dtype=torch.float16,
+    device_map=device_map)
+
+
+tokenizer = AutoTokenizer.from_pretrained("/path/to/meta-llama3-8b/",add_eos_token=True) 
+
+tokenizer.pad_token_id = tokenizer.eos_token_id + 1
+tokenizer.padding_side = "right" 
+
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id, max_new_tokens=100)
+
+test_dataset = load_dataset("json", data_files={'test':'/path/to/parser_test_moves_15.jsonl'})["test"]
+
+def is_first_moves(sample):
+    answer = 0
+    slist = sample.split('\n')
+    if slist[0].startswith('Context: 0 <Buil> Mission has started.'):
+        struct = [i for i in slist if i.startswith('Structure:')]
+        rels = struct[0].split(':')[1].strip()
+        if len(rels) == 0:
+            answer = 1
+    return answer
+
+
+def check_endpoints(struct, head):
+    """
+    takes a struct string and a head int and returns only 
+    the struct rels with sources that are >= head
+    """
+    new_rels_list = []
+    new_rels = None
+    if struct:
+        rels = struct.split(' ')
+        for rel in rels:
+            if len(rel) > 0:
+                source = int(rel.split('(')[1].split(',')[0].strip())
+                if source >= head:
+                    new_rels_list.append(rel)
+        if len(new_rels_list) > 0:
+            new_rels = ' '.join(new_rels_list)
+    return new_rels
+
+def add_previous(sample, previous, predictions):
+    new_output = []
+    keep_str = None
+    #get head
+    slist = sample.split('\n')
+    head = int(slist[0].split('Context:')[1].split('<')[0].strip())
+    # check current structure
+    for s in slist:
+        if s.startswith('Structure:'):
+            new_structure = check_endpoints(previous, head)
+            if new_structure:
+                s = 'Structure: ' + new_structure + ' ' + predictions
+                keep_str = new_structure + ' ' + predictions
+            else:
+                s = 'Structure: ' + predictions
+                keep_str = predictions
+        new_output.append(s)
+    new_output_string = '\n'.join(new_output)
+    return keep_str, new_output_string
+
+def format_gen(preds):
+    labels = ['COM','CONTR','CORR','QAP','ACK','ELAB','CLARIFQ','COND','CONTIN',
+              'RES','EXPL','QELAB','ALT','NARR','CONFQ','SEQ']
+    split_list = [st.strip() for st in preds.split(' ')]
+    clean_list = []
+    for a in split_list:
+        s_tuple = None
+        rel = None
+        try:
+            s = a.split('(')[1].split(')')[0].split(',')
+            r = a.split('(')[0].strip()
+        except IndexError:
+            print('split error one')
+        else:
+            try:
+                s_tuple = (int(s[0]), int(s[1]))
+            except IndexError:
+                print('split error two')
+            except ValueError:
+                print('value error three')
+            if r in labels:
+                #make sure the label is well-formed 
+                rel = r
+        if rel != None and s_tuple != None:
+            clean_list.append(rel + '(' + str(s_tuple[0]) + ',' + str(s_tuple[1]) + ')')
+    clean_preds = ' '.join(clean_list)
+    return clean_preds
+
+
+def formatting_prompts_func(example):
+    output_text = '<|begin_of_text|>Identify the discourse structure (DS) for the new turn in the following excerpt :\n' + example + '\n ### DS:'
+    return output_text
+
+
+f = open("/path/to/val-output-file.txt","w")
+
+new_generations = None
+previous_generations = None
+for datum in tqdm(test_dataset['sample']):
+
+    #figure out if it's a first example
+    if is_first_moves(datum):
+        text = formatting_prompts_func(datum)
+        previous_generations = None
+    else:
+        #need to make sure head edu and relations match up
+        update_prev, amended_text = add_previous(datum, previous_generations, new_generations)
+        previous_generations = update_prev
+        text = formatting_prompts_func(amended_text)
+    generated = pipe(text)[0]['generated_text']
+    print(generated, file=f)
+    new_generations = format_gen(generated.split('### DS:')[1])
+
+f.close()
+