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modeling_gzipembed.py

@@ -17,6 +17,12 @@ class GZIPEmbeddingModel(PreTrainedModel):
             self.dummy_parameter = torch.nn.Parameter(torch.ones(1))
 
     def forward(self, prompt, num_procs=16):
+        global calculate_ncd_row
+        global p
+        def calculate_ncd_row(data_row):
+            i = data_row[0]
+            row = model.ncd(data_row[1], prompt)
+            return i, row
         if type(prompt) == str:
             prompt = [prompt]
         x = []
@@ -24,7 +30,7 @@ class GZIPEmbeddingModel(PreTrainedModel):
             ncd = [0] * len(self.config.corpus)
             with multiprocessing.Pool(num_procs) as pool:
                 data = enumerate(self.config.corpus)
-                results = pool.map(self.ncd_r,(data,p))
+                results = pool.map(calculate_ncd_row,(data,p))
             for i,row in results:
                 ncd[i]=row
             x.append(ncd)