feat(server): allow gpt-neox models with odd vocab sizes to be sharded (#48)

README.md

@@ -26,7 +26,7 @@ to power Bloom, BloomZ and MT0-XXL api-inference widgets.
 - [MT0-XXL](https://huggingface.co/bigscience/mt0-xxl)
 - ~~[Galactica](https://huggingface.co/facebook/galactica-120b)~~ (deactivated)
 - [SantaCoder](https://huggingface.co/bigcode/santacoder)
-- [GPT-Neox 20B](https://huggingface.co/EleutherAI/gpt-neox-20b): use `--revision refs/pr/13`
+- [GPT-Neox 20B](https://huggingface.co/EleutherAI/gpt-neox-20b): use `--revision pr/13`
 
 Other models are supported on a best effort basis using:
 

server/text_generation/models/gpt_neox.py

@@ -145,7 +145,7 @@ class GPTNeoxSharded(GPTNeox):
                         start = rank * block_size
                         stop = (rank + 1) * block_size
                         tensor = slice_[start:stop]
-                    elif name == "embed_out.weight":
+                    elif name == "embed_out.weight" and model.gpt_neox.tp_embeddings:
                         size = slice_.get_shape()[0]
                         block_size = size // world_size
                         start = rank * block_size
@@ -176,9 +176,9 @@ class GPTNeoxSharded(GPTNeox):
                             )
 
                         if (
-                                type(module)
+                            type(module)
-                                in [TensorParallelRowLinear, TensorParallelColumnLinear]
+                            in [TensorParallelRowLinear, TensorParallelColumnLinear]
-                                and param_name == "weight"
+                            and param_name == "weight"
                         ):
                             tensor = Int8Params(
                                 tensor,
@@ -229,16 +229,24 @@ class GPTNeoxSharded(GPTNeox):
     def forward(
         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
     ):
-        outputs = self.model.forward(
+        if self.model.gpt_neox.tp_embeddings:
-            input_ids=input_ids,
+            outputs = self.model.forward(
-            attention_mask=attention_mask,
+                input_ids=input_ids,
-            past_key_values=past_key_values,
+                attention_mask=attention_mask,
-            use_cache=True,
+                past_key_values=past_key_values,
-        )
+                use_cache=True,
+            )
 
-        # Logits are sharded, so we need to gather them
+            # Logits are sharded, so we need to gather them
-        logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
+            logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
-        torch.distributed.all_gather(logits, outputs.logits, group=self.process_group)
+            torch.distributed.all_gather(
-        logits = torch.cat(logits, dim=2)
+                logits, outputs.logits, group=self.process_group
+            )
+            logits = torch.cat(logits, dim=2)
 
-        return logits, outputs.past_key_values
+            return logits, outputs.past_key_values
+        # While the model itself is sharded, the embeddings might not as they might not be dividable by num-shard
+        else:
+            return super(GPTNeoxSharded, self).forward(
+                input_ids, attention_mask, position_ids, past_key_values
+            )

server/text_generation/utils.py

@@ -91,7 +91,7 @@ class NextTokenChooser:
             top_p=pb.top_p,
             do_sample=pb.do_sample,
             seed=pb.seed,
-            device=str(device),
+            device=device,
         )