inspect signature for position ids

.github/workflows/build.yaml

@@ -213,13 +213,12 @@ jobs:
           sudo mount /dev/nvme1n1 ${{ env.DOCKER_VOLUME }}
       - name: Install
         run: |
-          pip install pytest-xdist 
           make install-integration-tests
       - name: Run tests
         run: |
           export DOCKER_IMAGE=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:sha-${{ env.GITHUB_SHA_SHORT }}
           export HUGGING_FACE_HUB_TOKEN=${{ secrets.HUGGING_FACE_HUB_TOKEN }}
-          pytest -s -vv -n 2 --dist loadfile integration-tests
+          pytest -s -vv integration-tests
 
   stop-runner:
     name: Stop self-hosted EC2 runner

server/text_generation_server/models/__init__.py

@@ -261,12 +261,17 @@ def get_model(
     if trust_remote_code and auto_map is not None:
         if "AutoModelForCausalLM" in auto_map.keys():
             return CausalLM(
-                model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
+                model_id,
+                revision,
+                quantize=quantize,
+                trust_remote_code=trust_remote_code,
             )
         if "AutoModelForSeq2SeqLM" in auto_map.keys:
             return Seq2SeqLM(
-                model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
+                model_id,
+                revision,
+                quantize=quantize,
+                trust_remote_code=trust_remote_code,
             )
 
-
     raise ValueError(f"Unsupported model type {model_type}")

server/text_generation_server/models/causal_lm.py

@@ -1,4 +1,5 @@
 import torch
+import inspect
 
 from dataclasses import dataclass
 from opentelemetry import trace
@@ -473,17 +474,28 @@ class CausalLM(Model):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map="auto" if torch.cuda.is_available() and torch.cuda.device_count() > 1 else None,
+            device_map="auto"
+            if torch.cuda.is_available() and torch.cuda.device_count() > 1
+            else None,
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
         if torch.cuda.is_available() and torch.cuda.device_count() == 1:
             model = model.cuda()
 
-        tokenizer.pad_token_id = (
-            model.config.pad_token_id
-            if model.config.pad_token_id is not None
-            else model.config.eos_token_id
+        if tokenizer.pad_token_id is None:
+            if model.config.pad_token_id is not None:
+                tokenizer.pad_token_id = model.config.pad_token_id
+            elif model.config.eos_token_id is not None:
+                tokenizer.pad_token_id = model.config.eos_token_id
+            elif tokenizer.eos_token_id is not None:
+                tokenizer.pad_token_id = tokenizer.eos_token_id
+            else:
+                tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+
+        self.has_position_ids = (
+            inspect.signature(model.forward).parameters.get("position_ids", None)
+            is not None
         )
 
         super(CausalLM, self).__init__(
@@ -507,14 +519,17 @@ class CausalLM(Model):
         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
     ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
         # Model Forward
-        outputs = self.model.forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            use_cache=True,
-            return_dict=True,
-        )
+        kwargs = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "use_cache": True,
+            "return_dict": True,
+        }
+        if self.has_position_ids:
+            kwargs["position_ids"] = position_ids
+
+        outputs = self.model.forward(**kwargs)
         return outputs.logits, outputs.past_key_values
 
     @tracer.start_as_current_span("generate_token")

server/text_generation_server/models/seq2seq_lm.py

@@ -519,7 +519,9 @@ class Seq2SeqLM(Model):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map="auto" if torch.cuda.is_available() and torch.cuda.device_count() > 1 else None,
+            device_map="auto"
+            if torch.cuda.is_available() and torch.cuda.device_count() > 1
+            else None,
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )