feat: adjust rotary embed and avoid cuda graphs of size 2 and smaller

integration-tests/models/__snapshots__/test_flash_qwen2_vl_warmup/test_flash_qwen2_vl_simple.json

@@ -0,0 +1,26 @@
+{
+  "choices": [
+    {
+      "finish_reason": "stop",
+      "index": 0,
+      "logprobs": null,
+      "message": {
+        "content": "The correct answer is: blue",
+        "name": null,
+        "role": "assistant",
+        "tool_calls": null
+      },
+      "usage": null
+    }
+  ],
+  "created": 1733445131,
+  "id": "",
+  "model": "Qwen/Qwen2-VL-2B-Instruct",
+  "object": "chat.completion",
+  "system_fingerprint": "2.4.2-dev0-native",
+  "usage": {
+    "completion_tokens": 7,
+    "prompt_tokens": 27,
+    "total_tokens": 34
+  }
+}

integration-tests/models/test_flash_qwen2_vl_warmup.py

@@ -5,7 +5,7 @@ import pytest
 def flash_qwen2_vl_handle(launcher):
     with launcher(
         "Qwen/Qwen2-VL-2B-Instruct",
-        max_input_tokens=40,
+        max_input_length=40,
         max_batch_prefill_tokens=50,
         max_total_tokens=51,
     ) as handle:

server/text_generation_server/models/__init__.py

@@ -29,6 +29,7 @@ from text_generation_server.models.custom_modeling.bloom_modeling import (
     BloomForCausalLM,
 )
 from text_generation_server.models.globals import ATTENTION
+import text_generation_server.models.globals as globals
 from text_generation_server.models.seq2seq_lm import Seq2SeqLM
 from text_generation_server.models.galactica import GalacticaCausalLMBatch
 from text_generation_server.models.custom_modeling.neox_modeling import (
@@ -1217,6 +1218,11 @@ def get_model(
         else:
             raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
     if model_type == QWEN2_VL:
+        # TODO: remove edge case when cuda graph issue is resolved for BS=2 with Qwen2-VL
+        logger.warning(
+            "Qwen2-VL requires cuda graphs to be greater than 2. Removing all cuda graphs with a batch size equal or less than 2."
+        )
+        globals.CUDA_GRAPHS = list(filter(lambda x: x > 2, globals.CUDA_GRAPHS))
         return VlmCausalLM(
             model_id=model_id,
             model_class=Qwen2VLForConditionalGeneration,

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

@@ -138,7 +138,12 @@ class Qwen2Attention(torch.nn.Module):
                 dim=-1,
             )
 
-        self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
+        self.rotary_emb(
+            query,
+            torch.select(kv, dim=1, index=0),
+            cos[: query.shape[0], ...],
+            sin[: query.shape[0], ...],
+        )
 
         if prefill_cache_indices is not None:
             kv_to_cache = kv[prefill_cache_indices]