Qwen2.5_VL Example Script Update (#598)

Signed-off-by: Mohit Soni <mohisoni@qti.qualcomm.com>

Author: mohiso22

QEfficient/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py

@@ -752,8 +752,8 @@ def get_dummy_inputs(self, kv_offload: bool = False, **kwargs):
             seq_len=constants.ONNX_EXPORT_EXAMPLE_SEQ_LEN,
         )
 
-        lang_inputs["past_key_values"] = [[] for _ in range(self.model.config.num_hidden_layers)]
-        for i in range(self.model.config.num_hidden_layers):
+        lang_inputs["past_key_values"] = [[] for _ in range(self.model.config.text_config.num_hidden_layers)]
+        for i in range(self.model.config.text_config.num_hidden_layers):
             for kv in ["key", "value"]:
                 lang_inputs["past_key_values"][i].append(torch.zeros(kv_cache_shape, dtype=torch.float32))
 
@@ -779,10 +779,10 @@ def get_specializations(
         **compiler_options,
     ):
         if height is None or width is None:
-            height = 1365
-            width = 2048
+            height = constants.QWEN2_5_VL_HEIGHT
+            width = constants.QWEN2_5_VL_WIDTH
             logger.warning(
-                "Setting height and width to be 1365 and 2048 respectively, as it was neither passed nor found in vision_config"
+                f"Setting height and width to be {height} and {width} respectively, as it was neither passed nor found in vision_config"
             )
         prefill_seq_len = prefill_seq_len if prefill_seq_len else 128
         ctx_len = ctx_len if ctx_len else constants.INTERN_CTX_LEN
@@ -882,7 +882,7 @@ def smart_resize(
 
     def get_onnx_dynamic_axes(self, kv_offload: bool = False):
         # Define dynamic axes
-        num_layers = self.config.num_hidden_layers
+        num_layers = self.config.text_config.num_hidden_layers
 
         vision_dynamic_axes = {
             "pixel_values": {0: "grid_height", 1: "grid_width"},
@@ -900,6 +900,7 @@ def get_onnx_dynamic_axes(self, kv_offload: bool = False):
             lang_dynamic_axes[f"past_value.{i}"] = {0: "batch_size", 2: "ctx_len"}
 
         dynamic_axes = {}
+
         if kv_offload:
             dynamic_axes["vision"] = vision_dynamic_axes
             dynamic_axes["lang"] = lang_dynamic_axes
@@ -911,7 +912,7 @@ def get_onnx_dynamic_axes(self, kv_offload: bool = False):
     def get_output_names(self, kv_offload: bool = False):
         vision_output_names = ["vision_embeds"]
         lang_output_names = ["logits"]
-        for i in range(self.model.config.num_hidden_layers):
+        for i in range(self.model.config.text_config.num_hidden_layers):
             for kv in ["key", "value"]:
                 lang_output_names.append(f"past_{kv}.{i}_RetainedState")
 
@@ -927,6 +928,32 @@ def get_output_names(self, kv_offload: bool = False):
             return lang_output_names
         return output_names
 
+    def prepare_inputs_for_generation(self, inputs, prefill_seq_len=128, batch_size=1):
+        input_ids_length = inputs["input_ids"].shape[1]
+
+        inputs["position_ids"] = torch.arange(input_ids_length).view(1, 1, input_ids_length).expand(-1, batch_size, -1)
+
+        pos_ids, rope_deltas = self.model.get_rope_index(
+            inputs["input_ids"],
+            None if "image_grid_thw" not in inputs else inputs["image_grid_thw"],
+            video_grid_thw=None,
+            second_per_grid_ts=None,
+            attention_mask=inputs["attention_mask"],
+        )
+
+        inputs["position_ids"] = torch.cat((inputs["position_ids"], pos_ids), dim=0)
+
+        num_chunks = -(input_ids_length // -prefill_seq_len)  # ceil divide without float
+        padded_len = num_chunks * prefill_seq_len  # Convert to a multiple of prompt_len
+
+        inputs["position_ids"] = F.pad(
+            inputs["position_ids"], pad=(0, padded_len - input_ids_length), mode="constant", value=-1
+        )
+
+        inputs.pop("image_grid_thw", None)
+
+        return inputs
+
     def get_inputs_info(self):
         return [
             IOInfo(name="input_ids", datatype=torch.int64, shape=("batch_size", "seq_len")),

QEfficient/utils/constants.py

@@ -125,6 +125,10 @@ def get_models_dir():
 # Wav2Vec2 Constant
 WAV2VEC2_MAX_SEQ_LEN = 480000  # 30 seconds of audio at 16 kHz sampling rate (16,000 samples/sec × 30 sec)
 
+# Qwen2_5_vl Constants
+QWEN2_5_VL_HEIGHT = 354
+QWEN2_5_VL_WIDTH = 536
+
 
 class Constants:
     # Export Constants.

examples/qwen2_5_vl_example.py

@@ -6,8 +6,6 @@
 # -----------------------------------------------------------------------------
 
 import requests
-import torch
-import torch.nn.functional as F
 import transformers
 from PIL import Image
 from qwen_vl_utils import process_vision_info
@@ -18,8 +16,7 @@
 ## For AWQ model update pytorch version to 2.8.*
 model_id = "Qwen/Qwen2.5-VL-32B-Instruct"
 config = AutoConfig.from_pretrained(model_id)
-
-## Use complete model without changing num_hidden_layers as it will not work for TF version 4.55.0 for Qwen2.5VL model
+config.text_config.num_hidden_layers = 2
 
 qeff_model = QEFFAutoModelForImageTextToText.from_pretrained(
     model_id, attn_implementation="eager", kv_offload=True, config=config
@@ -28,13 +25,13 @@
 processor = AutoProcessor.from_pretrained(model_id)
 
 ### use skip_vision=Ture, if want to run only text, ow false ###
-skip_vision = False
+skip_vision = True
 
 if skip_vision:
     ## Only Text ##
 
     ## Set Batch_Size ##
-    batch_size = 2
+    batch_size = 1
     qeff_model.compile(
         batch_size=batch_size,
         prefill_seq_len=128,
@@ -68,25 +65,7 @@
         return_tensors="pt",
     )
 
-    pos_ids, rope_deltas = qeff_model.model.get_rope_index(
-        inputs["input_ids"],
-        image_grid_thw=None,
-        video_grid_thw=None,
-        second_per_grid_ts=None,
-        attention_mask=inputs["attention_mask"],
-    )
-
-    input_ids_length = inputs["input_ids"].shape[1]
-
-    inputs["position_ids"] = torch.cat([pos_ids, pos_ids[0].unsqueeze(0)], dim=0)
-
-    prefill_seq_len = 128
-    num_chunks = -(input_ids_length // -prefill_seq_len)  # ceil divide without float
-    padded_len = num_chunks * prefill_seq_len  # Convert to a multiple of prompt_len
-
-    inputs["position_ids"] = F.pad(
-        inputs["position_ids"], pad=(0, padded_len - input_ids_length), mode="constant", value=-1
-    )
+    inputs = qeff_model.model.prepare_inputs_for_generation(inputs=inputs, prefill_seq_len=128, batch_size=batch_size)
 
     streamer = TextStreamer(tokenizer)
     output = qeff_model.generate(inputs=inputs, generation_len=100)
@@ -148,29 +127,9 @@
         padding=True,
         return_tensors="pt",
     )
-    input_ids_length = inputs["input_ids"].shape[1]
-
-    inputs["position_ids"] = torch.arange(input_ids_length).view(1, 1, input_ids_length).expand(-1, batch_size, -1)
-
-    pos_ids, rope_deltas = qeff_model.model.model.get_rope_index(
-        inputs["input_ids"],
-        inputs["image_grid_thw"],
-        video_grid_thw=None,
-        second_per_grid_ts=None,
-        attention_mask=inputs["attention_mask"],
-    )
 
-    inputs["position_ids"] = torch.cat((inputs["position_ids"], pos_ids), dim=0)
-
-    prefill_seq_len = 128
-    num_chunks = -(input_ids_length // -prefill_seq_len)  # ceil divide without float
-    padded_len = num_chunks * prefill_seq_len  # Convert to a multiple of prompt_len
-
-    inputs["position_ids"] = F.pad(
-        inputs["position_ids"], pad=(0, padded_len - input_ids_length), mode="constant", value=-1
-    )
+    inputs = qeff_model.model.prepare_inputs_for_generation(inputs=inputs, prefill_seq_len=128, batch_size=batch_size)
 
-    inputs.pop("image_grid_thw")
     streamer = TextStreamer(tokenizer)
     output = qeff_model.generate(inputs=inputs, generation_len=100)
     print(output.generated_ids)

tests/transformers/models/test_image_text_to_text_models.py

@@ -134,6 +134,17 @@
         "Can you describe the image in detail.",
         1,
     ),
+    (
+        "Qwen/Qwen2.5-VL-3B-Instruct",
+        True,
+        1,
+        128,
+        4096,
+        1540,
+        "https://picsum.photos/id/237/536/354",
+        "Can you describe the image in detail.",
+        1,
+    ),
     # (
     #     "meta-llama/Llama-3.2-11B-Vision-Instruct",
     #     True,
@@ -320,6 +331,10 @@ def check_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
         qnn_config=qnn_config,
     )
     inputs = processor(images=image, text=prompt, return_tensors="pt")
+    if hasattr(qeff_model.model.config, "model_type") and qeff_model.model.config.model_type == "qwen2_5_vl":
+        inputs = qeff_model.model.prepare_inputs_for_generation(
+            inputs=inputs, prefill_seq_len=prompt_len, batch_size=batch_size
+        )
     if "pixel_values" in inputs:
         inputs["pixel_values"] = inputs["pixel_values"].to(torch.float32)
     print("QPC Outputs (QAIC):")