Arm backend: Add BF16 layer tests for Qwen (pytorch#19767)

* Add layers that run in BF16 in the HF model

Change-Id: If75434db138059f3a433a70abda3f3e26f6dd3b6

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils
@Sebastian-Larsson @robell @rascani

---------

Signed-off-by: Tom Allsop <tom.allsop@arm.com>

Author: tom-arm

backends/arm/test/models/Qwen3_VL/test_qwen3_vl_layers.py

@@ -33,7 +33,7 @@
     Qwen3VLVisionRotaryEmbedding,
 )
 
-input_t = Tuple[torch.Tensor, ...]
+input_t = Tuple[torch.Tensor | int, ...]
 
 
 def _make_qwen3_vl_2b_instruct_layer_config():
@@ -99,6 +99,19 @@ def prepare_model_and_inputs(cls):
         raise NotImplementedError
 
 
+def _to_bfloat16(
+    model: torch.nn.Module, inputs: input_t
+) -> tuple[torch.nn.Module, input_t]:
+    return model.to(torch.bfloat16), tuple(
+        (
+            x.to(torch.bfloat16)
+            if isinstance(x, torch.Tensor) and x.is_floating_point()
+            else x
+        )
+        for x in inputs
+    )
+
+
 class Qwen3VLVisionMLPModel(Qwen3VLTestModule):
     def __init__(self, config) -> None:
         super().__init__()
@@ -442,6 +455,18 @@ class Qwen3VLTestCase:
 
 VGF_NO_QUANT_TEST_CASES: dict[str, Qwen3VLTestCase] = TOSA_FP_TEST_CASES
 
+TOSA_BF16_TEST_CASES: dict[str, Qwen3VLTestCase] = {
+    "vision_mlp": TOSA_FP_TEST_CASES["vision_mlp"],
+    "vision_patch_embed": TOSA_FP_TEST_CASES["vision_patch_embed"],
+    "vision_rotary_embedding": TOSA_FP_TEST_CASES["vision_rotary_embedding"],
+    "vision_rotary_apply": TOSA_FP_TEST_CASES["vision_rotary_apply"],
+    "vision_attention": TOSA_FP_TEST_CASES["vision_attention"],
+    "vision_block": TOSA_FP_TEST_CASES["vision_block"],
+    "vision_patch_merger": TOSA_FP_TEST_CASES["vision_patch_merger"],
+    "text_rms_norm": TOSA_FP_TEST_CASES["text_rms_norm"],
+    "qk_norm": TOSA_FP_TEST_CASES["qk_norm"],
+}
+
 
 @common.parametrize(
     "test_case",
@@ -460,6 +485,27 @@ def test_qwen3_vl_tosa_FP(test_case: Qwen3VLTestCase):
         pipeline.run()
 
 
+@common.parametrize(
+    "test_case",
+    TOSA_BF16_TEST_CASES,
+)
+def test_qwen3_vl_tosa_FP_bf16(test_case: Qwen3VLTestCase):
+    model, inputs = test_case.model_cls.prepare_model_and_inputs()
+    model, inputs = _to_bfloat16(model, inputs)
+    with torch.no_grad():
+        pipeline = TosaPipelineFP[input_t](
+            model,
+            inputs,
+            aten_op=[],
+            exir_op=[],
+            transform_passes=list(test_case.transform_passes),
+            tosa_extensions=["bf16"],
+            atol=1e-2,
+            rtol=1e-2,
+        )
+        pipeline.run()
+
+
 @common.SkipIfNoModelConverter
 @common.parametrize(
     "test_case",