[XLA] InternalError: mixed floating-point precision in TanhGrad during XLA compilation — f32 multiply sees mismatched types despite no explicit mixed precision

TensorFlow XLA compilation fails during gradient lowering when a float16 activation flows through `ResizeBilinear`, which promotes tensors to float32 internally. The forward pass succeeds in both eager and XLA modes, and eager gradients also succeed. However, XLA gradient compilation fails with: ``` tensorflow.python.framework.errors_impl.InternalError: during context [Unknown]: Seen floating point types of different precisions in %TanhGrad.9 = f32[5,4,8]{2,1,0} multiply(%unstack.5, %TanhGrad.8), metadata={op_type="TanhGrad" op_name="gradient_tape/TanhGrad" source_file="/home/test/.venv/lib/python3.13/site-packages/tensorflow/python/framework/ops.py" source_line=1221}, but mixed precision is disallowed. [Op:__inference_grad_fn_29] ``` This appears to happen because: - `tf.nn.tanh` output is float16 - `ResizeBilinear` promotes downstream tensors to float32 - during backprop, `TanhGrad` receives: - upstream gradient (`dy`) in float32 - saved forward activation (`y`) in float16 XLA then emits a mixed-precision multiply instead of inserting an explicit cast. Observed behavior: * eager forward: OK * eager gradient: OK * XLA forward: OK * XLA gradient compilation: FAILS Minimal Code to reproduce: ```python import tensorflow as tf print(tf.__version__) x = tf.random.uniform( [5, 4, 8], minval=-1.0, maxval=1.0, dtype=tf.float16, ) @tf.function(jit_compile=True) def grad_fn(x): with tf.GradientTape() as tape: tape.watch(x) y = tf.nn.tanh(x) y = tf.stack([y, tf.sign(x)], axis=3) # fp16 -> fp32 transition happens here y = tf.raw_ops.ResizeBilinear( images=y, size=tf.constant([4, 8], tf.int32), ) loss = tf.reduce_sum(y) return tape.gradient(loss, x) print(grad_fn(x)) ``` Error logs WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1779385617.959124 1655238 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. I0000 00:00:1779385617.985273 1655238 cpu_feature_guard.cc:227] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1779385618.691055 1655238 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2.21.0 I0000 00:00:1779385618.915190 1655238 gpu_device.cc:2043] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22107 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:01:00.0, compute capability: 8.9 E0000 00:00:1779385619.071301 1655238 util.cc:131] oneDNN supports DT_HALF only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present. I0000 00:00:1779385619.129295 1655238 service.cc:153] XLA service 0x98e2b80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: I0000 00:00:1779385619.129308 1655238 service.cc:161] StreamExecutor [0]: NVIDIA GeForce RTX 4090, Compute Capability 8.9 (Driver: 13.0.0; Runtime: 12.9.0; Toolkit: 12.5.0; DNN: 9.19.0) I0000 00:00:1779385619.131926 1655238 dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. W0000 00:00:1779385619.138950 1655238 op_kernel.cc:1858] OP_REQUIRES failed at xla_ops.cc:602 : INTERNAL: during context [Unknown]: Seen floating point types of different precisions in %TanhGrad.9 = f32[5,4,8]{2,1,0} multiply(%unstack.5, %TanhGrad.8), metadata={op_type="TanhGrad" op_name="gradient_tape/TanhGrad" source_file="/home/test/.venv/lib/python3.13/site-packages/tensorflow/python/framework/ops.py" source_line=1221}, but mixed precision is disallowed. Traceback (most recent call last): File "/home/test/bugs/tf/crash_5e89acba.py", line 31, in <module> print(grad_fn(x)) ~~~~~~~^^^ File "/home/test/.venv/lib/python3.13/site-packages/tensorflow/python/util/traceback_utils.py", line 167, in error_handler raise e.with_traceback(filtered_tb) from None File "/home/test/.venv/lib/python3.13/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute except TypeError as e: ...<5 lines>... raise e tensorflow.python.framework.errors_impl.InternalError: during context [Unknown]: Seen floating point types of different precisions in %TanhGrad.9 = f32[5,4,8]{2,1,0} multiply(%unstack.5, %TanhGrad.8), metadata={op_type="TanhGrad" op_name="gradient_tape/TanhGrad" source_file="/home/test/.venv/lib/python3.13/site-packages/tensorflow/python/framework/ops.py" source_line=1221}, but mixed precision is disallowed. [Op:__inference_grad_fn_29] Versions Tensorflow-vesion ; 2.21.0