Cherrypick of PR 3462 (#3483)

Co-authored-by: Dheeraj Peri <[email protected]>

Author: apbose

py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py

@@ -3,6 +3,7 @@
 from typing import Any, Callable, Optional, Union
 
 import numpy as np
+import tensorrt as trt
 import torch
 from torch.fx.node import Target
 from torch_tensorrt import _enums
@@ -15,11 +16,10 @@
     get_trt_tensor,
     has_dynamic_shape,
     set_layer_name,
+    to_torch,
 )
 from torch_tensorrt.dynamo.types import TRTElementWiseOp, TRTTensor
 
-import tensorrt as trt
-
 
 def get_python_op_from_trt_elementwise_op(
     trt_op: TRTElementWiseOp,
@@ -125,10 +125,9 @@ def convert_binary_elementwise(
     # dtype but we don't have a way to detect whether it makes sense for the
     # scalar to be float or half. Hence we go with the lhs dtype.
     if is_lhs_trt_tensor and isinstance(rhs_val, (float, int, bool)):
-        rhs_val = np.array([rhs_val], dtype=_enums.dtype._from(lhs_dtype).to(np.dtype))
+        rhs_val = to_torch(rhs_val, dtype=lhs_dtype)
     if is_rhs_trt_tensor and isinstance(lhs_val, (float, int, bool)):
-        lhs_val = np.array([lhs_val], dtype=_enums.dtype._from(rhs_dtype).to(np.dtype))
-
+        lhs_val = to_torch(lhs_val, dtype=rhs_dtype)
     lhs_val = get_trt_tensor(ctx, lhs_val, f"{name}_lhs", lhs_dtype)
     rhs_val = get_trt_tensor(ctx, rhs_val, f"{name}_rhs", rhs_dtype)
 

tests/py/dynamo/conversion/test_binary_ops_aten.py

@@ -228,6 +228,28 @@ def forward(self, x, y):
         ]
         self.run_test_with_dynamic_shape(Op(), input_specs)
 
+    @parameterized.expand(
+        [
+            (f"bf16_{op[0].__name__}_one_constant", op[0])
+            for op in elementwise_ops
+            if op[0].__name__ not in ["pow.Tensor_Tensor", "fmod.Tensor"]
+        ]
+    )
+    def test_elementwise_ops_bf16(self, _, orig_op):
+        class TestModule(nn.Module):
+            def __init__(self, orig_op):
+                super().__init__()
+                self.constant = torch.randn(1)
+                self.orig_op = orig_op
+
+            def forward(self, x):
+                x = self.orig_op(x, self.constant)
+                return self.orig_op(x, -2)
+
+        m = TestModule(orig_op)
+        inputs = [torch.randn(2, 2, dtype=torch.bfloat16)]
+        self.run_test(m, inputs)
+
 
 if __name__ == "__main__":
     run_tests()