Update pyproject.toml to use 0.1.34 MLIR-TRT (#261)

Signed-off-by: yizhuoz004 <[email protected]>

Author: yizhuoz004

tripy/pyproject.toml

@@ -8,8 +8,8 @@ requires-python = ">= 3.9"
 license = {text = "Apache 2.0"}
 dependencies = [
   "tensorrt~=10.0",
-  "mlir-tensorrt-compiler==0.1.32+cuda12.trt102",
-  "mlir-tensorrt-runtime==0.1.32+cuda12.trt102",
+  "mlir-tensorrt-compiler==0.1.34+cuda12.trt102",
+  "mlir-tensorrt-runtime==0.1.34+cuda12.trt102",
   "colored==2.2.3",
 ]
 

tripy/tests/integration/test_conv_transpose.py

@@ -186,7 +186,7 @@ def test_transposed_convolution_2d(self, torch_dtype, tp_dtype, test_case):
         expected = conv_layer_torch(input_torch).to(torch_dtype)
         output = conv_layer(input)
 
-        rtol_ = 1e-3
+        rtol_ = 1e-2
         assert tp.allclose(output, tp.Tensor(expected), rtol=rtol_)
         assert output.shape == expected.shape
 

tripy/tripy/frontend/trace/ops/binary_elementwise.py

@@ -494,7 +494,7 @@ def __rtruediv__(self: numbers.Number, other: "tripy.types.TensorLike") -> "trip
 @TENSOR_METHOD_REGISTRY("__floordiv__")
 @frontend_utils.convert_inputs_to_tensors(sync_arg_types=[("self", "other")])
 @constraints.dtype_info(
-    dtype_variables={"T1": ["float32", "float16", "bfloat16", "int4", "int8", "int32", "int64"]},
+    dtype_variables={"T1": ["float32", "float16", "bfloat16", "float8", "int4", "int8", "int32", "int64"]},
     dtype_constraints={"self": "T1", "other": "T1", constraints.RETURN_VALUE: "T1"},
 )
 def __floordiv__(self: Union["tripy.Tensor", Any], other: Union["tripy.Tensor", Any]) -> "tripy.Tensor":
@@ -530,7 +530,7 @@ def __floordiv__(self: Union["tripy.Tensor", Any], other: Union["tripy.Tensor",
 @TENSOR_METHOD_REGISTRY("__rfloordiv__")
 @frontend_utils.convert_inputs_to_tensors(sync_arg_types=[("self", "other")])
 @constraints.dtype_info(
-    dtype_variables={"T1": ["float32", "float16", "bfloat16", "int4", "int8", "int32", "int64"]},
+    dtype_variables={"T1": ["float32", "float16", "bfloat16", "float8", "int4", "int8", "int32", "int64"]},
     dtype_constraints={"self": "T1", "other": "T1", constraints.RETURN_VALUE: "T1"},
 )
 def __rfloordiv__(self: Union["tripy.Tensor", Any], other: Union["tripy.Tensor", Any]) -> "tripy.Tensor":
@@ -566,7 +566,7 @@ def __rfloordiv__(self: Union["tripy.Tensor", Any], other: Union["tripy.Tensor",
 @TENSOR_METHOD_REGISTRY("__mod__")
 @frontend_utils.convert_inputs_to_tensors(sync_arg_types=[("self", "other")])
 @constraints.dtype_info(
-    dtype_variables={"T1": ["float32", "float16", "bfloat16"]},
+    dtype_variables={"T1": ["float32", "float16", "bfloat16", "float8"]},
     dtype_constraints={"self": "T1", "other": "T1", constraints.RETURN_VALUE: "T1"},
 )
 def __mod__(self: Union["tripy.Tensor", Any], other: Union["tripy.Tensor", Any]) -> "tripy.Tensor":
@@ -597,7 +597,7 @@ def __mod__(self: Union["tripy.Tensor", Any], other: Union["tripy.Tensor", Any])
 @TENSOR_METHOD_REGISTRY("__rmod__")
 @frontend_utils.convert_inputs_to_tensors(sync_arg_types=[("self", "other")])
 @constraints.dtype_info(
-    dtype_variables={"T1": ["float32", "float16", "bfloat16"]},
+    dtype_variables={"T1": ["float32", "float16", "bfloat16", "float8"]},
     dtype_constraints={"self": "T1", "other": "T1", constraints.RETURN_VALUE: "T1"},
 )
 def __rmod__(self: Union["tripy.Tensor", Any], other: Union["tripy.Tensor", Any]) -> "tripy.Tensor":