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fix: Remove type casting in matmul and add scalar tensor conversion #3713

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fix: Remove type casting in matmul and add scalar tensor conversion #3713

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427fcb3
fix: Remove type casting in matmul and add scalar tensor conversion
keehyuna Jul 24, 2025
1b8dbd8
chore: Handle scalar tensor type promotion
keehyuna Jul 29, 2025
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9 changes: 8 additions & 1 deletion py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@
import warnings
from typing import Any, Callable, Optional, Union

import numpy as np
import tensorrt as trt
import torch
from torch.fx.node import Target
Expand Down Expand Up @@ -103,6 +102,14 @@ def convert_binary_elementwise(
rhs_dtype = rhs_val.dtype
is_rhs_trt_tensor = True

# Handle scalar tensor type promotion for elementwise operations
# When one operand is a scalar tensor (0-dimensional), promote its dtype to match the other operand
# This ensures consistent type handling in Torch elementwise operations
if isinstance(lhs_val, torch.Tensor) and len(lhs_val.shape) == 0:
lhs_dtype = rhs_dtype
if isinstance(rhs_val, torch.Tensor) and len(rhs_val.shape) == 0:
rhs_dtype = lhs_dtype

if not is_lhs_trt_tensor and not is_rhs_trt_tensor:
warnings.warn(
f"Both operands of the binary elementwise op {name} "
Expand Down
11 changes: 0 additions & 11 deletions py/torch_tensorrt/dynamo/conversion/impl/matmul.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
from torch_tensorrt.dynamo.conversion.converter_utils import (
broadcast,
cast_trt_tensor,
get_trt_tensor,
set_layer_name,
)
Expand Down Expand Up @@ -48,16 +47,6 @@ def matrix_multiply(
input, other = broadcast(
ctx, input, other, f"{name}_input", f"{name}_other", preset_diff
)
if ctx.net.get_flag(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED):
promoted_type = _enums.dtype._from(
torch.promote_types(
_enums.dtype._from(input.dtype).to(torch.dtype),
_enums.dtype._from(other.dtype).to(torch.dtype),
)
)
trt_promoted_type = promoted_type.to(trt.DataType)
input = cast_trt_tensor(ctx, input, trt_promoted_type, f"{name}_input_casted")
other = cast_trt_tensor(ctx, other, trt_promoted_type, f"{name}_other_casted")

layer = ctx.net.add_matrix_multiply(input, input_matrix_op, other, other_matrix_op)
set_layer_name(layer, target, name, source_ir)
Expand Down
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