Add support for bool in elementwise ops

[narendasan]

Description

Adds support for bool tensors in element wise ops

Type of change

Please delete options that are not relevant and/or add your own.

• New feature (non-breaking change which adds functionality)

Checklist:

• My code follows the style guidelines of this project (You can use the linters)
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas and hacks
• I have made corresponding changes to the documentation
• I have added tests to verify my fix or my feature
• New and existing unit tests pass locally with my changes

[narendasan]

@inocsin Are these changes that would only be relevant to mul or other ops in general? Also did you write tests for this? I didnt see them in the commits

[inocsin]

Currently, I only find the Bool * Int operation in my models. I think other operation like Int / Bool or Int +/- Bool doesn't make any sense. I will add test case later.

[inocsin]

The input type of trtorch is set to float in default, I have to modify the TRTorch/core/conversion/conversion.cpp:150

auto trt_in = ctx->net->addInput(name.c_str(), ctx->input_type, dims.input_shape);

to support input type of Bool

[inocsin]

we can try this #327

[narendasan]

What does it mean to do bool * int? should that give you a bool out? like what does False * 8 mean?

[inocsin]

[True, False] * [8, 8] = [8, 0], this can be a mask operation, check demo graph here #327

[inocsin]

we can close this PR if we merge #327

[github-actions]

Code conforms to Python style guidelines

[github-actions]

There are some changes that do not conform to C++ style guidelines:

diff --git a/workspace/core/conversion/converters/impl/element_wise.cpp b/tmp/changes.txt
index da582c9..f35c52e 100644
--- a/workspace/core/conversion/converters/impl/element_wise.cpp
+++ b/tmp/changes.txt
@@ -360,7 +360,7 @@ auto element_wise_registrations TRTORCH_UNUSED =
                    auto self = args[0].ITensorOrFreeze(ctx);
                    auto other = args[1].ITensorOrFreeze(ctx);
                    nvinfer1::ILayer* mul = nullptr;
-                    if (self->getType() ==nvinfer1::DataType::kBOOL || other->getType() == nvinfer1::DataType::kBOOL) {
+                    if (self->getType() == nvinfer1::DataType::kBOOL || other->getType() == nvinfer1::DataType::kBOOL) {
                      auto self_id = ctx->net->addIdentity(*self);
                      auto other_id = ctx->net->addIdentity(*other);
                      if (self->getType() == nvinfer1::DataType::kBOOL) {
@@ -369,11 +369,15 @@ auto element_wise_registrations TRTORCH_UNUSED =
                      if (other->getType() == nvinfer1::DataType::kBOOL) {
                        other_id->getOutput(0)->setType(nvinfer1::DataType::kINT32);
                      }
-                      mul =
-                        add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, self_id->getOutput(0), other_id->getOutput(0), util::node_info(n));
+                      mul = add_elementwise(
+                          ctx,
+                          nvinfer1::ElementWiseOperation::kPROD,
+                          self_id->getOutput(0),
+                          other_id->getOutput(0),
+                          util::node_info(n));
                    } else {
                      mul =
-                        add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, self, other, util::node_info(n));
+                          add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, self, other, util::node_info(n));
                    }
                    TRTORCH_CHECK(mul, "Unable to create mul layer from node: " << *n);

ERROR: Some files do not conform to style guidelines

[peri044]

We support aten::to operator now which should handle this. Check out https://github.com/NVIDIA/TRTorch/blob/master/tests/core/conversion/converters/test_cast.cpp for usage.