manupa-arm commented on a change in pull request #9409:
URL: https://github.com/apache/tvm/pull/9409#discussion_r752091426
##########
File path: tests/python/contrib/test_cmsisnn/test_conv2d.py
##########
@@ -129,8 +128,11 @@ def make_model(
@pytest.mark.parametrize("kernel_size", [(3, 3)])
@pytest.mark.parametrize("padding", ["SAME", "VALID"])
@pytest.mark.parametrize("strides, dilation", [((2, 2), (1, 1)), ((1, 1), (1,
1))])
[email protected]("enable_bias", [True, False])
@pytest.mark.parametrize("relu_type", ["NONE", "RELU"])
[email protected](
+ "conv_type, depth_multiplier, enable_bias",
+ [("conv2d", 1, True), ("conv2d", 1, False), ("depthwise", 1, True),
("depthwise", 3, True)],
Review comment:
Do we need to run all the tests again for a different depth_multiplier ?
##########
File path: src/relay/backend/contrib/cmsisnn/generate_constants.cc
##########
@@ -105,11 +105,25 @@ class GenerateConstantsMutator : public MixedModeMutator {
conv2d_call = requantize_input;
}
- // Transpose weights: HWIO -> OHWI
auto* conv2d_attrs = conv2d_call->attrs.as<Conv2DAttrs>();
- tvm::Attrs new_conv2d_attrs;
- Expr transposed_kernel =
- ConvertKernelLayout(conv2d_call->args[1], conv2d_attrs,
&new_conv2d_attrs);
+ tvm::Attrs new_conv2d_attrs = conv2d_call->attrs;
+ Expr conv2d_kernel = conv2d_call->args[1];
+
+ bool is_depthwise = false;
+ Array<PrimExpr> input_shape =
conv2d_call->args[0]->type_as<TensorTypeNode>()->shape;
+ Array<PrimExpr> kernel_shape =
conv2d_call->args[1]->type_as<TensorTypeNode>()->shape;
+ std::string kernel_layout = conv2d_attrs->kernel_layout.c_str();
+ int kernel_pos_o = kernel_layout.find("O");
+ int groups = conv2d_attrs->groups;
+ if (groups == qnn::get_const_int(input_shape[3]) &&
+ groups == qnn::get_const_int(kernel_shape[kernel_pos_o])) {
+ is_depthwise = true;
+ }
+
+ // Transpose weights: HWIO -> OHWI for Conv2D
+ if (!is_depthwise) {
Review comment:
nit: do we need this seperated out ? - as opposed to being inside the
if above
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