tlopex commented on code in PR #20243:
URL: https://github.com/apache/tvm/pull/20243#discussion_r3909687607
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src/relax/transform/combine_parallel_matmul.cc:
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@@ -210,6 +217,25 @@ ffi::TypedFunction<ffi::Map<Var, Expr>(ffi::Map<DFPattern,
Var>, ffi::Map<Var, E
continue;
}
+ if (branch_info.bias_dim) {
+ std::vector<ffi::Array<PrimExpr>> bias_shapes;
+ bool bias_shape_unknown = false;
+ for (const auto& bias_var : bias) {
+ auto bias_shape_opt = GetTensorType(bias_var)->GetShape();
+ if (!bias_shape_opt) {
+ bias_shape_unknown = true;
+ break;
+ }
+ bias_shapes.push_back(bias_shape_opt.value());
+ }
+ if (bias_shape_unknown) {
+ return ffi::Map<Var, Expr>{};
+ }
+ if (!shapes_compatible_excluding_trailing_axes(bias_shapes, 1)) {
Review Comment:
Please also require each bias’s last dimension to match the corresponding
`splits[i].split_size`. For weights `[3,4]`/`[3,5]` and biases `[2,1]`/`[2,1]`,
both original adds are valid via broadcasting, but this check passes and
produces a `[2,2]` concatenated bias that cannot broadcast to the combined
`[2,9]` output. This case should skip fusion, with a regression test added.
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