yunligou711-commits opened a new issue, #20479: URL: https://github.com/apache/tvm/issues/20479
Thanks for participating in the TVM community! We use https://discuss.tvm.apache.org/ for any general usage questions and discussions. The issue tracker is used for actionable items such as feature proposals discussion, roadmaps, and bug tracking. You are always welcomed to post on the forum first :smile_cat: Issues that are inactive for a period of time may get closed. We adopt this policy so that we won't lose track of actionable issues that may fall at the bottom of the pile. Feel free to reopen a new one if you feel there is an additional problem that needs attention when an old one gets closed. ## Expected behavior `relax.transform.AllocateWorkspace` should return an `IRModule` in which every referenced `GlobalVar` is defined. In particular: - `relax.analysis.well_formed` should pass. - `relax.build` should succeed. - `relax.VirtualMachine` construction should succeed. --- ## Actual behavior The returned module calls the old `GlobalVar` of each function renamed by the pass, even though that `GlobalVar` has been removed. As a result, `well_formed` fails, and `VirtualMachine` construction aborts: ```text === after AllocateWorkspace === global vars: ['inner1', 'inner_tir', 'main', 'outer1'] ValueError: GlobalVar I.GlobalVar("inner") is not defined. InternalError: Check failed: (func.has_value()) is false: Error: Cannot find ffi::Function inner in either Relax VM kernel library, or in TVM runtime ffi::Function registry, or in global Relax functions of the VM executable ``` `AllocateWorkspace` itself returns successfully, and `relax.build` also succeeds. The failure surfaces only when constructing the `VirtualMachine`. No BYOC backend is installed or registered — the backend name is never resolved. The module is already malformed before any code generation runs. --- ## Environment - **TVM version:** `0.26.dev0` - **Commit:** `8312a17f8734ddfd56e5f3977cd5df25b83ec49f` - **OS:** Linux x86_64, Ubuntu - **Kernel:** `5.15` - **Python:** `3.11` - **Target:** `llvm` - **Device:** CPU only - **External codegen backend:** Not required - **Reproduces with:** `relax.transform.AllocateWorkspace` alone --- ## Steps to reproduce ```python import tvm from tvm import relax from tvm.script import ir as I, tirx as T, relax as R @I.ir_module class Module: @T.prim_func def inner_tir( x: T.Buffer((4,), "float32"), y: T.Buffer((4,), "float32"), ): for i in T.serial(4): y[i] = x[i] + T.float32(1) @R.function def main( x: R.Tensor((4,), dtype="float32"), ) -> R.Tensor((4,), dtype="float32"): cls = Module with R.dataflow(): gv = cls.outer(x) R.output(gv) return gv @R.function def outer( x: R.Tensor((4,), dtype="float32"), ) -> R.Tensor((4,), dtype="float32"): R.func_attr({"Codegen": "my_backend"}) cls = Module with R.dataflow(): gv = cls.inner(x) R.output(gv) return gv @R.function def inner( x: R.Tensor((4,), dtype="float32"), ) -> R.Tensor((4,), dtype="float32"): R.func_attr( { "Composite": "my_backend.op", "Primitive": 1, "WorkspaceSize": 1024, } ) cls = Module with R.dataflow(): gv = R.call_tir( cls.inner_tir, (x,), out_ty=R.Tensor((4,), dtype="float32"), ) R.output(gv) return gv print("=== before ===") relax.analysis.well_formed(Module) # passes after = relax.transform.AllocateWorkspace()(Module) print("=== after AllocateWorkspace ===") print(sorted(str(g.name_hint) for g in after.get_global_vars())) relax.analysis.well_formed(after) # fails ex = relax.build(after, target="llvm") vm = relax.VirtualMachine(ex, tvm.cpu()) ``` The expected output is the block shown under **Actual behavior**. The module is already malformed before any codegen runs. --- ## Triage - `needs-triage` - `bug` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
