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Modified
SDFGState.unordered_arglist() (#1708)
This PR fixes the way how arguments are detected in scopes. Technically this only affects GPU code generation, but it is a side effect of how the code is generated. In GPU mode a `Map` is translated into one kernel, for this reason a signature must be computed (this is the reason why CPU code generation is not affected, no function call is produced). To compute this signature the `unsorted_arglist()` function scans what is needed. However, this was implemented not correctly. Assume that AccessNode for array `A` is outside the map and inside the map a temporary scalar, `tmp_in` is defined and initialized to `tmp_in = A[__i0, __i1]`, see also this image:  If the `data` property of the Memlet that connects the MapEntry with the AccessNode for `tmp_in` is referencing `A` then the (old) function would find that `A` is needed inside, although there is no AccessNode for `A` inside the map. If however, this Memlet referrers `tmp_in` (which is not super standard, but should be allowed), then the old version would not pick up. This would then lead to a code generation error. This PR modifies the function such that such cases are handled. This is done by following all edges that are adjacent to the MapEntry (from the inside) to where the are actually originate.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,197 @@ | ||
| import dace | ||
| import copy | ||
|
|
||
| def test_argument_signature_test(): | ||
| """Tests if the argument signature is computed correctly. | ||
| The test is focused on if data dependencies are picked up if they are only | ||
| referenced indirectly. This effect is only directly visible for GPU. | ||
| The test also runs on GPU, but will only compile for GPU. | ||
| """ | ||
|
|
||
| def make_sdfg() -> dace.SDFG: | ||
| sdfg = dace.SDFG("Repr") | ||
| state = sdfg.add_state(is_start_block=True) | ||
| N = dace.symbol(sdfg.add_symbol("N", dace.int32)) | ||
| for name in "BC": | ||
| sdfg.add_array( | ||
| name=name, | ||
| dtype=dace.float64, | ||
| shape=(N, N), | ||
| strides=(N, 1), | ||
| transient=False, | ||
| ) | ||
|
|
||
| # `A` uses a stride that is not used by any of the other arrays. | ||
| # However, the stride is used if we want to index array `A`. | ||
| second_stride_A = dace.symbol(sdfg.add_symbol("second_stride_A", dace.int32)) | ||
| sdfg.add_array( | ||
| name="A", | ||
| dtype=dace.float64, | ||
| shape=(N,), | ||
| strides=(second_stride_A,), | ||
| transient=False, | ||
|
|
||
| ) | ||
|
|
||
| # Also array `D` uses a stride that is not used by any other array. | ||
| second_stride_D = dace.symbol(sdfg.add_symbol("second_stride_D", dace.int32)) | ||
| sdfg.add_array( | ||
| name="D", | ||
| dtype=dace.float64, | ||
| shape=(N, N), | ||
| strides=(second_stride_D, 1), | ||
| transient=False, | ||
|
|
||
| ) | ||
|
|
||
| # Simplest way to generate a mapped Tasklet, we will later modify it. | ||
| state.add_mapped_tasklet( | ||
| "computation", | ||
| map_ranges={"__i0": "0:N", "__i1": "0:N"}, | ||
| inputs={ | ||
| "__in0": dace.Memlet("A[__i1]"), | ||
| "__in1": dace.Memlet("B[__i0, __i1]"), | ||
| }, | ||
| code="__out = __in0 + __in1", | ||
| outputs={"__out": dace.Memlet("C[__i0, __i1]")}, | ||
| external_edges=True, | ||
| ) | ||
|
|
||
| # Instead of going from the MapEntry to the Tasklet we will go through | ||
| # an temporary AccessNode that is only used inside the map scope. | ||
| # Thus there is no direct reference to `A` inside the map scope, that would | ||
| # need `second_stride_A`. | ||
| sdfg.add_scalar("tmp_in", transient=True, dtype=dace.float64) | ||
| tmp_in = state.add_access("tmp_in") | ||
| for e in state.edges(): | ||
| if e.dst_conn == "__in0": | ||
| iedge = e | ||
| break | ||
| state.add_edge( | ||
| iedge.src, | ||
| iedge.src_conn, | ||
| tmp_in, | ||
| None, | ||
| # The important thing is that the Memlet, that connects the MapEntry with the | ||
| # AccessNode, does not refers to the memory outside (its source) but to the transient | ||
| # inside (its destination) | ||
| dace.Memlet(data="tmp_in", subset="0", other_subset="__i1"), # This does not work! | ||
| #dace.Memlet(data="A", subset="__i1", other_subset="0"), # This would work! | ||
| ) | ||
| state.add_edge( | ||
| tmp_in, | ||
| None, | ||
| iedge.dst, | ||
| iedge.dst_conn, | ||
| dace.Memlet(f"{tmp_in.data}[0]"), | ||
| ) | ||
| state.remove_edge(iedge) | ||
|
|
||
| # Here we are doing something similar as for `A`, but this time for the output. | ||
| # The output of the Tasklet is stored inside a temporary scalar. | ||
| # From that scalar we then go to `C`, here the Memlet on the inside is still | ||
| # referring to `C`, thus it is referenced directly. | ||
| # We also add a second output that goes to `D` , but the inner Memlet does | ||
| # not refer to `D` but to the temporary. Thus there is no direct mention of | ||
| # `D` inside the map scope. | ||
| sdfg.add_scalar("tmp_out", transient=True, dtype=dace.float64) | ||
| tmp_out = state.add_access("tmp_out") | ||
| for e in state.edges(): | ||
| if e.src_conn == "__out": | ||
| oedge = e | ||
| assert oedge.data.data == "C" | ||
| break | ||
|
|
||
| state.add_edge( | ||
| oedge.src, | ||
| oedge.src_conn, | ||
| tmp_out, | ||
| None, | ||
| dace.Memlet(data="tmp_out", subset="0"), | ||
| ) | ||
| state.add_edge( | ||
| tmp_out, | ||
| None, | ||
| oedge.dst, | ||
| oedge.dst_conn, | ||
| dace.Memlet(data="C", subset="__i0, __i1"), | ||
| ) | ||
|
|
||
| # Now we create a new output that uses `tmp_out` but goes into `D`. | ||
| # The memlet on the inside will not use `D` but `tmp_out`. | ||
| state.add_edge( | ||
| tmp_out, | ||
| None, | ||
| oedge.dst, | ||
| "IN_D", | ||
| dace.Memlet(data=tmp_out.data, subset="0", other_subset="__i1, __i0"), | ||
| ) | ||
| state.add_edge( | ||
| oedge.dst, | ||
| "OUT_D", | ||
| state.add_access("D"), | ||
| None, | ||
| dace.Memlet(data="D", subset="__i0, __i1", other_subset="0"), | ||
| ) | ||
| oedge.dst.add_in_connector("IN_D", force=True) | ||
| oedge.dst.add_out_connector("OUT_D", force=True) | ||
| state.remove_edge(oedge) | ||
|
|
||
| # Without this the test does not work properly | ||
| # It is related to [Issue#1703](https://github.com/spcl/dace/issues/1703) | ||
| sdfg.validate() | ||
| for edge in state.edges(): | ||
| edge.data.try_initialize(edge=edge, sdfg=sdfg, state=state) | ||
|
|
||
| for array in sdfg.arrays.values(): | ||
| if isinstance(array, dace.data.Array): | ||
| array.storage = dace.StorageType.GPU_Global | ||
| else: | ||
| array.storage = dace.StorageType.Register | ||
| sdfg.apply_gpu_transformations(simplify=False) | ||
| sdfg.validate() | ||
|
|
||
| return sdfg | ||
|
|
||
| # Build the SDFG | ||
| sdfg = make_sdfg() | ||
|
|
||
| map_entry = None | ||
| for state in sdfg.states(): | ||
| for node in state.nodes(): | ||
| if isinstance(node, dace.nodes.MapEntry): | ||
| map_entry = node | ||
| break | ||
| if map_entry is not None: | ||
| break | ||
|
|
||
| # Now get the argument list of the map. | ||
| res_arglist = { k:v for k, v in state.scope_subgraph(map_entry).arglist().items()} | ||
|
|
||
| ref_arglist = { | ||
| 'A': dace.data.Array, | ||
| 'B': dace.data.Array, | ||
| 'C': dace.data.Array, | ||
| 'D': dace.data.Array, | ||
| 'N': dace.data.Scalar, | ||
| 'second_stride_A': dace.data.Scalar, | ||
| 'second_stride_D': dace.data.Scalar, | ||
| } | ||
|
|
||
| assert len(ref_arglist) == len(res_arglist), f"Expected {len(ref_arglist)} but got {len(res_arglist)}" | ||
| for aname in ref_arglist.keys(): | ||
| atype_ref = ref_arglist[aname] | ||
| atype_res = res_arglist[aname] | ||
| assert isinstance(atype_res, atype_ref), f"Expected '{aname}' to have type {atype_ref}, but it had {type(atype_res)}." | ||
|
|
||
| # If we have cupy we will also compile it. | ||
| try: | ||
| import cupy as cp | ||
| except ImportError: | ||
| return | ||
|
|
||
| csdfg = sdfg.compile() | ||
|
|
||
| if __name__ == "__main__": | ||
| test_argument_signature_test() |