denise-k commented on a change in pull request #9065:
URL: https://github.com/apache/tvm/pull/9065#discussion_r715202163
##########
File path: tests/python/relay/aot/test_crt_aot.py
##########
@@ -589,5 +590,41 @@ def test_memory_planning(workspace_byte_alignment,
main_workspace_size, sum_work
)
+def test_aot_codegen_backend_alloc_workspace_calls():
+ dtype = "float32"
+
+ # These shapes should create small tensors that would
+ # get lowered to stack allocations in the CPU PrimFuncs.
+ # However, the AoT executor codegen should retain them
+ # as TVMBAW calls
+ ishape = (1, 4, 4, 4)
+ wshape = (4, 4, 3, 3)
+
+ data0 = relay.var("data", shape=ishape, dtype=dtype)
+ weight0 = relay.var("weight", shape=wshape, dtype=dtype)
+ out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1, 1),
groups=1)
+ main_f = relay.Function([data0, weight0], out)
+ mod = tvm.IRModule()
+ mod["main"] = main_f
+ mod = transform.InferType()(mod)
+
+ i_data = np.random.uniform(0, 1, ishape).astype(dtype)
+ w1_data = np.random.uniform(0, 1, wshape).astype(dtype)
+
+ inputs = OrderedDict([("data", i_data), ("weight", w1_data)])
+ output_list = generate_ref_data(mod, inputs)
+
+ compiled_runtime_modules = compile_models(
Review comment:
@areusch roadmap item and task tracking have been created.
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