[tvm] branch unity updated: [Unity][VM] LibComparator using dtype from input (#14623)

[tqchen]

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tqchen pushed a commit to branch unity
in repository https://gitbox.apache.org/repos/asf/tvm.git


The following commit(s) were added to refs/heads/unity by this push:
     new bcad5a9abe [Unity][VM] LibComparator using dtype from input (#14623)
bcad5a9abe is described below

commit bcad5a9abe789d40ddcd4c8b434600726c2d4a00
Author: Ruihang Lai <ruiha...@cs.cmu.edu>
AuthorDate: Fri Apr 14 07:51:01 2023 -0400

    [Unity][VM] LibComparator using dtype from input (#14623)
    
    Previously the LibComparator omitted the dtype argument when creating
    a NDArray, which leads to the dtype always float32. This PR fixes this
    behavior.
---
 python/tvm/relax/testing/lib_comparator.py |  2 +-
 tests/python/relax/test_vm_instrument.py   | 19 +++++++++++++++++--
 2 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/python/tvm/relax/testing/lib_comparator.py 
b/python/tvm/relax/testing/lib_comparator.py
index a9cecc69dc..af21ef7a7c 100644
--- a/python/tvm/relax/testing/lib_comparator.py
+++ b/python/tvm/relax/testing/lib_comparator.py
@@ -119,7 +119,7 @@ class LibCompareVMInstrument:
         # not always true, true for most ops.
         ret_indices = (len(args) - 1,)
         for i, arg in enumerate(args):
-            arr = tvm.nd.empty(arg.shape, device=self.device)
+            arr = tvm.nd.empty(arg.shape, arg.dtype, device=self.device)
             # copy from cpu since we look at different device
             if i not in ret_indices:
                 arr.copyfrom(arg.copyto(tvm.cpu()))
diff --git a/tests/python/relax/test_vm_instrument.py 
b/tests/python/relax/test_vm_instrument.py
index 8297da1b74..6158542644 100644
--- a/tests/python/relax/test_vm_instrument.py
+++ b/tests/python/relax/test_vm_instrument.py
@@ -49,6 +49,21 @@ def get_exec(data_shape):
     return relax.build(mod, target)
 
 
+def get_exec_int32(data_shape):
+    builder = relax.BlockBuilder()
+
+    with builder.function("main"):
+        model = nn.ReLU()
+        data = nn.Placeholder(data_shape, dtype="int32", name="data")
+        output = model(data)
+        params = [data] + model.parameters()
+        builder.emit_func_output(output, params=params)
+
+    mod = builder.get()
+    target = "llvm"
+    return relax.build(mod, target)
+
+
 def test_conv2d_cpu():
     data_np = np.random.randn(1, 64).astype("float32")
     ex = get_exec(data_np.shape)
@@ -74,8 +89,8 @@ def test_conv2d_cpu():
 
 
 def test_lib_comparator():
-    data_np = np.random.randn(1, 64).astype("float32")
-    ex = get_exec(data_np.shape)
+    data_np = np.random.randn(1, 64).astype("int32")
+    ex = get_exec_int32(data_np.shape)
     vm = relax.VirtualMachine(ex, tvm.cpu())
     # compare against library module
     cmp = LibCompareVMInstrument(vm.module.imported_modules[0], tvm.cpu(), 
verbose=False)