[Relax][Bugfix] Support torch.unbind op and fix bugs for expand && split

python/tvm/contrib/msc/core/frontend/translate.py

@@ -119,6 +119,7 @@ def from_relax(
         )(mod)
     patterns = get_patterns_with_prefix("msc.")
     passes = [
+        tvm.relax.transform.ExpandTupleArguments(),
         msc_transform.SetExprName(),
         msc_transform.SetExprLayout(trans_config.get("allow_layout_missing", True)),
         tvm.relax.transform.FuseOpsByPattern(
@@ -310,6 +311,7 @@ def byoc_partition(
     def _partition_mod(mod, as_msc=True):
         patterns = get_patterns_with_prefix(target)
         passes = [
+            tvm.relax.transform.ExpandTupleArguments(),
             msc_transform.SetExprName(),
             msc_transform.SetExprLayout(trans_config.get("allow_layout_missing", True)),
             tvm.relax.transform.FuseOpsByPattern(patterns, bind_constants=not as_msc),

python/tvm/relax/frontend/torch/fx_translator.py

@@ -526,6 +526,22 @@ def _einsum(self, node: fx.node.Node) -> relax.Var:
             return self.block_builder.emit(relax.op.einsum(tuple(args[1]), args[0]))
         return self.block_builder.emit(relax.op.einsum(args[1:], args[0]))
 
+    def _unbind(self, node: fx.node.Node) -> relax.Var:
+        if len(node.args) == 2:
+            assert isinstance(node.args[1], int), "Expected 2nd argument of unbind as int"
+            dim = node.args[1]
+        elif "dim" in node.kwargs:
+            dim = node.kwargs["dim"]
+        else:
+            dim = 0
+        x = self.env[node.args[0]]
+        selections = self.shape_of(x)[dim].value
+        n_section = list(range(1, selections + 1))
+        ret, split = [], self.block_builder.emit(relax.op.split(x, n_section, dim))
+        for i in range(selections):
+            ret.append(self.block_builder.emit(relax.op.squeeze(split[i], axis=dim)))
+        return self.block_builder.emit(relax.Tuple(ret))
+
     ########## Manipulation ##########
 
     def _cat(self, node: fx.node.Node) -> relax.Var:
@@ -535,7 +551,13 @@ def _cat(self, node: fx.node.Node) -> relax.Var:
 
     def _expand(self, node: fx.node.Node) -> relax.Var:
         args = self.retrieve_args(node)
-        return self.block_builder.emit(relax.op.broadcast_to(args[0], args[1:]))
+        broadcast_shape, in_shape = [], self.shape_of(args[0])
+        for idx, i in enumerate(args[1:]):
+            if isinstance(i, int) and i == -1:
+                broadcast_shape.append(in_shape[idx])
+            else:
+                broadcast_shape.append(i)
+        return self.block_builder.emit(relax.op.broadcast_to(args[0], broadcast_shape))
 
     def _flatten(self, node: fx.node.Node) -> relax.Var:
         x = self.env[node.args[0]]
@@ -580,7 +602,13 @@ def _split(self, node: fx.node.Node) -> relax.Var:
             dim = node.kwargs["dim"]
         else:
             dim = 0
-        n_section = (self.shape_of(x)[dim].value + split_size - 1) // split_size
+        if isinstance(split_size, (list, tuple)):
+            n_section = []
+            for s in split_size[:-1]:
+                cum_sum = 0 if not n_section else n_section[-1]
+                n_section.append(s + cum_sum)
+        else:
+            n_section = (self.shape_of(x)[dim].value + split_size - 1) // split_size
         return self.block_builder.emit(relax.op.split(x, n_section, dim))
 
     def _chunk(self, node: fx.node.Node) -> relax.Var:
@@ -1501,6 +1529,7 @@ def create_convert_map(self):
             "cross_entropy": self._cross_entropy,
             "scaled_dot_product_attention": self._scaled_dot_product_attention,
             "einsum": self._einsum,
+            "unbind": self._unbind,
         }
 
     def update_convert_map(self, custom_convert_map: dict):

tests/python/contrib/test_msc/test_graph_build.py

@@ -1345,11 +1345,15 @@ def forward(self, x_1, x_2, x_3):
 def test_split():
     """test graph builder for split"""
 
-    class Split(Module):
+    class Split1(Module):
         def forward(self, data):
             return torch.split(data, 1, dim=1)
 
-    expected = {
+    class Split2(Module):
+        def forward(self, data):
+            return torch.split(data, [1, 2], dim=1)
+
+    expected1 = {
         "inputs": [
             {"name": "inp_0", "shape": [1, 3, 10, 10], "dtype": "float32", "layout": "ABCD"}
         ],
@@ -1361,8 +1365,43 @@ def forward(self, data):
         "nodes": {"total": 2, "input": 1, "split": 1},
     }
 
+    expected2 = {
+        "inputs": [
+            {"name": "inp_0", "shape": [1, 3, 10, 10], "dtype": "float32", "layout": "ABCD"}
+        ],
+        "outputs": [
+            {"name": "split_0", "shape": [1, 1, 10, 10], "dtype": "float32", "layout": "ABCD"},
+            {"name": "split_1", "shape": [1, 2, 10, 10], "dtype": "float32", "layout": "ABCD"},
+        ],
+        "nodes": {"total": 2, "input": 1, "split": 1},
+    }
+
+    input_info = [([1, 3, 10, 10], "float32")]
+    verify_model(Split1(), input_info, expected1)
+    verify_model(Split2(), input_info, expected2)
+
+
+def test_unbind():
+    """test graph builder for unbind"""
+
+    class Unbind(Module):
+        def forward(self, data):
+            return torch.unbind(data, dim=1)
+
+    expected = {
+        "inputs": [
+            {"name": "inp_0", "shape": [1, 3, 10, 10], "dtype": "float32", "layout": "ABCD"}
+        ],
+        "outputs": [
+            {"name": "tuple_0", "shape": [1, 10, 10], "dtype": "float32", "layout": "ACD"},
+            {"name": "tuple_1", "shape": [1, 10, 10], "dtype": "float32", "layout": "ACD"},
+            {"name": "tuple_2", "shape": [1, 10, 10], "dtype": "float32", "layout": "ACD"},
+        ],
+        "nodes": {"total": 9, "input": 1, "split": 1, "get_item": 3, "squeeze": 3, "tuple": 1},
+    }
+
     input_info = [([1, 3, 10, 10], "float32")]
-    verify_model(Split(), input_info, expected)
+    verify_model(Unbind(), input_info, expected)
 
 
 def test_cumsum():
@@ -1547,10 +1586,14 @@ def forward(self, x):
 def test_expand():
     """test graph builder for expand"""
 
-    class Expand(Module):
+    class Expand1(Module):
         def forward(self, x):
             return x.expand(4, 2, 3, 4)
 
+    class Expand2(Module):
+        def forward(self, x):
+            return x.expand(4, -1, -1, 4)
+
     expected = {
         "inputs": [{"name": "inp_0", "shape": [1, 2, 3, 4], "dtype": "float32", "layout": ""}],
         "outputs": [
@@ -1560,7 +1603,8 @@ def forward(self, x):
     }
 
     input_info = [([1, 2, 3, 4], "float32")]
-    verify_model(Expand(), input_info, expected)
+    verify_model(Expand1(), input_info, expected)
+    verify_model(Expand2(), input_info, expected)
 
 
 def test_reduce():

tests/python/contrib/test_msc/test_pipeline.py

@@ -38,7 +38,7 @@ def _get_config(model_type, compile_type, inputs, outputs, dynamic=False, atol=1
     path = "test_pipe_{}_{}_{}".format(model_type, compile_type, "dynamic" if dynamic else "static")
     return {
         "workspace": msc_utils.msc_dir(path),
-        "verbose": "info",
+        "verbose": "critical",
         "model_type": model_type,
         "inputs": inputs,
         "outputs": outputs,

tests/python/contrib/test_msc/test_translate_relax.py

@@ -67,7 +67,12 @@ def _run_relax(relax_mod):
 
     orig_output = _run_relax(orig_mod)
     rt_output = _run_relax(rt_mod)
-    tvm.testing.assert_allclose(orig_output, rt_output)
+    if not isinstance(orig_output, (list, tuple)):
+        orig_output = [orig_output]
+    if not isinstance(rt_output, (list, tuple)):
+        rt_output = [rt_output]
+    for o_out, r_out in zip(orig_output, rt_output):
+        tvm.testing.assert_allclose(o_out, r_out)
 
 
 def test_conv1d():
@@ -750,12 +755,33 @@ def forward(self, x_1, x_2, x_3):
 def test_split():
     """test relax translator for split"""
 
-    class Split(Module):
+    class Split1(Module):
         def forward(self, data):
             return torch.split(data, 1, dim=1)
 
+    class Split2(Module):
+        def forward(self, data):
+            return torch.split(data, [1, 2], dim=1)
+
     input_info = [([1, 3, 10, 10], "float32")]
-    _verify_model(Split(), input_info)
+    _verify_model(Split1(), input_info)
+    _verify_model(Split2(), input_info)
+
+
+def test_unbind():
+    """test relax translator for unbind"""
+
+    class Unbind1(Module):
+        def forward(self, data):
+            return torch.unbind(data)
+
+    class Unbind2(Module):
+        def forward(self, data):
+            return torch.unbind(data, dim=1)
+
+    input_info = [([3, 3, 10, 10], "float32")]
+    _verify_model(Unbind1(), input_info)
+    _verify_model(Unbind2(), input_info)
 
 
 def test_cumsum():
@@ -874,12 +900,17 @@ def forward(self, x):
 def test_expand():
     """test relax translator for expand"""
 
-    class Expand(Module):
+    class Expand1(Module):
         def forward(self, x):
             return x.expand(4, 2, 3, 4)
 
+    class Expand2(Module):
+        def forward(self, x):
+            return x.expand(4, -1, -1, 4)
+
     input_info = [([1, 2, 3, 4], "float32")]
-    _verify_model(Expand(), input_info)
+    _verify_model(Expand1(), input_info)
+    _verify_model(Expand2(), input_info)
 
 
 def test_reduce():

tests/python/contrib/test_msc/test_translate_relay.py

@@ -731,12 +731,33 @@ def forward(self, x_1, x_2, x_3):
 def test_split():
     """test relay to relax for split"""
 
-    class Split(Module):
+    class Split1(Module):
         def forward(self, data):
             return torch.split(data, 1, dim=1)
 
+    class Split2(Module):
+        def forward(self, data):
+            return torch.split(data, [1, 2], dim=1)
+
     input_info = [([1, 3, 10, 10], "float32")]
-    verify_model(Split(), input_info, build_target="llvm")
+    verify_model(Split1(), input_info, build_target="llvm")
+    verify_model(Split2(), input_info, build_target="llvm")
+
+
+def test_unbind():
+    """test relay to relax for unbind"""
+
+    class Unbind1(Module):
+        def forward(self, data):
+            return torch.unbind(data)
+
+    class Unbind2(Module):
+        def forward(self, data):
+            return torch.unbind(data, dim=1)
+
+    input_info = [([3, 3, 10, 10], "float32")]
+    verify_model(Unbind1(), input_info, build_target="llvm")
+    verify_model(Unbind2(), input_info, build_target="llvm")
 
 
 def test_cumsum():
@@ -859,12 +880,17 @@ def forward(self, x):
 def test_expand():
     """test relay to relax for expand"""
 
-    class Expand(Module):
+    class Expand1(Module):
         def forward(self, x):
             return x.expand(4, 2, 3, 4)
 
+    class Expand2(Module):
+        def forward(self, x):
+            return x.expand(4, -1, -1, 4)
+
     input_info = [([1, 2, 3, 4], "float32")]
-    verify_model(Expand(), input_info, build_target="llvm")
+    verify_model(Expand1(), input_info, build_target="llvm")
+    verify_model(Expand2(), input_info, build_target="llvm")
 
 
 def test_reduce():

tests/python/contrib/test_msc/test_translate_tensorrt.py

@@ -673,12 +673,34 @@ def forward(self, x_1, x_2, x_3):
 def test_split():
     """test tensorrt translator for split"""
 
-    class Split(Module):
+    class Split1(Module):
         def forward(self, data):
             return torch.split(data, 1, dim=1)
 
+    class Split2(Module):
+        def forward(self, data):
+            return torch.split(data, [1, 2], dim=1)
+
     input_info = [([1, 3, 10, 10], "float32")]
-    verify_model(Split(), input_info)
+    verify_model(Split1(), input_info)
+    verify_model(Split2(), input_info)
+
+
+@requires_tensorrt
+def test_unbind():
+    """test tensorrt to relax for unbind"""
+
+    class Unbind1(Module):
+        def forward(self, data):
+            return torch.unbind(data)
+
+    class Unbind2(Module):
+        def forward(self, data):
+            return torch.unbind(data, dim=1)
+
+    input_info = [([3, 3, 10, 10], "float32")]
+    verify_model(Unbind1(), input_info)
+    verify_model(Unbind2(), input_info)
 
 
 @requires_tensorrt
@@ -697,13 +719,19 @@ def forward(self, data):
 def test_expand():
     """test tensorrt translator for expand"""
 
-    class Expand(Module):
+    class Expand1(Module):
         def forward(self, x):
             x = x + 1.0
             return x.expand(4, 2, 3, 4)
 
+    class Expand2(Module):
+        def forward(self, x):
+            x = x + 1.0
+            return x.expand(4, -1, -1, 4)
+
     input_info = [([1, 2, 3, 4], "float32")]
-    verify_model(Expand(), input_info)
+    verify_model(Expand1(), input_info)
+    verify_model(Expand2(), input_info)
 
 
 @requires_tensorrt