[Bugfix][SLM] Produce well-formed Relax for nn.modules.KVCache

python/tvm/relax/frontend/nn/exporter.py

@@ -21,7 +21,7 @@
 from tvm import tir
 from tvm.ir import IRModule
 
-from ... import expr as rx
+from .... import relax as rx
 from ...block_builder import BlockBuilder
 from ...struct_info import ObjectStructInfo, ShapeStructInfo, TupleStructInfo
 from . import core, extern
@@ -136,6 +136,8 @@ def _effects() -> typing.List[typing.Tuple[str, core.Effect]]:
                         outputs, inputs = _emit_method(self.builder, method_spec, params, effects)
                     self.builder.emit_func_output(outputs, inputs)
         mod = self.builder.finalize()
+        assert rx.analysis.well_formed(mod)
+
         return mod, params, ext_mods
 
 

python/tvm/relax/frontend/nn/modules.py

@@ -19,7 +19,7 @@
 from typing import List, Optional, Sequence, Union
 
 from tvm import relax as rx
-from tvm import tir
+from tvm import tir, ir
 
 from . import op
 from .core import Effect, Module, ModuleList, Parameter, Tensor, get_default_dtype
@@ -517,8 +517,13 @@ def emit_init(self, name_hint: str, bb: rx.BlockBuilder):  # pylint: disable=arg
         return [
             bb.emit(
                 rx.Call(
-                    rx.extern("vm.builtin.attention_kv_cache_create"),
-                    args=[rx.op.zeros(init_shape, self.dtype), init_shape, rx.PrimValue(0)],
+                    ir.Op.get("relax.call_pure_packed"),
+                    args=[
+                        rx.extern("vm.builtin.attention_kv_cache_create"),
+                        rx.op.zeros(init_shape, self.dtype),
+                        init_shape,
+                        rx.PrimValue(0),
+                    ],
                     sinfo_args=[rx.ObjectStructInfo()],
                 ),
                 name_hint=name_hint,
@@ -588,8 +593,12 @@ def view(self, seq_len: tir.Var) -> Tensor:
         return Tensor(
             _expr=rx.BlockBuilder.current().emit(
                 rx.Call(
-                    rx.extern("vm.builtin.attention_kv_cache_view"),
-                    args=[self.cache, shape],
+                    ir.Op.get("relax.call_pure_packed"),
+                    args=[
+                        rx.extern("vm.builtin.attention_kv_cache_view"),
+                        self.cache,
+                        shape,
+                    ],
                     sinfo_args=[rx.TensorStructInfo(shape, self.dtype)],
                 )
             )
@@ -611,8 +620,12 @@ def append(self, new_element: Tensor) -> None:
             )
         self.cache = rx.BlockBuilder.current().emit(
             rx.Call(
-                rx.extern("vm.builtin.attention_kv_cache_append"),
-                args=[self.cache, new_element._expr],
+                ir.Op.get("relax.call_pure_packed"),
+                args=[
+                    rx.extern("vm.builtin.attention_kv_cache_append"),
+                    self.cache,
+                    new_element._expr,
+                ],
                 sinfo_args=[rx.ObjectStructInfo()],
             )
         )

tests/python/relax/test_frontend_nn_modules.py

@@ -451,15 +451,15 @@ def _initialize_effect() -> R.Tuple(R.Object, R.Object):
                 lv: R.Tensor((8, 2, 4), dtype="float32") = R.zeros(
                     R.shape([8, 2, 4]), dtype="float32"
                 )
-                cache: R.Object = R.call_packed(
+                cache: R.Object = R.call_pure_packed(
                     "vm.builtin.attention_kv_cache_create",
                     lv,
                     R.shape([8, 2, 4]),
                     R.prim_value(0),
                     sinfo_args=(R.Object,),
                 )
-                lv1: R.Tuple(R.Object, R.Object) = _io, cache
-                gv: R.Tuple(R.Object, R.Object) = lv1
+                lv1 = _io, cache
+                gv = lv1
                 R.output(gv)
             return gv
 
@@ -469,10 +469,10 @@ def forward(
         ) -> R.Tuple(R.Tensor((4, 2, 4), dtype="float32"), R.Tuple(R.Object, R.Object)):
             R.func_attr({"num_input": 3})
             with R.dataflow():
-                lv2: R.Object = R.call_packed(
+                lv2: R.Object = R.call_pure_packed(
                     "vm.builtin.attention_kv_cache_append", cache, x, sinfo_args=(R.Object,)
                 )
-                lv3: R.Tensor((4, 2, 4), dtype="float32") = R.call_packed(
+                lv3: R.Tensor((4, 2, 4), dtype="float32") = R.call_pure_packed(
                     "vm.builtin.attention_kv_cache_view",
                     lv2,
                     R.shape([4, 2, 4]),