tqchen commented on code in PR #16815:
URL: https://github.com/apache/tvm/pull/16815#discussion_r1544107187
##########
tests/python/relax/test_transform_rewrite_cuda_graph.py:
##########
@@ -757,5 +757,118 @@ def main() -> R.Tuple:
tvm.ir.assert_structural_equal(mod, Expected)
+def test_dynamic_capture():
+ @I.ir_module
+ class Before:
+ @T.prim_func
+ def add_one(x_handle: T.handle, y_handle: T.handle):
+ m = T.int64()
+ x = T.match_buffer(x_handle, (m,), "float32")
+ y = T.match_buffer(y_handle, (m,), "float32")
+ for i in range(m):
+ with T.block("add"):
+ vi = T.axis.remap("S", [i])
+ y[vi] = x[vi] + T.float32(1)
+
+ @R.function
+ def main(x: R.Tensor(("m",), "float32")) -> R.Tensor(("m",),
"float32"):
+ R.func_attr(
+ {"relax.rewrite_cuda_graph.capture_symbolic_vars": ["m"],
"relax.force_pure": True}
+ )
+ m = T.int64()
+ storage: R.Object = R.memory.alloc_storage(
+ R.shape([16]), 0, "global", "float32"
+ ) # assume m is upper-bounded
+ alloc1: R.Tensor((m,), "float32") = R.memory.alloc_tensor(
+ storage, 0, R.shape([m]), "float32"
+ )
+ _ = Before.add_one(x, alloc1)
+ storage1: R.Object = R.memory.alloc_storage(R.shape([16]), 0,
"global", "float32")
+ alloc2: R.Tensor((m,), "float32") = R.memory.alloc_tensor(
+ storage1, 0, R.shape([m]), "float32"
+ )
+ _ = Before.add_one(alloc1, alloc2)
+ alloc3: R.Tensor((m,), "float32") = R.builtin.alloc_tensor(
+ R.shape([m]), "float32", 0, "global"
+ )
+ _ = Before.add_one(alloc2, alloc3)
+ return alloc3
+
+ @I.ir_module
+ class Expected:
+ @T.prim_func
+ def add_one(x_handle: T.handle, y_handle: T.handle):
+ m = T.int64()
+ x = T.match_buffer(x_handle, (m,))
+ y = T.match_buffer(y_handle, (m,))
+ # with T.block("root"):
+ for i in range(m):
+ with T.block("add"):
+ vi = T.axis.spatial(m, i)
+ T.reads(x[vi])
+ T.writes(y[vi])
+ y[vi] = x[vi] + T.float32(1)
+
+ @R.function(private=True)
+ def cuda_graph_alloc() -> R.Tuple(R.Object, R.Object):
+ R.func_attr({"relax.force_pure": True})
+ storage: R.Object = R.memory.alloc_storage(
+ R.shape([16]), R.prim_value(0), R.str("global"),
R.dtype("float32")
+ )
+ storage1: R.Object = R.memory.alloc_storage(
+ R.shape([16]), R.prim_value(0), R.str("global"),
R.dtype("float32")
+ )
+ gv: R.Tuple(R.Object, R.Object) = storage, storage1
+ return gv
+
+ @R.function(private=True)
+ def cuda_graph_capture(
+ alloc1: R.Tensor(("m",), dtype="float32"),
+ alloc2: R.Tensor(("m",), dtype="float32"),
+ shape_expr: R.Shape(["m"]),
+ ):
+ m = T.int64()
+ R.func_attr({"relax.force_pure": True})
+ cls = Expected
+ cls.add_one(alloc1, alloc2)
+ gv = R.tuple()
+ return R.tuple()
+
+ @R.function
+ def main(x: R.Tensor(("m",), dtype="float32")) -> R.Tensor(("m",),
dtype="float32"):
+ m = T.int64()
+ R.func_attr(
+ {"relax.force_pure": True,
"relax.rewrite_cuda_graph.capture_symbolic_vars": ["m"]}
+ )
+ cls = Expected
+ gv: R.Tuple(R.Object, R.Object) = R.call_builtin_with_ctx(
+ "vm.builtin.cuda_graph.get_cached_alloc",
+ (cls.cuda_graph_alloc, R.prim_value(0)),
+ sinfo_args=(R.Tuple(R.Object, R.Object),),
+ )
+ storage: R.Object = gv[0]
+ alloc1: R.Tensor((m,), dtype="float32") = R.memory.alloc_tensor(
+ storage, R.prim_value(0), R.shape([m]), R.dtype("float32")
+ )
+ cls.add_one(x, alloc1)
+ storage1: R.Object = gv[1]
+ alloc2: R.Tensor((m,), dtype="float32") = R.memory.alloc_tensor(
+ storage1, R.prim_value(0), R.shape([m]), R.dtype("float32")
+ )
+ R.call_builtin_with_ctx(
Review Comment:
consider explicitly pass in the shape to the function, namely
(cls.cuda_graph_capture, (alloc1, alloc2, R.shape([m])), R.prim_value(0),
R.shape([m])),
##########
tests/python/relax/test_transform_rewrite_cuda_graph.py:
##########
@@ -757,5 +757,118 @@ def main() -> R.Tuple:
tvm.ir.assert_structural_equal(mod, Expected)
+def test_dynamic_capture():
+ @I.ir_module
+ class Before:
+ @T.prim_func
+ def add_one(x_handle: T.handle, y_handle: T.handle):
+ m = T.int64()
+ x = T.match_buffer(x_handle, (m,), "float32")
+ y = T.match_buffer(y_handle, (m,), "float32")
+ for i in range(m):
+ with T.block("add"):
+ vi = T.axis.remap("S", [i])
+ y[vi] = x[vi] + T.float32(1)
+
+ @R.function
+ def main(x: R.Tensor(("m",), "float32")) -> R.Tensor(("m",),
"float32"):
+ R.func_attr(
+ {"relax.rewrite_cuda_graph.capture_symbolic_vars": ["m"],
"relax.force_pure": True}
+ )
+ m = T.int64()
+ storage: R.Object = R.memory.alloc_storage(
+ R.shape([16]), 0, "global", "float32"
+ ) # assume m is upper-bounded
+ alloc1: R.Tensor((m,), "float32") = R.memory.alloc_tensor(
+ storage, 0, R.shape([m]), "float32"
+ )
+ _ = Before.add_one(x, alloc1)
+ storage1: R.Object = R.memory.alloc_storage(R.shape([16]), 0,
"global", "float32")
+ alloc2: R.Tensor((m,), "float32") = R.memory.alloc_tensor(
+ storage1, 0, R.shape([m]), "float32"
+ )
+ _ = Before.add_one(alloc1, alloc2)
+ alloc3: R.Tensor((m,), "float32") = R.builtin.alloc_tensor(
+ R.shape([m]), "float32", 0, "global"
+ )
+ _ = Before.add_one(alloc2, alloc3)
+ return alloc3
+
+ @I.ir_module
+ class Expected:
+ @T.prim_func
+ def add_one(x_handle: T.handle, y_handle: T.handle):
+ m = T.int64()
+ x = T.match_buffer(x_handle, (m,))
+ y = T.match_buffer(y_handle, (m,))
+ # with T.block("root"):
+ for i in range(m):
+ with T.block("add"):
+ vi = T.axis.spatial(m, i)
+ T.reads(x[vi])
+ T.writes(y[vi])
+ y[vi] = x[vi] + T.float32(1)
+
+ @R.function(private=True)
+ def cuda_graph_alloc() -> R.Tuple(R.Object, R.Object):
+ R.func_attr({"relax.force_pure": True})
+ storage: R.Object = R.memory.alloc_storage(
+ R.shape([16]), R.prim_value(0), R.str("global"),
R.dtype("float32")
+ )
+ storage1: R.Object = R.memory.alloc_storage(
+ R.shape([16]), R.prim_value(0), R.str("global"),
R.dtype("float32")
+ )
+ gv: R.Tuple(R.Object, R.Object) = storage, storage1
+ return gv
+
+ @R.function(private=True)
+ def cuda_graph_capture(
+ alloc1: R.Tensor(("m",), dtype="float32"),
+ alloc2: R.Tensor(("m",), dtype="float32"),
+ shape_expr: R.Shape(["m"]),
+ ):
+ m = T.int64()
+ R.func_attr({"relax.force_pure": True})
+ cls = Expected
+ cls.add_one(alloc1, alloc2)
+ gv = R.tuple()
+ return R.tuple()
+
+ @R.function
+ def main(x: R.Tensor(("m",), dtype="float32")) -> R.Tensor(("m",),
dtype="float32"):
+ m = T.int64()
+ R.func_attr(
+ {"relax.force_pure": True,
"relax.rewrite_cuda_graph.capture_symbolic_vars": ["m"]}
+ )
+ cls = Expected
+ gv: R.Tuple(R.Object, R.Object) = R.call_builtin_with_ctx(
+ "vm.builtin.cuda_graph.get_cached_alloc",
+ (cls.cuda_graph_alloc, R.prim_value(0)),
+ sinfo_args=(R.Tuple(R.Object, R.Object),),
+ )
+ storage: R.Object = gv[0]
+ alloc1: R.Tensor((m,), dtype="float32") = R.memory.alloc_tensor(
+ storage, R.prim_value(0), R.shape([m]), R.dtype("float32")
+ )
+ cls.add_one(x, alloc1)
+ storage1: R.Object = gv[1]
+ alloc2: R.Tensor((m,), dtype="float32") = R.memory.alloc_tensor(
+ storage1, R.prim_value(0), R.shape([m]), R.dtype("float32")
+ )
+ R.call_builtin_with_ctx(
Review Comment:
consider explicitly pass in the shape to the function, namely
`(cls.cuda_graph_capture, (alloc1, alloc2, R.shape([m])), R.prim_value(0),
R.shape([m])),`
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