class ComputeShapeSignature:
"""
Here we use the base name as the suffix of the signature to avoid generating for in-place variants.
"""
def __init__(self, kernel_name: str, f: NativeFunction):
self.__schema = LazyIrSchema(f.func)
self.__dispatch_args = ', '.join(
[a.decl() for a in dispatcher.arguments(f.func)])
self.__call_args = ", ".join([
f"{arg.name}"
for arg in self.__schema.filtered_args(generator=True)
])
self.__kernel_name = kernel_name
def __decl_suffix(self) -> str:
return f"{self.__kernel_name}({self.__dispatch_args})"
def __call_suffix(self) -> str:
return f"{self.__kernel_name}({self.__call_args})"
@property
def shape_decl(self) -> str:
return f"TORCH_API std::vector<Shape> compute_shape_{self.__decl_suffix()}"
@property
def shape_call(self) -> str:
return f"torch::lazy::compute_shape_{self.__call_suffix()}"
def node_ctor_inputs(schema: LazyIrSchema) -> str:
"""
Produce a formatted string with the arguments as passed into the constructor of a node class.
"""
node_ctor_values = [
node_ctor_arg_rvalue_string(arg) for arg in schema.filtered_args()
]
return ",\n ".join(node_ctor_values)
def gen_fallback_code(schema: LazyIrSchema, overload_name: str) -> str:
"""
Generate code that falls back to eager conditioned on a predicate
"""
fallback_args = ",\n ".join([str(arg.name) for arg in schema.filtered_args(generator=True)])
if len(overload_name):
aten_op_str = f"ATEN_OP2({schema.aten_name}, {overload_name})"
else:
aten_op_str = f"ATEN_OP({schema.aten_name})"
or_has_generator = ""
if schema.generator_arg:
# generators are always optional and there is never more than one, at least currently
or_has_generator = f" || ({schema.generator_arg.name}.has_value() && {schema.generator_arg.name}->defined())"
return f"""
def __call__(self, f: NativeFunction) -> List[str]:
sig = kernel_signature(f, self.backend_index)
metadata = self.backend_index.get_kernel(f)
assert metadata is not None
schema = LazyIrSchema(f.func)
value_args = schema.filtered_args(values=True, scalars=False)
lazy_tensor_decls_str = lazy_tensor_decls(value_args, self.tensor_class)
node_ctor_input_str = node_ctor_inputs(schema)
# Only generate shape/dtype fn for non-structured kernels,
# since we just use the meta function for structured kernels
if not f.structured and f.structured_delegate is None:
shape_sig = ComputeShapeSignature(metadata.kernel, f)
return ["\n".join([f"{shape_sig.shape_decl};"])]
else:
return []
def gen_shape_call(self, func: NativeFunction) -> str:
metadata = self.backend_index.get_kernel(func)
assert metadata is not None
schema = LazyIrSchema(func.func)
all_args = schema.filtered_args()
returns_length = len(schema.returns)
# call the meta kernel if it exists, to compute output shape/dtype for our IR
if func.structured or func.structured_delegate is not None:
meta_out = """std::vector<Shape> shapes{Shape(out_meta.scalar_type(), out_meta.sizes().vec())};"""
if returns_length > 1:
def this_shape(i: int) -> str:
return f"Shape(std::get<{i}>(out_meta).scalar_type(), std::get<{i}>(out_meta).sizes().vec())"
shapes_str = ",".join(
[this_shape(i) for i in range(returns_length)])
meta_out = "std::vector<Shape> shapes{" + shapes_str + "};"
meta_str = f"""auto out_meta = at::meta::{schema.aten_name}({', '.join(str(a.name) for a in all_args)});
{meta_out}"""
else:
shape_sig = ComputeShapeSignature(metadata.kernel, func)
meta_str = f"""
auto shapes = {shape_sig.shape_call};"""
meta_str += f"""
TORCH_INTERNAL_ASSERT(shapes.size() == {returns_length});"""
# Calculating which dimensions are symbolic
func_schema_str = "aten::" + str(func.func)
meta_str += f"""
if(symbolicShapeEnabled()){{
std::vector<jit::IValue> inputs = {{ {', '.join(str(a.name) for a in all_args)} }};
char* schema_str = "{func_schema_str}";
applySymbolicShapesOnLT(schema_str, inputs, shapes);
}}
"""
return meta_str
def __call__(self, func: NativeFunction) -> List[str]:
sig = kernel_signature(func, self.backend_index)
metadata = self.backend_index.get_kernel(func)
assert metadata is not None
schema = LazyIrSchema(func.func)
value_args = schema.filtered_args(values=True, scalars=False)
returns_length = len(schema.returns)
fallback_str = ""
if self.gen_forced_fallback_code:
fallback_str = gen_fallback_code(
schema, overload_name=func.func.name.overload_name)
value_types_names = [
f"{a.name}" for a in value_args if not a.is_wrapped_scalar
]
assert (len(value_types_names) >
0), "Code below assumes there is at least one tensor arg"
get_device_str = f"""auto common_device = torch::lazy::GetBackendDevice({', '.join(value_types_names)});
TORCH_INTERNAL_ASSERT(common_device);
"""
lazy_tensor_decls_str = lazy_tensor_decls(value_args,
self.tensor_class)
node_ctor_input_str = node_ctor_inputs(schema)
shape_str = self.gen_shape_call(func)
node_str = f"""auto node = torch::lazy::MakeNode<{schema.node_name}>({node_ctor_input_str},
std::move(shapes));"""
first_tensor_name = value_types_names[0]
bridge_str = """auto result = torch::lazy::CreateAtenFromLtcTensor(
torch::lazy::LazyTensor::Create(std::move(node), *common_device));"""
if returns_length > 1:
bridge_str = f"""std::vector<{self.tensor_class}Ptr> lazy_tensors;
for (int i = 0; i < {returns_length}; i++) {{
lazy_tensors.push_back(torch::lazy::LazyTensor::Create(torch::lazy::Value(node, i), *common_device));
}}
auto result = torch::lazy::TupleAtenFromLtcTensors<{returns_length}>(lazy_tensors);"""
if schema.name.name.inplace or func.func.is_out_fn():
assert returns_length == 1, (
"We assumed there was no such case where an op is an in-place variant "
f"and has tuple outputs, but got tuple of len {returns_length}."
)
bridge_str = f"""lazy_{first_tensor_name}->SetInPlaceIrValue(node);
auto& result = {first_tensor_name};"""
return [
f"""\
{sig.decl(name=f"{self.class_method_name}::{metadata.kernel}")} {{
{fallback_str}
TORCH_LAZY_FN_COUNTER("lazy::");
{get_device_str}
{lazy_tensor_decls_str}
{shape_str}
{node_str}
{bridge_str}
return result;
}};\n
"""
]
def gen(self, f: Union[NativeFunctionsGroup, NativeFunction]) -> List[str]:
# for now, we just want one IR class decl and soon after also the method defs
# and we use the functional version not out/inplace.
func = f.functional.func if isinstance(
f, NativeFunctionsGroup) else f.func
schema = LazyIrSchema(func)
all_args = schema.filtered_args()
value_args = schema.filtered_args(values=True, scalars=False)
scalar_args = schema.filtered_args(values=False, scalars=True)
node_ctor_args = ", ".join(
[f"const {i.lazy_type.cpp_type()}& {i.name}" for i in all_args])
scalar_initializers = ",\n ".join(
[f"{a.name}({a.name})" for a in scalar_args])
comma_if_scalar_initializers = ",\n" if len(
scalar_initializers) else ""
scalar_decls = "\n ".join([
f"std::string {a.name};" if a.lazy_type.cpp_type()
== "c10::string_view" else f"{a.lazy_type.cpp_type()} {a.name};"
for a in scalar_args
])
scalar_hashes = ", ".join([f"{a.name}" for a in scalar_args])
base_ctor_value_args_list = []
optional_values = []
for arg in value_args:
if isinstance(arg.lazy_type, BaseCType) or isinstance(
arg.lazy_type, VectorCType):
base_ctor_value_args_list.append(f"{arg.name}")
elif isinstance(arg.lazy_type, OptionalCType):
base_ctor_value_args_list.append(
f"{arg.name}.value_or(kNullValue)")
optional_values.append(arg.name)
else:
raise AssertionError(
f"TODO not sure if there are other valid types to handle here ({arg.lazy_type})"
)
base_ctor_value_args = ", ".join(base_ctor_value_args_list)
has_optional_decls = "\n ".join(
[f"bool has_{value}: 1;" for value in optional_values])
has_optional_defs = "\n ".join(
[f"has_{value} = !!{value};" for value in optional_values])
members_to_string = []
for arg in scalar_args:
if isinstance(arg.lazy_type, OptionalCType):
members_to_string.append(f"""if ({arg.name}.has_value()) {{
ss << ", {arg.name}=" << {arg.name}.value();
}} else {{
ss << ", {arg.name}=null";
}}""")
else:
members_to_string.append(
f'ss << ", {arg.name}=" << {arg.name};')
members_to_string_str = "\n ".join(members_to_string)
return [
f"""\
class {schema.node_name} : public {self.node_base} {{
public:
{schema.node_name}({node_ctor_args}, std::vector<Shape>&& shapes)
: {self.node_base}(torch::lazy::OpKind({aten_symbol(schema)}),
{{{base_ctor_value_args}}}, std::move(shapes),
/* num_outputs */ {len(func.returns)},
torch::lazy::MHash({scalar_hashes})){comma_if_scalar_initializers}
{scalar_initializers}
{{
{has_optional_defs}
}}
std::string ToString() const override {{
std::stringstream ss;
ss << {self.node_base}::ToString();
{members_to_string_str}
return ss.str();
}}
{self.lowering_function(f)}
{scalar_decls}
{has_optional_decls}
}};
""",
]
def __call__(self, func: NativeFunction) -> List[str]:
sig = kernel_signature(func, self.backend_index)
metadata = self.backend_index.get_kernel(func)
assert metadata is not None
schema = LazyIrSchema(func.func)
all_args = schema.filtered_args()
value_args = schema.filtered_args(values=True, scalars=False)
returns_length = len(schema.returns)
fallback_str = gen_fallback_code(schema, overload_name=func.func.name.overload_name)
value_types_names = [f"{a.name}" for a in value_args if not a.is_wrapped_scalar]
assert len(value_types_names) > 0, "Code below assumes there is at least one tensor arg"
get_device_str = f"""auto common_device = torch::lazy::GetBackendDevice({', '.join(value_types_names)});
TORCH_INTERNAL_ASSERT(common_device);
"""
lazy_tensor_decls_str = lazy_tensor_decls(value_args, self.tensor_class)
node_ctor_input_str = node_ctor_inputs(schema)
# call the meta kernel if it exists, to compute output shape/dtype for our IR
if func.structured or func.structured_delegate is not None:
meta_out = """std::vector<Shape> shapes{Shape(out_meta.scalar_type(), out_meta.sizes().vec())};"""
if returns_length > 1:
def this_shape(i: int) -> str:
return f"Shape(std::get<{i}>(out_meta).scalar_type(), std::get<{i}>(out_meta).sizes().vec())"
shapes_str = ','.join([this_shape(i) for i in range(returns_length)])
meta_out = "std::vector<Shape> shapes{" + shapes_str + "};"
meta_str = f"""auto out_meta = at::meta::{schema.aten_name}({', '.join(str(a.name) for a in all_args)});
{meta_out}"""
else:
shape_sig = ComputeShapeSignature(metadata.kernel, func)
meta_str = f"""
auto shapes = {shape_sig.shape_call};"""
meta_str += f"""
TORCH_INTERNAL_ASSERT(shapes.size() == {returns_length});"""
node_str = f"""auto node = torch::lazy::MakeNode<{schema.node_name}>({node_ctor_input_str},
std::move(shapes));"""
first_tensor_name = value_types_names[0]
bridge_str = """auto result = torch::lazy::CreateAtenFromLtcTensor(
torch::lazy::LazyTensor::Create(std::move(node), *common_device));"""
if returns_length > 1:
bridge_str = f"""std::vector<{self.tensor_class}Ptr> lazy_tensors;
for (int i = 0; i < {returns_length}; i++) {{
lazy_tensors.push_back(torch::lazy::LazyTensor::Create(torch::lazy::Value(node, i), *common_device));
}}
auto result = torch::lazy::TupleAtenFromLtcTensors<{returns_length}>(lazy_tensors);"""
if schema.name.name.inplace or func.func.is_out_fn():
assert returns_length == 1, "We assumed there was no such case where an op is an in-place variant " \
f"and has tuple outputs, but got tuple of len {returns_length}."
bridge_str = f"""lazy_{first_tensor_name}->SetInPlaceIrValue(node);
auto& result = {first_tensor_name};"""
return [f"""\
{sig.decl(name=f"{self.class_method_name}::{metadata.kernel}")} {{
{fallback_str}
TORCH_LAZY_FN_COUNTER("lazy::");
{get_device_str}
{lazy_tensor_decls_str}
{meta_str}
{node_str}
{bridge_str}
return result;
}};\n
"""]
