[BYOC] AOT BYOC Calling External Library

I am looking to create a simple custom c-codegen to interact with an external library and I need some guidance.

I started to use the RelayToTIR + TIRToRuntime method in order to take advantage of unified static memory planning (as described in the “Additional Target Hooks” RFC) and I’m having trouble creating a custom lowering method in RelayToTIR

I have created a partition function which includes the MergeCompilerRegions pass and the OutlineCompilerFunctionsWithExistingGlobalSymbols pass with the target “toy_cg_thflow” and the composite functions “dense_bias” and “dense_bias_relu”

Here is an example global variable at the input to my RelayToTIR pass:

def @tvmgen_default_toy_cg_thflow_main_0(%toy_cg_thflow_0_i0: Tensor[(64, 800), float32] /* ty=Tensor[(64, 800), float32] */, %toy_cg_thflow_0_i1: Tensor[(500, 800), float32] /* ty=Tensor[(500, 800), float32] */, %toy_cg_thflow_0_i2: Tensor[(500), float32] /* ty=Tensor[(500), float32] */, %toy_cg_thflow_0_i3: Tensor[(10, 500), float32] /* ty=Tensor[(10, 500), float32] */, %toy_cg_thflow_0_i4: Tensor[(10), float32] /* ty=Tensor[(10), float32] */, Compiler="toy_cg_thflow", Primitive=1, Inline=1, global_symbol="tvmgen_default_toy_cg_thflow_main_0") -> Tensor[(64, 10), float32] {
	%13 = fn (%FunctionVar_0_01: Tensor[(64, 800), float32] /* ty=Tensor[(64, 800), float32] */, %FunctionVar_0_11: Tensor[(500, 800), float32] /* ty=Tensor[(500, 800), float32] */, %FunctionVar_0_21: Tensor[(500), float32] /* ty=Tensor[(500), float32] */, PartitionedFromPattern="nn.dense_nn.bias_add_nn.relu_", Composite="toy_cg_thflow.dense_bias_relu") -> Tensor[(64, 500), float32] {
		%11 = nn.dense(%FunctionVar_0_01, %FunctionVar_0_11, units=None) /* ty=Tensor[(64, 500), float32] span=aten::linear_0:0:0 */;
		%12 = nn.bias_add(%11, %FunctionVar_0_21, axis=-1) /* ty=Tensor[(64, 500), float32] span=aten::linear_0:0:0 */;
		nn.relu(%12) /* ty=Tensor[(64, 500), float32] span=aten::relu_2:0:0 */
	} /* ty=fn (Tensor[(64, 800), float32], Tensor[(500, 800), float32], Tensor[(500), float32]) -> Tensor[(64, 500), float32] */;
	%14 = %13(%toy_cg_thflow_0_i0, %toy_cg_thflow_0_i1, %toy_cg_thflow_0_i2) /* ty=Tensor[(64, 500), float32] */;
	%15 = fn (%FunctionVar_0_0: Tensor[(64, 500), float32] /* ty=Tensor[(64, 500), float32] */, %FunctionVar_0_1: Tensor[(10, 500), float32] /* ty=Tensor[(10, 500), float32] */, %FunctionVar_0_2: Tensor[(10), float32] /* ty=Tensor[(10), float32] */, PartitionedFromPattern="nn.dense_nn.bias_add_", Composite="toy_cg_thflow.dense_bias") -> Tensor[(64, 10), float32] {
		%10 = nn.dense(%FunctionVar_0_0, %FunctionVar_0_1, units=None) /* ty=Tensor[(64, 10), float32] span=aten::linear_1:0:0 */;
		nn.bias_add(%10, %FunctionVar_0_2, axis=-1) /* ty=Tensor[(64, 10), float32] span=aten::linear_1:0:0 */
	} /* ty=fn (Tensor[(64, 500), float32], Tensor[(10, 500), float32], Tensor[(10), float32]) -> Tensor[(64, 10), float32] */;
	%15(%14, %toy_cg_thflow_0_i3, %toy_cg_thflow_0_i4) /* ty=Tensor[(64, 10), float32] */

I am now attempting to create a custom lowering method that will simply allow me to call an external function for each composite node, passing in the inputs, weights, biases, and output buffer

My naive way of attempting to do this is a pass like this in python:

class LowerFusedNodesSimple(ExprMutator):

# Convert Call node to tir.call_extern
def visit_call(self, call: Call):
    fn = call.op
    if(isinstance(fn, Function) and "Composite" in fn.attrs):
        if(fn.attrs["Composite"].split(".")[0] == 'toy_cg_thflow'):
            call_ext_args = []
            for arg in call.args:

            return tir.call_extern(fn.checked_type.ret_type.dtype, fn.attrs["Composite"].split(".")[1], *call_ext_args)
    return None    

# Convert Function node to PrimFunc
def visit_function(self, fn: Function):

    # Recursively visit params
    new_params = [self.visit(x) for x in fn.params]
    new_body = self.visit(fn.body)
    if new_params == list(fn.params) and new_body == fn.body:
        return fn
    if("global_symbol" in fn.attrs and "toy_cg_thflow" in fn.attrs["global_symbol"]):
        buffer_dict = {self.memo_map[arg]:tir.decl_buffer(shape = arg.type_annotation.shape, dtype = arg.type_annotation.dtype, name = arg.name_hint) for arg in fn.params}
        attrs = {"global_symbol": fn.attrs["global_symbol"],
                "target": tvm.target.Target("toy_cg_thflow"),
                "tir.noalias": True}
        dict_attrs = tvm.ir.make_node("DictAttrs", **attrs)

        return tir.PrimFunc(params = buffer_dict.keys(), body = Evaluate(self.memo_map[fn.body]), ret_type = fn.checked_type.ret_type, buffer_map=buffer_dict, attrs=dict_attrs)

# Convert relay Var to tir Var
def visit_var(self, var: Var):
    return tir.Var(var.name_hint, var.type_annotation.dtype)

# Convert relay const to TIR const
def visit_constant(self, const):
    return tir.FloatImm(const)

But this results in nested function calls without intermediate buffers

int32_t tvmgen_default_toy_cg_thflow_main_0(float* toy_cg_thflow_0_i0, float* toy_cg_thflow_0_i1, float* toy_cg_thflow_0_i2, float* toy_cg_thflow_0_i3, float* toy_cg_thflow_0_i4, uint8_t* global_const_workspace_16_var, uint8_t* global_workspace_17_var) {
    dense_bias(dense_bias_relu(toy_cg_thflow_0_i0, toy_cg_thflow_0_i1, toy_cg_thflow_0_i2), toy_cg_thflow_0_i3, toy_cg_thflow_0_i4);
    return 0;

Does anyone know of an easy way to lower composite nodes to custom external function calls with intermediate buffers similar to how default codegen works? It seems like this should be a simple use-case of BYOC

1 Like

I have a similar problem, have you solved it?

I ended up abandoning this but now that I’m more familiar with TVM I can tell you what I was doing wrong.

  1. TIR is representation meant to mirror output code, so if you nest function calls in TIR, the generated output calls will also be nested. Instead, my custom lowering method should have traversed the relay function, and added the function calls to a tir.SeqStmt so that the functions are called sequentially.

  2. Not nesting functions also means that one of the input variables to your external function needs to be an output pointer of type tir.Var which may then be passed as an input to future layers. The space for these intermediate buffers must be allocated before use with a tir.Allocate statement.

  3. To make this a bit easier and more readable, you can use tvm.tir.ir_builder. This takes care of wrapping sequential statements in tir.SeqStmt and dealing with buffer variables which can be a bit tricky. You can see examples of it being used here. The steps should loosely be

    1. Traverse your subgraph and allocate all the necessary intermediate buffers with ir_builder.allocate (this is usually done first before calling functions)
    2. Traverse your subgraph again and add all your external function calls using ir_builder.emit(tvm.tir.call_extern())
    3. Return your generated TIR code with ir_builder.get()

Getting familiar with TVM has been a process so I hope this is helpful!

1 Like

thanks for your sharing, it helps me a lot :smiling_face_with_three_hearts:!!