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Posted to commits@tvm.apache.org by mo...@apache.org on 2022/06/17 16:42:58 UTC
[tvm] branch main updated: [CMSIS-NN] Fixed the case with repeating operands in the QNN binary ops (#11732)
This is an automated email from the ASF dual-hosted git repository.
mousius pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/main by this push:
new dffc3108bb [CMSIS-NN] Fixed the case with repeating operands in the QNN binary ops (#11732)
dffc3108bb is described below
commit dffc3108bbd406e7da49693533983adba634b19d
Author: Ashutosh Parkhi <86...@users.noreply.github.com>
AuthorDate: Fri Jun 17 17:42:49 2022 +0100
[CMSIS-NN] Fixed the case with repeating operands in the QNN binary ops (#11732)
---
python/tvm/relay/op/contrib/cmsisnn.py | 1 -
.../backend/contrib/cmsisnn/extract_constants.cc | 13 ++++-
src/relay/backend/contrib/cmsisnn/relay_to_tir.cc | 14 ++++-
.../contrib/cmsisnn/scalar_to_tensor_constant.cc | 6 +++
.../python/contrib/test_cmsisnn/test_binary_ops.py | 61 +++++++++++++++++++++-
.../contrib/test_cmsisnn/test_extract_constants.py | 34 ++++++++++++
.../test_cmsisnn/test_scalar_to_tensor_constant.py | 41 +++++++++++++++
7 files changed, 165 insertions(+), 5 deletions(-)
diff --git a/python/tvm/relay/op/contrib/cmsisnn.py b/python/tvm/relay/op/contrib/cmsisnn.py
index 09831929e5..8d714b7269 100644
--- a/python/tvm/relay/op/contrib/cmsisnn.py
+++ b/python/tvm/relay/op/contrib/cmsisnn.py
@@ -223,7 +223,6 @@ def pattern_table():
def check_qnn_max_pool2d(pattern):
"""Check if max pool2d is supported by CMSIS-NN."""
output = pattern
- input_op = None
if str(pattern.op.name) == "clip":
pooling = pattern.args[0]
diff --git a/src/relay/backend/contrib/cmsisnn/extract_constants.cc b/src/relay/backend/contrib/cmsisnn/extract_constants.cc
index 1cbe36e30f..c6ed7af9ff 100644
--- a/src/relay/backend/contrib/cmsisnn/extract_constants.cc
+++ b/src/relay/backend/contrib/cmsisnn/extract_constants.cc
@@ -164,7 +164,18 @@ class ExtractConstantsMutator : public MixedModeMutator {
function_signature.push_back(arg);
} else {
if (arg.as<VarNode>()) {
- function_signature.push_back(arg);
+ // Only push if its not already present as multiple consumers of any input var
+ // will appear only once in the function signature.
+ bool found_in_existing_signature = false;
+ for (auto& sign : function_signature) {
+ if (arg.same_as(sign)) {
+ found_in_existing_signature = true;
+ break;
+ }
+ }
+ if (!found_in_existing_signature) {
+ function_signature.push_back(arg);
+ }
}
new_args.push_back(arg);
}
diff --git a/src/relay/backend/contrib/cmsisnn/relay_to_tir.cc b/src/relay/backend/contrib/cmsisnn/relay_to_tir.cc
index 524735caa9..5c99061fa8 100644
--- a/src/relay/backend/contrib/cmsisnn/relay_to_tir.cc
+++ b/src/relay/backend/contrib/cmsisnn/relay_to_tir.cc
@@ -556,7 +556,12 @@ class RelayToTIRVisitor : public MixedModeMutator {
BufferCreator buffer_creator;
tir::Var input_0 = buffer_creator.CreateBufferVar("input_0", DataType::Handle(8));
- tir::Var input_1 = buffer_creator.CreateBufferVar("input_1", DataType::Handle(8));
+ tir::Var input_1;
+ if (mul_call->args[0].same_as(mul_call->args[1])) {
+ input_1 = input_0;
+ } else {
+ input_1 = buffer_creator.CreateBufferVar("input_1", DataType::Handle(8));
+ }
tir::Var output = buffer_creator.CreateBufferVar("output", DataType::Handle(8));
tvm::Array<PrimExpr> args = {
@@ -626,7 +631,12 @@ class RelayToTIRVisitor : public MixedModeMutator {
BufferCreator buffer_creator;
tir::Var input_0 = buffer_creator.CreateBufferVar("input_0", DataType::Handle(8));
- tir::Var input_1 = buffer_creator.CreateBufferVar("input_1", DataType::Handle(8));
+ tir::Var input_1;
+ if (add_call->args[0].same_as(add_call->args[1])) {
+ input_1 = input_0;
+ } else {
+ input_1 = buffer_creator.CreateBufferVar("input_1", DataType::Handle(8));
+ }
tir::Var output = buffer_creator.CreateBufferVar("output", DataType::Handle(8));
tvm::Array<PrimExpr> args = {
diff --git a/src/relay/backend/contrib/cmsisnn/scalar_to_tensor_constant.cc b/src/relay/backend/contrib/cmsisnn/scalar_to_tensor_constant.cc
index 2448bfc766..40fd773eb2 100644
--- a/src/relay/backend/contrib/cmsisnn/scalar_to_tensor_constant.cc
+++ b/src/relay/backend/contrib/cmsisnn/scalar_to_tensor_constant.cc
@@ -179,6 +179,12 @@ class ScalarToTensorConstantMutator : public MixedModeMutator {
auto new_body = VisitExpr(func->body);
Function new_func = WithFields(func, FreeVars(new_body), new_body, func->ret_type,
FreeTypeVars(new_body, mod_), func->attrs);
+
+ // Updating new_func parameters could result into uniquification of function parameters.
+ // Call arguments need to be aligned to the number of arguments expected by new_func.
+ if (new_args[0].same_as(new_args[1])) {
+ new_args.erase(new_args.begin());
+ }
return Call(new_func, new_args);
}
diff --git a/tests/python/contrib/test_cmsisnn/test_binary_ops.py b/tests/python/contrib/test_cmsisnn/test_binary_ops.py
index fec18c197e..26604da0a6 100644
--- a/tests/python/contrib/test_cmsisnn/test_binary_ops.py
+++ b/tests/python/contrib/test_cmsisnn/test_binary_ops.py
@@ -101,7 +101,7 @@ def make_model(
def test_op_int8(
op, relu_type, input_0_scale, input_0_zero_point, input_1_scale, input_1_zero_point
):
- """Tests QNN Conv2D operator for CMSIS-NN"""
+ """Tests QNN binary operator for CMSIS-NN"""
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
@@ -145,6 +145,65 @@ def test_op_int8(
)
+@skip_if_no_reference_system
+@tvm.testing.requires_cmsisnn
+@pytest.mark.parametrize("op", [relay.qnn.op.mul, relay.qnn.op.add])
+@pytest.mark.parametrize("relu_type", ["RELU", "NONE"])
+def test_same_input_to_binary_op(op, relu_type):
+ """Tests QNN binary operator for CMSIS-NN where both inputs are the same"""
+ interface_api = "c"
+ use_unpacked_api = True
+ test_runner = AOT_USMP_CORSTONE300_RUNNER
+
+ dtype = "int8"
+ shape = [1, 16, 16, 3]
+ input_ = generate_variable("input")
+ input_scale = 0.256
+ input_zero_point = 33
+
+ model = make_model(
+ op,
+ input_,
+ input_,
+ input_scale,
+ input_zero_point,
+ input_scale,
+ input_zero_point,
+ relu_type,
+ )
+ orig_mod = make_module(model)
+
+ cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
+
+ # validate pattern matching
+ assert_partitioned_function(orig_mod, cmsisnn_mod)
+
+ # Check if the number of internal function parameter is 1
+ cmsisnn_global_func = cmsisnn_mod["tvmgen_default_cmsis_nn_main_0"]
+ assert (
+ isinstance(cmsisnn_global_func.body, tvm.relay.expr.Call)
+ and len(cmsisnn_global_func.body.args) == 1
+ ), "Composite function for the binary op should have only 1 parameter."
+
+ # validate the output
+ in_min, in_max = get_range_for_dtype_str(dtype)
+ inputs = {
+ "input": np.random.randint(in_min, high=in_max, size=shape, dtype=dtype),
+ }
+ output_list = generate_ref_data(orig_mod["main"], inputs)
+ compile_and_run(
+ AOTTestModel(
+ module=cmsisnn_mod,
+ inputs=inputs,
+ outputs=output_list,
+ output_tolerance=1,
+ ),
+ test_runner,
+ interface_api,
+ use_unpacked_api,
+ )
+
+
def parameterize_for_constant_inputs(test):
"""Generates parameters in such a way so that at least one of the inputs is a constant,
both can't be variables, both can't be scalars.
diff --git a/tests/python/contrib/test_cmsisnn/test_extract_constants.py b/tests/python/contrib/test_cmsisnn/test_extract_constants.py
index 8831596d40..7d3e81a9c7 100644
--- a/tests/python/contrib/test_cmsisnn/test_extract_constants.py
+++ b/tests/python/contrib/test_cmsisnn/test_extract_constants.py
@@ -116,6 +116,40 @@ def test_nested_function():
relay.transform.InferType()(mod)
+@tvm.testing.requires_cmsisnn
+def test_internal_function_with_duplicate_arguments():
+ """Tests the pass ExternConstants when a composite function
+ is present within global function with repeating arguments
+ to one of the binary ops.
+ """
+ input0 = relay.var("input0", shape=(8, 8))
+ binary_op0 = input0 + input0
+ binary_op1 = binary_op0 * relay.const(5.0, "float32")
+ local_func = relay.Function([input0], binary_op1, relay.TensorType((8, 8), "float32"))
+ local_func = set_composite_func_attr(local_func, "cmsis-nn")
+
+ arg = relay.var("arg", shape=(8, 8))
+ call_local_func = relay.Call(local_func, [arg])
+ extern_func = relay.Function([arg], call_local_func, relay.TensorType((8, 8), "float32"))
+
+ global_arg = relay.var("global_var", shape=(8, 8))
+ global_var = relay.GlobalVar("external_function")
+ extern_func = set_external_func_attr(extern_func, "cmsis-nn", global_var.name_hint)
+ call_extern_func = relay.Call(global_var, [global_arg])
+ main_func = relay.Function([global_arg], call_extern_func, relay.TensorType((8, 8), "float32"))
+ main_var = relay.GlobalVar("main")
+
+ mod = tvm.IRModule()
+ mod[global_var] = extern_func
+ mod[main_var] = main_func
+
+ mod = ExtractConstantsFromPartitionedFunction()(mod)
+ constant_verifier = CheckFunctionsForConstants()
+ constant_verifier.visit_function(mod[global_var])
+ constant_verifier.check_num_constants()
+ relay.transform.InferType()(mod)
+
+
@tvm.testing.requires_cmsisnn
def test_multiple_functions():
"""Tests the pass ExternConstants when global function
diff --git a/tests/python/contrib/test_cmsisnn/test_scalar_to_tensor_constant.py b/tests/python/contrib/test_cmsisnn/test_scalar_to_tensor_constant.py
index 557a65aeff..df54f7ce55 100644
--- a/tests/python/contrib/test_cmsisnn/test_scalar_to_tensor_constant.py
+++ b/tests/python/contrib/test_cmsisnn/test_scalar_to_tensor_constant.py
@@ -256,6 +256,47 @@ def test_all_primary_operands_tensor_constants():
assert tvm.ir.structural_equal(mod[global_var].body, new_mod[global_var].body)
+@tvm.testing.requires_cmsisnn
+def test_duplicate_constant_arguments():
+ """Tests the pass when repeating operands are arguments to the binary op"""
+ dtype = "int8"
+ shape = (1, 3, 3, 32)
+ operand0 = generate_variable("operand0", shape, dtype)
+ operand1 = generate_variable("operand1", shape, dtype)
+ binary_op = make_binary_op(
+ relay.qnn.op.add,
+ operand0,
+ operand0,
+ input_0_scale=0.0128,
+ input_0_zero_point=32,
+ input_1_scale=0.256,
+ input_1_zero_point=-64,
+ )
+
+ local_func = relay.Function([operand0, operand1], binary_op, relay.TensorType(shape, dtype))
+ local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_add")
+
+ rng = np.random.default_rng(12345)
+ arg0 = relay.const(rng.integers(-128, high=127, size=shape, dtype=dtype))
+ call_local_func = relay.Call(local_func, [arg0, arg0])
+ extern_func = relay.Function([], call_local_func, relay.TensorType(shape, dtype))
+
+ global_var = relay.GlobalVar("external_function")
+ extern_func = set_external_func_attr(extern_func, "cmsis-nn", global_var.name_hint)
+ call_extern_func = relay.Call(global_var, [])
+ main_func = relay.Function([], call_extern_func, relay.TensorType(shape, dtype))
+ main_var = relay.GlobalVar("main")
+
+ mod = tvm.IRModule()
+ mod[global_var] = extern_func
+ mod[main_var] = main_func
+
+ mod = relay.transform.InferType()(mod)
+ mod = ScalarToTensorConstants()(mod)
+ new_mod = relay.transform.InferType()(mod)
+ assert tvm.ir.structural_equal(mod[global_var].body, new_mod[global_var].body)
+
+
@tvm.testing.requires_cmsisnn
def test_non_cmsisnn_ext_func():
"""Non CMSISNN functions should not be altered."""