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Fix cast removal bug (#17953)
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The `RemoveDuplicateCastTransformer` fairly naively removed Cast nodes
from the graph without considering precision loss when using the same
`TypeGroup`. For instance, F64 -> F32 -> F64 would be optimised out of
the graph.

I also noticed that signedness was not accounted for, which is not
covered by any existing issue but is a problem. For example doing int ->
unsigned int -> int produces very different values for negative inputs
and so should not be optimised out

One could argue that we shouldn't be performing such cast elimination at
all (at least not in this transformer). The original scope might be well
restricted to only eliminating unnecessary casts from the
`InsertCastTransformer` and no others.

### Motivation and Context
This should fix #17565,
ttps://github.com//issues/9915 and
#8787.
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@adityagoel4512
adityagoel4512 authored Oct 31, 2023
1 parent 20f2dd8 commit ed41a28
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Showing 2 changed files with 133 additions and 18 deletions.
86 changes: 68 additions & 18 deletions onnxruntime/core/optimizer/insert_cast_transformer.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ onnxruntime::NodeArg* AddCastNode(onnxruntime::Graph& graph,
int64_t to_type,
onnxruntime::ProviderType providerType) {
// insert cast op to cast input
std::string node_name = graph.GenerateNodeName("InsertedCast_" + old_arg->Name());
std::string node_name = graph.GenerateNodeName("InsertedPrecisionFreeCast_" + old_arg->Name());

auto* new_arg = &graph.GetOrCreateNodeArg(node_name, new_type);

Expand Down Expand Up @@ -235,33 +235,95 @@ enum TypeGroup {
Unknown = -1,
Bool = 0,
Integer = 1,
Float = 2,
Unsigned = 2,
Float = 3,
};

TypeGroup GetTypeGroup(DataType type) {
if (*type == "tensor(bool)") {
return Bool;
}

if (*type == "tensor(int16)" || *type == "tensor(int32)" || *type == "tensor(int64)" || *type == "tensor(int8)" ||
*type == "tensor(uint16)" || *type == "tensor(uint32)" || *type == "tensor(uint64)" || *type == "tensor(uint8)") {
if (*type == "tensor(int16)" || *type == "tensor(int32)" || *type == "tensor(int64)" || *type == "tensor(int8)") {
return Integer;
}

if (*type == "tensor(uint16)" || *type == "tensor(uint32)" || *type == "tensor(uint64)" || *type == "tensor(uint8)") {
return Unsigned;
}

if (*type == "tensor(bfloat16)" || *type == "tensor(double)" || *type == "tensor(float)" || *type == "tensor(float16)") {
return Float;
}

return Unknown;
}

int BitLength(DataType type) {
if (*type == "tensor(bool)") {
return 1;
} else if (*type == "tensor(uint8)" || *type == "tensor(int8)") {
return 8;
} else if (*type == "tensor(int16)" || *type == "tensor(uint16)" || *type == "tensor(bfloat16)" || *type == "tensor(float16)") {
return 16;
} else if (*type == "tensor(int32)" || *type == "tensor(uint32)" || *type == "tensor(float)") {
return 32;
} else if (*type == "tensor(int64)" || *type == "tensor(uint64)" || *type == "tensor(double)") {
return 64;
} else {
return -1;
}
}

/** Transformer to remove duplicate Cast nodes. */
class RemoveDuplicateCastTransformer : public GraphTransformer {
public:
RemoveDuplicateCastTransformer() : GraphTransformer("RemoveDuplicateCastTransformer") {
}

private:
static bool UnsafeCast(DataType src_type, DataType dst_type, const Node& node) {
// This is not a complete cast optimisation pass, and is more conservative than it could be.
// For instance, certain integral -> floating point casts could be optimised but this is left to an explicit cast optimisation pass.

// The comparison with "InsertedPrecisionFreeCast_" reflects cast nodes that are inserted by InsertCastTransformer.
// Such casts should not be considered as loss of precision - the inserted upcasts (f16 -> f32) and downcasts (f32 -> f16) are inserted to support kernels when on a CPU EP without F16 support.
auto src_type_group = GetTypeGroup(src_type);
auto dst_type_group = GetTypeGroup(dst_type);
if (Unknown == src_type_group || Unknown == dst_type_group) {
return true;
}

// Do not remove any signed -> unsigned cast.
if ((src_type_group != Bool && src_type_group != Unsigned) && Unsigned == dst_type_group) {
return true;
}

// Do not remove any floating point -> non floating point cast.
if (Float == src_type_group && Float != dst_type_group) {
return true;
}

auto src_bit_length = BitLength(src_type);
auto dst_bit_length = BitLength(dst_type);

// unsigned integer -> integer cast may overflow if the destination integer is smaller or equal to the source integer.
if (Unsigned == src_type_group && Integer == dst_type_group) {
return dst_bit_length <= src_bit_length;
}

// integral -> floating cast may overflow if integer cannot be encoded in the mantissa. This check could be more precise.
if ((Integer == src_type_group || Unsigned == src_type_group) && Float == dst_type_group) {
return dst_bit_length <= src_bit_length;
}

if ((*src_type == "tensor(float16)" && *dst_type == "tensor(bfloat16)") || (*src_type == "tensor(bfloat16)" && *dst_type == "tensor(float16)")) {
return true;
}

return src_bit_length > dst_bit_length && (node.Name().compare(0, 26, "InsertedPrecisionFreeCast_"));
}

Status ApplyImpl(Graph& graph, bool& modified, int graph_level, const logging::Logger& logger) const override {
auto output_args = graph.GetOutputs();
InlinedHashSet<const onnxruntime::NodeArg*> graph_outputs;
Expand Down Expand Up @@ -293,17 +355,8 @@ class RemoveDuplicateCastTransformer : public GraphTransformer {
// - for each consumer cast node, it meets above condition for this optimization.
auto src_type = node.InputDefs()[0]->Type();
auto dst_type = node.OutputDefs()[0]->Type();
TypeGroup src_type_group = GetTypeGroup(src_type);
TypeGroup dst_type_group = GetTypeGroup(dst_type);
if (src_type_group == Unknown || dst_type_group == Unknown) {
continue;
}

bool loss_precision_cast = false;
if (src_type_group > dst_type_group) {
loss_precision_cast = true;
}

bool loss_precision_cast = UnsafeCast(src_type, dst_type, node);
size_t num_children = node.GetOutputEdgesCount();

bool inconsistent_casts = false;
Expand All @@ -312,10 +365,7 @@ class RemoveDuplicateCastTransformer : public GraphTransformer {
if (output_node.OpType() == "Cast") {
auto src_type1 = output_node.InputDefs()[0]->Type();
auto dst_type1 = output_node.OutputDefs()[0]->Type();
TypeGroup src_type_group1 = GetTypeGroup(src_type1);
TypeGroup dst_type_group1 = GetTypeGroup(dst_type1);
if (src_type_group1 == Unknown || dst_type_group1 == Unknown ||
(loss_precision_cast && dst_type_group1 > src_type_group1)) {
if (loss_precision_cast && UnsafeCast(dst_type1, src_type1, output_node)) {
inconsistent_casts = true;
break;
}
Expand Down
65 changes: 65 additions & 0 deletions onnxruntime/test/framework/insert_cast_transformer_test.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
#include "core/framework/allocator.h"
#include "core/optimizer/insert_cast_transformer.h"
#include "core/graph/model.h"
#include "core/graph/node_attr_utils.h"
#include "gtest/gtest.h"
#include "test_utils.h"
#include "test/test_environment.h"
Expand Down Expand Up @@ -110,6 +111,70 @@ TEST(TransformerTest, InsertCastAllCPUTest) {
}
}

TEST(TransformerTest, CastRemovalDoesNotLowerPrecisionTest) {
auto model = std::make_shared<onnxruntime::Model>("test", false, DefaultLoggingManager().DefaultLogger());
onnxruntime::Graph& graph = model->MainGraph();
TypeProto tensor_float_32;
tensor_float_32.mutable_tensor_type()->set_elem_type(TensorProto_DataType_FLOAT);
TypeProto tensor_float_64;
tensor_float_64.mutable_tensor_type()->set_elem_type(TensorProto_DataType_DOUBLE);
onnxruntime::NodeArg n1_def("N1", &tensor_float_64),
n2_def("N2", &tensor_float_32),
n3_def("N3", &tensor_float_64);

NodeAttributes n1_attrs = {{"to", utils::MakeAttribute("to", static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT))}};
NodeAttributes n2_attrs = {{"to", utils::MakeAttribute("to", static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_DOUBLE))}};

graph.AddNode("node1", "Cast", "F64 to F32 cast", ArgMap{&n1_def}, ArgMap{&n2_def}, &n1_attrs);
graph.AddNode("node2", "Cast", "F32 to F64 cast", ArgMap{&n2_def}, ArgMap{&n3_def}, &n2_attrs);

auto status = graph.Resolve();
ASSERT_TRUE(status.IsOK()) << status.ErrorMessage();

InsertCastTransformer cast_inserter("Test", DefaultCpuExecutionProvider()->GetKernelRegistry().get());

bool modified = true;
status = cast_inserter.Apply(graph, modified, DefaultLoggingManager().DefaultLogger());
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
status = graph.Resolve();
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();

// When casting f64 -> f32 -> f64 we should not be optimising away the cast since there is a loss of precision.
EXPECT_EQ(graph.NumberOfNodes(), 2);
}

TEST(TransformerTest, CastRemovalDoesNotRemoveSignednessTest) {
auto model = std::make_shared<onnxruntime::Model>("test", false, DefaultLoggingManager().DefaultLogger());
onnxruntime::Graph& graph = model->MainGraph();
TypeProto tensor_uint32;
tensor_uint32.mutable_tensor_type()->set_elem_type(TensorProto_DataType_UINT32);
TypeProto tensor_int32;
tensor_int32.mutable_tensor_type()->set_elem_type(TensorProto_DataType_INT32);
onnxruntime::NodeArg n1_def("N1", &tensor_int32),
n2_def("N2", &tensor_uint32),
n3_def("N3", &tensor_int32);

NodeAttributes n1_attrs = {{"to", utils::MakeAttribute("to", static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_UINT32))}};
NodeAttributes n2_attrs = {{"to", utils::MakeAttribute("to", static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_INT32))}};

graph.AddNode("node1", "Cast", "I32 to UI32 cast", ArgMap{&n1_def}, ArgMap{&n2_def}, &n1_attrs);
graph.AddNode("node2", "Cast", "UI32 to I32 cast", ArgMap{&n2_def}, ArgMap{&n3_def}, &n2_attrs);

auto status = graph.Resolve();
ASSERT_TRUE(status.IsOK()) << status.ErrorMessage();

InsertCastTransformer cast_inserter("Test", DefaultCpuExecutionProvider()->GetKernelRegistry().get());

bool modified = true;
status = cast_inserter.Apply(graph, modified, DefaultLoggingManager().DefaultLogger());
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
status = graph.Resolve();
EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();

// When casting i32 -> ui32 -> i32 we should not be optimising away the cast since applying the casts produces a very different result.
EXPECT_EQ(graph.NumberOfNodes(), 2);
}

// test that when there are 3 Cast ops in a row we remove the correct ones
TEST(TransformerTest, ThreeInARowRemoval) {
auto model_uri = MODEL_FOLDER ORT_TSTR("triple-cast.onnx");
Expand Down

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