Add Optional Activation node to NodeUnit #22888
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| Original file line number | Diff line number | Diff line change | ||||
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@@ -215,4 +215,141 @@ | |||||
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| #endif // !defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD) | ||||||
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| namespace { | ||||||
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| bool GetDataTypeMinMax(int32_t data_type, int32_t& min, int32_t& max) { | ||||||
| switch (data_type) { | ||||||
| case ONNX_NAMESPACE::TensorProto::INT8: | ||||||
| min = static_cast<int32_t>(std::numeric_limits<int8_t>::min()); | ||||||
| max = static_cast<int32_t>(std::numeric_limits<int8_t>::max()); | ||||||
| break; | ||||||
| case ONNX_NAMESPACE::TensorProto::UINT8: | ||||||
| min = static_cast<int32_t>(std::numeric_limits<uint8_t>::min()); | ||||||
| max = static_cast<int32_t>(std::numeric_limits<uint8_t>::max()); | ||||||
| break; | ||||||
| case ONNX_NAMESPACE::TensorProto::INT16: | ||||||
| min = static_cast<int32_t>(std::numeric_limits<int16_t>::min()); | ||||||
| max = static_cast<int32_t>(std::numeric_limits<int16_t>::max()); | ||||||
| break; | ||||||
| case ONNX_NAMESPACE::TensorProto::UINT16: | ||||||
| min = static_cast<int32_t>(std::numeric_limits<uint16_t>::min()); | ||||||
| max = static_cast<int32_t>(std::numeric_limits<uint16_t>::max()); | ||||||
| break; | ||||||
| default: | ||||||
| return false; | ||||||
| } | ||||||
| return true; | ||||||
| } | ||||||
| bool GetQSalarScaleZp(const GraphViewer& graph_viewer, const Node& q_node, float& scale, int32_t& zp, | ||||||
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Suggested change
Would be good to refactor some of the utils here as there seems to be a fair bit of duplication. e.g. maybe a general purpose helper that reads the scale and zp values (scalar or otherwise), and has a bool to indicate if they're scalar. that helper could be used by many of the utils here. |
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| int32_t& data_type) { | ||||||
| assert(q_node.OpType() == QOpName); | ||||||
| const auto& q_input_defs = q_node.InputDefs(); | ||||||
| if (q_input_defs.size() != 3 || !q_input_defs[2]->Exists()) { | ||||||
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There was a problem hiding this comment. zp is optional and defaults to zero, so do we need to require 3 inputs here? |
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| return false; | ||||||
| } | ||||||
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| const ONNX_NAMESPACE::TensorProto* scale_tensor_proto = | ||||||
| graph_viewer.GetConstantInitializer(q_input_defs[1]->Name(), true); | ||||||
| if (!scale_tensor_proto) { | ||||||
| return false; | ||||||
| } | ||||||
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| // Support scalar float scale only for now. Need to extend to other float types if needed. | ||||||
| Initializer scale_initializer(*scale_tensor_proto, graph_viewer.ModelPath()); | ||||||
| if (scale_initializer.dims().size() != 0 || scale_initializer.data_type() != ONNX_NAMESPACE::TensorProto::FLOAT) { | ||||||
| return false; | ||||||
| } | ||||||
| scale = *scale_initializer.data<float>(); | ||||||
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| const ONNX_NAMESPACE::TensorProto* zp_tensor_proto = | ||||||
| graph_viewer.GetConstantInitializer(q_input_defs[2]->Name(), true); | ||||||
| if (!zp_tensor_proto) { | ||||||
| return false; | ||||||
| } | ||||||
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| Initializer zp_initializer(*zp_tensor_proto, graph_viewer.ModelPath()); | ||||||
| if (zp_initializer.dims().size() != 0) { | ||||||
| return false; | ||||||
| } | ||||||
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| data_type = zp_initializer.data_type(); | ||||||
| switch (data_type) { | ||||||
| case ONNX_NAMESPACE::TensorProto::INT8: | ||||||
| zp = static_cast<int32_t>(*zp_initializer.data<int8_t>()); | ||||||
| break; | ||||||
| case ONNX_NAMESPACE::TensorProto::UINT8: | ||||||
| zp = static_cast<int32_t>(*zp_initializer.data<uint8_t>()); | ||||||
| break; | ||||||
| case ONNX_NAMESPACE::TensorProto::INT16: | ||||||
| zp = static_cast<int32_t>(*zp_initializer.data<int16_t>()); | ||||||
| break; | ||||||
| case ONNX_NAMESPACE::TensorProto::UINT16: | ||||||
| zp = static_cast<int32_t>(*zp_initializer.data<uint16_t>()); | ||||||
| break; | ||||||
| default: | ||||||
| return false; | ||||||
| } | ||||||
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| return true; | ||||||
| } | ||||||
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| bool CanRemoveRelu(const GraphViewer& graph_viewer, const Node& q_node) { | ||||||
| float scale = 0.0f; | ||||||
| int32_t zp = 0; | ||||||
| int32_t data_type = 0; | ||||||
| if (!GetQSalarScaleZp(graph_viewer, q_node, scale, zp, data_type)) { | ||||||
| return false; | ||||||
| } | ||||||
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| int32_t data_type_min = 0; | ||||||
| int32_t data_type_max = 0; | ||||||
| if (!GetDataTypeMinMax(data_type, data_type_min, data_type_max)) { | ||||||
| return false; | ||||||
| } | ||||||
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| // Relu can be removed if the zero-point is set to the smallest quantized value. | ||||||
| return zp == data_type_min; | ||||||
| } | ||||||
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| bool CanRemoveClip(const GraphViewer& graph_viewer, const Node& clip_node, const Node& q_node) { | ||||||
| float scale = 0.0f; | ||||||
| int32_t zp = 0; | ||||||
| int32_t data_type = 0; | ||||||
| if (!GetQSalarScaleZp(graph_viewer, q_node, scale, zp, data_type)) { | ||||||
| return false; | ||||||
| } | ||||||
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| float min = 0.0f; | ||||||
| float max = 0.0f; | ||||||
| if (!optimizer_utils::GetClipConstantMinMax(graph_viewer.GetGraph(), clip_node, min, max)) { | ||||||
| return false; | ||||||
| } | ||||||
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| int32_t q_clip_min = static_cast<int32_t>(::rint(min / scale)) + zp; | ||||||
| int32_t q_clip_max = static_cast<int32_t>(::rint(max / scale)) + zp; | ||||||
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| int32_t data_type_min = 0; | ||||||
| int32_t data_type_max = 0; | ||||||
| if (!GetDataTypeMinMax(data_type, data_type_min, data_type_max)) { | ||||||
| return false; | ||||||
| } | ||||||
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| // The Clip can be removed if its range entirely overlaps the quantization range. | ||||||
| // QClip range: [------------------] | ||||||
| // Quant range: [-------------] | ||||||
| return q_clip_min <= data_type_min && q_clip_max >= data_type_max; | ||||||
| } | ||||||
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| } // namespace | ||||||
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| bool CanFuseActivationQ(const GraphViewer& graph_viewer, const Node& activation_node, const Node& q_node) { | ||||||
| const std::string& activation_op_type = activation_node.OpType(); | ||||||
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Check warning on line 346 in onnxruntime/core/optimizer/qdq_transformer/qdq_util.cc
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| if (activation_op_type == "Relu") { | ||||||
| return CanRemoveRelu(graph_viewer, q_node); | ||||||
| } else if (activation_op_type == "Clip") { | ||||||
| return CanRemoveClip(graph_viewer, activation_node, q_node); | ||||||
| } | ||||||
| return false; | ||||||
| } | ||||||
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| } // namespace onnxruntime::QDQ | ||||||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -15,6 +15,7 @@ namespace onnxruntime { | |
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| class Node; | ||
| class Path; | ||
| class GraphViewer; | ||
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| namespace QDQ { | ||
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@@ -76,5 +77,9 @@ bool MatchQNode(const Node& node); | |
| // Check DQ node op type, version, and domain. | ||
| bool MatchDQNode(const Node& node); | ||
| #endif // !defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD) | ||
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| // Check if an activation node can be fused with a Q node. | ||
| bool CanFuseActivationQ(const GraphViewer& graph_viewer, const Node& activation_node, const Node& q_node); | ||
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| } // namespace QDQ | ||
| } // namespace onnxruntime | ||
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We should have a comment explaining what the 'activation' node is and how it is expected to be used.
IIUC
If that's correct I'd almost be inclined to call it something like redundant_clip_node (given Relu is a form of Clip).
Also as the OpenVINO EP (IIRC) is doing the fallback to higher precision does it need an update to be aware of the activation node in the NodeUnit?