[luci/service] Migrate reshape shape inference rule to sinf::Algorithm #13989
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| Original file line number | Diff line number | Diff line change | ||||||
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@@ -14,8 +14,36 @@ | |||||||
| * limitations under the License. | ||||||||
| */ | ||||||||
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| #include "luci/Service/CircleShapeInference.h" | ||||||||
| #include "Check.h" | ||||||||
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| #include "CircleShapeInferenceHelper.h" | ||||||||
| #include "CircleCloneNode.h" | ||||||||
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| #include <luci/Log.h> | ||||||||
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| namespace | ||||||||
| { | ||||||||
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| std::ostream &operator<<(std::ostream &os, const loco::TensorShape &tensor_shape) | ||||||||
| { | ||||||||
| os << "["; | ||||||||
| for (uint32_t r = 0; r < tensor_shape.rank(); ++r) | ||||||||
| { | ||||||||
| if (r) | ||||||||
| os << ","; | ||||||||
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| if (tensor_shape.dim(r).known()) | ||||||||
| os << tensor_shape.dim(r).value(); | ||||||||
| else | ||||||||
| os << "?"; | ||||||||
| } | ||||||||
| os << "]"; | ||||||||
| return os; | ||||||||
| } | ||||||||
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| } // namespace | ||||||||
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| namespace luci | ||||||||
| { | ||||||||
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@@ -34,4 +62,101 @@ luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReshape *node) | |||||||
| return cloned; | ||||||||
| } | ||||||||
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| namespace sinf | ||||||||
| { | ||||||||
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| loco::TensorShape Algorithm::visit(const luci::CircleReshape *node) | ||||||||
| { | ||||||||
| LOGGER(l); | ||||||||
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| const loco::DataType S32 = loco::DataType::S32; | ||||||||
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| loco::TensorShape shape_by_input; | ||||||||
| { | ||||||||
| LUCI_ASSERT(node->shape(), "2nd input shape() should not be nullptr"); | ||||||||
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| // Only support node's shape() is CircleConst with S32 | ||||||||
| // TODO support other node with other types | ||||||||
| auto const_shape_node = dynamic_cast<luci::CircleConst *>(node->shape()); | ||||||||
| if (const_shape_node != nullptr) | ||||||||
| { | ||||||||
| LUCI_ASSERT(const_shape_node->dtype() == S32, "Only support int32 CircleConst"); | ||||||||
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| shape_by_input.rank(const_shape_node->size<S32>()); | ||||||||
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| for (uint32_t axis = 0; axis < shape_by_input.rank(); ++axis) | ||||||||
| { | ||||||||
| shape_by_input.dim(axis) = const_shape_node->at<S32>(axis); | ||||||||
| } | ||||||||
| } | ||||||||
| else | ||||||||
| { | ||||||||
| // We use shape from the node itself | ||||||||
| loco::TensorShape shape; | ||||||||
| shape.rank(node->rank()); | ||||||||
| for (uint32_t r = 0; r < node->rank(); ++r) | ||||||||
| { | ||||||||
| // TODO remove this copy from `use_own(node);` | ||||||||
| // Shape inference rules in this file did not consider unknown dimension. | ||||||||
|
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like #13987 (review) There was a problem hiding this comment. I have applied this suggestion. Thanks! |
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| // If some node has unknown dimension, 0 is inserted and wrong shape | ||||||||
| // inference was done as a result. | ||||||||
| // To fix this, new shape inference algorithm is being implemented. | ||||||||
| // Until new inference algorithm is fully implemented, unknown dimension | ||||||||
| // would be represented as 1 along with TFLite expression. | ||||||||
| shape.dim(r) = node->dim(r).known() ? node->dim(r).value() : 1; | ||||||||
| } | ||||||||
| shape_by_input = shape; | ||||||||
| } | ||||||||
| } | ||||||||
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| loco::TensorShape shape_by_attr; | ||||||||
| { | ||||||||
| shape_by_attr.rank(node->newShape()->rank()); | ||||||||
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| for (uint32_t axis = 0; axis < shape_by_attr.rank(); ++axis) | ||||||||
| { | ||||||||
| shape_by_attr.dim(axis) = node->newShape()->dim(axis); | ||||||||
| } | ||||||||
| } | ||||||||
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| if (!(shape_by_input == shape_by_attr)) | ||||||||
| { | ||||||||
| INFO(l) << "CircleReshape: Two new shape information mismatched : " << std::endl; | ||||||||
| INFO(l) << " shape_by_input : " << shape_by_input << std::endl; | ||||||||
| INFO(l) << " shape_by_attr : " << shape_by_attr << std::endl; | ||||||||
| } | ||||||||
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| loco::TensorShape output_shape = shape_by_input; | ||||||||
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| // One of the dimensions can have special value -1, meaning its actual value should be inferred. | ||||||||
| const auto input = loco::must_cast<luci::CircleNode *>(node->tensor()); | ||||||||
| const auto input_shape = circle_shape(input); | ||||||||
| uint32_t input_element_count = 1; | ||||||||
| uint32_t output_element_count = 1; | ||||||||
| uint32_t unknown_dim_index = UINT32_MAX; | ||||||||
| for (uint32_t i = 0; i < input_shape.rank(); ++i) | ||||||||
| input_element_count *= (input_shape.dim(i).known() ? input_shape.dim(i).value() : 1); | ||||||||
| for (uint32_t dim_index = 0; dim_index < output_shape.rank(); ++dim_index) | ||||||||
| { | ||||||||
| const uint32_t dim_value = output_shape.dim(dim_index).value(); | ||||||||
| if (static_cast<int>(dim_value) == -1) | ||||||||
| { | ||||||||
| LUCI_ASSERT(unknown_dim_index == UINT32_MAX, "More than one unknown dimension"); | ||||||||
| unknown_dim_index = dim_index; | ||||||||
| } | ||||||||
| else | ||||||||
| { | ||||||||
| output_element_count *= dim_value; | ||||||||
| } | ||||||||
| } | ||||||||
| if (unknown_dim_index != UINT32_MAX) | ||||||||
| { | ||||||||
| output_shape.dim(unknown_dim_index) = input_element_count / output_element_count; | ||||||||
| } | ||||||||
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| return output_shape; | ||||||||
| } | ||||||||
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| } // namespace sinf | ||||||||
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| } // namespace luci | ||||||||
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(Not for this PR)
I find out that there are many
std::ostream &operator<<overloadings forTensorShapeinluci/services.operator<<(std::ostream &os, const loco::TensorShapeincompiler/luci/service/How about introduce new file name
compiler/luci/service/src/logHelper.hand making one implementation?