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Arm-Runtime-for-YOLO26-on-Raspberry-Pi-5 / src /runtime /patches /xnnpack-graph-api-pruning.patch
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diff --git a/sources/xnnpack/include/xnnpack.h b/sources/xnnpack/include/xnnpack.h
index 1b5d0dd..0818889 100644
--- a/sources/xnnpack/include/xnnpack.h
+++ b/sources/xnnpack/include/xnnpack.h
@@ -604,19 +604,6 @@ enum xnn_status xnn_define_unary(
uint32_t output_id,
uint32_t flags);
-/// Define a Convert Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Convert Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_convert(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a 2D Convolution Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -812,84 +799,6 @@ enum xnn_status xnn_define_depth_to_space(
uint32_t block_size,
uint32_t flags);
-/// Define a 1D Global Average Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 1D Global Average Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_average_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2D Global Average Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
-/// dimensions.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 2D Global Average Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_average_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 1D Global Sum Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 1D Global Sum Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_sum_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2D Global Sum Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
-/// dimensions.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 2D Global Sum Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_sum_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a 2D Average Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1171,60 +1080,6 @@ enum xnn_status xnn_define_binary(
uint32_t output_id,
uint32_t flags);
-/// Define a 2-Input Add Node and add it to a Subgraph.
-///
-/// The 2-Input Add Node computes elementwise addition of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Add Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_add2(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2-Input Multiply Node and add it to a Subgraph.
-///
-/// The 2-Input Multiply Node computes elementwise multiplication of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Multiply Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_multiply2(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
// Cap operations applied to logits (Q * K) of attention operator.
enum xnn_attention_logits_cap_type {
// No capping.
@@ -1238,135 +1093,6 @@ struct xnn_attention_logits_cap_tanh_params {
float cap;
};
-/// Define a Subtract Node and add it to a Subgraph.
-///
-/// The Subtract Node computes elementwise subtraction of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Subtract Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_subtract(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Divide Node and add it to a Subgraph.
-///
-/// The Divide Node computes elementwise division of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Divide Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_divide(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2-Input Maximum Node and add it to a Subgraph.
-///
-/// The 2-Input Maximum Node computes elementwise maximum of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Maximum Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_maximum2(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2-Input Minimum Node and add it to a Subgraph.
-///
-/// The 2-Input Minimum Node computes elementwise minimum of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Minimum Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_minimum2(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Squared Difference Node and add it to a Subgraph.
-///
-/// The Squared Difference Node computes elementwise squared difference of two tensor inputs with numpy broadcasting
-/// rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Squared Difference Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_squared_difference(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Constant Pad Node with static padding specification and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1466,26 +1192,6 @@ enum xnn_status xnn_define_split_dim( //
uint32_t output_id, //
uint32_t flags);
-/// Define a Mean Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param num_reduction_axes - number of axes along which mean is computed.
-/// @param reduction_axes - axes along which mean is computed.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with at least
-/// @a num_reduction_axes dimensions defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor defined in the
-/// @a subgraph with @a num_reduction_axes fewer dimensions than the input tensor (if
-/// XNN_FLAG_KEEP_DIMS is not specified), or has same dimension rank but the dimension at
-/// @a reduction_axes reduced to 1 (if XNN_FLAG_KEEP_DIMS is specified).
-/// @param flags - binary features of the Mean Node. The only currently supported value is XNN_FLAG_KEEP_DIMS
-XNN_DEPRECATED enum xnn_status xnn_define_static_mean(
- xnn_subgraph_t subgraph,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
enum xnn_reduce_operator {
xnn_reduce_invalid = -1,
xnn_reduce_sum,
@@ -1496,27 +1202,6 @@ enum xnn_reduce_operator {
xnn_reduce_min,
};
-/// Define a Reduce Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param num_reduction_axes - number of axes along which reduce is computed.
-/// @param reduction_axes - axes along which reduce is computed.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with at least
-/// @a num_reduction_axes dimensions defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor defined in the
-/// @a subgraph with @a num_reduction_axes fewer dimensions than the input tensor (if
-/// XNN_FLAG_KEEP_DIMS is not specified), or has same dimension rank but the dimension at
-/// @a reduction_axes reduced to 1 (if XNN_FLAG_KEEP_DIMS is specified).
-/// @param flags - binary features of the Reduce Node. The only currently supported value is XNN_FLAG_KEEP_DIMS
-enum xnn_status xnn_define_static_reduce(
- xnn_subgraph_t subgraph,
- enum xnn_reduce_operator reduce_operator_type,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Reduce Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1690,22 +1375,6 @@ XNN_DEPRECATED enum xnn_status xnn_define_concatenate5(
uint32_t output_id,
uint32_t flags);
-/// Define a Copy Sign Node and add it to a Subgraph.
-///
-/// The Copy Sign Node copies the sign of the second input to the first input.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be defined in the @a subgraph.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor.
-/// @param flags - binary features of the Copy Sign Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_copysign(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Copy Node and add it to a Subgraph.
///
/// The Copy Node copies an input tensor to an output tensor.
@@ -1746,95 +1415,6 @@ enum xnn_status xnn_define_even_split(
const uint32_t* outputs,
uint32_t flags);
-/// Define a 2-Output Split Node and add it to a Subgraph.
-///
-/// The 2-Output Split Node splits an input tensor into two output tensors along a specified axis evenly.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param split_dim - the dimension to split the input tensor along. If this is less than zero, the number of
-/// dimensions is added to it.
-/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
-/// subgraph.
-/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
-/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
-/// of the second output. The split_dim dimension is half of the input's split_dim.
-/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the first output. The split_dim dimension is half of the input's split_dim.
-/// @param flags - binary features of the Split Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_even_split2(
- xnn_subgraph_t subgraph,
- int32_t split_dim,
- uint32_t input_id,
- uint32_t output1_id,
- uint32_t output2_id,
- uint32_t flags);
-
-/// Define a 3-Output Split Node and add it to a Subgraph.
-///
-/// The 3-Output Split Node splits an input tensor into three output tensors along a specified axis evenly.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param split_dim - the dimension to split the input tensor along. If this is less than zero, the number of
-/// dimensions is added to it.
-/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
-/// subgraph.
-/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
-/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
-/// of the second and third output. The split_dim dimension is one third of the input's split_dim.
-/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the first and third output. The split_dim dimension is one third of the input's
-/// split_dim.
-/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the second and third output. The split_dim dimension is one third of the input's
-/// split_dim.
-/// @param flags - binary features of the Split Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_even_split3(
- xnn_subgraph_t subgraph,
- int32_t split_dim,
- uint32_t input_id,
- uint32_t output1_id,
- uint32_t output2_id,
- uint32_t output3_id,
- uint32_t flags);
-
-/// Define a 4-Output Split Node and add it to a Subgraph.
-///
-/// The 4-Output Split Node splits an input tensor into four output tensors along a specified axis evenly.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param split_dim - the dimension to split the input tensor along. If this is less than zero, the number of
-/// dimensions is added to it.
-/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
-/// subgraph.
-/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
-/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
-/// of the other output tensors. The split_dim dimension is one fourth of the input's split_dim.
-/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
-/// split_dim.
-/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
-/// split_dim.
-/// @param output4_id - Value ID for the fourth output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
-/// split_dim.
-/// @param flags - binary features of the Split Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_even_split4(
- xnn_subgraph_t subgraph,
- int32_t split_dim,
- uint32_t input_id,
- uint32_t output1_id,
- uint32_t output2_id,
- uint32_t output3_id,
- uint32_t output4_id,
- uint32_t flags);
-
/// Define a Reshape Node with static shape specification and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1889,23 +1469,6 @@ enum xnn_status xnn_define_static_resize_bilinear_2d(
uint32_t output_id,
uint32_t flags);
-/// Define a PReLU (Parametric ReLU) Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
-/// with [N, H, W, channels] dimensions.
-/// @param slope_id - Value ID for the slope tensor. The slope tensor must be a 1D tensor defined in the @a subgraph with
-/// either [1] or [channels] dimensions.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
-/// with [N, H, W, channels] dimensions.
-/// @param flags - binary features of the PReLU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_prelu(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t slope_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a RoPE (Rotary Positional Embeddings) Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1925,32 +1488,6 @@ enum xnn_status xnn_define_rope(
uint32_t output_id,
uint32_t flags);
-/// Define a Abs Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Abs Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_abs(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Bankers' Rounding Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Bankers' Rounding Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_bankers_rounding(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Batch Matrix Multiply Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1978,157 +1515,6 @@ enum xnn_status xnn_define_batch_matrix_multiply(
uint32_t output_id,
uint32_t flags);
-/// Define a Ceiling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Ceiling Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_ceiling(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Clamp Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Clamp Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_clamp(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define an ELU (Exponential Linear Unit) Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param alpha - scale factor for negative output elements.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the ELU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_elu(
- xnn_subgraph_t subgraph,
- float alpha,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Exp Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Exp Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_exp(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Floor Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Floor Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_floor(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define an GELU (Gaussian Error Linear Unit) Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the GELU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_gelu(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a HardSwish Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the HardSwish Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_hardswish(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Leaky ReLU Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param negative_slope - scale factor for negative input elements.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Leaky ReLU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_leaky_relu(
- xnn_subgraph_t subgraph,
- float negative_slope,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Log Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Log Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_log(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Negate Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Negate Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_negate(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Sigmoid Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Sigmoid Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_sigmoid(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a SoftMax Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -2164,48 +1550,6 @@ enum xnn_status xnn_define_space_to_depth_2d(
uint32_t output_id,
uint32_t flags);
-/// Define a Square Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Square Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_square(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Square Root Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Square Root Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_square_root(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Reciprocal Square Root Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be
-/// defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be
-/// defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Square Root Node. No supported flags
-/// are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_reciprocal_square_root(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
enum xnn_status xnn_define_static_slice(
xnn_subgraph_t subgraph,
size_t num_dims,
@@ -2282,19 +1626,6 @@ enum xnn_status xnn_define_static_transpose(
uint32_t output_id,
uint32_t flags);
-/// Define a Tanh Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Tanh Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_tanh(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Weights cache can be finalized in these ways:
enum xnn_weights_cache_finalization_kind {
/// Weights cache is finalized, no insert operations into the weights cache is allowed, even if the "inserted"
diff --git a/sources/xnnpack/src/subgraph/deprecated.c b/sources/xnnpack/src/subgraph/deprecated.c
index 650cbf9..1feb9a3 100644
--- a/sources/xnnpack/src/subgraph/deprecated.c
+++ b/sources/xnnpack/src/subgraph/deprecated.c
@@ -5,337 +5,6 @@
#include "include/xnnpack.h"
#include "src/xnnpack/subgraph.h"
-enum xnn_status xnn_define_add2(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_add, &params, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_subtract(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_subtract, &params, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_multiply2(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_multiply, &params, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_divide(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_divide, &params, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_maximum2(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_maximum, NULL, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_minimum2(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_minimum, NULL, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_squared_difference(xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_squared_difference, NULL,
- input1_id, input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_copysign(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_copysign, NULL, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_prelu(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_prelu, NULL,
- input1_id, input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_static_mean(xnn_subgraph_t subgraph,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_static_reduce(subgraph, xnn_reduce_mean, num_reduction_axes,
- reduction_axes, input_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_global_average_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
-
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
-
- reduction_axes[0] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = (xnn_define_static_reduce(
- subgraph, xnn_reduce_mean, 1, reduction_axes, input_id,
- output_id, flags));
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_global_average_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
-
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
-
- reduction_axes[0] = input_value->shape.num_dims - 3;
- reduction_axes[1] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = xnn_define_static_reduce(
- subgraph, xnn_reduce_mean, 2, reduction_axes, input_id,
- output_id, flags);
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_global_sum_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
- reduction_axes[0] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = xnn_define_static_reduce(
- subgraph, xnn_reduce_sum, 1, reduction_axes, input_id,
- output_id, flags);
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_global_sum_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
- reduction_axes[0] = input_value->shape.num_dims - 3;
- reduction_axes[1] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = xnn_define_static_reduce(
- subgraph, xnn_reduce_sum, 2, reduction_axes, input_id,
- output_id, flags);
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_convert(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_convert, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_abs(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_abs, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_bankers_rounding(xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_bankers_rounding, NULL, input_id,
- output_id, flags);
-}
-
-enum xnn_status xnn_define_ceiling(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_ceiling, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_clamp(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- union xnn_unary_params params;
- params.clamp.min = output_min;
- params.clamp.max = output_max;
- return xnn_define_unary(subgraph, xnn_unary_clamp, &params, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_elu(xnn_subgraph_t subgraph, float alpha,
- uint32_t input_id, uint32_t output_id,
- uint32_t flags) {
- union xnn_unary_params params;
- params.elu.alpha = alpha;
- return xnn_define_unary(subgraph, xnn_unary_elu, &params, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_exp(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_exp, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_floor(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_floor, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_gelu(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_gelu, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_hardswish(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_hardswish, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_leaky_relu(xnn_subgraph_t subgraph,
- float negative_slope, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- union xnn_unary_params params;
- params.leaky_relu.negative_slope = negative_slope;
- return xnn_define_unary(subgraph, xnn_unary_leaky_relu, &params, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_log(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_log, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_negate(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_negate, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_sigmoid(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_sigmoid, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_square(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_square, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_square_root(xnn_subgraph_t subgraph,
- uint32_t input_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_square_root, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_reciprocal_square_root(xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_reciprocal_square_root, NULL, input_id,
- output_id, flags);
-}
-
-enum xnn_status xnn_define_tanh(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_tanh, NULL, input_id, output_id,
- flags);
-}
-
enum xnn_status xnn_define_concatenate2(xnn_subgraph_t subgraph, int32_t axis, uint32_t input1_id,
uint32_t input2_id, uint32_t output_id, uint32_t flags) {
const uint32_t inputs_id[2] = {input1_id, input2_id};
@@ -366,23 +35,3 @@ enum xnn_status xnn_define_concatenate5(xnn_subgraph_t subgraph, int32_t axis, u
return xnn_define_concatenate(subgraph, axis, /*num_inputs=*/5,
inputs_id, output_id, flags);
}
-
-enum xnn_status xnn_define_even_split2(xnn_subgraph_t subgraph, int32_t split_dim, uint32_t input_id,
- uint32_t output1_id, uint32_t output2_id, uint32_t flags) {
- const uint32_t outputs_id[2] = {output1_id, output2_id};
- return xnn_define_even_split(subgraph, split_dim, input_id, /*num_outputs=*/2, outputs_id, flags);
-}
-
-enum xnn_status xnn_define_even_split3(xnn_subgraph_t subgraph, int32_t split_dim, uint32_t input_id,
- uint32_t output1_id, uint32_t output2_id,
- uint32_t output3_id, uint32_t flags) {
- const uint32_t outputs_id[3] = {output1_id, output2_id, output3_id};
- return xnn_define_even_split(subgraph, split_dim, input_id, /*num_outputs=*/3, outputs_id, flags);
-}
-
-enum xnn_status xnn_define_even_split4(xnn_subgraph_t subgraph, int32_t split_dim, uint32_t input_id,
- uint32_t output1_id, uint32_t output2_id,
- uint32_t output3_id, uint32_t output4_id, uint32_t flags) {
- const uint32_t outputs_id[4] = {output1_id, output2_id, output3_id, output4_id};
- return xnn_define_even_split(subgraph, split_dim, input_id, /*num_outputs=*/4, outputs_id, flags);
-}
diff --git a/sources/xnnpack/src/subgraph/max-pooling-2d.c b/sources/xnnpack/src/subgraph/max-pooling-2d.c
index 4a98c26..159d317 100644
--- a/sources/xnnpack/src/subgraph/max-pooling-2d.c
+++ b/sources/xnnpack/src/subgraph/max-pooling-2d.c
@@ -14,7 +14,6 @@
#include "src/xnnpack/node-type.h"
#include "src/xnnpack/operator-type.h"
#include "src/xnnpack/operator.h"
-#include "src/xnnpack/requantization.h"
#include "src/xnnpack/subgraph-validation.h"
#include "src/xnnpack/subgraph.h"
#include <pthreadpool.h>
@@ -28,101 +27,27 @@ static enum xnn_status create_max_pooling_operator(
{
assert(node->num_inputs == 1);
assert(node->num_outputs == 1);
- const uint32_t output_id = node->outputs[0];
- assert(output_id != XNN_INVALID_VALUE_ID);
- assert(output_id < num_values);
-
const uint32_t input_id = node->inputs[0];
assert(input_id < num_values);
const struct xnn_runtime_value *input_value = &values[input_id];
- const enum xnn_datatype datatype = input_value->datatype;
-
- enum xnn_status status;
- switch (datatype) {
- case xnn_datatype_fp16:
- status = xnn_create_max_pooling2d_nhwc_f16(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- node->activation.output_min,
- node->activation.output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- case xnn_datatype_fp32:
- status = xnn_create_max_pooling2d_nhwc_f32(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- node->activation.output_min,
- node->activation.output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- case xnn_datatype_qint8:
- {
- const float output_scale = values[output_id].quantization.scale;
- const int32_t output_zero_point = values[output_id].quantization.zero_point;
- const int8_t output_min = xnn_qs8_quantize(node->activation.output_min, output_scale, output_zero_point);
- const int8_t output_max = xnn_qs8_quantize(node->activation.output_max, output_scale, output_zero_point);
- status = xnn_create_max_pooling2d_nhwc_s8(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- output_min,
- output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- }
- case xnn_datatype_quint8:
- {
- const float output_scale = values[output_id].quantization.scale;
- const int32_t output_zero_point = values[output_id].quantization.zero_point;
- const uint8_t output_min = xnn_qu8_quantize(node->activation.output_min, output_scale, output_zero_point);
- const uint8_t output_max = xnn_qu8_quantize(node->activation.output_max, output_scale, output_zero_point);
- status = xnn_create_max_pooling2d_nhwc_u8(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- output_min,
- output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- }
- default:
- XNN_UNREACHABLE;
+ if (input_value->datatype != xnn_datatype_fp16) {
+ return xnn_status_invalid_parameter;
}
- return status;
+ return xnn_create_max_pooling2d_nhwc_f16(
+ node->params.pooling_2d.padding_top,
+ node->params.pooling_2d.padding_right,
+ node->params.pooling_2d.padding_bottom,
+ node->params.pooling_2d.padding_left,
+ node->params.pooling_2d.pooling_height,
+ node->params.pooling_2d.pooling_width,
+ node->params.pooling_2d.stride_height,
+ node->params.pooling_2d.stride_width,
+ node->params.pooling_2d.dilation_height,
+ node->params.pooling_2d.dilation_width,
+ node->activation.output_min,
+ node->activation.output_max,
+ node->flags,
+ &opdata->operator_objects[0]);
}
static enum xnn_status reshape_max_pooling_operator(
@@ -144,64 +69,21 @@ static enum xnn_status reshape_max_pooling_operator(
const size_t input_width = values[input_id].shape.dim[2];
const size_t channels = values[input_id].shape.dim[3];
- enum xnn_status status = xnn_status_invalid_state;
size_t output_height, output_width;
- switch (opdata->operator_objects[0]->type) {
- case xnn_operator_type_max_pooling_nhwc_f16:
- status = xnn_reshape_max_pooling2d_nhwc_f16(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- case xnn_operator_type_max_pooling_nhwc_f32:
- status = xnn_reshape_max_pooling2d_nhwc_f32(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- case xnn_operator_type_max_pooling_nhwc_s8:
- status = xnn_reshape_max_pooling2d_nhwc_s8(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- case xnn_operator_type_max_pooling_nhwc_u8:
- status = xnn_reshape_max_pooling2d_nhwc_u8(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- default:
- XNN_UNREACHABLE;
+ if (opdata->operator_objects[0]->type != xnn_operator_type_max_pooling_nhwc_f16) {
+ return xnn_status_invalid_state;
}
+ const enum xnn_status status = xnn_reshape_max_pooling2d_nhwc_f16(
+ opdata->operator_objects[0],
+ batch_size,
+ input_height,
+ input_width,
+ channels,
+ /*input_pixel_stride=*/channels,
+ /*output_pixel_stride=*/channels,
+ &output_height,
+ &output_width,
+ threadpool);
if (status != xnn_status_success) {
return status;
@@ -243,30 +125,13 @@ static enum xnn_status setup_max_pooling_operator(
void* output_data = output_value->data;
assert(output_data != NULL);
- switch (opdata->operator_objects[0]->type) {
- case xnn_operator_type_max_pooling_nhwc_f16:
- return xnn_setup_max_pooling2d_nhwc_f16(
- opdata->operator_objects[0],
- input_data,
- output_data);
- case xnn_operator_type_max_pooling_nhwc_f32:
- return xnn_setup_max_pooling2d_nhwc_f32(
- opdata->operator_objects[0],
- input_data,
- output_data);
- case xnn_operator_type_max_pooling_nhwc_s8:
- return xnn_setup_max_pooling2d_nhwc_s8(
- opdata->operator_objects[0],
- input_data,
- output_data);
- case xnn_operator_type_max_pooling_nhwc_u8:
- return xnn_setup_max_pooling2d_nhwc_u8(
- opdata->operator_objects[0],
- input_data,
- output_data);
- default:
- XNN_UNREACHABLE;
+ if (opdata->operator_objects[0]->type != xnn_operator_type_max_pooling_nhwc_f16) {
+ return xnn_status_invalid_state;
}
+ return xnn_setup_max_pooling2d_nhwc_f16(
+ opdata->operator_objects[0],
+ input_data,
+ output_data);
}
enum xnn_status xnn_define_max_pooling_2d(
@@ -345,18 +210,12 @@ enum xnn_status xnn_define_max_pooling_2d(
return status;
}
- switch (input_value->datatype) {
- case xnn_datatype_fp16:
- case xnn_datatype_fp32:
- case xnn_datatype_qint8:
- case xnn_datatype_quint8:
- break;
- default:
- xnn_log_error(
- "failed to define %s operator with input ID #%" PRIu32 ": unsupported Value datatype %s (%d)",
- xnn_node_type_to_string(xnn_node_type_max_pooling_2d), input_id,
- xnn_datatype_to_string(input_value->datatype), input_value->datatype);
- return xnn_status_invalid_parameter;
+ if (input_value->datatype != xnn_datatype_fp16) {
+ xnn_log_error(
+ "failed to define %s operator with input ID #%" PRIu32 ": unsupported Value datatype %s (%d); only FP16 is supported",
+ xnn_node_type_to_string(xnn_node_type_max_pooling_2d), input_id,
+ xnn_datatype_to_string(input_value->datatype), input_value->datatype);
+ return xnn_status_invalid_parameter;
}
status = xnn_subgraph_check_output_node_id(xnn_node_type_max_pooling_2d, output_id, subgraph->num_values);
@@ -370,21 +229,12 @@ enum xnn_status xnn_define_max_pooling_2d(
return status;
}
- switch (output_value->datatype) {
- case xnn_datatype_fp16:
- break;
- case xnn_datatype_fp32:
- break;
- case xnn_datatype_qint8:
- break;
- case xnn_datatype_quint8:
- break;
- default:
- xnn_log_error(
- "failed to define %s operator with output ID #%" PRIu32 ": unsupported Value datatype %s (%d)",
- xnn_node_type_to_string(xnn_node_type_max_pooling_2d), output_id,
- xnn_datatype_to_string(output_value->datatype), output_value->datatype);
- return xnn_status_invalid_parameter;
+ if (output_value->datatype != xnn_datatype_fp16) {
+ xnn_log_error(
+ "failed to define %s operator with output ID #%" PRIu32 ": unsupported Value datatype %s (%d); only FP16 is supported",
+ xnn_node_type_to_string(xnn_node_type_max_pooling_2d), output_id,
+ xnn_datatype_to_string(output_value->datatype), output_value->datatype);
+ return xnn_status_invalid_parameter;
}
status = xnn_subgraph_check_datatype_matches(
diff --git a/sources/xnnpack/src/subgraph/static-reduce.c b/sources/xnnpack/src/subgraph/static-reduce.c
index 766653a..304f3f9 100644
--- a/sources/xnnpack/src/subgraph/static-reduce.c
+++ b/sources/xnnpack/src/subgraph/static-reduce.c
@@ -245,24 +245,6 @@ static enum xnn_status setup_reduce_operator(
return xnn_setup_reduce_nd(opdata->operator_objects[0], workspace, input_data, output_data);
}
-enum xnn_status xnn_define_static_reduce(
- xnn_subgraph_t subgraph,
- enum xnn_reduce_operator reduce_operator,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- int64_t signed_reduction_axes[XNN_MAX_TENSOR_DIMS];
- for (int i = 0; i < num_reduction_axes; i++) {
- signed_reduction_axes[i] = reduction_axes[i];
- }
- return xnn_define_static_reduce_v2(subgraph, reduce_operator,
- num_reduction_axes, signed_reduction_axes,
- input_id, output_id, flags);
-}
-
enum xnn_status xnn_define_static_reduce_v2(
xnn_subgraph_t subgraph, enum xnn_reduce_operator reduce_operator,
size_t num_reduction_axes, const int64_t* reduction_axes, uint32_t input_id,
diff --git a/sources/xnnpack/src/subgraph/unary.c b/sources/xnnpack/src/subgraph/unary.c
index ec73ba0..e1bb956 100644
--- a/sources/xnnpack/src/subgraph/unary.c
+++ b/sources/xnnpack/src/subgraph/unary.c
@@ -390,10 +390,26 @@ enum xnn_status xnn_define_unary(
return status;
}
+ if (type == xnn_unary_log) {
+ xnn_log_error("failed to define unary log node: this runtime does not support unary log");
+ return xnn_status_unsupported_parameter;
+ }
+ if (type == xnn_unary_approxgelu) {
+ xnn_log_error("failed to define approximate GELU node: this runtime does not support approximate GELU");
+ return xnn_status_unsupported_parameter;
+ }
+ if (type == xnn_unary_gelu) {
+ xnn_log_error("failed to define GELU node: this runtime does not support GELU");
+ return xnn_status_unsupported_parameter;
+ }
+ if (type == xnn_unary_elu) {
+ xnn_log_error("failed to define ELU node: this runtime does not support ELU");
+ return xnn_status_unsupported_parameter;
+ }
+
switch (type) {
case xnn_unary_clamp:
case xnn_unary_leaky_relu:
- case xnn_unary_elu:
if (!params) {
xnn_log_error("failed to define %s node with input ID #%" PRIu32
" and output ID #%" PRIu32 ": missing clamp params",