Add PostOp support to GEMM and CLGEMM operators and functions Part 2
* Implement PostOp interface changes
* Remove spaces around "=" in TypePrinter
Partially resolves COMPMID-4435
Signed-off-by: SiCongLi <sicong.li@arm.com>
Change-Id: If1e2280554030a0f635e73339a2e86987f6dc41b
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6484
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sheri Zhang <sheri.zhang@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index bfe85ea..bff672c 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -30,6 +30,7 @@
#include "arm_compute/core/Size3D.h"
#include "arm_compute/core/Strides.h"
#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/experimental/IPostOp.h"
#include "arm_compute/core/utils/misc/Macros.h"
#include "support/Bfloat16.h"
#include "support/Half.h"
@@ -1963,6 +1964,7 @@
bool export_to_cl_image{ false }; /**< True if the reshaped rhs has to be exported to cl_image. n0 must be equal to 4 */
};
+class ITensorInfo;
/** GEMM information class. This class stores the necessary information to compute GEMM functions
*
* This object also contains the information about how matrix A and matrix B have been reshaped
@@ -1984,7 +1986,8 @@
_fp_mixed_precision(false),
_broadcast_bias(false),
_pretranspose_B(true),
- _activation_info()
+ _activation_info(),
+ _post_ops()
{
}
/** Constructor
@@ -2002,10 +2005,11 @@
* @param[in] fast_math (Optional) Use a data type of shorter width to improve performance
* @param[in] broadcast_bias (Optional) Broadcast the shape of the bias tensor from a vector to a matrix.
* @param[in] activation_info (Optional) Activation to apply after the matrix multiplication
+ * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation.
*/
GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 0, bool reinterpret_input_as_3d = false, bool retain_internal_weights = false,
GEMMLowpOutputStageInfo gemmlowp_output_stage = GEMMLowpOutputStageInfo(), bool fp_mixed_precision = false, bool fast_math = false, bool broadcast_bias = false,
- const ActivationLayerInfo &activation_info = ActivationLayerInfo()) noexcept
+ const ActivationLayerInfo &activation_info = ActivationLayerInfo(), const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *>()) noexcept
: _is_a_reshaped(is_a_reshaped),
_is_b_reshaped(is_b_reshaped),
_reshape_b_only_on_first_run(reshape_b_only_on_first_run),
@@ -2017,7 +2021,8 @@
_fp_mixed_precision(fp_mixed_precision),
_broadcast_bias(broadcast_bias),
_pretranspose_B(reshape_b_only_on_first_run),
- _activation_info(activation_info)
+ _activation_info(activation_info),
+ _post_ops(post_ops)
{
}
/** Flag which specifies if the matrix A has been reshaped
@@ -2142,20 +2147,37 @@
{
_activation_info = activation_info;
}
+ /** Post operations to apply after the matrix multiplication
+ *
+ * @return experimental::PostOpList object
+ */
+ const experimental::PostOpList<ITensorInfo *> &post_ops() const
+ {
+ return _post_ops;
+ }
+ /** Set post ops
+ *
+ * @param[in] post_ops experimental::PostOpList object to set
+ */
+ void set_post_ops(const experimental::PostOpList<ITensorInfo *> &post_ops)
+ {
+ _post_ops = post_ops;
+ }
private:
- bool _is_a_reshaped;
- bool _is_b_reshaped;
- bool _reshape_b_only_on_first_run;
- int _depth_output_gemm3d;
- bool _reinterpret_input_as_3d;
- bool _retain_internal_weights;
- GEMMLowpOutputStageInfo _gemmlowp_output_stage;
- bool _fast_math;
- bool _fp_mixed_precision;
- bool _broadcast_bias;
- bool _pretranspose_B;
- ActivationLayerInfo _activation_info;
+ bool _is_a_reshaped;
+ bool _is_b_reshaped;
+ bool _reshape_b_only_on_first_run;
+ int _depth_output_gemm3d;
+ bool _reinterpret_input_as_3d;
+ bool _retain_internal_weights;
+ GEMMLowpOutputStageInfo _gemmlowp_output_stage;
+ bool _fast_math;
+ bool _fp_mixed_precision;
+ bool _broadcast_bias;
+ bool _pretranspose_B;
+ ActivationLayerInfo _activation_info;
+ experimental::PostOpList<ITensorInfo *> _post_ops;
};
/** Winograd information */
diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
index 0f092bd..38a4019 100644
--- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
@@ -26,6 +26,7 @@
#include "arm_compute/core/CL/CLCompileContext.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/core/experimental/IPostOp.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
@@ -118,9 +119,11 @@
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
* available which may introduce a drop of accuracy as well. Default is false
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
+ * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation.
*/
void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
- const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1);
+ const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1,
+ const experimental::PostOpList<ICLTensor *> &post_ops = experimental::PostOpList<ICLTensor *> {});
/** Set the input and output tensors.
*
* @param[in] compile_context The compile context to be used.
@@ -140,10 +143,11 @@
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
* available which may introduce a drop of accuracy as well. Default is false
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
+ * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation.
*/
void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false,
- unsigned int num_groups = 1);
+ unsigned int num_groups = 1, const experimental::PostOpList<ICLTensor *> &post_ops = experimental::PostOpList<ICLTensor *> {});
/** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayer
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
@@ -162,12 +166,13 @@
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
* available which may introduce a drop of accuracy as well. Default is false
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
+ * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false,
- unsigned int num_groups = 1);
+ unsigned int num_groups = 1, const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *> {});
/** Static function to check if given info will return the convolution called by @ref CLConvolutionLayer
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
diff --git a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
index d7a4e7f..9918a61 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
@@ -24,6 +24,7 @@
#ifndef ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H
#define ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H
+#include "arm_compute/core/experimental/IPostOp.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/CLTypes.h"
#include "arm_compute/runtime/IFunction.h"
@@ -93,9 +94,11 @@
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
+ * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation.
*/
void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
- const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);
+ const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1,
+ const experimental::PostOpList<ICLTensor *> &post_ops = experimental::PostOpList<ICLTensor *> {});
/** Set the input and output tensors.
*
* @param[in] compile_context The compile context to be used.
@@ -114,10 +117,12 @@
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
+ * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation.
*/
void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo(),
- const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);
+ const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1,
+ const experimental::PostOpList<ICLTensor *> &post_ops = experimental::PostOpList<ICLTensor *> {});
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer.
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
@@ -135,11 +140,13 @@
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
+ * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);
+ const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1,
+ const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *> {});
// Inherited methods overridden:
void run() override;
diff --git a/arm_compute/runtime/FunctionDescriptors.h b/arm_compute/runtime/FunctionDescriptors.h
index 07a8f66..face8a6 100644
--- a/arm_compute/runtime/FunctionDescriptors.h
+++ b/arm_compute/runtime/FunctionDescriptors.h
@@ -57,20 +57,22 @@
{
Conv2dInfo() = default;
- Conv2dInfo(const PadStrideInfo &conv_info,
- const Size2D &dilation,
- const ActivationLayerInfo &act_info,
- bool enable_fast_math,
- unsigned int num_groups)
- : conv_info(conv_info), dilation(dilation), act_info(act_info), enable_fast_math(enable_fast_math), num_groups(num_groups)
+ Conv2dInfo(const PadStrideInfo &conv_info,
+ const Size2D &dilation,
+ const ActivationLayerInfo &act_info,
+ bool enable_fast_math,
+ unsigned int num_groups,
+ const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *> {})
+ : conv_info(conv_info), dilation(dilation), act_info(act_info), enable_fast_math(enable_fast_math), num_groups(num_groups), post_ops(post_ops)
{
}
- PadStrideInfo conv_info{};
- Size2D dilation{ 1U, 1U };
- ActivationLayerInfo act_info{};
- bool enable_fast_math{ false };
- unsigned int num_groups{ 1 };
+ PadStrideInfo conv_info{};
+ Size2D dilation{ 1U, 1U };
+ ActivationLayerInfo act_info{};
+ bool enable_fast_math{ false };
+ unsigned int num_groups{ 1 };
+ experimental::PostOpList<ITensorInfo *> post_ops{};
};
/** Descriptor used by the 3d Convolution function */
diff --git a/src/gpu/cl/operators/ClConv2d.cpp b/src/gpu/cl/operators/ClConv2d.cpp
index 7fe0de7..d633c8f 100644
--- a/src/gpu/cl/operators/ClConv2d.cpp
+++ b/src/gpu/cl/operators/ClConv2d.cpp
@@ -92,6 +92,7 @@
case ConvolutionMethod::WINOGRAD:
{
ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1);
+ ARM_COMPUTE_ERROR_ON(conv2d_info.post_ops.size() > 0);
auto f = std::make_unique<ClWinogradConv2d>();
f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math);
_operator = std::move(f);
@@ -100,6 +101,7 @@
case ConvolutionMethod::DIRECT:
{
ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1);
+ ARM_COMPUTE_ERROR_ON(conv2d_info.post_ops.size() > 0);
auto f = std::make_unique<ClDirectConv2d>();
f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info);
_operator = std::move(f);
@@ -133,6 +135,7 @@
{
//Validate Winograd
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClWinogradConv2d is not supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClWinogradConv2d does not support PostOps");
ARM_COMPUTE_RETURN_ON_ERROR(ClWinogradConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math));
break;
}
@@ -140,6 +143,7 @@
{
// Validate direct convolution layer
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClDirectConv2d is not supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClDirectConv2d does not support PostOps");
ARM_COMPUTE_RETURN_ON_ERROR(ClDirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info));
break;
}
diff --git a/src/gpu/cl/operators/ClGemm.cpp b/src/gpu/cl/operators/ClGemm.cpp
index d2d0f8f..e05256e 100644
--- a/src/gpu/cl/operators/ClGemm.cpp
+++ b/src/gpu/cl/operators/ClGemm.cpp
@@ -38,6 +38,7 @@
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "arm_compute/runtime/ITensorAllocator.h"
+#include "arm_compute/core/experimental/IPostOp.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/MemoryHelpers.h"
#include "src/core/utils/helpers/float_ops.h"
@@ -64,7 +65,7 @@
{
inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type)
{
- return kernel_type == CLGEMMKernelType::NATIVE? false : true;
+ return kernel_type == CLGEMMKernelType::NATIVE ? false : true;
}
//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type
inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run, bool constant_weights)
@@ -203,6 +204,7 @@
void ClGemm::configure_native(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
const GEMMInfo &gemm_info)
{
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
DataType data_type = a->data_type();
bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
@@ -252,6 +254,7 @@
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = broadcast_bias;
kernel_info.activation_info = gemm_info.activation_info();
+ kernel_info.post_ops = gemm_info.post_ops();
// Set the target for the kernels
_reshape_lhs_kernel->set_target(gpu_target);
@@ -278,6 +281,7 @@
void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
const GEMMInfo &gemm_info)
{
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
DataType data_type = a->data_type();
bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
@@ -330,6 +334,7 @@
{
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_UNUSED(output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
// Get the GPU target
const GPUTarget gpu_target = CLScheduler::get().target();
@@ -386,6 +391,7 @@
kernel_info.reinterpret_input_as_3d = false;
kernel_info.broadcast_bias = broadcast_bias;
kernel_info.activation_info = gemm_info.activation_info();
+ kernel_info.post_ops = gemm_info.post_ops();
GEMMLHSMatrixInfo lhs_info;
GEMMRHSMatrixInfo rhs_info;
@@ -412,6 +418,7 @@
{
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_UNUSED(output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.post_ops().size() > 0, "PostOps are not supported in this kernel");
TensorInfo tmp_b_info{};
@@ -588,8 +595,10 @@
ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } };
CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false);
}
-
- ITensorPack gemm_reshaped_pack{ { ACL_SRC_0, lhs_reshaped.get() }, { ACL_SRC_1, rhs_reshaped.get() }, { ACL_SRC_2, src2 }, { ACL_DST, dst } };
+ // Copy original tensor pack and overwrite lhs and rhs with reshaped counterparts
+ ITensorPack gemm_reshaped_pack(tensors);
+ gemm_reshaped_pack.add_const_tensor(ACL_SRC_0, lhs_reshaped.get());
+ gemm_reshaped_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get());
if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED)
{
diff --git a/src/gpu/cl/operators/ClGemm.h b/src/gpu/cl/operators/ClGemm.h
index fd53648..e084e53 100644
--- a/src/gpu/cl/operators/ClGemm.h
+++ b/src/gpu/cl/operators/ClGemm.h
@@ -81,8 +81,8 @@
* @param[in] alpha Weight of the matrix product
* @param[in] beta Weight of matrix C
* @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
- * if the reshape of matrix B should happen only for the first run. GEMMInfo also contains information about the reshaping
- * in case matrix A and matrix B have been already transformed.
+ * if the reshape of matrix B should happen only for the first run. GEMMInfo also contains information about the reshaping
+ * in case matrix A and matrix B have been already transformed.
*/
void configure(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
/** Static function to check if given info will lead to a valid configuration
diff --git a/src/gpu/cl/operators/ClGemmConv2d.cpp b/src/gpu/cl/operators/ClGemmConv2d.cpp
index 785f1f1..7db5fa0 100644
--- a/src/gpu/cl/operators/ClGemmConv2d.cpp
+++ b/src/gpu/cl/operators/ClGemmConv2d.cpp
@@ -54,14 +54,14 @@
{
ClGemmConv2d::ClGemmConv2d()
: _weights_reshape_kernel(nullptr), _im2col_kernel(nullptr), _mm_gemm(nullptr), _mm_gemmlowp(nullptr), _col2im_kernel(nullptr), _activation_kernel(nullptr), _im2col_output(), _weights_reshaped(),
- _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _append_bias(false), _is_prepared(false), _aux_mem(AuxTensorIdx::Count)
+ _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _append_bias(false), _is_prepared(false), _use_post_ops(false), _aux_mem(AuxTensorIdx::Count)
{
}
ClGemmConv2d::~ClGemmConv2d() = default;
void ClGemmConv2d::configure_mm(const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
- int gemm_3d_depth, const ActivationLayerInfo &act_info)
+ int gemm_3d_depth, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights);
ARM_COMPUTE_ERROR_THROW_ON(validate_mm(src, weights, biases, dst, gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info));
@@ -76,11 +76,14 @@
false, // fast_math
false, // fp_mixed_precision
true, // broadcast_bias
- act_info); // activation_info
+ act_info, // activation_info
+ post_ops // post ops
+ );
TensorInfo tmp_src{ *src };
if(_is_quantized)
{
+ ARM_COMPUTE_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops");
// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
// Extract and negate input and weights offset
const QuantizationInfo input_quantization_info = src->quantization_info();
@@ -115,7 +118,7 @@
}
Status ClGemmConv2d::validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
- const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info)
+ const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops)
{
const bool is_quantized = is_data_type_quantized_asymmetric(src->data_type());
@@ -129,10 +132,13 @@
false, // fast_math
false, // fp_mixed_precision
true, // broadcast_bias
- act_info); // activation_info
+ act_info, // activation_info
+ post_ops // post ops
+ );
if(is_quantized)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops");
// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
// Extract and negate input and weights offset
const QuantizationInfo input_quantization_info = src->quantization_info();
@@ -183,6 +189,7 @@
// Only for quantize there are few cases where we cannot fuse the activation function in GEMM
_fuse_activation = true;
+ _use_post_ops = conv2d_info.post_ops.size() > 0;
const ITensorInfo *gemm_input_to_use = src;
ITensorInfo *gemm_output_to_use = dst;
@@ -311,10 +318,11 @@
// In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
- configure_mm(compile_context, gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, conv2d_info.act_info);
+ configure_mm(compile_context, gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, conv2d_info.act_info, conv2d_info.post_ops);
if(!_skip_col2im)
{
+ ARM_COMPUTE_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClGemmConv2d does not support post ops with col2im operation"); // Post ops must be performed after every other op
// Set the GPU target for col2im
_col2im_kernel = std::make_unique<opencl::kernels::ClCol2ImKernel>();
_col2im_kernel->set_target(CLScheduler::get().target());
@@ -326,7 +334,8 @@
ARM_COMPUTE_ERROR_ON_MSG((dst->dimension(idx_width) != conv_w) || (dst->dimension(idx_height) != conv_h),
"Output shape does not match the expected one");
- if(!_fuse_activation)
+ // Disable running of activation kernel if post ops are used
+ if(!_fuse_activation && !_use_post_ops)
{
_activation_kernel = std::make_unique<opencl::kernels::ClActivationKernel>();
_activation_kernel->configure(compile_context, dst, nullptr, conv2d_info.act_info);
@@ -376,6 +385,7 @@
&& conv2d_info.conv_info.stride().second == 1);
const bool skip_col2im = data_layout == DataLayout::NHWC;
bool fuse_activation = true;
+ bool use_post_ops = conv2d_info.post_ops.size() > 0;
ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * conv2d_info.num_groups) != src->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
@@ -507,16 +517,19 @@
// In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, conv2d_info.act_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, conv2d_info.act_info,
+ conv2d_info.post_ops));
// Validate Col2Im
if(!skip_col2im)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClGemmConv2d does not support post ops with col2im operation"); // Post ops must be performed after every other op
ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClCol2ImKernel::validate(gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups));
}
- //Validate Activation Layer
- if(!fuse_activation)
+ // Validate Activation Layer
+ // Disable running (thus validation) of activation kernel if post ops are used
+ if(!fuse_activation && !use_post_ops)
{
ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClActivationKernel::validate(dst, nullptr, conv2d_info.act_info));
}
@@ -585,7 +598,8 @@
}
//Run Activation Layer if we cannot fuse in GEMM
- if(!_fuse_activation)
+ // Disable running of activation kernel if post ops are used
+ if(!_fuse_activation && !_use_post_ops)
{
ITensorPack pack =
{
diff --git a/src/gpu/cl/operators/ClGemmConv2d.h b/src/gpu/cl/operators/ClGemmConv2d.h
index 9a5e381..afde7c5 100644
--- a/src/gpu/cl/operators/ClGemmConv2d.h
+++ b/src/gpu/cl/operators/ClGemmConv2d.h
@@ -26,6 +26,7 @@
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/core/experimental/IPostOp.h"
#include "arm_compute/runtime/FunctionDescriptors.h"
#include "src/gpu/cl/ClCompileContext.h"
#include "src/gpu/cl/IClOperator.h"
@@ -132,7 +133,7 @@
*/
void configure_mm(const CLCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
- int gemm_3d_depth, const ActivationLayerInfo &act_info);
+ int gemm_3d_depth, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *> {});
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer matrix multiply routines
*
* @param[in] src Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
@@ -149,7 +150,7 @@
* @return a status
*/
static Status validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
- int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info);
+ int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *> {});
enum AuxTensorIdx
{
@@ -177,6 +178,7 @@
bool _fuse_activation;
bool _append_bias;
bool _is_prepared;
+ bool _use_post_ops;
experimental::MemoryRequirements _aux_mem;
};
diff --git a/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp b/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp
index 234a0df..e8affc0 100644
--- a/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp
+++ b/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp
@@ -29,10 +29,11 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "src/common/utils/Log.h"
#include "src/core/CL/kernels/CLBatchNormalizationLayerKernel.h"
+#include "src/common/utils/Log.h"
+
namespace arm_compute
{
CLBatchNormalizationLayer::CLBatchNormalizationLayer()
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index eaca6ee..d75f54f 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -60,21 +60,26 @@
CLConvolutionLayer::~CLConvolutionLayer() = default;
void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
- const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups)
+ const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups, const experimental::PostOpList<ICLTensor *> &post_ops)
{
- configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups);
+ configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups, post_ops);
}
void CLConvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info,
- const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups)
+ const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups, const experimental::PostOpList<ICLTensor *> &post_ops)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info,
enable_fast_math, num_groups));
- ARM_COMPUTE_LOG_PARAMS(input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups);
+ ARM_COMPUTE_LOG_PARAMS(input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups, post_ops);
- const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, enable_fast_math, num_groups);
+ // Convert post op arguments to ITensorInfo
+ auto transformed_post_ops = experimental::transform_post_op_list_arguments<ICLTensor *, ITensorInfo *>(post_ops, [](auto tensor)
+ {
+ return tensor->info();
+ });
+ const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, enable_fast_math, num_groups, transformed_post_ops);
switch(opencl::ClConv2d::get_convolution_method(input->info(), weights->info(), output->info(), conv2d_info,
weights_info, CLScheduler::get().target()))
@@ -90,6 +95,7 @@
}
case ConvolutionMethod::FFT:
{
+ ARM_COMPUTE_ERROR_ON_MSG(post_ops.size() > 0, "CLFFTConvolutionLayer does not support post ops");
auto f = std::make_unique<CLFFTConvolutionLayer>(_impl->memory_manager);
f->configure(compile_context, input, weights, biases, output, conv_info, act_info, enable_fast_math);
_impl->func = std::move(f);
@@ -102,22 +108,30 @@
if(_impl->op)
{
- _impl->memory_group = MemoryGroup(std::move(_impl->memory_manager));
- _impl->aux_mem_req = _impl->op->workspace();
- _impl->run_pack = { { ACL_SRC_0, input }, { ACL_SRC_1, weights }, { ACL_SRC_2, biases }, { ACL_DST, output } };
- _impl->prep_pack = { { ACL_SRC_1, weights }, { ACL_SRC_2, biases } };
- _impl->workspace = manage_workspace<CLTensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack);
+ _impl->memory_group = MemoryGroup(std::move(_impl->memory_manager));
+ _impl->aux_mem_req = _impl->op->workspace();
+ _impl->run_pack = { { ACL_SRC_0, input }, { ACL_SRC_1, weights }, { ACL_SRC_2, biases }, { ACL_DST, output } };
+ size_t post_op_tensor_index = 0;
+ for(const auto &op : post_ops.get_list())
+ {
+ for(auto &tensor : op->arguments())
+ {
+ _impl->run_pack.add_const_tensor(experimental::get_post_op_arg_type(post_op_tensor_index++), *tensor);
+ }
+ }
+ _impl->prep_pack = { { ACL_SRC_1, weights }, { ACL_SRC_2, biases } };
+ _impl->workspace = manage_workspace<CLTensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack);
}
}
Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups)
+ const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups, const experimental::PostOpList<ITensorInfo *> &post_ops)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported");
const GPUTarget gpu_target = CLScheduler::get().target();
- const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, enable_fast_math, num_groups);
+ const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, enable_fast_math, num_groups, post_ops);
switch(opencl::ClConv2d::get_convolution_method(input, weights, output, conv2d_info, weights_info, gpu_target))
{
@@ -131,6 +145,7 @@
case ConvolutionMethod::FFT:
{
// Validate FFT-based convolution layer
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(post_ops.size() > 0, "CLFFTConvolutionLayer does not support post ops");
ARM_COMPUTE_RETURN_ON_ERROR(CLFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math));
break;
}
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 837527b..1eabee6 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -31,6 +31,7 @@
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "src/core/experimental/PostOp.h"
#include "src/core/helpers/MemoryHelpers.h"
#include "src/gpu/cl/operators/ClGemmConv2d.h"
#include "support/Cast.h"
@@ -68,19 +69,24 @@
CLGEMMConvolutionLayer::~CLGEMMConvolutionLayer() = default;
void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
- const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups)
+ const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups, const experimental::PostOpList<ICLTensor *> &post_ops)
{
- configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info, dilation, act_info, num_groups);
+ configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info, dilation, act_info, num_groups, post_ops);
}
void CLGEMMConvolutionLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups)
+ const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups, const experimental::PostOpList<ICLTensor *> &post_ops)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- _impl->weights = weights;
- _impl->op = std::make_unique<opencl::ClGemmConv2d>();
- const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups);
+ _impl->weights = weights;
+ _impl->op = std::make_unique<opencl::ClGemmConv2d>();
+ // Convert post op arguments to ITensorInfo
+ auto transformed_post_ops = experimental::transform_post_op_list_arguments<ICLTensor *, ITensorInfo *>(post_ops, [](auto tensor)
+ {
+ return tensor->info();
+ });
+ const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups, transformed_post_ops);
_impl->op->configure(compile_context, input->info(), weights->info(), (biases != nullptr ? biases->info() : nullptr), output->info(), conv2d_info, weights_info);
_impl->run_pack =
@@ -90,6 +96,15 @@
{ TensorType::ACL_SRC_2, biases },
{ TensorType::ACL_DST, output }
};
+ // Add post op tensors
+ size_t post_op_tensor_index = 0;
+ for(const auto &op : post_ops.get_list())
+ {
+ for(auto &tensor : op->arguments())
+ {
+ _impl->run_pack.add_const_tensor(experimental::get_post_op_arg_type(post_op_tensor_index++), *tensor);
+ }
+ }
_impl->prep_pack =
{
{ TensorType::ACL_SRC_1, weights },
@@ -100,9 +115,9 @@
}
Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups)
+ const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups, const experimental::PostOpList<ITensorInfo *> &post_ops)
{
- const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups);
+ const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups, post_ops);
return opencl::ClGemmConv2d::validate(input, weights, biases, output, conv2d_info, weights_info);
}
diff --git a/utils/TypePrinter.h b/utils/TypePrinter.h
index 30ba667..950d322 100644
--- a/utils/TypePrinter.h
+++ b/utils/TypePrinter.h
@@ -363,18 +363,18 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const GEMMKernelInfo &gemm_info)
{
- os << "( m= " << gemm_info.m;
- os << " n= " << gemm_info.n;
- os << " k= " << gemm_info.k;
- os << " depth_output_gemm3d= " << gemm_info.depth_output_gemm3d;
- os << " reinterpret_input_as_3d= " << gemm_info.reinterpret_input_as_3d;
- os << " broadcast_bias= " << gemm_info.broadcast_bias;
- os << " fp_mixed_precision= " << gemm_info.fp_mixed_precision;
- os << " mult_transpose1xW_width= " << gemm_info.mult_transpose1xW_width;
- os << " mult_interleave4x4_height= " << gemm_info.mult_interleave4x4_height;
- os << " a_offset= " << gemm_info.a_offset;
- os << " b_offset= " << gemm_info.b_offset;
- os << "post_ops= " << gemm_info.post_ops;
+ os << "( m=" << gemm_info.m;
+ os << " n=" << gemm_info.n;
+ os << " k=" << gemm_info.k;
+ os << " depth_output_gemm3d=" << gemm_info.depth_output_gemm3d;
+ os << " reinterpret_input_as_3d=" << gemm_info.reinterpret_input_as_3d;
+ os << " broadcast_bias=" << gemm_info.broadcast_bias;
+ os << " fp_mixed_precision=" << gemm_info.fp_mixed_precision;
+ os << " mult_transpose1xW_width=" << gemm_info.mult_transpose1xW_width;
+ os << " mult_interleave4x4_height=" << gemm_info.mult_interleave4x4_height;
+ os << " a_offset=" << gemm_info.a_offset;
+ os << " b_offset=" << gemm_info.b_offset;
+ os << "post_ops=" << gemm_info.post_ops;
os << ")";
return os;
}
@@ -388,7 +388,7 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const GEMMLHSMatrixInfo &gemm_info)
{
- os << "( m0= " << (unsigned int)gemm_info.m0 << " k0= " << gemm_info.k0 << " v0= " << gemm_info.v0 << " trans= " << gemm_info.transpose << " inter= " << gemm_info.interleave << "})";
+ os << "( m0=" << (unsigned int)gemm_info.m0 << " k0=" << gemm_info.k0 << " v0=" << gemm_info.v0 << " trans=" << gemm_info.transpose << " inter=" << gemm_info.interleave << "})";
return os;
}
@@ -401,7 +401,7 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const GEMMRHSMatrixInfo &gemm_info)
{
- os << "( n0= " << (unsigned int)gemm_info.n0 << " k0= " << gemm_info.k0 << " h0= " << gemm_info.h0 << " trans= " << gemm_info.transpose << " inter= " << gemm_info.interleave << " exp_img=" <<
+ os << "( n0=" << (unsigned int)gemm_info.n0 << " k0=" << gemm_info.k0 << " h0=" << gemm_info.h0 << " trans=" << gemm_info.transpose << " inter=" << gemm_info.interleave << " exp_img=" <<
gemm_info.export_to_cl_image << "})";
return os;
}
@@ -455,7 +455,7 @@
inline ::std::ostream &operator<<(::std::ostream &os, const BoundingBoxTransformInfo &bbox_info)
{
auto weights = bbox_info.weights();
- os << "(" << bbox_info.img_width() << "x" << bbox_info.img_height() << ")~" << bbox_info.scale() << "(weights = {" << weights[0] << ", " << weights[1] << ", " << weights[2] << ", " << weights[3] <<
+ os << "(" << bbox_info.img_width() << "x" << bbox_info.img_height() << ")~" << bbox_info.scale() << "(weights={" << weights[0] << ", " << weights[1] << ", " << weights[2] << ", " << weights[3] <<
"})";
return os;
}
@@ -1495,6 +1495,7 @@
os << "fp_mixed_precision=" << info.fp_mixed_precision() << ",";
os << "broadcast_bias=" << info.broadcast_bias() << ",";
os << "pretranspose_B=" << info.pretranspose_B() << ",";
+ os << "post_ops=" << info.post_ops() << "}";
return os;
}
@@ -2540,10 +2541,10 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const ConvolutionInfo &conv_info)
{
- os << "{PadStrideInfo = " << conv_info.pad_stride_info << ", "
- << "depth_multiplier = " << conv_info.depth_multiplier << ", "
- << "act_info = " << to_string(conv_info.act_info) << ", "
- << "dilation = " << conv_info.dilation << "}";
+ os << "{PadStrideInfo=" << conv_info.pad_stride_info << ", "
+ << "depth_multiplier=" << conv_info.depth_multiplier << ", "
+ << "act_info=" << to_string(conv_info.act_info) << ", "
+ << "dilation=" << conv_info.dilation << "}";
return os;
}
@@ -2569,13 +2570,13 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const FullyConnectedLayerInfo &layer_info)
{
- os << "{activation_info = " << to_string(layer_info.activation_info) << ", "
- << "weights_trained_layout = " << layer_info.weights_trained_layout << ", "
- << "transpose_weights = " << layer_info.transpose_weights << ", "
- << "are_weights_reshaped = " << layer_info.are_weights_reshaped << ", "
- << "retain_internal_weights = " << layer_info.retain_internal_weights << ", "
- << "constant_weights = " << layer_info.transpose_weights << ", "
- << "fp_mixed_precision = " << layer_info.fp_mixed_precision << "}";
+ os << "{activation_info=" << to_string(layer_info.activation_info) << ", "
+ << "weights_trained_layout=" << layer_info.weights_trained_layout << ", "
+ << "transpose_weights=" << layer_info.transpose_weights << ", "
+ << "are_weights_reshaped=" << layer_info.are_weights_reshaped << ", "
+ << "retain_internal_weights=" << layer_info.retain_internal_weights << ", "
+ << "constant_weights=" << layer_info.transpose_weights << ", "
+ << "fp_mixed_precision=" << layer_info.fp_mixed_precision << "}";
return os;
}
@@ -2643,17 +2644,17 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const GEMMLowpOutputStageInfo &gemm_info)
{
- os << "{type = " << gemm_info.type << ", "
- << "gemlowp_offset = " << gemm_info.gemmlowp_offset << ", "
- << "gemmlowp_multiplier" << gemm_info.gemmlowp_multiplier << ", "
- << "gemmlowp_shift = " << gemm_info.gemmlowp_shift << ", "
- << "gemmlowp_min_bound = " << gemm_info.gemmlowp_min_bound << ", "
- << "gemmlowp_max_bound = " << gemm_info.gemmlowp_max_bound << ", "
- << "gemmlowp_multipliers = " << gemm_info.gemmlowp_multiplier << ", "
- << "gemmlowp_shifts = " << gemm_info.gemmlowp_shift << ", "
- << "gemmlowp_real_multiplier = " << gemm_info.gemmlowp_real_multiplier << ", "
- << "is_quantized_per_channel = " << gemm_info.is_quantized_per_channel << ", "
- << "output_data_type = " << gemm_info.output_data_type << "}";
+ os << "{type=" << gemm_info.type << ", "
+ << "gemlowp_offset=" << gemm_info.gemmlowp_offset << ", "
+ << "gemmlowp_multiplier=" << gemm_info.gemmlowp_multiplier << ", "
+ << "gemmlowp_shift=" << gemm_info.gemmlowp_shift << ", "
+ << "gemmlowp_min_bound=" << gemm_info.gemmlowp_min_bound << ", "
+ << "gemmlowp_max_bound=" << gemm_info.gemmlowp_max_bound << ", "
+ << "gemmlowp_multipliers=" << gemm_info.gemmlowp_multiplier << ", "
+ << "gemmlowp_shifts=" << gemm_info.gemmlowp_shift << ", "
+ << "gemmlowp_real_multiplier=" << gemm_info.gemmlowp_real_multiplier << ", "
+ << "is_quantized_per_channel=" << gemm_info.is_quantized_per_channel << ", "
+ << "output_data_type=" << gemm_info.output_data_type << "}";
return os;
}
@@ -2679,11 +2680,12 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const Conv2dInfo &conv_info)
{
- os << "{conv_info = " << conv_info.conv_info << ", "
- << "dilation = " << conv_info.dilation << ", "
- << "act_info = " << to_string(conv_info.act_info) << ", "
- << "enable_fast_math = " << conv_info.enable_fast_math << ", "
- << "num_groups = " << conv_info.num_groups << "}";
+ os << "{conv_info=" << conv_info.conv_info << ", "
+ << "dilation=" << conv_info.dilation << ", "
+ << "act_info=" << to_string(conv_info.act_info) << ", "
+ << "enable_fast_math=" << conv_info.enable_fast_math << ", "
+ << "num_groups=" << conv_info.num_groups << ","
+ << "post_ops=" << conv_info.post_ops << "}";
return os;
}
@@ -2709,7 +2711,7 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const PixelValue &pixel_value)
{
- os << "{value.u64= " << pixel_value.get<uint64_t>() << "}";
+ os << "{value.u64=" << pixel_value.get<uint64_t>() << "}";
return os;
}
@@ -2735,13 +2737,13 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const ScaleKernelInfo &scale_info)
{
- os << "{interpolation_policy = " << scale_info.interpolation_policy << ", "
- << "BorderMode = " << scale_info.border_mode << ", "
- << "PixelValue = " << scale_info.constant_border_value << ", "
- << "SamplingPolicy = " << scale_info.sampling_policy << ", "
- << "use_padding = " << scale_info.use_padding << ", "
- << "align_corners = " << scale_info.align_corners << ", "
- << "data_layout = " << scale_info.data_layout << "}";
+ os << "{interpolation_policy=" << scale_info.interpolation_policy << ", "
+ << "BorderMode=" << scale_info.border_mode << ", "
+ << "PixelValue=" << scale_info.constant_border_value << ", "
+ << "SamplingPolicy=" << scale_info.sampling_policy << ", "
+ << "use_padding=" << scale_info.use_padding << ", "
+ << "align_corners=" << scale_info.align_corners << ", "
+ << "data_layout=" << scale_info.data_layout << "}";
return os;
}
@@ -2803,8 +2805,8 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const FFT1DInfo &fft1d_info)
{
- os << "{axis = " << fft1d_info.axis << ", "
- << "direction = " << fft1d_info.direction << "}";
+ os << "{axis=" << fft1d_info.axis << ", "
+ << "direction=" << fft1d_info.direction << "}";
return os;
}
@@ -2830,9 +2832,9 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const FFT2DInfo &fft2d_info)
{
- os << "{axis = " << fft2d_info.axis0 << ", "
- << "axis = " << fft2d_info.axis1 << ", "
- << "direction = " << fft2d_info.direction << "}";
+ os << "{axis=" << fft2d_info.axis0 << ", "
+ << "axis=" << fft2d_info.axis1 << ", "
+ << "direction=" << fft2d_info.direction << "}";
return os;
}
@@ -2858,8 +2860,8 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const Coordinates2D &coord_2d)
{
- os << "{x = " << coord_2d.x << ", "
- << "y = " << coord_2d.y << "}";
+ os << "{x=" << coord_2d.x << ", "
+ << "y=" << coord_2d.y << "}";
return os;
}
@@ -2921,10 +2923,10 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const SoftmaxKernelInfo &info)
{
- os << "{beta = " << info.beta << ", "
- << "is_log = " << info.is_log << ", "
- << "input_data_type = " << info.input_data_type << ", "
- << "axis = " << info.axis << "}";
+ os << "{beta=" << info.beta << ", "
+ << "is_log=" << info.is_log << ", "
+ << "input_data_type=" << info.input_data_type << ", "
+ << "axis=" << info.axis << "}";
return os;
}
@@ -3052,15 +3054,15 @@
*/
inline ::std::ostream &operator<<(::std::ostream &os, const BoxNMSLimitInfo &info)
{
- os << "{score_thresh = " << info.score_thresh() << ", "
- << "nms = " << info.nms() << ", "
- << "detections_per_im = " << info.detections_per_im() << ", "
- << "soft_nms_enabled = " << info.soft_nms_enabled() << ", "
- << "soft_nms_min_score_thres = " << info.soft_nms_min_score_thres() << ", "
- << "suppress_size = " << info.suppress_size() << ", "
- << "min_size = " << info.min_size() << ", "
- << "im_width = " << info.im_width() << ", "
- << "im_height = " << info.im_height() << "}";
+ os << "{score_thresh=" << info.score_thresh() << ", "
+ << "nms=" << info.nms() << ", "
+ << "detections_per_im=" << info.detections_per_im() << ", "
+ << "soft_nms_enabled=" << info.soft_nms_enabled() << ", "
+ << "soft_nms_min_score_thres=" << info.soft_nms_min_score_thres() << ", "
+ << "suppress_size=" << info.suppress_size() << ", "
+ << "min_size=" << info.min_size() << ", "
+ << "im_width=" << info.im_width() << ", "
+ << "im_height=" << info.im_height() << "}";
return os;
}