Compute Library
 21.02
CpuSoftmax.cpp
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25 
33 
34 namespace arm_compute
35 {
36 namespace cpu
37 {
38 template <bool IS_LOG>
40  : _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _max(nullptr), _tmp(nullptr), _input_permuted(nullptr), _output_permuted(nullptr), _needs_permute(false)
41 {
42 }
43 
44 template <bool IS_LOG>
45 void CpuSoftmaxGeneric<IS_LOG>::configure(const ITensorInfo *src, ITensorInfo *dst, float beta, int32_t axis)
46 {
47  // Perform validation step
50 
51  const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(src->num_dimensions())));
52 
53  _needs_permute = actual_axis > 0;
54 
55  if(_needs_permute)
56  {
57  _input_permuted = std::make_unique<TensorInfo>();
58  _permute_input.configure(src, _input_permuted.get(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
59  }
60 
61  // We want to deal with a 2D input. Either it is the permuted version of the original input (4D case)
62  // or it is the original input case (2D case)
63  const ITensorInfo *tmp_input = (_needs_permute ? _input_permuted.get() : src);
64 
65  // Create intermediate tensors shapes
66  TensorShape max_sum_shape = tmp_input->tensor_shape();
67  max_sum_shape.set(0, 1);
68  const TensorInfo input_info = tmp_input->clone()->reset_padding().set_is_resizable(true);
69  DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->data_type()) ? DataType::F32 : tmp_input->data_type();
70  TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type));
71  TensorInfo max_info(tmp_input->clone()->set_tensor_shape(max_sum_shape));
72 
73  // Init intermediate tensors
74  _max = std::make_unique<TensorInfo>(max_info);
75  _tmp = std::make_unique<TensorInfo>(tensor_info_tmp);
76 
77  // Configure kernels
78  auto mk = std::make_unique<kernels::CpuLogits1DMaxKernel>();
79  mk->configure(tmp_input, _max.get());
80  _max_kernel = std::move(mk);
81 
82  auto sm = std::make_unique<kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>();
83  if(_needs_permute)
84  {
85  _output_permuted = std::make_unique<TensorInfo>();
86 
87  // The normalization kernel stores the result in a permuted output tensor
88  sm->configure(tmp_input, _max.get(), _output_permuted.get(), beta, _tmp.get());
89 
90  // Re-permute the permuted output into the requested (4D) output
91  _permute_output.configure(_output_permuted.get(), dst, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
92  }
93  else
94  {
95  // Softmax 2D case
96  sm->configure(tmp_input, _max.get(), dst, beta, _tmp.get());
97  }
98  _softmax_kernel = std::move(sm);
99 }
100 
101 template <bool IS_LOG>
102 Status CpuSoftmaxGeneric<IS_LOG>::validate(const ITensorInfo *src, const ITensorInfo *dst, float beta, int32_t axis)
103 {
104  // Perform validation step
106  ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->num_dimensions() > 4, "Only up to 4 dimensions are supported");
107  ARM_COMPUTE_UNUSED(beta);
108  ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-src->num_dimensions()) || static_cast<int32_t>(src->num_dimensions()) <= axis);
109 
110  // Create intermediate tensor info
111  DataType tmp_data_type = src->data_type();
112  const TensorInfo tensor_info_tmp(src->clone()->set_data_type(tmp_data_type).set_is_resizable(true));
113 
114  TensorShape max_sum_shape = src->tensor_shape();
115  max_sum_shape.set(0, 1);
116  const TensorInfo tensor_info_max_sum(src->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(src->quantization_info()).set_is_resizable(true));
117  const TensorInfo dont_care;
118 
119  const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(src->num_dimensions())));
120 
121  const bool needs_permute = actual_axis > 0;
122 
123  if(needs_permute)
124  {
126  const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*src, permutation_vector);
127  TensorInfo input_permuted(src->clone()->set_tensor_shape(permuted_shape));
128  ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(src, &input_permuted, permutation_vector));
129  TensorInfo output_permuted(dst->clone()->set_tensor_shape(permuted_shape));
130  ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(&output_permuted, dst, permutation_vector));
131  }
132 
134  ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuLogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, dst, beta, &dont_care));
135 
136  return Status{};
137 }
138 
139 template <bool IS_LOG>
141 {
142  ARM_COMPUTE_ERROR_ON_MSG(tensors.empty(), "No inputs provided");
143 
144  ITensorPack max_pack;
145  ITensorPack softmax_pack;
146 
147  if(_needs_permute)
148  {
149  ITensorPack permute_in_pack;
150  permute_in_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC));
151  permute_in_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_2));
152  _permute_input.run(permute_in_pack);
153 
154  max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_2));
155 
156  softmax_pack.add_tensor(TensorType::ACL_SRC_0, tensors.get_tensor(ACL_INT_2));
157  softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1));
158  softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_INT_3));
159  softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0));
160  }
161  else
162  {
163  max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC));
165  softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1));
166  softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_DST));
167  softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0));
168  }
169 
170  max_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_1));
171 
172  NEScheduler::get().schedule_op(_max_kernel.get(), Window::DimY, _max_kernel->window(), max_pack);
173  NEScheduler::get().schedule_op(_softmax_kernel.get(), Window::DimY, _softmax_kernel->window(), softmax_pack);
174 
175  if(_needs_permute)
176  {
177  ITensorPack permute_out_pack;
178  permute_out_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_3));
179  permute_out_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_DST));
180  _permute_output.run(permute_out_pack);
181  }
182 }
183 
184 template <bool IS_LOG>
186 {
188 
189  req.push_back({ TensorType::ACL_INT_0, _tmp->total_size(), 0 });
190  req.push_back({ TensorType::ACL_INT_1, _max->total_size(), 0 });
191 
192  if(_needs_permute)
193  {
194  req.push_back({ TensorType::ACL_INT_2, _input_permuted->total_size(), 0 });
195  req.push_back({ TensorType::ACL_INT_3, _output_permuted->total_size(), 0 });
196  }
197 
198  return req;
199 }
200 
201 template class CpuSoftmaxGeneric<false>;
202 template class CpuSoftmaxGeneric<true>;
203 } // namespace cpu
204 } // namespace arm_compute
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
static Status validate(const ITensorInfo *src, const ITensorInfo *dst)
Static function to check if given info will lead to a valid configuration of CpuLogits1DMaxKernel.
Shape of a tensor.
Definition: TensorShape.h:39
TensorShape compute_permutation_output_shape(const ITensorInfo &input, const PermutationVector &perm)
Calculate the permuted shape of an input given a permutation vector.
std::unique_ptr< ITensorInfo > clone() const override
Provide a clone of the current object of class T.
Definition: TensorInfo.cpp:316
PermutationVector get_permutation_vector_from_softmax_axis(size_t axis)
Given a softmax axis, this function returns the permutation vector required to put the axis to the fr...
static Status validate(const ITensorInfo *src, const ITensorInfo *dst, float beta=1.0f, int32_t axis=0)
Static function to check if given info will lead to a valid configuration of CpuSoftmax.
Definition: CpuSoftmax.cpp:102
bool empty() const
Checks if pack is empty.
Definition: ITensorPack.cpp:61
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
virtual DataType data_type() const =0
Data type used for each element of the tensor.
virtual void schedule_op(ICPPKernel *kernel, const Hints &hints, const Window &window, ITensorPack &tensors)=0
Runs the kernel in the same thread as the caller synchronously.
1 channel, 1 F32 per channel
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Interface for softmax computation for QASYMM8 with pre-computed max.
Status class.
Definition: Error.h:52
#define ARM_COMPUTE_RETURN_ERROR_ON(cond)
If the condition is true, an error is returned.
Definition: Error.h:296
SimpleTensor< float > src
Definition: DFT.cpp:155
Copyright (c) 2017-2021 Arm Limited.
std::vector< MemoryInfo > MemoryRequirements
Definition: Types.h:71
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:163
const ITensor * get_const_tensor(int id) const
Get constant tensor of a given id.
Definition: ITensorPack.cpp:40
T wrap_around(T x, T m)
Wrap-around a number within the range 0 <= x < m.
Definition: Helpers.h:231
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
void configure(const ITensorInfo *src, ITensorInfo *dst, float beta=1.0f, int32_t axis=0)
Set the input and output tensors.
Definition: CpuSoftmax.cpp:45
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)
Definition: Error.h:456
void run(ITensorPack &tensors) override
Run the kernels contained in the function.
Definition: INEOperator.cpp:40
virtual std::unique_ptr< T > clone() const =0
Provide a clone of the current object of class T.
static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const PermutationVector &perm)
Static function to check if given info will lead to a valid configuration of CpuPermute.
Definition: CpuPermute.cpp:39
virtual QuantizationInfo quantization_info() const =0
Get the quantization settings (scale and offset) of the tensor.
bool is_data_type_quantized_asymmetric(DataType dt)
Check if a given data type is of asymmetric quantized type.
Definition: Utils.h:1190
Strides of an item in bytes.
Definition: Strides.h:37
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
void run(ITensorPack &tensors) override
Run the kernels contained in the function.
Definition: CpuSoftmax.cpp:140
ITensor * get_tensor(int id)
Get tensor of a given id from the pac.
Definition: ITensorPack.cpp:50
experimental::MemoryRequirements workspace() const override
Return the memory requirements required by the workspace.
Definition: CpuSoftmax.cpp:185
void configure(const ITensorInfo *src, ITensorInfo *dst, const PermutationVector &perm)
Configure operator for a given list of arguments.
Definition: CpuPermute.cpp:32
#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)
If the condition is true, an error is returned.
Definition: Error.h:244
Tensor packing service.
Definition: ITensorPack.h:37
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:161
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:45
DataType
Available data types.
Definition: Types.h:77
TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true, bool increase_dim_unit=true)
Accessor to set the value of one of the dimensions.
Definition: TensorShape.h:79
Basic function to compute a SoftmaxLayer and a Log SoftmaxLayer.
Definition: CpuSoftmax.h:57
void add_tensor(int id, ITensor *tensor)
Add tensor to the pack.
Definition: ITensorPack.cpp:30
static IScheduler & get()
Access the scheduler singleton.
Definition: Scheduler.cpp:94