Compute Library
 21.02
GCGEMM.cpp
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2  * Copyright (c) 2017-2019 Arm Limited.
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25 
26 #include "arm_compute/core/Error.h"
35 #include "arm_compute/core/Types.h"
39 
40 using namespace arm_compute;
41 
42 namespace
43 {
44 Status validate_arguments(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo())
45 {
46  ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
47 
50  ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
51  ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
52 
53  if(c != nullptr)
54  {
56  ARM_COMPUTE_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
57  ARM_COMPUTE_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B");
58  }
59 
60  if(output->total_size() != 0)
61  {
62  ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B");
63  ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A");
64  }
65 
66  ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
67 
68  ARM_COMPUTE_UNUSED(alpha);
69  ARM_COMPUTE_UNUSED(beta);
70  ARM_COMPUTE_UNUSED(gemm_info);
71  return Status{};
72 }
73 } // namespace
74 
75 GCGEMM::GCGEMM(std::shared_ptr<IMemoryManager> memory_manager)
76  : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _original_b(nullptr), _is_interleaved_transposed(false),
77  _run_addition(false), _reshape_b_only_on_first_run(false), _is_prepared(false)
78 {
79 }
80 
81 void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
82 {
83  ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
84 
85  // Perform validation step
86  ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(a->info(), b->info(), c, output->info(), alpha, beta, gemm_info));
87 
88  // Check if we need to reshape the matrix B only on the first run
89  _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
90  _is_prepared = false;
91  _original_b = b;
92 
93  const IGCTensor *matrix_a = a;
94  const IGCTensor *matrix_b = b;
95 
96  // Get the GPU target
97  const GPUTarget gpu_target = GCScheduler::get().get_target();
98 
99  // Set the target for the kernels
100  _interleave_kernel.set_target(gpu_target);
101  _mm_kernel.set_target(gpu_target);
102 
103  // Arguments used by GEMMReshapeInfo
104  // If we pass the matrix A and matrix B reshaped to GCGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GCGEMMReshapeInfo
105  // in order to know how the matrices have been reshaped
106  const int m = a->info()->dimension(1);
107  const int n = b->info()->dimension(0);
108  const int k = a->info()->dimension(0);
109  int mult_transpose1xW_width = 1;
110  int mult_interleave4x4_height = 1;
111 
112  // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
113  _is_interleaved_transposed = a->info()->dimension(1) > 16;
114 
115  if(_is_interleaved_transposed)
116  {
117  matrix_a = &_tmp_a;
118  matrix_b = &_tmp_b;
119 
120  // Manage intermediate buffers
121  _memory_group.manage(&_tmp_a);
122  if(!_reshape_b_only_on_first_run)
123  {
124  _memory_group.manage(&_tmp_b);
125  }
126  // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel
127 
128  // Configure interleave kernel
129  _interleave_kernel.configure(a, &_tmp_a);
130 
131  // Configure transpose kernel
132  _transpose_kernel.configure(b, &_tmp_b);
133  }
134 
135  _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));
136 
137  if(_is_interleaved_transposed)
138  {
139  // Allocate intermediate tensors
140  _tmp_a.allocator()->allocate();
141  if(!_reshape_b_only_on_first_run)
142  {
143  _tmp_b.allocator()->allocate();
144  }
145  }
146 
147  // Configure matrix addition kernel
148  if(beta != 0 && c != nullptr)
149  {
150  _ma_kernel.configure(c, output, beta);
151  _run_addition = true;
152  }
153 }
154 
155 Status GCGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info)
156 {
157  ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(a, b, c, output, alpha, beta, gemm_info));
158  return Status{};
159 }
160 
162 {
163  prepare();
164 
165  MemoryGroupResourceScope scope_mg(_memory_group);
166 
167  if(_is_interleaved_transposed)
168  {
169  // Run interleave kernel
170  GCScheduler::get().dispatch(_interleave_kernel, false);
171 
172  if(!_reshape_b_only_on_first_run)
173  {
174  // Run transpose kernel
175  GCScheduler::get().dispatch(_transpose_kernel, false);
176  }
177 
179  }
180 
181  // Run matrix multiply kernel
182  GCScheduler::get().dispatch(_mm_kernel, !_run_addition);
183 
184  // Run matrix addition kernel
185  if(_run_addition)
186  {
188  GCScheduler::get().dispatch(_ma_kernel);
189  }
190 }
191 
193 {
194  if(!_is_prepared)
195  {
196  if(_is_interleaved_transposed && _reshape_b_only_on_first_run)
197  {
198  ARM_COMPUTE_ERROR_ON(!_original_b->is_used());
199 
200  // Run transpose kernel
201  _tmp_b.allocator()->allocate();
202  GCScheduler::get().dispatch(_transpose_kernel, false);
204 
205  // Mark original weights tensor as unused
206  _original_b->mark_as_unused();
207  }
208 
209  _is_prepared = true;
210  }
211 }
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
SimpleTensor< float > b
Definition: DFT.cpp:157
void dispatch(IGCKernel &kernel, bool flush=true)
Schedule the execution of the passed kernel if possible.
Definition: GCScheduler.cpp:77
void run() override
Run the kernels contained in the function.
Definition: GCGEMM.cpp:161
GEMM reshape information class.
Definition: Types.h:1831
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
bool is_used() const
Flags if the tensor is used or not.
Definition: ITensor.cpp:163
1 channel, 1 F32 per channel
void memory_barrier()
Defines a barrier ordering memory transactions.
Definition: GCScheduler.cpp:86
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
Definition: Error.h:466
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
Interface for GLES Compute tensor.
Definition: IGCTensor.h:35
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Status class.
Definition: Error.h:52
Copyright (c) 2017-2021 Arm Limited.
1 channel, 1 F16 per channel
void set_target(GPUTarget target)
Set the targeted GPU architecture.
Definition: IGCKernel.h:113
void mark_as_unused() const
Marks a tensor as unused.
Definition: ITensor.cpp:168
void manage(IMemoryManageable *obj) override
Sets a object to be managed by the given memory group.
Definition: MemoryGroup.h:79
static GCScheduler & get()
Access the scheduler singleton.
Definition: GCScheduler.cpp:70
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:543
#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)
Definition: Error.h:456
void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed=true, const GEMMReshapeInfo &reshape_info=GEMMReshapeInfo())
Initialise the kernel&#39;s input, output and alpha.
GCGEMM(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Default constructor.
Definition: GCGEMM.cpp:75
static Status validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info=GEMMInfo())
Static function to check if given info will lead to a valid configuration of GCGEMM.
Definition: GCGEMM.cpp:155
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
void prepare() override
Prepare the function for executing.
Definition: GCGEMM.cpp:192
#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:790
virtual void allocate()=0
Interface to be implemented by the child class to allocate the tensor.
Memory group resources scope handling class.
Definition: IMemoryGroup.h:82
virtual size_t total_size() const =0
Returns the total size of the tensor in bytes.
GPUTarget
Available GPU Targets.
Definition: GPUTarget.h:34
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
Interface to enqueue GLES kernels and get/set the GLES CommandQueue.
#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)
If the condition is true, an error is returned.
Definition: Error.h:244
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:161
bool reshape_b_only_on_first_run() const
Flag which specifies if the reshape of matrix B should executed only for the first.
Definition: Types.h:2074
void configure(const IGCTensor *input, IGCTensor *output, float beta)
Initialise the kernel&#39;s input, output and beta value.
GEMM information class.
Definition: Types.h:2003
GPUTarget get_target() const
Get the target GPU.
Definition: GCScheduler.h:81
void configure(const IGCTensor *input, IGCTensor *output)
Initialise the kernel&#39;s input and output.
void configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())
Initialise the kernel&#39;s inputs and output.
Definition: GCGEMM.cpp:81
ITensorAllocator * allocator()
Return a pointer to the tensor&#39;s allocator.
Definition: GCTensor.cpp:34
void configure(const IGCTensor *input, IGCTensor *output)
Initialise the kernel&#39;s input and output.