ArmNN
 24.02
ConvImpl.cpp
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1 //
2 // Copyright © 2017 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #include "ConvImpl.hpp"
7 
9 
10 #include <cmath>
11 #include <limits>
12 
13 namespace armnn
14 {
15 
17 {
18  ARMNN_ASSERT(multiplier >= 0.0f && multiplier < 1.0f);
19  if (multiplier == 0.0f)
20  {
21  m_Multiplier = 0;
22  m_RightShift = 0;
23  }
24  else
25  {
26  const double q = std::frexp(multiplier, &m_RightShift);
27  m_RightShift = -m_RightShift;
28  int64_t qFixed = static_cast<int64_t>(::round(q * (1ll << 31)));
29  ARMNN_ASSERT(qFixed <= (1ll << 31));
30  if (qFixed == (1ll << 31))
31  {
32  qFixed /= 2;
33  --m_RightShift;
34  }
35  ARMNN_ASSERT(m_RightShift >= 0);
36  ARMNN_ASSERT(qFixed <= std::numeric_limits<int32_t>::max());
37  m_Multiplier = static_cast<int32_t>(qFixed);
38  }
39 }
40 
42 {
43  int32_t x = SaturatingRoundingDoublingHighMul(rhs, m_Multiplier);
44  return RoundingDivideByPOT(x, m_RightShift);
45 }
46 
47 int32_t QuantizedMultiplierSmallerThanOne::SaturatingRoundingDoublingHighMul(int32_t a, int32_t b)
48 {
49  // Check for overflow.
50  if (a == b && a == std::numeric_limits<int32_t>::min())
51  {
52  return std::numeric_limits<int32_t>::max();
53  }
54  int64_t a_64(a);
55  int64_t b_64(b);
56  int64_t ab_64 = a_64 * b_64;
57  int32_t nudge = ab_64 >= 0 ? (1 << 30) : (1 - (1 << 30));
58  int32_t ab_x2_high32 = static_cast<std::int32_t>((ab_64 + nudge) / (1ll << 31));
59  return ab_x2_high32;
60 }
61 
62 int32_t QuantizedMultiplierSmallerThanOne::RoundingDivideByPOT(int32_t x, int exponent)
63 {
64  ARMNN_ASSERT(exponent >= 0 && exponent <= 31);
65  int32_t mask = (1 << exponent) - 1;
66  int32_t remainder = x & mask;
67  int32_t threshold = (mask >> 1) + (x < 0 ? 1 : 0);
68  return (x >> exponent) + (remainder > threshold ? 1 : 0);
69 }
70 
71 void Convolve(const TensorShape& rInputShape,
72  Decoder<float>& rInputDecoder,
73  const TensorShape& rOutputShape,
74  Encoder<float>& rOutputEncoder,
75  const TensorShape& rFilterShape,
76  Decoder<float>& rFilterDecoder,
77  bool biasEnabled,
78  Decoder<float>* pBiasDecoder,
79  DataLayout dataLayout,
80  unsigned int paddingTop,
81  unsigned int paddingLeft,
82  unsigned int xStride,
83  unsigned int yStride,
84  unsigned int xDilation,
85  unsigned int yDilation,
86  bool depthwise)
87 {
88  if (biasEnabled && !pBiasDecoder)
89  {
90  throw InvalidArgumentException("Bias is enabled but the bias data is invalid");
91  }
92  const armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout);
93 
94  const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
95  const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
96  const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
97 
98  // Weights layout:
99  // Conv2d: [O,H,W,I]
100  // Depthwise: [1,H,W,O]
101  const unsigned int inputChannels = rInputShape[channelsIndex];
102  const unsigned int outputChannels = rOutputShape[channelsIndex];
103  const unsigned int depthMultiplier = depthwise ? outputChannels/inputChannels : 1;
104 
105  const unsigned int batchSize = rOutputShape[0];
106  const unsigned int outputHeight = rOutputShape[heightIndex];
107  const unsigned int outputWidth = rOutputShape[widthIndex];
108  const unsigned int inputHeight = rInputShape[heightIndex];
109  const unsigned int inputWidth = rInputShape[widthIndex];
110 
111  const unsigned int filterHeight = depthwise ? rFilterShape[1] : rFilterShape[heightIndex];
112  const unsigned int filterWidth = depthwise ? rFilterShape[2] : rFilterShape[widthIndex];
113 
114  const std::vector<float> inputVec = rInputDecoder.DecodeTensor(rInputShape);
115  const std::vector<float> filterVec = rFilterDecoder.DecodeTensor(rFilterShape, depthwise);
116 
117  const TensorShape biasShape{outputChannels};
118  const std::vector<float> biasVec = biasEnabled ? pBiasDecoder->DecodeTensor(biasShape) : std::vector<float>();
119 
120  for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++)
121  {
122  for (unsigned int cOutput = 0; cOutput < outputChannels; cOutput++)
123  {
124  for (unsigned int yOutput = 0; yOutput < outputHeight; yOutput++)
125  {
126  for (unsigned int xOutput = 0; xOutput < outputWidth; xOutput++)
127  {
128  // This loop goes over each output element.
129  float sum = 0.0f;
130 
131  // For depthwise, each output channel corresponds to exactly one input channel.
132  // For normal, must loop over each input channel.
133  for (unsigned int cInput = 0; cInput < (depthwise ? 1 : inputChannels); cInput++)
134  {
135  for (unsigned int yFilter = 0; yFilter < filterHeight; yFilter++)
136  {
137  for (unsigned int xFilter = 0; xFilter < filterWidth; xFilter++)
138  {
139  // This loop goes over each input element for each output element.
140  unsigned int filterIndex = 0;
141 
142  // Since dimensionality of kernel depends on depthwiseness, so does index.
143  if (depthwise)
144  {
145  cInput = cOutput / depthMultiplier;
146  // filterDepth = outputChannels;
147  filterIndex = xFilter * outputChannels + cOutput +
148  yFilter * filterWidth * outputChannels;
149  }
150  else
151  {
152  // Keep this implementation, as using DataLayoutIndexed::GetIndex causes great
153  // performance regression.
154  if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC)
155  {
156  filterIndex = cOutput * filterHeight * filterWidth * inputChannels +
157  yFilter * filterWidth * inputChannels +
158  xFilter * inputChannels +
159  cInput;
160  }
161  else
162  {
163  filterIndex = cOutput * filterWidth * filterHeight * inputChannels +
164  cInput * filterWidth * filterHeight +
165  yFilter * filterWidth +
166  xFilter;
167  }
168  }
169 
170  unsigned int yInput = yOutput * yStride + yFilter * yDilation;
171  unsigned int xInput = xOutput * xStride + xFilter * xDilation;
172 
173  float inputValue;
174 
175  // Check if we're in the padding.
176  if (yInput < paddingTop || yInput >= inputHeight + paddingTop ||
177  xInput < paddingLeft || xInput >= inputWidth + paddingLeft)
178  {
179  inputValue = 0.0f;
180  }
181  else
182  {
183  unsigned int inputIndex = 0;
184 
185  // Keep this implementation, as using DataLayoutIndexed::GetIndex causes great
186  // performance regression.
187  if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC)
188  {
189  inputIndex = batchIdx * inputHeight * inputWidth * inputChannels +
190  (yInput - paddingTop) * inputWidth * inputChannels +
191  (xInput - paddingLeft) * inputChannels +
192  cInput;
193  }
194  else
195  {
196  inputIndex = batchIdx * inputWidth * inputHeight * inputChannels +
197  inputWidth * inputHeight * cInput +
198  inputWidth * (yInput - paddingTop) +
199  xInput - paddingLeft;
200  }
201  inputValue = inputVec[inputIndex];
202  }
203 
204  sum += filterVec[filterIndex] * inputValue;
205  }
206  }
207  }
208 
209  if (biasEnabled)
210  {
211  sum += biasVec[cOutput];
212  }
213 
214  unsigned int outIdx;
215  if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC)
216  {
217  outIdx = batchIdx * outputHeight * outputWidth * outputChannels +
218  yOutput * outputWidth * outputChannels +
219  xOutput * outputChannels +
220  cOutput;
221  }
222  else
223  {
224  outIdx = batchIdx * outputHeight * outputWidth * outputChannels +
225  cOutput * outputHeight * outputWidth +
226  yOutput * outputWidth +
227  xOutput;
228  }
229 
230  rOutputEncoder[outIdx];
231  rOutputEncoder.Set(sum);
232  }
233  }
234  }
235  }
236 }
237 
238 } // namespace armnn
ARMNN_ASSERT
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14
armnn::Decoder< float >
armnn::Encoder::Set
virtual void Set(IType right)=0
armnn::DataLayout
DataLayout
Definition: Types.hpp:62
armnn::DataLayout::NHWC
@ NHWC
armnnUtils::DataLayoutIndexed
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout.
Definition: DataLayoutIndexed.hpp:17
armnnUtils::DataLayoutIndexed::GetDataLayout
armnn::DataLayout GetDataLayout() const
Definition: DataLayoutIndexed.hpp:22
ConvImpl.hpp
armnn::QuantizedMultiplierSmallerThanOne::QuantizedMultiplierSmallerThanOne
QuantizedMultiplierSmallerThanOne(float multiplier)
Constructs a QuantizedMultiplierSmallerThanOne which will multiply by the given multiplier.
Definition: ConvImpl.cpp:16
armnnUtils::DataLayoutIndexed::GetHeightIndex
unsigned int GetHeightIndex() const
Definition: DataLayoutIndexed.hpp:24
armnn::QuantizedMultiplierSmallerThanOne::operator*
int32_t operator*(int32_t rhs) const
The implementation of this function is adapted from Android NN's MultiplyByQuantizedMultiplierSmaller...
Definition: ConvImpl.cpp:41
Assert.hpp
armnn::TensorShape
Definition: Tensor.hpp:20
armnn::Encoder< float >
armnn::InvalidArgumentException
Definition: Exceptions.hpp:80
armnn::Decoder::DecodeTensor
virtual std::vector< float > DecodeTensor(const TensorShape &tensorShape, bool isDepthwise=false)=0
armnnUtils::DataLayoutIndexed::GetWidthIndex
unsigned int GetWidthIndex() const
Definition: DataLayoutIndexed.hpp:25
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
armnnUtils::DataLayoutIndexed::GetChannelsIndex
unsigned int GetChannelsIndex() const
Definition: DataLayoutIndexed.hpp:23
armnn::Convolve
void Convolve(const TensorShape &rInputShape, Decoder< float > &rInputDecoder, const TensorShape &rOutputShape, Encoder< float > &rOutputEncoder, const TensorShape &rFilterShape, Decoder< float > &rFilterDecoder, bool biasEnabled, Decoder< float > *pBiasDecoder, DataLayout dataLayout, unsigned int paddingTop, unsigned int paddingLeft, unsigned int xStride, unsigned int yStride, unsigned int xDilation, unsigned int yDilation, bool depthwise)
Definition: ConvImpl.cpp:71