Operators Supported by TensorFlow - 2.0 English

Vitis AI User Guide (UG1414)
Document ID
UG1414
Release Date
2022-01-20
Version
2.0 English
Table 1. Operators Supported by TensorFlow
TensorFlow XIR　 DPU Implementations　
OP type Attributes OP name Attributes
placeholder / inputlayer* shape data shape Allocate memory for input data.　
data_type
const　　 　　　 const　　 data Allocate memory for const data.　　
  shape
  data_type
conv2d　　　　 filter conv2d　　　　 kernel Convolution Engine.
strides   stride
　   pad([0, 0, 0, 0])
padding   pad_mode(SAME or VALID)
dilations   dilation
conv2d* kernel_size conv2d kernel
strides stride
padding pad([0, 0, 0, 0])
dilation_rate dilation
use_bias  
group group
depthwiseconv2dnative　　　　 filter depthwise-conv2d　　　　 kernel Depthwise-Convolution Engine.　　　　
strides stride
explicit_paddings pad
padding pad_mode(SAME or VALID)
dilations dilation
conv2dbackpropinput / conv2dtranspose*　　　　 filter transposed-conv2d　　　　 kernel Convolution Engine.　　　　
strides stride
　 pad([0, 0, 0, 0])
padding pad_mode(SAME or VALID)
dilations dilation
spacetobacthnd + conv2d + batchtospacend　　　　　　　 block_shape conv2d　　　　　　　 dilation Spacetobatch, Conv2d and Batchtospace would be mapped to Convolution Engine when specific requirements that Xilinx sets have been met.　　　　　　　
padding pad
filter kernel
strides stride
padding pad_mode(SAME)
dilations dilation
block_shape 　
crops 　
matmul / dense*　 transpose_a conv2d / matmul　 transpose_a The matmul would be transformed to a conv2d operation once the equivalent conv2d meets the hardware requirements and can be mapped to DPU.　
transpose_b transpose_b
maxpool / maxpooling2d*　　　 ksize maxpool2d　　　 kernel Pooling Engine.　　　
strides stride
　 pad([0, 0, 0, 0])
padding pad_mode(SAME or VALID)
avgpool / averagepooling2d* / globalavgeragepooling2d*　　　　　 pool_size avgpool2d　　　　　 kernel Pooling Engine.　　　　　
strides stride
　 pad([0, 0, 0, 0])
padding pad_mode(SAME or VALID)
　 count_include_pad (false)
　 count_include_invalid (true)
mean　 axis avgpool / reduction_mean　 axis Mean operation would be transformed to avgpool if the equivalent avgpool meets the hardware requirements and can be mapped to DPU.　
keep_dims keep_dims
relu 　 relu 　 Activations would be fused to adjacent operations such as convolution, add, etc.　　
relu6 　 relu6 　
leakyrelu alpha leaky_relu alpha
fixneuron / quantizelayer*　　　 bit_width fix　　　 bit_width It would be divided into float2fix and fix2float during compilation, then the float2fix and fix2float operations would be fused with adjacent operations into course-grained operations.　　　
quantize_pos fix_point
　 if_signed
　 round_mode
identity 　 identity 　 Identity would be removed.
add, addv2 　 add 　 If the add is an element-wise add, the add would be mapped to DPU Element-wise Add Engine, if the add is an channel-wise add, Xilinx searches for opportunities to fuse the add with adjacent operations such as convolutions.
concatv2 / concatenate* axis concat axis Xilinx reduces the overhead resulting from the concat by special reading or writing strategies and allocating the on-chip memory carefully.
pad / zeropadding2d*　 paddings pad　 paddings "CONSTANT" padding would be fused adjacent operations. "SYMMETRIC" padding would be mapped to DPU instructions. "REFLECT" padding is not supported by DPU yet.　
mode mode
  constant_values
shape 　 shape 　 The shape operation would be removed.
stridedslice　　 begin stridedslice　　 begin If they are shape-related operations, they would be removed during compilation. If they are components of a coarse-grained operation, they would be fused with adjacent operations. Otherwise, they would be compiled into CPU implementations.　　　　　　　　　　　　　
end end
strides strides
pack axis stack axis
neg 　 neg 　
mul 　 mul 　
realdiv 　 div 　
sub 　 sub 　
prod　 axis reduction_product　 axis
keep_dims keep_dims
sum　 axis reduction_sum　 axis
keep_dims keep_dims
max　 axis reduction_max　 axis
keep_dims keep_dims
resizebilinear　　　 size resize　　　 size If the mode of the resize is 'BILINEAR', align_corner=false, half_pixel_centers = false, size = 2, 4, 8; align_corner=false, half_pixel_centers = true, size = 2, 4 can be transformed to DPU implementations (pad+depthwise-transposed conv2d). If the mode of the resize is 'NEAREST' and the size is an integer, the resize would be mapped to DPU implementations.　　　　　　　　　　　
align_corners align_corners
half_pixel_centers half_pixel_centers
　 mode="BILINEAR"
resizenearestneighbor　　　 size resize　　　 size
align_corners align_corners
half_pixel_centers half_pixel_centers
　 mode="NEAREST"
upsample2d/upsampling2d*　　　 size resize　　　 scale
　 align_corners
　 half_pixel_centers
interpolation mode
reshape shape reshape shape They would be transformed to the reshape operation in some cases. Otherwise they would be mapped to CPU.　　
reshape* target_shape
transpose perm transpose order
squeeze axis squeeze axis
exp 　 exp 　 They would only be compiled into CPU implementations.　　
softmax axis softmax axis
sigmoid 　 sigmoid 　
square+ rsqrt+ maximum　 　 l2_normalize　 axis output = x / sqrt(max(sum(x ^ 2), epsilon)) would be fused into a l2_normalize in XIR.
　 epsilon
  1. The OPs in TensorFlow listed above are supported in XIR. All of them have CPU implementations in the tool-chain.
  2. Operators with * represent that the version of TensorFlow > 2.0.