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# File 'lib/tensor_stream/evaluator/ruby/array_ops.rb', line 3
def self.included(klass)
klass.class_eval do
register_op :slice do |context, tensor, inputs|
input = inputs[0]
start = inputs[1]
size = complete_eval(tensor.options[:size], context)
raise "start index and size not of the same shape #{start.size} != #{size.size}" if start.size != size.size
slice_tensor(input, start.dup, size.dup)
end
register_op %i[flow_dynamic_stitch dynamic_stitch] do |context, tensor, inputs|
number_of_indexes = tensor.options[:n]
indexes = inputs[0...number_of_indexes]
data = inputs[number_of_indexes...inputs.size]
merged = []
merge_dynamic_stitch(merged, indexes, data, context)
merged
end
register_op :gather do |_context, tensor, inputs|
params, indexes = inputs
raise "axis !=0 not supported" if tensor.options[:axis] != 0
gather(params, indexes)
end
register_op %i[concat concat_v2] do |_context, _tensor, inputs|
axis = inputs.shift
concat_array(inputs, axis)
end
register_op :stack do |_context, tensor, inputs|
axis = tensor.options[:axis] || 0
shape = shape_eval(inputs[0])
rank = shape.size + 1
elem_size = shape.empty? ? 1 : shape.reduce(:*)
output_buffer = Array.new(inputs.size * elem_size) { 0 }
new_shape = [inputs.size]
shape.inject(new_shape) { |ns, s| ns << s }
divisors = new_shape.dup.drop(1).reverse.inject([1]) { |a, s|
a << s * a.last
}.reverse
axis = rank + axis if axis < 0
rotated_shape = Array.new(axis + 1) { new_shape.shift }
new_shape = rotated_shape.rotate! + new_shape
multipliers = new_shape.dup.drop(1).reverse.inject([1]) { |a, s|
a << s * a.last
}.reverse
inputs.each_with_index do |input, index|
raw_input = input.is_a?(Array) ? input.flatten : [input]
start = index * divisors.first
raw_input.each_with_index do |x, index2|
index_map = []
ptr = start + index2
divisors.each_with_object(index_map) do |div, a|
a << (ptr / div.to_f).floor
ptr = ptr % div
end
rotated_index = Array.new(axis + 1) { index_map.shift }
index_map = rotated_index.rotate! + index_map
ptr2 = 0
multipliers.each_with_index do |m, idx|
ptr2 += index_map[idx] * m
end
output_buffer[ptr2] = x
end
end
TensorShape.reshape(output_buffer, new_shape)
end
register_op :unstack do |_context, tensor, inputs|
axis = tensor.options[:axis] || 0
new_shape = shape_eval(inputs[0])
rank = new_shape.size - 1
divisors = new_shape.dup.drop(1).reverse.inject([1]) { |a, s|
a << s * a.last
}.reverse
axis = rank + axis if axis < 0
rotated_shape = Array.new(axis + 1) { new_shape.shift }
new_shape = rotated_shape.rotate!(-1) + new_shape
output_buffer = Array.new(new_shape.reduce(:*)) { 0 }
multipliers = new_shape.dup.drop(1).reverse.inject([1]) { |a, s|
a << s * a.last
}.reverse
inputs.each_with_index do |input, index|
raw_input = input.is_a?(Array) ? input.flatten : [input]
start = index * divisors.first
raw_input.each_with_index do |x, index2|
index_map = []
ptr = start + index2
divisors.each_with_object(index_map) do |div, a|
a << (ptr / div.to_f).floor
ptr = ptr % div
end
rotated_index = Array.new(axis + 1) { index_map.shift }
index_map = rotated_index.rotate!(-1) + index_map
ptr2 = 0
multipliers.each_with_index do |m, idx|
ptr2 += index_map[idx] * m
end
output_buffer[ptr2] = x
end
end
res = TensorShape.reshape(output_buffer, new_shape)
TensorStream::Evaluator::OutputGroup.new(res, res.map { tensor.inputs[0].data_type })
end
register_op :squeeze do |_context, tensor, inputs|
val = inputs[0]
shape = shape_eval(val)
axis = !tensor.options[:axis].is_a?(Array) ? [tensor.options[:axis]] : tensor.options[:axis]
if !axis.empty?
axis.each do |x|
raise TensorStream::ValueError, "unable to squeeze dimension that does not have a size of 1" if shape[x] != 1
shape[x] = nil
end
else
shape = shape.map { |s| s == 1 ? nil : s }
end
TensorShape.reshape(val, shape.compact)
end
register_op :expand_dims do |_context, _tensor, inputs|
val, axis = inputs
axis = axis.nil? ? 0 : axis
shape = shape_eval(val)
axis = -axis if axis == shape.size
new_shape = shape.dup.insert(axis, 1).compact
TensorShape.reshape([val], new_shape)
end
register_op :fill do |_context, tensor, inputs|
shape = inputs[0] || tensor.shape.shape
value = inputs[1]
func = -> { value }
if shape.is_a?(Array) && shape.size.zero?
func.call
else
shape = [shape.to_i] unless shape.is_a?(Array)
generate_vector(shape, generator: func)
end
end
register_op :invert_permutation do |_context, _tensor, inputs|
input = inputs[0]
output = input.dup
unless input.nil?
input.size.times.each do |index|
output[input[index]] = index
end
end
output
end
register_op :index, no_eval: true do |_context, _tensor, inputs|
f = inputs[0]
index = inputs[1]
if f.is_a?(TensorStream::Evaluator::OutputGroup)
f.outputs[index]
else
f[index]
end
end
register_op :setdiff1d do |_context, tensor, inputs|
input, remove = inputs
idx = []
out = []
input.each_with_index do |x, index|
next if remove.include?(x)
out << x
idx << index
end
idx = idx.map { |i| Tensor.cast_dtype(i, tensor.options[:index_dtype]) } unless tensor.options[:index_dtype] == :int32
TensorStream::Evaluator::OutputGroup.new([out, idx], tensor.inputs.map(&:data_type))
end
register_op :size do |_context, tensor, inputs|
input = inputs[0]
Tensor.cast_dtype(input.flatten.size, tensor.options[:out_type])
end
register_op :range do |_context, _tensor, inputs|
start, limit, delta = inputs
raise " delta !=0 " if delta.zero?
if limit.zero?
limit = start
start = 0
end
raise " Requires start <= limit when delta > 0" if (start > limit) && delta > 0
raise " Requires start >= limit when delta < 0" if (start < limit) && delta < 0
cur_step = start
r = []
Kernel.loop do
break if start == limit
break if (start < limit) && (cur_step >= limit)
break if (start > limit) && (cur_step <= limit)
r << cur_step
cur_step += delta
end
r
end
register_op :eye do |_context, tensor, inputs|
rows, columns = inputs
Array.new(rows) do |i|
Array.new(columns) do |col|
if fp_type?(tensor.data_type)
i == col ? 1.0 : 0.0
else
i == col ? 1 : 0
end
end
end
end
register_op %i[zeros ones zeros_like ones_like] do |_context, tensor, inputs|
shape = if %i[zeros_like ones_like].include?(tensor.operation)
shape_eval(inputs[0])
else
inputs[0] || tensor.shape.shape
end
func = if %i[zeros zeros_like].include?(tensor.operation)
-> { int_type?(tensor.data_type) ? 0 : 0.0 }
else
-> { int_type?(tensor.data_type) ? 1 : 1.0 }
end
if shape.is_a?(Array) && shape.size.zero?
func.call
else
shape = [shape.to_i] unless shape.is_a?(Array)
cache_key = "#{tensor.operation}_#{shape}"
if @context[:_cache].key?(cache_key)
@context[:_cache][cache_key]
else
generate_vector(shape, generator: func).tap do |v|
@context[:_cache][cache_key] = v
end
end
end
end
register_op :truncate do |_context, _tensor, inputs|
truncate(inputs[0], inputs[1])
end
register_op :rank do |_context, _tensor, inputs|
get_rank(inputs[0])
end
register_op :split do |_context, tensor, inputs|
value, num_split, axis = inputs
value_shape = shape_eval(value)
res = if num_split.is_a?(Array)
begin_index = 0
num_split.collect do |num|
end_index = begin_index + num
arr = split_tensor(value, begin_index, end_index, axis)
begin_index = end_index
arr
end
else
raise TensorStream::ValueError, "#{num_split} does not divide #{value_shape[axis]} evenly" if value_shape[axis] % num_split != 0
piece_sizes = value_shape[axis] / num_split
Array.new(num_split) do |num|
begin_index = num * piece_sizes
end_index = begin_index + piece_sizes
split_tensor(value, begin_index, end_index, axis)
end
end
TensorStream::Evaluator::OutputGroup.new(res, res.map { tensor.inputs[0].data_type })
end
register_op :reshape do |_context, _tensor, inputs|
arr, new_shape = inputs
arr = [arr] unless arr.is_a?(Array)
flat_arr = arr.flatten
if new_shape.size.zero? && flat_arr.size == 1
flat_arr[0]
else
TensorShape.reshape(flat_arr, new_shape)
end
end
register_op :pad do |_context, tensor, inputs|
arr_pad(inputs[0], inputs[1], tensor.data_type)
end
register_op :tile do |_context, _tensor, inputs|
input, multiples = inputs
rank = get_rank(input)
raise "1D or higher tensor required" if rank.zero?
raise "invalid multiple size passed #{rank} != #{multiples.size}" if rank != multiples.size
tile = tile_arr(input, 0, multiples)
tile.nil? ? [] : tile
end
register_op %i[select where] do |context, tensor, inputs|
pred = inputs[0]
call_3way_vector_op(pred, inputs[1], inputs[2], context) { |t, u, v| t ? u : v }
end
register_op :shape do |_context, tensor, inputs|
shape_eval(inputs[0], tensor.options[:out_type])
end
register_op :shape_n do |_context, tensor, inputs|
shapes = inputs.collect { |input|
shape_eval(input)
}
TensorStream::Evaluator::OutputGroup.new(shapes, shapes.map { tensor.options[:out_type] })
end
register_op :transpose do |_context, _tensor, inputs|
shape = shape_eval(inputs[0])
rank = get_rank(inputs[0])
perm = inputs[1] || (0...rank).to_a.reverse
if rank == 2 && perm.nil? inputs[0].transpose
else
arr = inputs[0].flatten
new_shape = perm.map { |p| shape[p] }
new_arr = Array.new(shape.reduce(:*)) { 0 }
transpose_with_perm(arr, new_arr, shape, new_shape, perm)
TensorShape.reshape(new_arr, new_shape)
end
end
register_op :case, noop: true do |context, tensor, _inputs|
pred = global_eval(tensor, tensor.inputs[0], context)
result = nil
if tensor.options[:exclusive]
p_true = pred.each_with_index.collect { |p, index| [p, index] }.select { |a| a[0] }
raise TensorStream::ValueError, "more than one predicate returns true pos #{p_true.map { |a| a[1] }.join(",")}" if p_true.size > 1
end
pred.each_with_index do |p, index|
next unless p
result = global_eval(tensor, tensor.inputs[2 + index], context)
end
result = global_eval(tensor, tensor.inputs[1], context) if result.nil?
result
end
register_op :case_grad do |_context, tensor, inputs|
index, pred, func, grad = inputs
if index < 0 && !pred.find { |p| !!p }
grad
elsif index >= 0 && pred[index]
grad
else
func = -> { int_type?(tensor.data_type) ? 0 : 0.0 }
shape = shape_eval(func)
generate_vector(shape, generator: func)
end
end
register_op :dynamic_partition do |context, tensor, inputs|
data, partitions = inputs
num_partitions = tensor.options[:num_partitions]
output_arr = Array.new(num_partitions) { [] }
partitions.each_with_index do |part, index|
output_arr[part] << data[index]
end
TensorStream::Evaluator::OutputGroup.new(output_arr, num_partitions.times.map { tensor.data_type })
end
register_op :gather_grad do |context, tensor, inputs|
grad, indexes, input_shape = inputs
output = Array.new(input_shape.reduce(:*)) { fp_type?(tensor.data_type) ? 0.0 : 0 }
indexes.each_with_index.map do |x, index|
output[x] += grad[index]
end
TensorShape.reshape(output, input_shape)
end
register_op :strided_slice do |_context, _tensor, inputs|
value, b_index, e_index, stride = inputs
slices = b_index.zip(e_index).zip(stride).map do |params|
selection, stride = params
s, e = selection
[s, e, stride]
end
strided_slice(value, slices)
end
register_op :strided_slice_grad do |_context, tensor, inputs|
x, b_index, e_index, stride, grad = inputs
slices = b_index.zip(e_index).zip(stride).map do |params|
selection, stride = params
s, e = selection
[s, e, stride]
end
target_val = generate_vector(x, generator: ->() { fp_type?(tensor.data_type) ? 0.0 : 0 })
strided_slice_grad(target_val, grad, x.dup, slices.dup)
target_val
end
def merge_dynamic_stitch(merged, indexes, data, context)
indexes.each_with_index do |ind, m|
if ind.is_a?(Array)
merge_dynamic_stitch(merged, ind, data[m], context)
else
ind = ind.is_a?(Tensor) ? complete_eval(ind, context) : ind
merged[ind] = data[m]
end
end
end
end
end
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