def simulated_quantize_compute(attrs, inputs, out_type, target):
"""Compiler for simulated_quantize."""
assert len(inputs) == 4
assert attrs.sign
assert attrs.rounding == "round"
data, scale, clip_min, clip_max = inputs
# simulate rounding error
scaled_data = topi.divide(data, scale)
clipped_data = topi.maximum(topi.minimum(scaled_data, clip_max), clip_min)
round_data = topi.round(clipped_data)
# recover data
rdata = topi.multiply(round_data, scale)
return [rdata]
def simulated_quantize_compute(attrs, inputs, out_type, target):
"""Compiler for simulated_quantize."""
assert len(inputs) == 4
assert attrs.sign
assert attrs.rounding == "round"
data, scale, clip_min, clip_max = inputs
# simulate rounding error
scaled_data = topi.divide(data, scale)
clipped_data = topi.maximum(topi.minimum(scaled_data, clip_max), clip_min)
round_data = topi.round(clipped_data)
# recover data
rdata = topi.multiply(round_data, scale)
return [rdata]
def simulated_quantize_compute(attrs, inputs, out_type):
"""Compiler for simulated_quantize."""
assert len(inputs) == 4
assert attrs.sign
assert attrs.rounding == "round"
data, scale, clip_min, clip_max = inputs
if attrs.kind == QAnnotateKind.IDENTITY:
return [topi.identity(data)]
# simulate rounding error
scaled_data = topi.divide(data, scale)
clipped_data = topi.maximum(topi.minimum(scaled_data, clip_max), clip_min)
round_data = topi.round(clipped_data)
# recover data
rdata = topi.multiply(round_data, scale)
return [rdata]
def divide_compute(attrs, inputs, output_type, target):
assert len(inputs) == 2
return [topi.divide(inputs[0], inputs[1])]
# https://rufflewind.com/2016-12-30/reverse-mode-automatic-differentiation
import tvm
import topi
import numpy
x = tvm.te.placeholder((3, ), name='x')
w = tvm.te.placeholder((3, ), name='w')
z1 = topi.multiply(x, w)
z2 = topi.sum(z1)
z3 = topi.multiply(z2, -1)
z4 = topi.exp(z3)
z5 = topi.add(z4, 1)
z6 = topi.divide(1, z5)
[dw] = tvm.te.gradient(z6, w)
s = tvm.te.create_schedule(dw.op)
g = tvm.build(s, [x, w, dw])
# The default tensor type in tvm
dtype = "float32"
target = 'llvm'
ctx = tvm.context(target, 0)
# # Random generated tensor for testing
x1 = tvm.nd.array(numpy.array([1, 3, 2]).astype(dtype), ctx)
w1 = tvm.nd.array(numpy.array([2, 1, -2]).astype(dtype), ctx)
dw1 = tvm.nd.empty(shape=(3, ), dtype='float32', ctx=ctx)
g(x1, w1, dw1)
print("ret=", dw1)
