def before():
x = relay.var("x", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.squeeze(y)
u = relay.transpose(y, axes=[0, 1])
w = relay.left_shift(z, u)
return relay.Function([x], w)
def before():
x = relay.var("x", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.squeeze(y)
u = relay.transpose(y, axes=[0, 1])
w = relay.left_shift(z, u)
return relay.Function([x], w)
def quantize(data, shift_bits, target_bits=relay.const(7, dtype='int32')):
"""Quantize output of layer, to be consistent with source code @yx
Question: should the shift_bits participating to network control flow?
At mxnet quantization with truman's code, the bits number of max_v
is converted to normal interger using function `asscalar()`. However,
I cannot find the related function in relay.
I am confused with the control flow logic in model network, whether
the condition `shift_bits == -1` should join in model network or just
left it in python code flow. By Longtao.Wang
Parameters
----------
shift_bits: tvm.relay.Expr
The shift_bits parameter is never used according to @yx's source code,
which always be constant Expr(-1).
"""
max_v = relay.max(relay.abs(data))
min_v = relay.min(data)
ln_max_v = relay.log(relay.cast(max_v, 'float32'))
ln_2 = relay.log(relay.const(2.))
total_bits = relay.ceil(relay.divide(ln_max_v, ln_2)) # ceil( ln(max_v) / ln(2) )
shift_bits = relay.subtract(total_bits.astype('int32'), target_bits)
shift_bits = relay.maximum(shift_bits, relay.const(0))
denominator = relay.left_shift(relay.const(1),
relay.cast(shift_bits, 'int32'))
out = relay.divide(data, denominator)
# According to @yx's code, use divide operation instead of shift op for
# possible negative number round.
# out = relay.right_shift(data, shift_bits)
out = relay.cast(relay.clip(out, a_min=-128, a_max=127), 'int8')
return out, max_v, min_v, shift_bits
def create_graph():
input1 = relay.var("x1", shape=ifm_shape, dtype=dtype)
input2 = relay.var("x2", shape=ifm2_shape, dtype=dtype)
c1 = relay.left_shift(input1, input2)
f = relay.Function([input1, input2], c1)
mod = tvm.IRModule()
mod["main"] = f
return mod
def create_model():
ifm = relay.var("ifm", shape=ifm_shape, dtype=dtype)
ifm2 = relay.var("ifm2", shape=ifm2_shape, dtype=dtype)
c1 = relay.left_shift(ifm, ifm2)
f = relay.Function([ifm, ifm2], c1)
mod = tvm.IRModule()
mod["main"] = f
return mod
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.squeeze(y)
u = relay.transpose(y, axes=[0, 1])
w = relay.left_shift(z, u)
f1 = relay.Function([x], w)
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
def approx_exp(x):
x = relay.minimum(relay.maximum(x, C((- 88.0))), C(88.0))
x = (C(127.0) + (x * C(1.44269504)))
xf = relay.floor(x)
i = relay.cast(xf, 'int32')
x = (x - xf)
Y = (C(0.99992522) + (x * (C(0.69583354) + (x * (C(0.22606716) + (x * C(0.078024523)))))))
exponent = relay.left_shift(i, relay.expr.const(23, 'int32'))
exponent = relay.reinterpret(exponent, 'float32')
return (exponent * Y)
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.squeeze(y)
u = relay.transpose(y, axes=[0, 1])
w = relay.left_shift(z, u)
f1 = relay.Function([x], w)
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.squeeze(y)
u = relay.transpose(y, axes=[0, 1])
w = relay.left_shift(z, u)
f1 = relay.Function([x], w)
f1 = f1.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
def approx_exp(x):
# An approximation derived from Opus,
# https://github.com/xiph/opus/blob/c1c247/celt/mathops.h#L147-L165
x = relay.minimum(relay.maximum(x, C(-88.0)), C(88.0))
x = C(127.0) + x * C(1.44269504)
xf = relay.floor(x)
i = relay.cast(xf, "int32")
x = x - xf
Y = C(0.99992522) + x * (C(0.69583354) + x * (C(0.22606716) + x * C(0.078024523)))
exponent = relay.left_shift(i, relay.expr.const(23, "int32"))
exponent = relay.reinterpret(exponent, "float32")
return exponent * Y
def generate_relay_counter_array(self, counter):
"""Generate relay symbolic uint64 counter array for Philox2x32 RNG.
Generate a relay vector of 64-bits integers
which encodes couples (counter, i) for i in range(n)
counter must be a relay expression
(e.g. a relay constant or variable).
"""
c = relay.cast(counter, "uint64")
b = relay.op.transform.full(c, (self.n, ), "uint64")
d = relay.left_shift(b, RELAY_UINT64_32)
e = relay.arange(relay.const(self.n, "uint64"), dtype="uint64")
return relay.bitwise_or(d, e)
def __impl_philox_2x_round(self, ctr, key):
"""Compute a round in Philox2x32.
:param ctr: uint64 vector
:param key: uint32 scalar
:return:
"""
ctr_0 = relay.right_shift(ctr, RELAY_UINT64_32)
ctr_1 = relay.bitwise_and(ctr, RELAY_UINT64_CLEAR_HIGH)
# mul_hi_lo
product = relay.multiply(RELAY_PHILOX_M2x32_0, ctr_0)
key_64 = relay.cast(key, "uint64")
ctr_1_xor_key = relay.bitwise_xor(ctr_1, key_64)
ctr_1_xor_key_up = relay.left_shift(ctr_1_xor_key, RELAY_UINT64_32)
return relay.bitwise_xor(product, ctr_1_xor_key_up)
def create_model():
ifm = relay.var("ifm", shape=ifm_shape, dtype=dtype)
ifm2 = relay.var("ifm2", shape=ifm2_shape, dtype=dtype)
c1 = relay.left_shift(ifm, ifm2)
return tvm.IRModule.from_expr(relay.Function([ifm, ifm2], c1))
