def hardware_bernoulli(rng_key, p=np.float32(0.5), shape=None):
"""Faster RNG."""
y = 1.0
x = 0.0
if FLAGS.use_bfloat16_activation:
y = jnp.bfloat16(y)
x = jnp.bfloat16(0.0)
p = jnp.bfloat16(p)
y = lax.tie_in(rng_key, y)
m = lax.rng_uniform(x, y, shape)
if FLAGS.use_bfloat16_activation:
assert m.dtype == jnp.bfloat16
return m < p
def test_weak_types(self):
mul = jax.jit(jnp.multiply)
# The value `2` here should be weakly typed, and should not lead to
# promotion.
tf_fn = jax2tf.convert(lambda x: mul(x, 2.))
self.assertAllClose(tf_fn(tf.constant(1.375, tf.bfloat16)).numpy(),
jnp.bfloat16(2.750))
def test_bfloat16_constant(self):
def jax_fn_scalar(x):
x = x.astype(jnp.bfloat16)
x *= 2.
return x
def jax_fn_array(x):
x = x.astype(jnp.bfloat16)
x *= np.array([1.5, 2.5, 3.5], jnp.bfloat16)
return x
tf_fn_scalar = jax2tf.convert(jax_fn_scalar)
self.assertAllClose(tf_fn_scalar(1.375).numpy(), jnp.bfloat16(2.750))
tf_fn_array = jax2tf.convert(jax_fn_array)
self.assertAllClose(tf_fn_array(np.array([3, 4, 5])),
np.array([4.5, 10, 17.5], jnp.bfloat16))
def main(_):
# CHECK-LABEL: TEST: abs int32[]
# CHECK: mhlo.abs
# CHECK-SAME: tensor<i32>
print_ir(np.int32(0))(lax.abs)
# CHECK-LABEL: TEST: add float32[] float32[]
# CHECK: mhlo.add
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.add)
# CHECK-LABEL: TEST: acos float32[]
# CHECK: mhlo.atan2
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1))(lax.acos)
# CHECK-LABEL: TEST: acosh float32[]
# CHECK: xla_fallback_acosh
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.acosh)
# CHECK-LABEL: TEST: asin float32[]
# CHECK: mhlo.atan2
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1))(lax.asin)
# CHECK-LABEL: TEST: asinh float32[]
# CHECK: xla_fallback_asinh
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.asinh)
# CHECK-LABEL: TEST: atan float32[]
# CHECK: mhlo.atan2
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1))(lax.atan)
# CHECK-LABEL: TEST: atanh float32[]
# CHECK: xla_fallback_atanh
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.atanh)
# CHECK-LABEL: TEST: atan2 float64[] float64[]
# CHECK: mhlo.atan2
# CHECK-SAME: tensor<f64>
print_ir(np.float64(1), np.float64(2))(lax.atan2)
# CHECK-LABEL: TEST: bessel_i0e float32[]
# CHECK: xla_fallback_bessel_i0e
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.bessel_i0e)
# CHECK-LABEL: TEST: bessel_i1e float32[]
# CHECK: xla_fallback_bessel_i1e
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.bessel_i1e)
# CHECK-LABEL: TEST: betainc float32[] float32[] float32[]
# CHECK: xla_fallback_regularized_incomplete_beta
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0), np.float32(0), np.float32(0))(lax.betainc)
# CHECK-LABEL: TEST: bitcast_convert_type uint32[7]
# CHECK: mhlo.bitcast_convert
# CHECK-SAME: tensor<7xui32>
# CHECK-SAME: tensor<7xf32>
print_ir(np.empty((7,), np.uint32))(
partial(lax.bitcast_convert_type, new_dtype=np.float32))
# CHECK-LABEL: TEST: bitwise_and int32[] int32[]
# CHECK: mhlo.and
# CHECK-SAME: tensor<i32>
print_ir(np.int32(1), np.int32(2))(lax.bitwise_and)
# CHECK-LABEL: TEST: bitwise_and bool[] bool[]
# CHECK: mhlo.and
# CHECK-SAME: tensor<i1>
print_ir(np.bool_(0), np.bool_(0))(lax.bitwise_and)
# CHECK-LABEL: TEST: bitwise_or int32[] int32[]
# CHECK: mhlo.or
# CHECK-SAME: tensor<i32>
print_ir(np.int32(1), np.int32(2))(lax.bitwise_or)
# CHECK-LABEL: TEST: bitwise_or bool[] bool[]
# CHECK: mhlo.or
# CHECK-SAME: tensor<i1>
print_ir(np.bool_(0), np.bool_(0))(lax.bitwise_or)
# CHECK-LABEL: TEST: bitwise_xor int32[] int32[]
# CHECK: mhlo.xor
# CHECK-SAME: tensor<i32>
print_ir(np.int32(1), np.int32(2))(lax.bitwise_xor)
# CHECK-LABEL: TEST: bitwise_xor bool[] bool[]
# CHECK: mhlo.xor
# CHECK-SAME: tensor<i1>
print_ir(np.bool_(0), np.bool_(0))(lax.bitwise_xor)
# CHECK-LABEL: TEST: cbrt bfloat16[]
# CHECK: mhlo.cbrt
# CHECK-SAME: tensor<bf16>
print_ir(jnp.bfloat16(0))(lax.cbrt)
# CHECK-LABEL: TEST: clamp bfloat16[] bfloat16[] bfloat16[]
# CHECK: mhlo.clamp
# CHECK-SAME: tensor<bf16>
print_ir(jnp.bfloat16(0), jnp.bfloat16(0), jnp.bfloat16(0))(lax.clamp)
# CHECK-LABEL: TEST: ceil float16[7]
# CHECK: mhlo.ceil
# CHECK-SAME: tensor<7xf16>
print_ir(np.empty((7,), np.float16))(lax.ceil)
# CHECK-LABEL: TEST: convert_element_type float16[7]
# CHECK: mhlo.convert
# CHECK-SAME: tensor<7xf16>
# CHECK-SAME: tensor<7xf32>
print_ir(np.empty((7,), np.float16))(
partial(lax.convert_element_type, new_dtype=np.float32))
# CHECK-LABEL: TEST: convert_element_type complex64[7]
# CHECK: mhlo.real
# CHECK-SAME: tensor<7xcomplex<f32>>
# CHECK-SAME: tensor<7xf32>
print_ir(np.empty((7,), np.complex64))(
partial(lax.convert_element_type, new_dtype=np.float32))
# CHECK-LABEL: TEST: convert_element_type float32[7]
# CHECK: mhlo.compare
# CHECK-SAME: tensor<7xf32>
# CHECK-SAME: tensor<7xi1>
print_ir(np.empty((7,), np.float32))(
partial(lax.convert_element_type, new_dtype=np.bool_))
# CHECK-LABEL: TEST: clz uint32[]
# CHECK: mhlo.count_leading_zeros
# CHECK-SAME: tensor<ui32>
print_ir(np.uint32(0))(lax.clz)
# CHECK-LABEL: TEST: conj complex64[]
# CHECK-DAG: mhlo.real
# CHECK-DAG: mhlo.imag
# CHECK-DAG: mhlo.neg
# CHECK-DAG: mhlo.complex
# CHECK-SAME: tensor<complex<f32>>
print_ir(np.complex64(0))(lax.conj)
# CHECK-LABEL: TEST: cos float32[]
# CHECK: mhlo.cos
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.cos)
# CHECK-LABEL: TEST: cosh float32[]
# CHECK: xla_fallback_cosh
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.cosh)
# CHECK-LABEL: TEST: digamma float32[]
# CHECK: chlo.digamma
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.digamma)
# CHECK-LABEL: TEST: div float32[] float32[]
# CHECK: mhlo.div
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.div)
# CHECK-LABEL: TEST: eq float32[] float32[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type FLOAT">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction EQ">
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.eq)
# CHECK-LABEL: TEST: eq complex128[] complex128[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type FLOAT">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction EQ">
# CHECK-SAME: tensor<complex<f64>>
print_ir(np.complex128(1), np.complex128(2))(lax.eq)
# CHECK-LABEL: TEST: eq int64[] int64[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type SIGNED">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction EQ">
# CHECK-SAME: tensor<i64>
print_ir(np.int64(1), np.int64(2))(lax.eq)
# CHECK-LABEL: TEST: eq uint16[] uint16[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type UNSIGNED">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction EQ">
# CHECK-SAME: tensor<ui16>
print_ir(np.uint16(1), np.uint16(2))(lax.eq)
# CHECK-LABEL: TEST: erf float32[]
# CHECK: xla_fallback_erf
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.erf)
# CHECK-LABEL: TEST: erfc float32[]
# CHECK: xla_fallback_erfc
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.erfc)
# CHECK-LABEL: TEST: erf_inv float32[]
# CHECK: xla_fallback_erf_inv
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.erf_inv)
# CHECK-LABEL: TEST: exp float16[]
# CHECK: mhlo.exp
# CHECK-SAME: tensor<f16>
print_ir(np.float16(0))(lax.exp)
# CHECK-LABEL: TEST: expm1 bfloat16[]
# CHECK: mhlo.exponential_minus_one
# CHECK-SAME: tensor<bf16>
print_ir(jnp.bfloat16(0))(lax.expm1)
# CHECK-LABEL: TEST: floor bfloat16[2,3]
# CHECK: mhlo.floor
# CHECK-SAME: tensor<2x3xbf16>
print_ir(np.empty((2, 3), jnp.bfloat16))(lax.floor)
# CHECK-LABEL: TEST: ge float32[] float32[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type FLOAT">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction GE">
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.ge)
# CHECK-LABEL: TEST: gt float32[] float32[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type FLOAT">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction GT">
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.gt)
# CHECK-LABEL: TEST: igamma float32[] float32[]
# CHECK: xla_fallback_igamma
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0), np.float32(0))(lax.igamma)
# CHECK-LABEL: TEST: igammac float32[] float32[]
# CHECK: xla_fallback_igammac
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0), np.float32(0))(lax.igammac)
# CHECK-LABEL: TEST: igamma_grad_a float32[] float32[]
# CHECK: xla_fallback_igamma_grad_a
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0), np.float32(0))(lax.igamma_grad_a)
# CHECK-LABEL: TEST: imag complex64[]
# CHECK: mhlo.imag
# CHECK-SAME: tensor<complex<f32>>
print_ir(np.complex64(0))(lax.imag)
# CHECK-LABEL: TEST: integer_pow float32[]
# CHECK-DAG: mhlo.mul
# CHECK-SAME: tensor<f32>
@print_ir(np.float32(1))
def integer_pow(x): return lax.integer_pow(x, 3)
# CHECK-LABEL: TEST: is_finite float64[]
# CHECK: mhlo.is_finite
# CHECK-SAME: tensor<f64>
print_ir(np.float64(0))(lax.is_finite)
# CHECK-LABEL: TEST: le float32[] float32[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type FLOAT">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction LE">
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.le)
# CHECK-LABEL: TEST: lgamma float32[]
# CHECK: chlo.lgamma
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.lgamma)
# CHECK-LABEL: TEST: log float32[]
# CHECK: mhlo.log
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.log)
# CHECK-LABEL: TEST: log1p float32[]
# CHECK: mhlo.log_plus_one
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.log1p)
# CHECK-LABEL: TEST: lt float32[] float32[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type FLOAT">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction LT">
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.lt)
# CHECK-LABEL: TEST: max float32[] float32[]
# CHECK: mhlo.max
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.max)
# CHECK-LABEL: TEST: min float32[] float32[]
# CHECK: mhlo.min
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.min)
# CHECK-LABEL: TEST: mul float32[] float32[]
# CHECK: mhlo.mul
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.mul)
# CHECK-LABEL: TEST: ne float32[] float32[]
# CHECK: mhlo.compare
# CHECK-SAME: compare_type = #mhlo<"comparison_type FLOAT">
# CHECK-SAME: comparison_direction = #mhlo<"comparison_direction NE">
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.ne)
# CHECK-LABEL: TEST: neg int64[]
# CHECK: mhlo.negate
# CHECK-SAME: tensor<i64>
print_ir(np.int64(0))(lax.neg)
# CHECK-LABEL: TEST: nextafter float32[] float32[]
# CHECK: chlo.next_after
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0), np.float32(0))(lax.nextafter)
# CHECK-LABEL: TEST: bitwise_not int64[]
# CHECK: mhlo.not
# CHECK-SAME: tensor<i64>
print_ir(np.int64(0))(lax.bitwise_not)
# CHECK-LABEL: TEST: bitwise_not bool[]
# CHECK: mhlo.not
# CHECK-SAME: tensor<i1>
print_ir(np.bool_(0))(lax.bitwise_not)
# CHECK-LABEL: TEST: population_count uint32[]
# CHECK: mhlo.popcnt
# CHECK-SAME: tensor<ui32>
print_ir(np.uint32(0))(lax.population_count)
# CHECK-LABEL: TEST: pow float32[] float32[]
# CHECK: mhlo.power
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.pow)
# CHECK-LABEL: TEST: random_gamma_grad float32[] float32[]
# CHECK: xla_fallback_random_gamma_grad
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0), np.float32(0))(lax.random_gamma_grad)
# CHECK-LABEL: TEST: real complex128[]
# CHECK: mhlo.real
# CHECK-SAME: tensor<complex<f64>>
print_ir(np.complex128(0))(lax.real)
# CHECK-LABEL: TEST: reduce_precision bfloat16[]
# CHECK: mhlo.reduce_precision
# CHECK-SAME: tensor<bf16>
print_ir(jnp.bfloat16(0))(
partial(lax.reduce_precision, exponent_bits=2, mantissa_bits=2))
# CHECK-LABEL: TEST: rem float32[] float32[]
# CHECK: mhlo.rem
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.rem)
# CHECK-LABEL: TEST: round float64[7,1]
# CHECK: mhlo.round
# CHECK-SAME: tensor<7x1xf64>
print_ir(np.empty((7,1), np.float64))(
partial(lax.round, rounding_method=lax.RoundingMethod.AWAY_FROM_ZERO))
# CHECK-LABEL: TEST: rsqrt complex64[]
# CHECK: mhlo.rsqrt
# CHECK-SAME: tensor<complex<f32>>
print_ir(jnp.complex64(0))(lax.rsqrt)
# CHECK-LABEL: TEST: shift_left uint32[] uint32[]
# CHECK: mhlo.shift_left
# CHECK-SAME: tensor<ui32>
print_ir(np.uint32(0), np.uint32(0))(lax.shift_left)
# CHECK-LABEL: TEST: shift_right_arithmetic uint8[] uint8[]
# CHECK: mhlo.shift_right_arithmetic
# CHECK-SAME: tensor<ui8>
print_ir(np.uint8(0), np.uint8(0))(lax.shift_right_arithmetic)
# CHECK-LABEL: TEST: shift_right_logical uint16[] uint16[]
# CHECK: mhlo.shift_right_logical
# CHECK-SAME: tensor<ui16>
print_ir(np.uint16(0), np.uint16(0))(lax.shift_right_logical)
# CHECK-LABEL: TEST: sign int64[]
# CHECK: mhlo.sign
# CHECK-SAME: tensor<i64>
print_ir(np.int64(0))(lax.sign)
# CHECK-LABEL: TEST: sign uint32[]
# CHECK: mhlo.compare
# CHECK-SAME: tensor<ui32>
print_ir(np.uint32(0))(lax.sign)
# CHECK-LABEL: TEST: sin float32[]
# CHECK: mhlo.sin
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.sin)
# CHECK-LABEL: TEST: sinh float32[]
# CHECK: xla_fallback_sinh
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.sinh)
# CHECK-LABEL: TEST: sub float32[] float32[]
# CHECK: mhlo.sub
# CHECK-SAME: tensor<f32>
print_ir(np.float32(1), np.float32(2))(lax.sub)
# CHECK-LABEL: TEST: sqrt bfloat16[]
# CHECK: mhlo.sqrt
# CHECK-SAME: tensor<bf16>
print_ir(jnp.bfloat16(0))(lax.sqrt)
# CHECK-LABEL: TEST: tan float16[]
# CHECK: mhlo.sine
# CHECK-SAME: tensor<f32>
# CHECK: mhlo.cosine
# CHECK-SAME: tensor<f32>
print_ir(np.float16(0))(lax.tan)
# CHECK-LABEL: TEST: tanh float32[]
# CHECK: mhlo.tanh
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.tanh)
def main(_):
# CHECK-LABEL: TEST: bitwise_not bool[7]
# CHECK: mhlo.not
# CHECK-SAME: tensor<7xi1>
print_ir(np.empty([7], np.bool_))(lax.bitwise_not)
# CHECK-LABEL: TEST: neg int8[]
# CHECK: mhlo.negate
# CHECK-SAME: tensor<i8>
print_ir(np.int8(0))(lax.neg)
# CHECK-LABEL: TEST: neg int16[0]
# CHECK: mhlo.negate
# CHECK-SAME: tensor<0xi16>
print_ir(np.empty([0], np.int16))(lax.neg)
# CHECK-LABEL: TEST: neg int32[2,3]
# CHECK: mhlo.negate
# CHECK-SAME: tensor<2x3xi32>
print_ir(np.empty([2, 3], np.int32))(lax.neg)
# CHECK-LABEL: TEST: neg int64[2,3,4]
# CHECK: mhlo.negate
# CHECK-SAME: tensor<2x3x4xi64>
print_ir(np.empty([2, 3, 4], np.int64))(lax.neg)
# CHECK-LABEL: TEST: add uint8[4,0,1] uint8[4,0,1]
# CHECK: mhlo.add
# CHECK-SAME: tensor<4x0x1xui8>
print_ir(np.empty([4, 0, 1], np.uint8), np.empty([4, 0, 1],
np.uint8))(lax.add)
# CHECK-LABEL: TEST: add uint16[] uint16[]
# CHECK: mhlo.add
# CHECK-SAME: tensor<ui16>
print_ir(np.uint16(0), np.uint16(0))(lax.add)
# CHECK-LABEL: TEST: add uint32[] uint32[]
# CHECK: mhlo.add
# CHECK-SAME: tensor<ui32>
print_ir(np.uint32(0), np.uint32(0))(lax.add)
# CHECK-LABEL: TEST: add uint64[] uint64[]
# CHECK: mhlo.add
# CHECK-SAME: tensor<ui64>
print_ir(np.uint64(0), np.uint64(0))(lax.add)
# CHECK-LABEL: TEST: sin float16[]
# CHECK: mhlo.sine
# CHECK-SAME: tensor<f16>
print_ir(np.float16(0))(lax.sin)
# CHECK-LABEL: TEST: sin bfloat16[]
# CHECK: mhlo.sine
# CHECK-SAME: tensor<bf16>
print_ir(jnp.bfloat16(0))(lax.sin)
# CHECK-LABEL: TEST: sin float32[]
# CHECK: mhlo.sine
# CHECK-SAME: tensor<f32>
print_ir(np.float32(0))(lax.sin)
# CHECK-LABEL: TEST: sin float64[]
# CHECK: mhlo.sine
# CHECK-SAME: tensor<f64>
print_ir(np.float64(0))(lax.sin)
# CHECK-LABEL: TEST: cos complex64[]
# CHECK: mhlo.cosine
# CHECK-SAME: tensor<complex<f32>>
print_ir(np.complex64(0))(lax.cos)
# CHECK-LABEL: TEST: cos complex128[]
# CHECK: mhlo.cosine
# CHECK-SAME: tensor<complex<f64>>
print_ir(np.complex128(0))(lax.cos)
def temporal_shift_tpu(x: types.TensorLike,
num_frames: int,
channel_shift_fraction: float = 0.125) -> jnp.ndarray:
"""Performs a temporal shift: https://arxiv.org/abs/1811.08383.
TPU optimized version of TSM. Reshape is avoided by having the images
reshaped in [T * B, :] so that frames corresponding to same time frame in
videos are contiguous in memory. Thanks to cr/288510308 which allows to fuse
pad->slice into convolution, we reformulate the slice pad into a pad then
slice. Finally, to avoid concatenate that prevent some fusion from happening
we simply sum masked version of the features.
Args:
x: Input expected to be [T * B, H, W, C] (where the batch has been reshaped
from a time major version of the input).
num_frames: number of frames T per video.
channel_shift_fraction: fraction of the channel to shift forward and
backward.
Returns:
The temporal shifted version of x.
"""
# B, T, H, W, C = batch_size, num_frames, im_height, im_width, channels
# Input is (T * B, H, W, C)
original_shape = list(x.shape)
batch_size = int(original_shape[0] / num_frames)
n_channels = int(original_shape[-1])
n_shift = int(n_channels * channel_shift_fraction)
# Cast to bfloat16.
x = x.astype(jnp.bfloat16)
# For the following, assume that x has 3 channels [x1, x2, x3] and n_shift=1.
# Shift backward, we first pad by zeros [x1, x2, x3, 0, 0].
orig_shp = list(x.shape)
shifted_backward_padding = ((0, batch_size, 0), (0, 0, 0), (0, 0, 0),
(0, n_channels - n_shift, 0))
x_backward_padding = jax.lax.pad(x,
padding_value=jnp.bfloat16(0.),
padding_config=shifted_backward_padding)
# The following shift gets to [x3^+1, 0, 0] (where +1 means from the future).
shifted_backward = jax.lax.slice(x_backward_padding,
(batch_size, 0, 0, n_channels - n_shift),
(orig_shp[0] + batch_size, orig_shp[1],
orig_shp[2], 2 * n_channels - n_shift))
# Shift forward, we first pad by zeros [0, 0, x1, x2, x3].
shifted_forward_padding = ((batch_size, 0, 0), (0, 0, 0), (0, 0, 0),
(n_channels - n_shift, 0, 0))
x_forward_padding = jax.lax.pad(x,
padding_value=jnp.bfloat16(0.),
padding_config=shifted_forward_padding)
# The following shift gets to [0, 0, x1^-1] (where -1 means from the past).
shifted_forward = jax.lax.slice(
x_forward_padding, (0, 0, 0, 0),
(orig_shp[0], orig_shp[1], orig_shp[2], n_channels))
# No shift is in the middle, this gets [0, x2, 0].
mask_noshift = (jnp.reshape(
(jnp.arange(n_channels) >= n_shift) &
(jnp.arange(n_channels) < n_channels - n_shift),
(1, 1, 1, -1))).astype(jnp.bfloat16)
no_shift = mask_noshift * x
# By summing everything together, we end up with [x3^+1, x2, x1^-1].
# Note: channels have been reordered but that doesn't matter for the model.
shifted_x = shifted_backward + shifted_forward + no_shift
return shifted_x.astype(jnp.float32)
def self_attention(inputs,
variable_dictionary,
num_heads: int,
qkv_features: int = None,
padding_mask: List[bool] = None,
dropout_rate: float = 0.,
deterministic: bool = False,
precision: Precision = None,
kernel_init: List[float] = nn.linear.default_kernel_init,
bias_init: List[float] = nn.initializers.zeros,
dtype: jnp.dtype = jnp.float32,
bias: bool = True):
"""Applies Multi-head self-attention on the input data.
Args:
inputs: input data of shape `[bs, dim1, dim2, ..., dimN, features]`.
variable_dictionary: Parameter dictionary.
num_heads: number of attention heads. Features (i.e. inputs.shape[-1])
should be divisible by the number of heads.
qkv_features: dimension of the key, query, and value.
padding_mask: boolean specifying tokens that are pad token.
dropout_rate: dropout rate
deterministic: bool, deterministic or not (to apply dropout)
precision: numerical precision of the computation see `jax.lax.Precision`
for details.
kernel_init: initializer for the kernel of the Dense layers.
bias_init: initializer for the bias of the Dense layers.
dtype: datatype for the activiations, jnp.bfloat16 or jnp.float32
bias: bool: whether pointwise QKVO dense transforms use bias.
Returns:
output of shape `[bs, dim1, dim2, ..., dimN, features//num_heads]`.
"""
features = inputs.shape[-1]
qkv_features = qkv_features or features
assert qkv_features % num_heads == 0, (
'Memory dimension must be divisible by number of heads.')
head_dim = qkv_features // num_heads
inputs = inputs.astype(dtype)
if FLAGS.use_einsum:
dense_module = Dense3D
else:
dense_module = attention.DenseGeneral
query = dense_module.call(variable_dictionary['query'],
inputs,
axis=-1,
features=(num_heads, head_dim),
kernel_init=kernel_init,
bias_init=bias_init,
bias=bias,
precision=precision,
dtype=dtype,
name='query')
query = jnp.multiply(query, 1.0 / math.sqrt(float(head_dim)))
key = dense_module.call(variable_dictionary['key'],
inputs,
axis=-1,
features=(num_heads, head_dim),
kernel_init=kernel_init,
bias_init=bias_init,
bias=bias,
precision=precision,
dtype=dtype,
name='key')
value = dense_module.call(variable_dictionary['value'],
inputs,
axis=-1,
features=(num_heads, head_dim),
kernel_init=kernel_init,
bias_init=bias_init,
bias=bias,
precision=precision,
dtype=dtype,
name='value')
assert query.dtype == dtype
assert key.dtype == dtype
assert value.dtype == dtype
# get raw attention scores from dot product between key and query
# B = batch size (number of sequences)
# F = `from_tensor` sequence length
# T = `to_tensor` sequence length
# N = `num_heads`
# H = `head_dim` (qkv_features // num_heads)
attention_scores = jnp.einsum('BTNH,BFNH->BNFT', key, query)
assert attention_scores.dtype == dtype
assert attention_scores.dtype == dtype
# create attention masks
if padding_mask is not None:
assert padding_mask.dtype == bool, ('Mask should have bool type.')
attention_mask = jnp.expand_dims(padding_mask, axis=1)
adder = (1.0 - attention_mask) * NEG_INFINITY
attention_scores += adder.astype(dtype)
assert attention_scores.dtype == dtype
attention_scores = attention_scores - lax.stop_gradient(
jnp.max(attention_scores, axis=-1, keepdims=True))
attention_scores = jnp.exp(attention_scores)
attention_sum = jnp.sum(attention_scores, axis=-1, keepdims=True)
keep_prob = 1 - dropout_rate
if not deterministic:
keep_mask = jax.random.bernoulli(nn.make_rng(), keep_prob,
attention_scores.shape).astype(dtype)
assert keep_mask.dtype == dtype
attention_probs = jnp.multiply(keep_mask, attention_scores)
else:
attention_probs = attention_scores
assert attention_probs.dtype == dtype
attention_probs = jnp.einsum('BNFT,BTNH->BFNH', attention_probs, value)
assert attention_probs.dtype == dtype
attention_probs = attention_probs / jnp.transpose(attention_sum,
[0, 2, 1, 3])
# split mask and scaling ops in dropout
# move the scaling from dropout to here to save same mul ops
# TODO(yuemmawang) automate this optimization in xla
if not deterministic:
scale = 1 / keep_prob
if dtype == jnp.bfloat16:
scale = jnp.bfloat16(scale)
attention_probs = jnp.multiply(attention_probs, scale)
assert attention_probs.dtype == dtype
return attention_probs
