def test_return_type(self):
x_bf16 = jnp.array(1.0, dtype=jnp.bfloat16)
y_bf16 = fp_cast.downcast_sat_ftz(
x_bf16, exp_min=-11, exp_max=4, sig_bits=3)
self.assertEqual(x_bf16.dtype, y_bf16.dtype)
xf32 = jnp.array(1.0, dtype=jnp.bfloat16)
yf32 = fp_cast.downcast_sat_ftz(
xf32, exp_min=-11, exp_max=4, sig_bits=3)
self.assertEqual(xf32.dtype, yf32.dtype)
def to_quantized(self, x, *, dtype):
"""Quantizes the argument to the target format.
integer: "upscales", rounds or floors and clips.
floating-point: optionally upscales, then downcasts to target precision.
Args:
x: Argument to be quantized.
dtype: Type of returned quantized value of x. If quantized x is an input
to a matmul, we might be want to set it to jnp.int8. If quantized x is
weights stored in memory, same applies. In fake_quant style we might
prefer to set dtype=SCALE_DTYPE, since quantized x might get constant
folded with rescale op (`from_quantized`). Please take a look at the
comment on SCALE_DTYPE.
Returns:
Quantized value of x.
"""
if isinstance(self._prec, _FloatQuant):
if self._prec.is_scaled:
x = jnp.multiply(x, self._scale).astype(x.dtype)
fp_spec = self._prec.fp_spec
return fp_cast.downcast_sat_ftz(
x,
fp_spec.exp_min,
fp_spec.exp_max,
fp_spec.sig_bits,
)
else:
if self._symmetric:
quantize = primitives.round_and_clip_to_signed_int
else:
quantize = primitives.floor_and_clip_to_unsigned_int
scaled_x = jnp.multiply(x, self._scale)
return quantize(scaled_x, prec=self._prec, dtype=dtype)
def test_downcast_sat_ftz(self, dtype, argument_result_values):
argument_result = jnp.array(
argument_result_values,
dtype=dtype,
)
y = fp_cast.downcast_sat_ftz(
argument_result[:, 0],
exp_min=-11,
exp_max=4,
sig_bits=3,
)
onp.testing.assert_equal(
onp.array(argument_result[:, 1], dtype=onp.float32),
onp.array(y, dtype=onp.float32),
)
def test_attributes_create_weights_op_fp(
self,
weight_range,
weight_shape,
fp_quant,
):
weights = jnp.array(
fp32(onp.random.uniform(*weight_range, size=weight_shape)))
axis = None if weight_shape[1] == 1 else 0
weights_quant_op = QuantOps.create_weights_ops(
w=weights,
weight_params=QuantOps.WeightParams(prec=fp_quant, axis=axis))
max_weight = onp.max(abs(weights), axis=0)
onp.testing.assert_array_equal(
jnp.squeeze(weights_quant_op._scale),
jnp.exp2(-jnp.floor(jnp.log2(max_weight))))
self.assertEqual(weights_quant_op._symmetric, True)
self.assertIs(weights_quant_op._prec, fp_quant)
weights_scaled = (weights * weights_quant_op._scale).astype(
weights.dtype)
weights_quant_expected = fp_cast.downcast_sat_ftz(
weights_scaled,
fp_quant.fp_spec.exp_min,
fp_quant.fp_spec.exp_max,
fp_quant.fp_spec.sig_bits,
)
weights_quant_calculated = weights_quant_op.to_quantized(
weights, dtype=SCALE_DTYPE)
onp.testing.assert_array_equal(weights_quant_expected,
weights_quant_calculated)
# Test the lower (23 - fp_quant.fp_spec.sig_bits) bits of the calculated
# quantized weights are zero.
sig_mask = jnp.int32((1 << (23 - fp_quant.fp_spec.sig_bits)) - 1)
onp.testing.assert_array_equal(
weights_quant_calculated.view(jnp.int32) & sig_mask,
jnp.zeros_like(weights))
def test_invalid_argument_type(self):
x_s8 = jnp.array(1, dtype=jnp.int8)
with self.assertRaises(ValueError):
fp_cast.downcast_sat_ftz(x_s8, exp_min=-11, exp_max=4, sig_bits=3)
def downcast_and_sum(x):
return jnp.sum(
fp_cast.downcast_sat_ftz(x,
sig_bits=sig_bits,
exp_min=exp_min,
exp_max=exp_max))
def test_sig_bits_zero(self):
x = jnp.array(2.11111)
y = fp_cast.downcast_sat_ftz(x, exp_min=-11, exp_max=4, sig_bits=0)
self.assertEqual(y.item(), 2.0)
