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Add narrow_range to DequantizeOp

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PiperOrigin-RevId: 272068762
This commit is contained in:
Paul Donnelly 2019-09-30 14:40:18 -07:00 committed by TensorFlower Gardener
parent ddc1f64e08
commit 720a51829b
10 changed files with 73 additions and 59 deletions
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49
tensorflow/core/api_def/base_api/api_def_Dequantize.pbtxt
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@ -15,7 +15,7 @@ END
summary: "Dequantize the \'input\' tensor into a float Tensor."
description: <<END
[min_range, max_range] are scalar floats that specify the range for
the 'input' data. The 'mode' attribute controls exactly which calculations are
the output. The 'mode' attribute controls exactly which calculations are
used to convert the float values to their quantized equivalents.
In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
@ -47,45 +47,24 @@ const double offset_input = static_cast<double>(input) - lowest_quantized;
result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
```
*SCALED mode Example*
If the mode is `SCALED`, dequantization is performed by multiplying each
input value by a scaling_factor. (Thus an input of 0 always maps to 0.0).
`SCALED` mode matches the quantization approach used in
`QuantizeAndDequantize{V2|V3}`.
The scaling_factor is determined from `min_range`, `max_range`, and
`narrow_range` in a way that is compatible with `QuantizeAndDequantize{V2|V3}`
and `QuantizeV2`, using the following algorithm:
If the mode is `SCALED`, we do not use the full range of the output type,
choosing to elide the lowest possible value for symmetry (e.g., output range is
-127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
0.
We first find the range of values in our tensor. The
range we use is always centered on 0, so we find m such that
```c++
m = max(abs(input_min), abs(input_max))
```
Our input tensor range is then `[-m, m]`.
const int min_expected_T = std::numeric_limits<T>::min() +
(narrow_range ? 1 : 0);
const int max_expected_T = std::numeric_limits<T>::max();
const float max_expected_T = std::numeric_limits<float>::max();
Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.
If T is signed, this is
```
num_bits = sizeof(T) * 8
[min_fixed, max_fixed] =
[-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
```
Otherwise, if T is unsigned, the fixed-point range is
```
[min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
```
From this we compute our scaling factor, s:
```c++
s = (2 * m) / (max_fixed - min_fixed)
```
Now we can dequantize the elements of our tensor:
```c++
result = input * s
const float scale_factor =
(std::numeric_limits<T>::min() == 0) ? (max_range / max_expected_T)
: std::max(min_range / min_expected_T,
max_range / max_expected_T);
```
END
}

10
tensorflow/core/kernels/dequantize_op.cc
View File

@ -56,6 +56,7 @@ class DequantizeOp : public OpKernel {
} else if (mode_string == "SCALED") {
mode_ = QUANTIZE_MODE_SCALED;
}
OP_REQUIRES_OK(ctx, ctx->GetAttr("narrow_range", &narrow_range_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("axis", &axis_));
}
@ -133,10 +134,12 @@ class DequantizeOp : public OpKernel {
output);
}
} else if (mode_ == QUANTIZE_MODE_SCALED) {
const int min_output_value =
std::numeric_limits<T>::min() + (narrow_range_ ? 1 : 0);
const float scale_factor =
std::numeric_limits<T>::min() == 0
? (max_range / std::numeric_limits<T>::max())
: std::max(min_range / std::numeric_limits<T>::min(),
: std::max(min_range / min_output_value,
max_range / std::numeric_limits<T>::max());
const auto& input_tensor = input.flat<T>();
output->flat<float>() =
@ -168,10 +171,12 @@ class DequantizeOp : public OpKernel {
((input.template cast<float>() + half_range) * scale_factor) +
min_range;
} else if (mode_ == QUANTIZE_MODE_SCALED) {
const int min_output_value =
std::numeric_limits<T>::min() + (narrow_range_ ? 1 : 0);
const float scale_factor =
std::numeric_limits<T>::min() == 0
? (max_range / std::numeric_limits<T>::max())
: std::max(min_range / std::numeric_limits<T>::min(),
: std::max(min_range / min_output_value,
max_range / std::numeric_limits<T>::max());
output.device(d) = input.template cast<float>() * scale_factor;
}
@ -180,6 +185,7 @@ class DequantizeOp : public OpKernel {
private:
int mode_;
int axis_;
bool narrow_range_;
};
REGISTER_KERNEL_BUILDER(

1
tensorflow/core/ops/array_ops.cc
View File

@ -3073,6 +3073,7 @@ REGISTER_OP("Dequantize")
.Output("output: float")
.Attr("T: quantizedtype")
.Attr("mode: {'MIN_COMBINED', 'MIN_FIRST', 'SCALED'} = 'MIN_COMBINED'")
.Attr("narrow_range: bool = false")
.Attr("axis: int = -1")
.SetShapeFn([](InferenceContext* c) {
int axis = -1;

15
tensorflow/python/ops/array_ops.py
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@ -4514,7 +4514,7 @@ def quantize_v2(
raise ValueError("input should have known rank to use negative axis.")
axis %= input.shape.ndims
if compat.forward_compatible(2019, 9, 25) or axis >= 0:
if compat.forward_compatible(2019, 9, 25) or axis >= 0 or narrow_range:
return gen_array_ops.quantize_v2(
input,
min_range,
@ -4579,14 +4579,14 @@ def quantize(
@tf_export("quantization.dequantize", v1=["quantization.dequantize",
"dequantize"])
@deprecation.deprecated_endpoints("dequantize")
def dequantize(
def dequantize( # pylint: disable=missing-docstring
input, # pylint: disable=redefined-builtin
min_range,
max_range,
mode="MIN_COMBINED",
name=None,
axis=None):
"""Dequantize tensor to the specified range."""
axis=None,
narrow_range=False):
if axis is None:
axis = -1
elif axis < 0:
@ -4594,12 +4594,15 @@ def dequantize(
raise ValueError("input should have known rank to use negative axis.")
axis %= input.shape.ndims
if compat.forward_compatible(2019, 9, 25) or axis >= 0:
if compat.forward_compatible(2019, 10, 22) or axis >= 0 or narrow_range:
return gen_array_ops.dequantize(
input, min_range, max_range, mode=mode, name=name, axis=axis)
input, min_range, max_range, mode=mode, name=name,
narrow_range=narrow_range, axis=axis)
return gen_array_ops.dequantize(
input, min_range, max_range, mode=mode, name=name)
dequantize.__doc__ = gen_array_ops.dequantize.__doc__
@tf_export("quantization.quantize_and_dequantize")
def quantize_and_dequantize(

47
tensorflow/python/ops/dequantize_op_test.py
View File

@ -31,10 +31,12 @@ class DequantizeOpTest(test.TestCase):
def __init__(self, method_name="runTest"):
super(DequantizeOpTest, self).__init__(method_name)
def _testDequantizeOp(self, inputs, min_range, max_range, dtype):
def _testDequantizeOp(self, inputs, min_range, max_range, dtype,
mode="MIN_COMBINED", narrow_range=False):
with self.cached_session():
input_op = constant_op.constant(inputs, shape=[len(inputs)], dtype=dtype)
dequantized = array_ops.dequantize(input_op, min_range, max_range)
dequantized = array_ops.dequantize(input_op, min_range, max_range,
mode=mode, narrow_range=narrow_range)
tf_ans = self.evaluate(dequantized)
# TODO(vrv): Add support for DT_QINT32 quantization if needed.
@ -44,19 +46,26 @@ class DequantizeOpTest(test.TestCase):
dtypes.quint16: np.uint16,
dtypes.qint16: np.int16
}
self.assertTrue(dtype in type_dict.keys())
self.assertIn(dtype, type_dict.keys())
v_max = np.iinfo(type_dict[dtype]).max
v_min = np.iinfo(type_dict[dtype]).min
self.assertTrue(min_range >= v_min)
self.assertTrue(max_range <= v_max)
self.assertGreaterEqual(min_range, v_min)
self.assertLessEqual(max_range, v_max)
type_range = v_max - v_min
if v_min < 0:
half_range = (type_range + 1) / 2
else:
half_range = 0.0
np_ans = ((inputs.astype(np.float32) + half_range) *
(max_range - min_range) / type_range) + min_range
if mode == "MIN_COMBINED":
if v_min < 0:
half_range = (type_range + 1) / 2
else:
half_range = 0.0
np_ans = ((inputs.astype(np.float32) + half_range) *
(max_range - min_range) / type_range) + min_range
elif mode == "SCALED":
if narrow_range:
v_min += 1
scale_factor = max(min_range / v_min, max_range / v_max)
np_ans = inputs.astype(np.float32) * scale_factor
self.assertAllClose(tf_ans, np_ans, rtol=1e-5, atol=1e-5)
def testBasicQuint8(self):
@ -70,6 +79,22 @@ class DequantizeOpTest(test.TestCase):
self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8)
self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8)
def testScaledMode(self):
self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8,
mode="SCALED")
self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8,
mode="SCALED")
self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8,
mode="SCALED")
def testNarrowRange(self):
self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8,
mode="SCALED", narrow_range=True)
self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8,
mode="SCALED", narrow_range=True)
self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8,
mode="SCALED", narrow_range=True)
def testAxis(self):
# Generates a tensor of the specified `shape` using values from `values`
# scaled by (slice_idx + 1) along `axis` dimension.

2
tensorflow/tools/api/golden/v1/tensorflow.pbtxt
View File

@ -1106,7 +1106,7 @@ tf_module {
}
member_method {
name: "dequantize"
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\', \'axis\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\', \'None\'], "
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\', \'axis\', \'narrow_range\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\', \'None\', \'False\'], "
}
member_method {
name: "deserialize_many_sparse"

2
tensorflow/tools/api/golden/v1/tensorflow.quantization.pbtxt
View File

@ -2,7 +2,7 @@ path: "tensorflow.quantization"
tf_module {
member_method {
name: "dequantize"
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\', \'axis\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\', \'None\'], "
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\', \'axis\', \'narrow_range\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\', \'None\', \'False\'], "
}
member_method {
name: "fake_quant_with_min_max_args"

2
tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt
View File

@ -1026,7 +1026,7 @@ tf_module {
}
member_method {
name: "Dequantize"
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'-1\', \'None\'], "
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'narrow_range\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'False\', \'-1\', \'None\'], "
}
member_method {
name: "DeserializeIterator"

2
tensorflow/tools/api/golden/v2/tensorflow.quantization.pbtxt
View File

@ -2,7 +2,7 @@ path: "tensorflow.quantization"
tf_module {
member_method {
name: "dequantize"
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\', \'axis\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\', \'None\'], "
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\', \'axis\', \'narrow_range\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\', \'None\', \'False\'], "
}
member_method {
name: "fake_quant_with_min_max_args"

2
tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt
View File

@ -1026,7 +1026,7 @@ tf_module {
}
member_method {
name: "Dequantize"
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'-1\', \'None\'], "
argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'narrow_range\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'False\', \'-1\', \'None\'], "
}
member_method {
name: "DeserializeIterator"