This is an automated email from the ASF dual-hosted git repository.
lukhut pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/main by this push:
new 1222398342 [TFLite] Support quantized GREATER op in TFLite frontend
(#12754)
1222398342 is described below
commit 12223983422868bbbc5444f66d175aeb9318b71f
Author: Dhruv Chauhan <89972057+dchauhan-...@users.noreply.github.com>
AuthorDate: Mon Sep 12 21:03:56 2022 +0100
[TFLite] Support quantized GREATER op in TFLite frontend (#12754)
Support GREATER quantization operation conversion as part of issue #9187
Continuation of #11519.
---
python/tvm/relay/frontend/tflite.py | 19 ++++++-----
tests/python/frontend/tflite/test_forward.py | 49 +++++++++++++++-------------
2 files changed, 37 insertions(+), 31 deletions(-)
diff --git a/python/tvm/relay/frontend/tflite.py
b/python/tvm/relay/frontend/tflite.py
index c38191b389..6c68230e0e 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -1291,7 +1291,13 @@ class OperatorConverter(object):
return out
- def _convert_elemwise(self, relay_op, op, ignore_qnn_params=False):
+ def _convert_elemwise(
+ self,
+ relay_op,
+ op,
+ ignore_qnn_params=False,
+ comparison_op=False,
+ ):
"""Generic method to Convert TFLite elemwise"""
try:
from tflite.AddOptions import AddOptions
@@ -1316,7 +1322,7 @@ class OperatorConverter(object):
# TFLite format demands equal scale and zero_point tuple parameters
for some operations
# to allow us to use non-quantized operation instead of quantized if
ignore_qnn_params=True
- if ignore_qnn_params:
+ if ignore_qnn_params and not comparison_op:
assert (
lhs_tensor.qnn_params
and self.has_same_qnn_params(lhs_tensor, output_tensor)
@@ -1431,12 +1437,7 @@ class OperatorConverter(object):
def convert_greater(self, op):
"""Convert TFLite GREATER"""
- # Check if the input tensor is quantized, call QNN op
- if self.is_quantized(op):
- raise tvm.error.OpNotImplemented(
- "TFlite quantized GREATER operator is not supported yet."
- )
- return self._convert_elemwise(_op.greater, op)
+ return self._convert_elemwise(_op.greater, op, self.is_quantized(op),
comparison_op=True)
def convert_squared_difference(self, op):
"""Convert TFLite SQUARED DIFFERENCE"""
@@ -1475,7 +1476,7 @@ class OperatorConverter(object):
def convert_equal(self, op):
"""Convert TFLite EQUAL"""
- return self._convert_elemwise(_op.equal, op, self.is_quantized(op))
+ return self._convert_elemwise(_op.equal, op, self.is_quantized(op),
comparison_op=True)
def convert_not_equal(self, op):
"""Convert TFLite NOT_EQUAL"""
diff --git a/tests/python/frontend/tflite/test_forward.py
b/tests/python/frontend/tflite/test_forward.py
index 7267b72548..18045b8e83 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -2254,6 +2254,7 @@ def _test_elemwise(
quantized=False,
qnn_op=None,
same_qnn_params=False,
+ comparison_op=False,
):
"""One iteration of elemwise"""
@@ -2298,7 +2299,7 @@ def _test_elemwise(
if x[0] is not None
}
- if math_op is math_ops.equal:
+ if comparison_op:
out = math_op(inq_data[0], inq_data[1])
out = with_fused_activation_function(out,
fused_activation_function)
@@ -2307,6 +2308,9 @@ def _test_elemwise(
[x + ":0" for x in input_range.keys()],
[x[1] for x in zip(in_data, inq_data) if x[0] is not None],
[out],
+ quantized=True,
+ input_range=input_range,
+ experimental_new_converter=same_qnn_params,
)
else:
out = math_op(inq_data[0], inq_data[1])
@@ -2314,6 +2318,7 @@ def _test_elemwise(
out = tf.quantization.fake_quant_with_min_max_args(
out, min=out_min, max=out_max, name="out"
)
+
# Note same_qnn_params uses experimental_new_converter as toco
failed
compare_tflite_with_tvm(
[x[1] for x in zip(in_data, data) if x[0] is not None],
@@ -2440,9 +2445,17 @@ def _test_minimum(data, fused_activation_function=None,
quantized=False, qnn_op=
# -------
-def _test_greater(data):
+def _test_greater(data, fused_activation_function=None, quantized=False,
qnn_op=None):
"""One iteration of greater"""
- return _test_elemwise(math_ops.greater, data)
+ return _test_elemwise(
+ math_ops.greater,
+ data,
+ fused_activation_function,
+ quantized,
+ qnn_op,
+ same_qnn_params=True,
+ comparison_op=True,
+ )
#######################################################################
@@ -2489,6 +2502,7 @@ def _test_equal(data, fused_activation_function=None,
quantized=False, qnn_op=No
quantized,
qnn_op,
same_qnn_params=True,
+ comparison_op=True,
)
@@ -2555,25 +2569,14 @@ def _test_forward_elemwise(testop):
def _test_forward_elemwise_quantized(testop):
- if testop is not _test_equal:
- testop(
- [
- np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8),
- np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8),
- ],
- quantized=True,
- qnn_op=testop,
- )
- else:
- # no need for fake_quant to hold tensors in float32 until conversion
- testop(
- [
- np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.float32),
- np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.float32),
- ],
- quantized=True,
- qnn_op=testop,
- )
+ testop(
+ [
+ np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8),
+ np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8),
+ ],
+ quantized=True,
+ qnn_op=testop,
+ )
def _test_elemwise_qnn_out_range(qnn_op):
@@ -2585,6 +2588,7 @@ def _test_elemwise_qnn_out_range(qnn_op):
_test_maximum: (-112, 111),
_test_minimum: (-128, 127),
_test_equal: (-150, 150),
+ _test_greater: (-150, 150),
}
return qnn_out_range[qnn_op]
@@ -2615,6 +2619,7 @@ def test_all_elemwise():
_test_forward_elemwise(_test_minimum)
_test_forward_elemwise_quantized(_test_minimum)
_test_forward_elemwise(_test_greater)
+ _test_forward_elemwise_quantized(_test_greater)
_test_forward_elemwise(_test_squared_difference)
_test_forward_elemwise(_test_greater_equal)
_test_forward_elemwise(_test_less)
