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new 495ddc57f4 [TFLite][Frontend] Support for quantized squared difference
(#14667)
495ddc57f4 is described below
commit 495ddc57f416cae4325e4311a7ed5186d3be069d
Author: Ashutosh Parkhi <[email protected]>
AuthorDate: Thu Apr 20 16:07:25 2023 +0100
[TFLite][Frontend] Support for quantized squared difference (#14667)
In line with the TFLu implementation, this commit
enables mapping of quantized squared difference
to Relay's float32 squred difference.
Co-authored-by: Shai Maor <[email protected]>
Co-authored-by: Leandro Nunes <[email protected]>
---
python/tvm/relay/frontend/tflite.py | 15 ++++++++---
tests/python/frontend/tflite/test_forward.py | 37 +++++++++++++++++++++++-----
2 files changed, 43 insertions(+), 9 deletions(-)
diff --git a/python/tvm/relay/frontend/tflite.py
b/python/tvm/relay/frontend/tflite.py
index 9daf7f716f..78b9844a13 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -1466,10 +1466,19 @@ class OperatorConverter(object):
def convert_squared_difference(self, op):
"""Convert TFLite SQUARED DIFFERENCE"""
# Check if the input tensor is quantized, call QNN op
+ #
(https://github.com/tensorflow/tflite-micro/blob/bc35c3ed9c7ab93b3a13b46fce936f854bcfce2c/tensorflow/lite/micro/kernels/squared_difference.cc#L157)
# pylint: disable=line-too-long
if self.is_quantized(op):
- raise tvm.error.OpNotImplemented(
- "TFlite quantized squared difference operator is not supported
yet."
- )
+ input_tensors = self.get_input_tensors(op)
+ output_tensors = self.get_output_tensors(op)
+ lhs_expr = self.get_tensor_expr(input_tensors[0])
+ rhs_expr = self.get_tensor_expr(input_tensors[1])
+ assert len(input_tensors) == 2, "input tensors length should be 2"
+ assert len(output_tensors) == 1, "output tensors length should be
1"
+ lhs_expr_f32 = self.dequantize(lhs_expr, input_tensors[0])
+ rhs_expr_f32 = self.dequantize(rhs_expr, input_tensors[1])
+ out_f32 = _op.subtract(lhs_expr_f32, rhs_expr_f32)
+ return self.quantize(out_f32 * out_f32, output_tensors[0])
+
difference = self._convert_elemwise(_op.subtract, op)
# _convert_elemwise has guaranteed only have one output tensor
exp_type =
self.get_tensor_type_str(self.get_output_tensors(op)[0].tensor.Type())
diff --git a/tests/python/frontend/tflite/test_forward.py
b/tests/python/frontend/tflite/test_forward.py
index 41eb1f3067..fa4844f070 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -305,7 +305,7 @@ def compare_tflite_with_tvm(
mode="graph_executor",
experimental_new_converter=False,
fp16_quantized=False,
- int_quant_dtype=tf.int8,
+ int_quant_dtype=tf.uint8,
):
"""Generic function to generate and compare TFLite and TVM output"""
in_data = convert_to_list(in_data)
@@ -334,6 +334,8 @@ def compare_tflite_with_tvm(
converter.target_spec.supported_ops = [
tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8
]
+ elif int_quant_dtype == tf.int8:
+ converter.inference_type = tf.lite.constants.INT8
else:
# default to int8 quantization
converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
@@ -2327,6 +2329,16 @@ def _test_elemwise(
def __test_elemwise(in_data):
assert len(in_data) == 2
if quantized:
+ int_quant_dtype = None
+ if data[0].dtype == "int8":
+ int_quant_dtype = tf.int8
+ elif data[0].dtype == "uint8":
+ int_quant_dtype = tf.uint8
+ elif data[0].dtype == "int16":
+ int_quant_dtype = tf.int16
+ else:
+ assert False, "Unsupported conversion from numpy to tflite
dtype!"
+
# set the fp32 output range with respect to the operation
out_min, out_max = _test_elemwise_qnn_out_range(qnn_op)
inq0_min, inq0_max = (-100, 100)
@@ -2375,6 +2387,7 @@ def _test_elemwise(
quantized=True,
input_range=input_range,
experimental_new_converter=same_qnn_params,
+ int_quant_dtype=int_quant_dtype,
)
else:
out = math_op(inq_data[0], inq_data[1])
@@ -2392,6 +2405,7 @@ def _test_elemwise(
quantized=True,
input_range=input_range,
experimental_new_converter=same_qnn_params,
+ int_quant_dtype=int_quant_dtype,
)
else:
out = math_op(
@@ -2585,9 +2599,16 @@ def _test_not_equal(data):
# ------------------
-def _test_squared_difference(data):
+def _test_squared_difference(data, fused_activation_function=None,
quantized=False, qnn_op=None):
"""One iteration of squared difference"""
- return _test_elemwise(math_ops.squared_difference, data)
+ return _test_elemwise(
+ math_ops.squared_difference,
+ data,
+ fused_activation_function,
+ quantized,
+ qnn_op,
+ same_qnn_params=True,
+ )
#######################################################################
@@ -2632,11 +2653,13 @@ def _test_forward_elemwise(testop):
)
-def _test_forward_elemwise_quantized(testop):
+def _test_forward_elemwise_quantized(testop, dtype=np.uint8):
+ type_info = np.iinfo(dtype)
+ _min, _max = type_info.min, type_info.max
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),
+ np.array(np.random.uniform(_min, _max, (3, 6)), dtype=dtype),
+ np.array(np.random.uniform(_min, _max, (3, 6)), dtype=dtype),
],
quantized=True,
qnn_op=testop,
@@ -2653,6 +2676,7 @@ def _test_elemwise_qnn_out_range(qnn_op):
_test_minimum: (-128, 127),
_test_equal: (-150, 150),
_test_greater: (-150, 150),
+ _test_squared_difference: (0, 65025),
}
return qnn_out_range[qnn_op]
@@ -2685,6 +2709,7 @@ def test_all_elemwise():
_test_forward_elemwise(_test_greater)
_test_forward_elemwise_quantized(_test_greater)
_test_forward_elemwise(_test_squared_difference)
+ _test_forward_elemwise_quantized(_test_squared_difference, np.int8)
_test_forward_elemwise(_test_greater_equal)
_test_forward_elemwise(_test_less)
_test_forward_elemwise(_test_less_equal)
