Skip to content

Commit

Permalink
[Frontend][TFLite] Dynamically calculate input_stats of any fake_quan…
Browse files Browse the repository at this point in the history 
…t range (apache#4789)

* [TFLite] Dynamically calculate input_stats of any fake_quant range

* pass the input range to the convertor and caclulate (mean, scale) there
* change the range of the second tensor in elemwise operations
  so that we test inputs with different quant params
* change the possible output range for elemwise ops wrt the updated ranges
* update the comments for (m, s) calculations
* add input range dict to reduce_mean op

* Apply requested changes

* add exception handling for zero division in input_stats
* fix range of the input tensor in elemwsie
  • Loading branch information
@inadob @zhiics
inadob authored and zhiics committed Mar 2, 2020
1 parent 2faf2a3 commit 2ce2ca7
Showing 1 changed file with 27 additions and 16 deletions.
43 changes: 27 additions & 16 deletions tests/python/frontend/tflite/test_forward.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -123,7 +123,8 @@ def run_tflite_graph(tflite_model_buf, input_data):


def compare_tflite_with_tvm(in_data, in_name, input_tensors,
output_tensors, init_global_variables=False, out_names=None, quantized=False):
output_tensors, init_global_variables=False,
out_names=None, quantized=False, input_range=None):
"""Generic function to generate and compare TFLite and TVM output"""
in_data = convert_to_list(in_data)
in_name = convert_to_list(in_name)
Expand All @@ -143,11 +144,16 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors,
converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
input_arrays = converter.get_input_arrays()
input_stats = {}
# hardcode the mean_values and std_dev_values (m,s) to be the same
# if all inputs are in (float_min; float_max) == (-100, 100)
# calculate the mean and quantization scale for every input tensor,
# with respect to its fp32 input range, defined in fake_quant.
# s = 255/(fmax-fmin); m = -fmin*s (the zero point)
for i in input_arrays:
input_stats[i] = (128., 1.275)
try:
quant_scale = 255 / (input_range[i][1] - input_range[i][0])
except ZeroDivisionError:
raise ZeroDivisionError('Min and max of the input range for tensor ' + i + ' can\'t be equal')
mean = - input_range[i][0] * quant_scale
input_stats[i] = (mean, quant_scale)
converter.quantized_input_stats = input_stats

tflite_model_buffer = converter.convert()
Expand Down Expand Up @@ -757,13 +763,15 @@ def _test_elemwise(math_op, data, fused_activation_function=None, quantized=Fals
if quantized:
# fake_quant will keep the tensors in float32 until the conversion in the session
inq_data = [tf.quantization.fake_quant_with_min_max_args(in_data[0], min=-100, max=100, name="inq_0"),
tf.quantization.fake_quant_with_min_max_args(in_data[1], min=-100, max=100, name="inq_1")]
tf.quantization.fake_quant_with_min_max_args(in_data[1], min=-50, max=50, name="inq_1")]
input_range = {'inq_0': (-100, 100), 'inq_1': (-50, 50)}
out = math_op(inq_data[0], inq_data[1])
out = with_fused_activation_function(out, fused_activation_function)
# set the quantized output range with respect to the operation
# set the fp32 output range with respect to the operation
out_min, out_max = _test_elemwise_qnn_out_range(qnn_op)
out = tf.quantization.fake_quant_with_min_max_args(out, min=out_min, max=out_max, name="out")
compare_tflite_with_tvm(data, ['inq_0:0', 'inq_1:0'], inq_data, [out], quantized=True)
compare_tflite_with_tvm(data, ['inq_0:0', 'inq_1:0'], inq_data, [out],
quantized=True, input_range=input_range)
else:
out = math_op(in_data[0], in_data[1])
out = with_fused_activation_function(out, fused_activation_function)
Expand All @@ -775,13 +783,14 @@ def _test_elemwise(math_op, data, fused_activation_function=None, quantized=Fals

if quantized:
inq_data = [tf.quantization.fake_quant_with_min_max_args(in_data[0], min=-100, max=100, name="inq_0")]
inq_const = tf.quantization.fake_quant_with_min_max_args(data[1], min=-100, max=100, name="const_tensor")
inq_const = tf.quantization.fake_quant_with_min_max_args(data[1], min=-50, max=50, name="const_tensor")
input_range = {'inq_0': (-100, 100)}
# the 2nd tensor is treated as constant and directly added as part of the operation
out = math_op(inq_data, ops.convert_to_tensor(inq_const, dtype='float32', name='inq_const'))
out = with_fused_activation_function(out, fused_activation_function)
out_min, out_max = _test_elemwise_qnn_out_range(qnn_op)
out = tf.quantization.fake_quant_with_min_max_args(out, min=out_min, max=out_max, name="out")
compare_tflite_with_tvm(data[0], ['inq_0:0'], inq_data, [out], quantized=True)
compare_tflite_with_tvm(data[0], ['inq_0:0'], inq_data, [out], quantized=True, input_range=input_range)
else:
out = math_op(in_data[0], ops.convert_to_tensor(data[1], dtype=data[1].dtype))
out = with_fused_activation_function(out, fused_activation_function)
Expand All @@ -793,13 +802,14 @@ def _test_elemwise(math_op, data, fused_activation_function=None, quantized=Fals

if quantized:
inq_const = tf.quantization.fake_quant_with_min_max_args(data[0], min=-100, max=100, name="const_tensor")
inq_data = [tf.quantization.fake_quant_with_min_max_args(in_data[0], min=-100, max=100, name="inq_1")]
inq_data = [tf.quantization.fake_quant_with_min_max_args(in_data[0], min=-50, max=50, name="inq_1")]
input_range = {'inq_1': (-50, 50)}
# the 1st tensor is treated as constant and directly added as part of the operation
out = math_op(ops.convert_to_tensor(inq_const, dtype='float32', name='inq_const'), inq_data)
out = with_fused_activation_function(out, fused_activation_function)
out_min, out_max = _test_elemwise_qnn_out_range(qnn_op)
out = tf.quantization.fake_quant_with_min_max_args(out, min=out_min, max=out_max, name="out")
compare_tflite_with_tvm(data[1], ['inq_1:0'], inq_data, [out], quantized=True)
compare_tflite_with_tvm(data[1], ['inq_1:0'], inq_data, [out], quantized=True, input_range=input_range)
else:
out = math_op(ops.convert_to_tensor(data[0], dtype=data[0].dtype), in_data[0])
out = with_fused_activation_function(out, fused_activation_function)
Expand Down Expand Up @@ -920,11 +930,11 @@ def _test_forward_elemwise_quantized(testop):
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):
# set the fake_quant output range if input tensors are in [-100, 100] float32
# set the fake_quant output range with respect to the input tensors float32 range
qnn_out_range = {
_test_add: (-200, 200),
_test_sub: (-200, 200),
_test_mul: (-1e+4, 1e+4),
_test_add: (-150, 150),
_test_sub: (-150, 150),
_test_mul: (-5e+3, 5e+3),
}

return qnn_out_range[qnn_op]
Expand Down Expand Up @@ -994,9 +1004,10 @@ def _test_reduce_quantize(math_op, data, keep_dims=None):
with tf.Graph().as_default():
in_data = [array_ops.placeholder(shape=data[0].shape, dtype="float32", name='in')]
inq_data = [tf.quantization.fake_quant_with_min_max_args(in_data[0], min=-100, max=100, name="inq_0")]
input_range = {'inq_0': (-100, 100)}
out = math_op(inq_data, data[1], keep_dims)
out = tf.quantization.fake_quant_with_min_max_args(out, min=-200, max=200, name="out")
compare_tflite_with_tvm([data[0]], ['inq_0:0'], [inq_data[0]], [out], quantized=True)
compare_tflite_with_tvm([data[0]], ['inq_0:0'], [inq_data[0]], [out], quantized=True, input_range=input_range)


#######################################################################
Expand Down

0 comments on commit 2ce2ca7

Please sign in to comment.