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Remove developer facing api from frontend exports. #5375

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Apr 19, 2020
Merged

Remove developer facing api from frontend exports. #5375

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4 changes: 0 additions & 4 deletions python/tvm/relay/frontend/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,10 +24,6 @@
from __future__ import absolute_import

from .mxnet import from_mxnet
from .mxnet_qnn_op_utils import dequantize_mxnet_min_max
from .mxnet_qnn_op_utils import quantize_mxnet_min_max
from .mxnet_qnn_op_utils import get_mkldnn_int8_scale
from .mxnet_qnn_op_utils import get_mkldnn_uint8_scale
from .mxnet_qnn_op_utils import quantize_conv_bias_mkldnn_from_var
from .keras import from_keras
from .onnx import from_onnx
Expand Down
37 changes: 19 additions & 18 deletions tests/python/frontend/mxnet/test_qnn_ops_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,10 +16,14 @@
# under the License.

import tvm
from tvm import te
import numpy as np
from tvm import relay
from tvm.contrib import graph_runtime
from tvm.relay.frontend.mxnet_qnn_op_utils import dequantize_mxnet_min_max, \
quantize_mxnet_min_max, \
get_mkldnn_int8_scale, \
get_mkldnn_uint8_scale, \
quantize_conv_bias_mkldnn_from_var


def test_mkldnn_dequantize():
Expand All @@ -29,11 +33,10 @@ def dequantize_test_driver(in_dtype, quant_args, in_data, verify_output_data):
input_data = relay.var("input_data", shape=shape, dtype=in_dtype)
min_range = quant_args['min_range']
max_range = quant_args['max_range']
dequantized_output = \
relay.frontend.dequantize_mxnet_min_max(input_data,
min_range=min_range,
max_range=max_range,
in_dtype=in_dtype)
dequantized_output = dequantize_mxnet_min_max(input_data,
min_range=min_range,
max_range=max_range,
in_dtype=in_dtype)
mod = relay.Function(relay.analysis.free_vars(dequantized_output), dequantized_output)
mod = tvm.IRModule.from_expr(mod)
with relay.build_config(opt_level=3):
Expand Down Expand Up @@ -79,17 +82,15 @@ def test_int8_to_float32():


def test_mkldnn_quantize():

def quantize_test_driver(out_dtype, quant_args, in_data, verify_output_data):
shape = in_data.shape
input_data = relay.var("input_data", shape=shape, dtype='float32')
min_range = quant_args['min_range']
max_range = quant_args['max_range']
quantized_output, _, _ = \
relay.frontend.quantize_mxnet_min_max(input_data,
min_range=min_range,
max_range=max_range,
out_dtype=out_dtype)
quantized_output, _, _ = quantize_mxnet_min_max(input_data,
min_range=min_range,
max_range=max_range,
out_dtype=out_dtype)
mod = relay.Function(relay.analysis.free_vars(quantized_output), quantized_output)
mod = tvm.IRModule.from_expr(mod)
with relay.build_config(opt_level=3):
Expand Down Expand Up @@ -140,24 +141,24 @@ def test_get_mkldnn_int8_scale():
range_min = -3.904039
range_max = 3.904039
expected = 0.03061991354976495
output = relay.frontend.get_mkldnn_int8_scale(range_max=range_max,
range_min=range_min)
output = get_mkldnn_int8_scale(range_max=range_max,
range_min=range_min)
assert np.allclose(output, expected)


def test_get_mkldnn_uint8_scale():
range_min = 0.0
range_max = 55.77269
expected = 0.21828841189047482
output = relay.frontend.get_mkldnn_uint8_scale(range_max=range_max,
range_min=range_min)
output = get_mkldnn_uint8_scale(range_max=range_max,
range_min=range_min)
assert np.allclose(output, expected)


def test_quantize_conv_bias_mkldnn_from_var():
bias_var = relay.var('bias', shape=(3,), dtype='float32')
bias_scale = tvm.nd.array(np.array([0.5, 0.6, 0.7]))
output = relay.frontend.quantize_conv_bias_mkldnn_from_var(bias_var, bias_scale)
output = quantize_conv_bias_mkldnn_from_var(bias_var, bias_scale)
assert isinstance(output, tvm.relay.expr.Call)
attrs = output.attrs
assert attrs.axis == 0
Expand All @@ -171,4 +172,4 @@ def test_quantize_conv_bias_mkldnn_from_var():
test_mkldnn_quantize()
test_get_mkldnn_int8_scale()
test_get_mkldnn_uint8_scale()
test_quantize_conv_bias_mkldnn_from_var()
test_quantize_conv_bias_mkldnn_from_var()