# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import tvm import numpy as np from tvm import relay from tvm.contrib import graph_runtime roundings = ["UPWARD", "TONEAREST"] def verify(mod, goldens): with relay.build_config(opt_level=3): graph, lib, params = relay.build(mod, "llvm", params=None) golden_data, golden_output = goldens rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0)) rt_mod.set_input("quantized_data",golden_data) rt_mod.set_input(**params) rt_mod.run() res = rt_mod.get_output(0).asnumpy() np.testing.assert_equal(res, golden_output) def get_mod(data_shape, data_dtype, out_dtype, input_scale, output_scale, input_zero_point=0, output_zero_point=0, rounding="TONEAREST"): quantized_data = relay.var("quantized_data", shape=data_shape, dtype=data_dtype) mod = relay.qnn.op.requantize( quantized_data, input_scale=input_scale, input_zero_point=input_zero_point, output_scale=output_scale, output_zero_point=output_zero_point, rounding=rounding, out_dtype=out_dtype) mod = relay.Function(relay.analysis.free_vars(mod), mod) mod = relay.Module.from_expr(mod) return mod def test_same_scale(): # Have same scales, everything within range golden_data = np.arange(-100, 100, 1).astype('int32') golden_output = golden_data for rounding in roundings: mod = get_mod(data_shape=(200, ), data_dtype='int32', out_dtype="int8", input_scale=0.5, output_scale=0.5, rounding=rounding) assert 'right_shift' not in mod.astext() verify(mod, (golden_data, golden_output)) def test_downscale(): for rounding in roundings: mod = get_mod(data_shape=(32, ), data_dtype='int32', out_dtype='int8', input_scale=1, output_scale=16, rounding=rounding) # Try positive values # 8 corresponds to 0.5, resulting in 1 golden_data = np.arange(0, 32, 1).astype('int32') golden_output = np.repeat([0, 1, 2], [8, 16, 8]) verify(mod, (golden_data, golden_output)) # Try negative values # -8 corresponds to -0.5. For UPWARD, this is 0 golden_data = np.arange(0, -32, -1).astype('int32') if rounding == "UPWARD": golden_output = np.repeat([0, -1, -2], [9, 16, 7]) else: golden_output = np.repeat([0, -1, -2], [8, 16, 8]) verify(mod, (golden_data, golden_output)) # Try a different scale mod = get_mod(data_shape=(32, ), data_dtype='int32', out_dtype="int8", input_scale=1, output_scale=4, rounding=rounding) # Try positive values # 2I corresponds to 0.5, resulting in 1 golden_data = np.arange(0, 32, 1).astype('int32') golden_output = np.repeat([0, 1, 2, 3, 4, 5, 6, 7, 8], [2, 4, 4, 4, 4, 4, 4, 4, 2]) verify(mod, (golden_data, golden_output)) # Try negative values # -8 corresponds to -0.5. For UPWARD, this is 0 golden_data = np.arange(0, -32, -1).astype('int32') if rounding == "UPWARD": golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8], [3, 4, 4, 4, 4, 4, 4, 4, 1]) else: golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8], [2, 4, 4, 4, 4, 4, 4, 4, 2]) verify(mod, (golden_data, golden_output)) # Try uint8 out_dtype mod = get_mod(data_shape=(32, ), data_dtype='int32', out_dtype='uint8', input_scale=1, output_scale=16, rounding=rounding) # Try positive values # 8 corresponds to 0.5, resulting in 1 golden_data = np.arange(0, 32, 1).astype('int32') golden_output = np.repeat([0, 1, 2], [8, 16, 8]) verify(mod, (golden_data, golden_output)) # Try uint8 in_dtyope and uint8 out_dtype mod = get_mod(data_shape=(32, ), data_dtype='uint8', out_dtype='uint8', input_scale=1, output_scale=16, rounding=rounding) # Try positive values # 8 corresponds to 0.5, resulting in 1 golden_data = np.arange(0, 32, 1).astype('int32') golden_output = np.repeat([0, 1, 2], [8, 16, 8]) verify(mod, (golden_data, golden_output)) def test_upscale(): for rounding in roundings: mod = get_mod(data_shape=(32, ), data_dtype='int32', out_dtype="int8", input_scale=2, output_scale=1, rounding=rounding) # Try positive values # 8 corresponds to 0.5, resulting in 1 golden_data = np.arange(0, 32, 1).astype('int32') golden_output = np.multiply(2, golden_data) verify(mod, (golden_data, golden_output)) # Try negative values # -8 corresponds to -0.5. For UPWARD, this is 0 golden_data = np.arange(0, -32, -1).astype('int32') golden_output = np.multiply(2, golden_data) verify(mod, (golden_data, golden_output)) def test_saturation(): for rounding in roundings: mod = get_mod(data_shape=(16, ), data_dtype='int32', out_dtype="int8", input_scale=0.5, output_scale=0.5, rounding=rounding) golden_data = np.arange(0, 16, 1).astype('int32') golden_data = np.add(120, golden_data) output = np.array([120, 121, 122, 123, 124, 125, 126, 127, 127, 127, 127, 127, 127, 127, 127, 127]) golden_output = output verify(mod, (golden_data, golden_output)) # Try negative numbers golden_data = np.arange(0, -16, -1).astype('int32') golden_data = np.add(-120, golden_data) output = np.array([-120, -121, -122, -123, -124, -125, -126, -127, -128, -128, -128, -128, -128, -128, -128, -128]) golden_output = output verify(mod, (golden_data, golden_output)) def test_zero_point(): # Output zero point for rounding in roundings: mod = get_mod(data_shape=(32, ), data_dtype='int32', out_dtype='int8', input_scale=1, output_scale=16, output_zero_point=1, rounding=rounding) # Try positive values # 8 corresponds to 0.5, resulting in 1 golden_data = np.arange(0, 32, 1).astype('int32') golden_output = np.repeat([0, 1, 2], [8, 16, 8]) golden_output = np.add(1, golden_output) verify(mod, (golden_data, golden_output)) # Try negative values # -8 corresponds to -0.5. For UPWARD, this is 0 golden_data = np.arange(-32, -64, -1).astype('int32') if rounding == "UPWARD": golden_output = np.repeat([-2, -3, -4], [9, 16, 7]) else: golden_output = np.repeat([-2, -3, -4], [8, 16, 8]) golden_output = np.add(1, golden_output) verify(mod, (golden_data, golden_output)) # Input zero point for rounding in roundings: mod = get_mod(data_shape=(32, ), data_dtype='int32', out_dtype='int8', input_scale=1, output_scale=16, input_zero_point=16, rounding=rounding) # Try positive values golden_data = np.arange(32, 64, 1).astype('int32') golden_output = np.repeat([2, 3, 4], [8, 16, 8]) golden_output = np.subtract(golden_output, 1) verify(mod, (golden_data, golden_output)) # Try negative values golden_data = np.arange(-32, -64, -1).astype('int32') if rounding == "UPWARD": golden_output = np.repeat([-2, -3, -4], [9, 16, 7]) else: golden_output = np.repeat([-2, -3, -4], [8, 16, 8]) golden_output = np.subtract(golden_output, 1) verify(mod, (golden_data, golden_output)) if __name__ == "__main__": test_same_scale() test_downscale() test_upscale() test_saturation() test_zero_point()