gussmith23 commented on a change in pull request #5812: URL: https://github.com/apache/incubator-tvm/pull/5812#discussion_r469608060
########## File path: tests/python/unittest/test_custom_datatypes_change_dtype.py ########## @@ -0,0 +1,481 @@ +# 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. +"""Utilities for changing datatypes of models.""" +import tvm +import topi.testing +import numpy as np +from numpy.random import MT19937, RandomState, SeedSequence +from tvm import relay +from tvm.relay.testing.inception_v3 import get_workload as get_inception +from tvm.relay.testing.resnet import get_workload as get_resnet +from tvm.relay.testing.mobilenet import get_workload as get_mobilenet +from tvm.target.datatype import register, register_min_func, register_op, create_lower_func, lower_ite +from nose.tools import nottest + +tgt = "llvm" +# we use a random seed to generate input_data +# to guarantee stable tests +rs = RandomState(MT19937(SeedSequence(123456789))) + +def convert_ndarray(dst_dtype, *arrays): + """Converts NDArray(s) into the specified datatype""" + def convert(array): + x = relay.var('x', shape=array.shape, dtype=str(array.dtype)) + cast = relay.Function([x], x.astype(dst_dtype)) + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + return relay.create_executor('graph').evaluate(cast)(array) + + return tuple([convert(x) for x in arrays]) + + +def change_dtype(src, dst, module, params): + module = relay.frontend.ChangeDatatype(src, dst)(module) + module = relay.transform.InferType()(module) + params = dict((p, convert_ndarray(dst, params[p])) for p in params) + return module, params + +def compare(module, input, src_dtype, dst_dtype, rtol, atol, params = {}): + ex = relay.create_executor("graph", mod=module) + + correct = ex.evaluate()(*input, **params) + + module, _ = change_dtype(src_dtype, dst_dtype, module, []) + ex = relay.create_executor("graph", mod=module) + # converts all inputs to dst_dtype + x_converted = convert_ndarray(dst_dtype, *input) + + # Vectorization is not implemented with custom datatypes + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + maybe_correct = ex.evaluate()(*x_converted, **params) + # TODO(andrew) this only works on single output + maybe_correct_converted = convert_ndarray(src_dtype, maybe_correct)[0] + np.testing.assert_allclose(maybe_correct_converted.asnumpy(), + correct.asnumpy(), + rtol=rtol, + atol=atol) + +def setup(): + """Set up tests + + Currently, this registers some custom datatypes using the Bring Your + Own Datatypes framework. + """ + + # To use datatype operations in an external library, you should first load + # the library containing the datatype implementation: + # CDLL("libposit.so", RTLD_GLOBAL) + # In this case, the datatype library we are using is built right into TVM, + # so we do not need to explicitly load any library. + + # You can pick a code for your datatype arbitrarily, as long as it is + # greater than 128 and has not already been chosen. + + register("posites2", 131) + + register_op(create_lower_func( + { + (32, 32): "FloatToPosit32es2", + (32, 16): "FloatToPosit16es2", + (32, 8): 'FloatToPosit8es2', + }), + "Cast", "llvm", "posites2", "float") + register_op(create_lower_func( + { + (32, 32): "Posit32es2ToFloat", + (16, 32): 'Posit16es2ToFloat', + (8, 32): 'Posit8es2ToFloat', + }), + "Cast", "llvm", "float", "posites2") + register_op(create_lower_func( + { + (4, 32): 'IntToPosit32es2', + (4, 16): 'IntToPosit16es2', + (4, 8): 'IntToPosit8es2' + }), + "Cast", "llvm", "posites2", "int") + register_op(create_lower_func({ + 32: 'Posit32es2Add', + 16: 'Posit16es2Add', + 8: 'Posit8es2Add' + }), "Add", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sub', + 16: 'Posit16es2Sub', + 8: 'Posit8es2Sub' + }), "Sub", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'FloatToPosit32es2', + 16: 'FloatToPosit16es2', + 8: 'FloatToPosit8es2' + }), "FloatImm", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Mul', + 16: 'Posit16es2Mul', + 8: 'Posit8es2Mul' + }), "Mul", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Div', + 16: 'Posit16es2Div', + 8: 'Posit8es2Div' + }), "Div", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Max', + 16: 'Posit16es2Max', + 8: 'Posit8es2Max' + }), "Max", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sqrt', + 16: 'Posit16es2Sqrt', + 8: 'Posit8es2Sqrt' + }), "Call", "llvm", "posites2", intrinsic_name="sqrt") + register_op(lower_ite, + "Call", + "llvm", + "posites2", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({ + 32: 'Posit32es2Exp', + 16: 'Posit16es2Exp', + 8: 'Posit8es2Exp' + }), "Call", "llvm", "posites2", intrinsic_name="exp") + register_op(create_lower_func({ + 32: 'Posit32es2Log', + 16: 'Posit16es2Log', + 8: 'Posit8es2Log' + }), "Call", "llvm", "posites2", intrinsic_name="log") + register_op(create_lower_func({ + 32: 'Posit32es2Sigmoid', + 16: 'Posit16es2Sigmoid', + 8: 'Posit8es2Sigmoid' + }), "Call", "llvm", "posites2", intrinsic_name="sigmoid") + register_op(create_lower_func({ + 32: 'Posit32es2Tanh', + 16: 'Posit16es2Tanh', + 8: 'Posit8es2Tanh' + }), "Call", "llvm", "posites2", intrinsic_name="tanh") + register_min_func(lambda num_bits: - (2 ** 2 ** 2) ** (num_bits - 2), "posites2") + + register("noptype", 132) Review comment: (see comment above about removing noptype) ########## File path: 3rdparty/nop-type/nop-type.cc ########## @@ -0,0 +1,30 @@ +#include <tvm/runtime/c_runtime_api.h> Review comment: I think we can delete noptype entirely, can we not? ########## File path: python/tvm/relay/backend/_backend.py ########## @@ -90,7 +90,11 @@ def _tensor_value_repr(tvalue): @tvm._ffi.register_func("relay._constant_repr") def _tensor_constant_repr(tvalue): - return str(tvalue.data.asnumpy()) + # TODO(gus) do this in a smarter way Review comment: Any ideas of how to do this better? Otherwise we can delete this comment. ########## File path: python/tvm/relay/backend/_backend.py ########## @@ -90,7 +90,11 @@ def _tensor_value_repr(tvalue): @tvm._ffi.register_func("relay._constant_repr") def _tensor_constant_repr(tvalue): - return str(tvalue.data.asnumpy()) + # TODO(gus) do this in a smarter way + try: Review comment: Here, we should actually check for a custom type (`tvm.datatype.get_type_registered()`). Using try/except was my lazy solution. So something like: ``` if tvm.datatype.get_type_registered(tvalue.data.dtype.code): ... else: return str(tvalue.data.asnumpy()) ``` ########## File path: Makefile ########## @@ -90,6 +90,10 @@ scalalint: lint: cpplint pylint jnilint +# Test scripts +pyunittest: + ./tests/scripts/task_python_unittest.sh Review comment: Is this just a convenience thing? ########## File path: CMakeLists.txt ########## @@ -258,7 +260,8 @@ if(NOT USE_RTTI) add_definitions(-DDMLC_ENABLE_RTTI=0) endif() -list(APPEND RUNTIME_SRCS 3rdparty/bfloat16/bfloat16.cc) +list(APPEND RUNTIME_SRCS 3rdparty/posit/posit-wrapper.cc) +list(APPEND RUNTIME_SRCS 3rdparty/nop-type/nop-type.cc) Review comment: (see previous comment about noptype being removed) ########## File path: python/tvm/target/datatype.py ########## @@ -73,7 +73,12 @@ def get_type_registered(type_code): return tvm.runtime._ffi_api._datatype_get_type_registered(type_code) -def register_op(lower_func, op_name, target, type_name, src_type_name=None): +def register_op(lower_func, + op_name, + target, + type_name, + src_type_name=None, + intrinsic_name=None): """Register an external function which computes the given op. Currently, this will only work with Casts and binary expressions Review comment: Hm, this docstring probably needs updating, it doesn't even make sense to me anymore... ########## File path: python/tvm/relay/testing/mobilenet.py ########## @@ -129,6 +129,7 @@ def mobile_net(num_classes=1000, data_shape=(1, 3, 224, 224), bias = relay.var('fc_bias') fc = relay.nn.dense(data=flatten, weight=weight, units=num_classes) fc = relay.nn.bias_add(fc, bias) + # TODO(gus) i think softmax is broken Review comment: We have tests for softmax now, right? So this comment is probably out of date? ########## File path: tests/python/unittest/test_custom_datatypes_change_dtype.py ########## @@ -0,0 +1,481 @@ +# 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. +"""Utilities for changing datatypes of models.""" +import tvm +import topi.testing +import numpy as np +from numpy.random import MT19937, RandomState, SeedSequence +from tvm import relay +from tvm.relay.testing.inception_v3 import get_workload as get_inception +from tvm.relay.testing.resnet import get_workload as get_resnet +from tvm.relay.testing.mobilenet import get_workload as get_mobilenet +from tvm.target.datatype import register, register_min_func, register_op, create_lower_func, lower_ite +from nose.tools import nottest + +tgt = "llvm" +# we use a random seed to generate input_data +# to guarantee stable tests +rs = RandomState(MT19937(SeedSequence(123456789))) + +def convert_ndarray(dst_dtype, *arrays): + """Converts NDArray(s) into the specified datatype""" + def convert(array): + x = relay.var('x', shape=array.shape, dtype=str(array.dtype)) + cast = relay.Function([x], x.astype(dst_dtype)) + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + return relay.create_executor('graph').evaluate(cast)(array) + + return tuple([convert(x) for x in arrays]) + + +def change_dtype(src, dst, module, params): + module = relay.frontend.ChangeDatatype(src, dst)(module) + module = relay.transform.InferType()(module) + params = dict((p, convert_ndarray(dst, params[p])) for p in params) + return module, params + +def compare(module, input, src_dtype, dst_dtype, rtol, atol, params = {}): + ex = relay.create_executor("graph", mod=module) + + correct = ex.evaluate()(*input, **params) + + module, _ = change_dtype(src_dtype, dst_dtype, module, []) + ex = relay.create_executor("graph", mod=module) + # converts all inputs to dst_dtype + x_converted = convert_ndarray(dst_dtype, *input) + + # Vectorization is not implemented with custom datatypes + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + maybe_correct = ex.evaluate()(*x_converted, **params) + # TODO(andrew) this only works on single output + maybe_correct_converted = convert_ndarray(src_dtype, maybe_correct)[0] + np.testing.assert_allclose(maybe_correct_converted.asnumpy(), + correct.asnumpy(), + rtol=rtol, + atol=atol) + +def setup(): + """Set up tests + + Currently, this registers some custom datatypes using the Bring Your + Own Datatypes framework. + """ + + # To use datatype operations in an external library, you should first load + # the library containing the datatype implementation: + # CDLL("libposit.so", RTLD_GLOBAL) + # In this case, the datatype library we are using is built right into TVM, + # so we do not need to explicitly load any library. + + # You can pick a code for your datatype arbitrarily, as long as it is + # greater than 128 and has not already been chosen. + + register("posites2", 131) + + register_op(create_lower_func( + { + (32, 32): "FloatToPosit32es2", + (32, 16): "FloatToPosit16es2", + (32, 8): 'FloatToPosit8es2', + }), + "Cast", "llvm", "posites2", "float") + register_op(create_lower_func( + { + (32, 32): "Posit32es2ToFloat", + (16, 32): 'Posit16es2ToFloat', + (8, 32): 'Posit8es2ToFloat', + }), + "Cast", "llvm", "float", "posites2") + register_op(create_lower_func( + { + (4, 32): 'IntToPosit32es2', + (4, 16): 'IntToPosit16es2', + (4, 8): 'IntToPosit8es2' + }), + "Cast", "llvm", "posites2", "int") + register_op(create_lower_func({ + 32: 'Posit32es2Add', + 16: 'Posit16es2Add', + 8: 'Posit8es2Add' + }), "Add", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sub', + 16: 'Posit16es2Sub', + 8: 'Posit8es2Sub' + }), "Sub", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'FloatToPosit32es2', + 16: 'FloatToPosit16es2', + 8: 'FloatToPosit8es2' + }), "FloatImm", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Mul', + 16: 'Posit16es2Mul', + 8: 'Posit8es2Mul' + }), "Mul", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Div', + 16: 'Posit16es2Div', + 8: 'Posit8es2Div' + }), "Div", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Max', + 16: 'Posit16es2Max', + 8: 'Posit8es2Max' + }), "Max", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sqrt', + 16: 'Posit16es2Sqrt', + 8: 'Posit8es2Sqrt' + }), "Call", "llvm", "posites2", intrinsic_name="sqrt") + register_op(lower_ite, + "Call", + "llvm", + "posites2", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({ + 32: 'Posit32es2Exp', + 16: 'Posit16es2Exp', + 8: 'Posit8es2Exp' + }), "Call", "llvm", "posites2", intrinsic_name="exp") + register_op(create_lower_func({ + 32: 'Posit32es2Log', + 16: 'Posit16es2Log', + 8: 'Posit8es2Log' + }), "Call", "llvm", "posites2", intrinsic_name="log") + register_op(create_lower_func({ + 32: 'Posit32es2Sigmoid', + 16: 'Posit16es2Sigmoid', + 8: 'Posit8es2Sigmoid' + }), "Call", "llvm", "posites2", intrinsic_name="sigmoid") + register_op(create_lower_func({ + 32: 'Posit32es2Tanh', + 16: 'Posit16es2Tanh', + 8: 'Posit8es2Tanh' + }), "Call", "llvm", "posites2", intrinsic_name="tanh") + register_min_func(lambda num_bits: - (2 ** 2 ** 2) ** (num_bits - 2), "posites2") + + register("noptype", 132) + register_op(create_lower_func({ + (32, 32): "FloatToNop32" + }), "Cast", "llvm", "noptype", "float") + register_op(create_lower_func({ + (32, 32): 'Nop32ToFloat' + }), "Cast", "llvm", "float", "noptype") + register_op(create_lower_func({ + (4, 32): "IntToNop32" + }), "Cast", "llvm", "noptype", "int") + register_op(create_lower_func({32: 'IntToNop32'}), "Add", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sub'}), "Sub", "llvm", "noptype") + register_op(create_lower_func({32: 'FloatToNop32'}), "FloatImm", "llvm", + "noptype") + register_op(create_lower_func({32: 'Nop32Mul'}), "Mul", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Div'}), "Div", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Max'}), "Max", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sqrt'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sqrt") + # TODO(gus) not sure if this will work... Review comment: Can delete these now ########## File path: tests/python/unittest/test_custom_datatypes_change_dtype.py ########## @@ -0,0 +1,481 @@ +# 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. +"""Utilities for changing datatypes of models.""" +import tvm +import topi.testing +import numpy as np +from numpy.random import MT19937, RandomState, SeedSequence +from tvm import relay +from tvm.relay.testing.inception_v3 import get_workload as get_inception +from tvm.relay.testing.resnet import get_workload as get_resnet +from tvm.relay.testing.mobilenet import get_workload as get_mobilenet +from tvm.target.datatype import register, register_min_func, register_op, create_lower_func, lower_ite +from nose.tools import nottest + +tgt = "llvm" +# we use a random seed to generate input_data +# to guarantee stable tests +rs = RandomState(MT19937(SeedSequence(123456789))) + +def convert_ndarray(dst_dtype, *arrays): + """Converts NDArray(s) into the specified datatype""" + def convert(array): + x = relay.var('x', shape=array.shape, dtype=str(array.dtype)) + cast = relay.Function([x], x.astype(dst_dtype)) + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + return relay.create_executor('graph').evaluate(cast)(array) + + return tuple([convert(x) for x in arrays]) + + +def change_dtype(src, dst, module, params): + module = relay.frontend.ChangeDatatype(src, dst)(module) + module = relay.transform.InferType()(module) + params = dict((p, convert_ndarray(dst, params[p])) for p in params) + return module, params + +def compare(module, input, src_dtype, dst_dtype, rtol, atol, params = {}): + ex = relay.create_executor("graph", mod=module) + + correct = ex.evaluate()(*input, **params) + + module, _ = change_dtype(src_dtype, dst_dtype, module, []) + ex = relay.create_executor("graph", mod=module) + # converts all inputs to dst_dtype + x_converted = convert_ndarray(dst_dtype, *input) + + # Vectorization is not implemented with custom datatypes + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + maybe_correct = ex.evaluate()(*x_converted, **params) + # TODO(andrew) this only works on single output + maybe_correct_converted = convert_ndarray(src_dtype, maybe_correct)[0] + np.testing.assert_allclose(maybe_correct_converted.asnumpy(), + correct.asnumpy(), + rtol=rtol, + atol=atol) + +def setup(): + """Set up tests + + Currently, this registers some custom datatypes using the Bring Your + Own Datatypes framework. + """ + + # To use datatype operations in an external library, you should first load + # the library containing the datatype implementation: + # CDLL("libposit.so", RTLD_GLOBAL) + # In this case, the datatype library we are using is built right into TVM, + # so we do not need to explicitly load any library. + + # You can pick a code for your datatype arbitrarily, as long as it is + # greater than 128 and has not already been chosen. + + register("posites2", 131) + + register_op(create_lower_func( + { + (32, 32): "FloatToPosit32es2", + (32, 16): "FloatToPosit16es2", + (32, 8): 'FloatToPosit8es2', + }), + "Cast", "llvm", "posites2", "float") + register_op(create_lower_func( + { + (32, 32): "Posit32es2ToFloat", + (16, 32): 'Posit16es2ToFloat', + (8, 32): 'Posit8es2ToFloat', + }), + "Cast", "llvm", "float", "posites2") + register_op(create_lower_func( + { + (4, 32): 'IntToPosit32es2', + (4, 16): 'IntToPosit16es2', + (4, 8): 'IntToPosit8es2' + }), + "Cast", "llvm", "posites2", "int") + register_op(create_lower_func({ + 32: 'Posit32es2Add', + 16: 'Posit16es2Add', + 8: 'Posit8es2Add' + }), "Add", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sub', + 16: 'Posit16es2Sub', + 8: 'Posit8es2Sub' + }), "Sub", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'FloatToPosit32es2', + 16: 'FloatToPosit16es2', + 8: 'FloatToPosit8es2' + }), "FloatImm", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Mul', + 16: 'Posit16es2Mul', + 8: 'Posit8es2Mul' + }), "Mul", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Div', + 16: 'Posit16es2Div', + 8: 'Posit8es2Div' + }), "Div", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Max', + 16: 'Posit16es2Max', + 8: 'Posit8es2Max' + }), "Max", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sqrt', + 16: 'Posit16es2Sqrt', + 8: 'Posit8es2Sqrt' + }), "Call", "llvm", "posites2", intrinsic_name="sqrt") + register_op(lower_ite, + "Call", + "llvm", + "posites2", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({ + 32: 'Posit32es2Exp', + 16: 'Posit16es2Exp', + 8: 'Posit8es2Exp' + }), "Call", "llvm", "posites2", intrinsic_name="exp") + register_op(create_lower_func({ + 32: 'Posit32es2Log', + 16: 'Posit16es2Log', + 8: 'Posit8es2Log' + }), "Call", "llvm", "posites2", intrinsic_name="log") + register_op(create_lower_func({ + 32: 'Posit32es2Sigmoid', + 16: 'Posit16es2Sigmoid', + 8: 'Posit8es2Sigmoid' + }), "Call", "llvm", "posites2", intrinsic_name="sigmoid") + register_op(create_lower_func({ + 32: 'Posit32es2Tanh', + 16: 'Posit16es2Tanh', + 8: 'Posit8es2Tanh' + }), "Call", "llvm", "posites2", intrinsic_name="tanh") + register_min_func(lambda num_bits: - (2 ** 2 ** 2) ** (num_bits - 2), "posites2") + + register("noptype", 132) + register_op(create_lower_func({ + (32, 32): "FloatToNop32" + }), "Cast", "llvm", "noptype", "float") + register_op(create_lower_func({ + (32, 32): 'Nop32ToFloat' + }), "Cast", "llvm", "float", "noptype") + register_op(create_lower_func({ + (4, 32): "IntToNop32" + }), "Cast", "llvm", "noptype", "int") + register_op(create_lower_func({32: 'IntToNop32'}), "Add", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sub'}), "Sub", "llvm", "noptype") + register_op(create_lower_func({32: 'FloatToNop32'}), "FloatImm", "llvm", + "noptype") + register_op(create_lower_func({32: 'Nop32Mul'}), "Mul", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Div'}), "Div", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Max'}), "Max", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sqrt'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sqrt") + # TODO(gus) not sure if this will work... + register_op(lower_ite, + "Call", + "llvm", + "noptype", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({32: 'Nop32Exp'}), + "Call", + "llvm", + "noptype", + intrinsic_name="exp") + register_op(create_lower_func({32: 'Nop32Log'}), + "Call", + "llvm", + "noptype", + intrinsic_name="log") + register_op(create_lower_func({32: 'Nop32Sigmoid'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sigmoid") + register_op(create_lower_func({32: 'Nop32Tanh'}), + "Call", + "llvm", + "noptype", + intrinsic_name="tanh") + # This can be anything, considering the type isn't functionally correct. + register_min_func(lambda num_bits: 0, "noptype") + + +def run_ops(src_dtype, dst_dtype, rtol=1e-7, atol=1e-7): + """Run the same op, but with two different datatypes""" + def check_unary_op(op, src_dtype, dst_dtype): + t1 = relay.TensorType((5, 10, 5), src_dtype) Review comment: Chooses an arbitrary tensor shape. That's probably fine, but can we define the shape at the top of the function? ########## File path: tests/python/unittest/test_custom_datatypes_change_dtype.py ########## @@ -0,0 +1,481 @@ +# Licensed to the Apache Software Foundation (ASF) under one Review comment: Let's rename this to just `test_custom_datatypes.py`. ########## File path: tests/python/unittest/test_custom_datatypes_change_dtype.py ########## @@ -0,0 +1,481 @@ +# 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. +"""Utilities for changing datatypes of models.""" +import tvm +import topi.testing +import numpy as np +from numpy.random import MT19937, RandomState, SeedSequence +from tvm import relay +from tvm.relay.testing.inception_v3 import get_workload as get_inception +from tvm.relay.testing.resnet import get_workload as get_resnet +from tvm.relay.testing.mobilenet import get_workload as get_mobilenet +from tvm.target.datatype import register, register_min_func, register_op, create_lower_func, lower_ite +from nose.tools import nottest + +tgt = "llvm" +# we use a random seed to generate input_data +# to guarantee stable tests +rs = RandomState(MT19937(SeedSequence(123456789))) + +def convert_ndarray(dst_dtype, *arrays): + """Converts NDArray(s) into the specified datatype""" + def convert(array): + x = relay.var('x', shape=array.shape, dtype=str(array.dtype)) + cast = relay.Function([x], x.astype(dst_dtype)) + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + return relay.create_executor('graph').evaluate(cast)(array) + + return tuple([convert(x) for x in arrays]) + + +def change_dtype(src, dst, module, params): + module = relay.frontend.ChangeDatatype(src, dst)(module) + module = relay.transform.InferType()(module) + params = dict((p, convert_ndarray(dst, params[p])) for p in params) + return module, params + +def compare(module, input, src_dtype, dst_dtype, rtol, atol, params = {}): + ex = relay.create_executor("graph", mod=module) + + correct = ex.evaluate()(*input, **params) + + module, _ = change_dtype(src_dtype, dst_dtype, module, []) + ex = relay.create_executor("graph", mod=module) + # converts all inputs to dst_dtype + x_converted = convert_ndarray(dst_dtype, *input) + + # Vectorization is not implemented with custom datatypes + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + maybe_correct = ex.evaluate()(*x_converted, **params) + # TODO(andrew) this only works on single output + maybe_correct_converted = convert_ndarray(src_dtype, maybe_correct)[0] + np.testing.assert_allclose(maybe_correct_converted.asnumpy(), + correct.asnumpy(), + rtol=rtol, + atol=atol) + +def setup(): + """Set up tests + + Currently, this registers some custom datatypes using the Bring Your + Own Datatypes framework. + """ + + # To use datatype operations in an external library, you should first load + # the library containing the datatype implementation: + # CDLL("libposit.so", RTLD_GLOBAL) + # In this case, the datatype library we are using is built right into TVM, + # so we do not need to explicitly load any library. + + # You can pick a code for your datatype arbitrarily, as long as it is + # greater than 128 and has not already been chosen. + + register("posites2", 131) + + register_op(create_lower_func( + { + (32, 32): "FloatToPosit32es2", + (32, 16): "FloatToPosit16es2", + (32, 8): 'FloatToPosit8es2', + }), + "Cast", "llvm", "posites2", "float") + register_op(create_lower_func( + { + (32, 32): "Posit32es2ToFloat", + (16, 32): 'Posit16es2ToFloat', + (8, 32): 'Posit8es2ToFloat', + }), + "Cast", "llvm", "float", "posites2") + register_op(create_lower_func( + { + (4, 32): 'IntToPosit32es2', + (4, 16): 'IntToPosit16es2', + (4, 8): 'IntToPosit8es2' + }), + "Cast", "llvm", "posites2", "int") + register_op(create_lower_func({ + 32: 'Posit32es2Add', + 16: 'Posit16es2Add', + 8: 'Posit8es2Add' + }), "Add", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sub', + 16: 'Posit16es2Sub', + 8: 'Posit8es2Sub' + }), "Sub", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'FloatToPosit32es2', + 16: 'FloatToPosit16es2', + 8: 'FloatToPosit8es2' + }), "FloatImm", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Mul', + 16: 'Posit16es2Mul', + 8: 'Posit8es2Mul' + }), "Mul", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Div', + 16: 'Posit16es2Div', + 8: 'Posit8es2Div' + }), "Div", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Max', + 16: 'Posit16es2Max', + 8: 'Posit8es2Max' + }), "Max", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sqrt', + 16: 'Posit16es2Sqrt', + 8: 'Posit8es2Sqrt' + }), "Call", "llvm", "posites2", intrinsic_name="sqrt") + register_op(lower_ite, + "Call", + "llvm", + "posites2", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({ + 32: 'Posit32es2Exp', + 16: 'Posit16es2Exp', + 8: 'Posit8es2Exp' + }), "Call", "llvm", "posites2", intrinsic_name="exp") + register_op(create_lower_func({ + 32: 'Posit32es2Log', + 16: 'Posit16es2Log', + 8: 'Posit8es2Log' + }), "Call", "llvm", "posites2", intrinsic_name="log") + register_op(create_lower_func({ + 32: 'Posit32es2Sigmoid', + 16: 'Posit16es2Sigmoid', + 8: 'Posit8es2Sigmoid' + }), "Call", "llvm", "posites2", intrinsic_name="sigmoid") + register_op(create_lower_func({ + 32: 'Posit32es2Tanh', + 16: 'Posit16es2Tanh', + 8: 'Posit8es2Tanh' + }), "Call", "llvm", "posites2", intrinsic_name="tanh") + register_min_func(lambda num_bits: - (2 ** 2 ** 2) ** (num_bits - 2), "posites2") + + register("noptype", 132) + register_op(create_lower_func({ + (32, 32): "FloatToNop32" + }), "Cast", "llvm", "noptype", "float") + register_op(create_lower_func({ + (32, 32): 'Nop32ToFloat' + }), "Cast", "llvm", "float", "noptype") + register_op(create_lower_func({ + (4, 32): "IntToNop32" + }), "Cast", "llvm", "noptype", "int") + register_op(create_lower_func({32: 'IntToNop32'}), "Add", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sub'}), "Sub", "llvm", "noptype") + register_op(create_lower_func({32: 'FloatToNop32'}), "FloatImm", "llvm", + "noptype") + register_op(create_lower_func({32: 'Nop32Mul'}), "Mul", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Div'}), "Div", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Max'}), "Max", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sqrt'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sqrt") + # TODO(gus) not sure if this will work... + register_op(lower_ite, + "Call", + "llvm", + "noptype", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({32: 'Nop32Exp'}), + "Call", + "llvm", + "noptype", + intrinsic_name="exp") + register_op(create_lower_func({32: 'Nop32Log'}), + "Call", + "llvm", + "noptype", + intrinsic_name="log") + register_op(create_lower_func({32: 'Nop32Sigmoid'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sigmoid") + register_op(create_lower_func({32: 'Nop32Tanh'}), + "Call", + "llvm", + "noptype", + intrinsic_name="tanh") + # This can be anything, considering the type isn't functionally correct. + register_min_func(lambda num_bits: 0, "noptype") + + +def run_ops(src_dtype, dst_dtype, rtol=1e-7, atol=1e-7): + """Run the same op, but with two different datatypes""" + def check_unary_op(op, src_dtype, dst_dtype): + t1 = relay.TensorType((5, 10, 5), src_dtype) + x = relay.var("x", t1) + z = op(x) + x_data = rs.rand(5, 10, 5).astype(t1.dtype) + + module = tvm.IRModule.from_expr(relay.Function([x], z)) + + compare(module, (x_data, ), src_dtype, dst_dtype, rtol, atol) + # print(maybe_correct_converted) + # print(correct) + + for op in [ + relay.nn.softmax, + tvm.relay.log, + tvm.relay.exp, + tvm.relay.sqrt, + tvm.relay.rsqrt, + tvm.relay.sigmoid, + tvm.relay.tanh, + relay.nn.relu, + ]: + check_unary_op(op, src_dtype, dst_dtype) + + def check_binary_op(opfunc, src_dtype, dst_dtype): + t1 = relay.TensorType((5, 10, 5), src_dtype) + t2 = relay.TensorType((5, ), src_dtype) + x = relay.var("x", t1) + y = relay.var("y", t2) + z = opfunc(x, y) + x_data = rs.rand(5, 10, 5).astype(t1.dtype) + y_data = rs.rand(5).astype(t2.dtype) + module = tvm.IRModule.from_expr(relay.Function([x, y], z)) + + compare(module, (x_data, y_data), src_dtype, dst_dtype, rtol, atol) + + for op in [ + relay.add, + relay.subtract, + relay.divide, + relay.multiply, + ]: + check_binary_op(op, src_dtype, dst_dtype) + + # we would like to test tvm_if_then_else + # but Relay.IfNode is not lowered to this intrinsic, + # so to keep our tests consistent with relay, we decide to not unit test + # Note: tvm_if_then_else is tested as part of the mobile_net model + + +def run_model(get_workload, + input_shape, + src_dtype, + dst_dtype, + num_classes, + rtol=0.0001, + atol=0.0001): + module, params = get_workload(image_shape=input_shape, + num_classes=num_classes) + + # Convert the input into the correct format. + input = tvm.nd.array(rs.rand(*input_shape).astype(src_dtype)) + + compare(module, (input, ), src_dtype, dst_dtype, rtol, atol, params) + + # # Simplifying inference is essential right now, as batch norms (which get + # # removed) are broken with custom datatypes. + # #expr = relay.ir_pass.simplify_inference(expr) Review comment: Is this still needed? Doesn't seem like it -- we can delete the comment. ########## File path: tests/python/unittest/test_custom_datatypes_change_dtype.py ########## @@ -0,0 +1,481 @@ +# 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. +"""Utilities for changing datatypes of models.""" +import tvm +import topi.testing +import numpy as np +from numpy.random import MT19937, RandomState, SeedSequence +from tvm import relay +from tvm.relay.testing.inception_v3 import get_workload as get_inception +from tvm.relay.testing.resnet import get_workload as get_resnet +from tvm.relay.testing.mobilenet import get_workload as get_mobilenet +from tvm.target.datatype import register, register_min_func, register_op, create_lower_func, lower_ite +from nose.tools import nottest + +tgt = "llvm" +# we use a random seed to generate input_data +# to guarantee stable tests +rs = RandomState(MT19937(SeedSequence(123456789))) + +def convert_ndarray(dst_dtype, *arrays): + """Converts NDArray(s) into the specified datatype""" + def convert(array): + x = relay.var('x', shape=array.shape, dtype=str(array.dtype)) + cast = relay.Function([x], x.astype(dst_dtype)) + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + return relay.create_executor('graph').evaluate(cast)(array) + + return tuple([convert(x) for x in arrays]) + + +def change_dtype(src, dst, module, params): + module = relay.frontend.ChangeDatatype(src, dst)(module) + module = relay.transform.InferType()(module) + params = dict((p, convert_ndarray(dst, params[p])) for p in params) + return module, params + +def compare(module, input, src_dtype, dst_dtype, rtol, atol, params = {}): + ex = relay.create_executor("graph", mod=module) + + correct = ex.evaluate()(*input, **params) + + module, _ = change_dtype(src_dtype, dst_dtype, module, []) + ex = relay.create_executor("graph", mod=module) + # converts all inputs to dst_dtype + x_converted = convert_ndarray(dst_dtype, *input) + + # Vectorization is not implemented with custom datatypes + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + maybe_correct = ex.evaluate()(*x_converted, **params) + # TODO(andrew) this only works on single output + maybe_correct_converted = convert_ndarray(src_dtype, maybe_correct)[0] + np.testing.assert_allclose(maybe_correct_converted.asnumpy(), + correct.asnumpy(), + rtol=rtol, + atol=atol) + +def setup(): + """Set up tests + + Currently, this registers some custom datatypes using the Bring Your + Own Datatypes framework. + """ + + # To use datatype operations in an external library, you should first load + # the library containing the datatype implementation: + # CDLL("libposit.so", RTLD_GLOBAL) + # In this case, the datatype library we are using is built right into TVM, + # so we do not need to explicitly load any library. + + # You can pick a code for your datatype arbitrarily, as long as it is + # greater than 128 and has not already been chosen. + + register("posites2", 131) + + register_op(create_lower_func( + { + (32, 32): "FloatToPosit32es2", + (32, 16): "FloatToPosit16es2", + (32, 8): 'FloatToPosit8es2', + }), + "Cast", "llvm", "posites2", "float") + register_op(create_lower_func( + { + (32, 32): "Posit32es2ToFloat", + (16, 32): 'Posit16es2ToFloat', + (8, 32): 'Posit8es2ToFloat', + }), + "Cast", "llvm", "float", "posites2") + register_op(create_lower_func( + { + (4, 32): 'IntToPosit32es2', + (4, 16): 'IntToPosit16es2', + (4, 8): 'IntToPosit8es2' + }), + "Cast", "llvm", "posites2", "int") + register_op(create_lower_func({ + 32: 'Posit32es2Add', + 16: 'Posit16es2Add', + 8: 'Posit8es2Add' + }), "Add", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sub', + 16: 'Posit16es2Sub', + 8: 'Posit8es2Sub' + }), "Sub", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'FloatToPosit32es2', + 16: 'FloatToPosit16es2', + 8: 'FloatToPosit8es2' + }), "FloatImm", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Mul', + 16: 'Posit16es2Mul', + 8: 'Posit8es2Mul' + }), "Mul", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Div', + 16: 'Posit16es2Div', + 8: 'Posit8es2Div' + }), "Div", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Max', + 16: 'Posit16es2Max', + 8: 'Posit8es2Max' + }), "Max", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sqrt', + 16: 'Posit16es2Sqrt', + 8: 'Posit8es2Sqrt' + }), "Call", "llvm", "posites2", intrinsic_name="sqrt") + register_op(lower_ite, + "Call", + "llvm", + "posites2", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({ + 32: 'Posit32es2Exp', + 16: 'Posit16es2Exp', + 8: 'Posit8es2Exp' + }), "Call", "llvm", "posites2", intrinsic_name="exp") + register_op(create_lower_func({ + 32: 'Posit32es2Log', + 16: 'Posit16es2Log', + 8: 'Posit8es2Log' + }), "Call", "llvm", "posites2", intrinsic_name="log") + register_op(create_lower_func({ + 32: 'Posit32es2Sigmoid', + 16: 'Posit16es2Sigmoid', + 8: 'Posit8es2Sigmoid' + }), "Call", "llvm", "posites2", intrinsic_name="sigmoid") + register_op(create_lower_func({ + 32: 'Posit32es2Tanh', + 16: 'Posit16es2Tanh', + 8: 'Posit8es2Tanh' + }), "Call", "llvm", "posites2", intrinsic_name="tanh") + register_min_func(lambda num_bits: - (2 ** 2 ** 2) ** (num_bits - 2), "posites2") + + register("noptype", 132) + register_op(create_lower_func({ + (32, 32): "FloatToNop32" + }), "Cast", "llvm", "noptype", "float") + register_op(create_lower_func({ + (32, 32): 'Nop32ToFloat' + }), "Cast", "llvm", "float", "noptype") + register_op(create_lower_func({ + (4, 32): "IntToNop32" + }), "Cast", "llvm", "noptype", "int") + register_op(create_lower_func({32: 'IntToNop32'}), "Add", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sub'}), "Sub", "llvm", "noptype") + register_op(create_lower_func({32: 'FloatToNop32'}), "FloatImm", "llvm", + "noptype") + register_op(create_lower_func({32: 'Nop32Mul'}), "Mul", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Div'}), "Div", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Max'}), "Max", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sqrt'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sqrt") + # TODO(gus) not sure if this will work... + register_op(lower_ite, + "Call", + "llvm", + "noptype", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({32: 'Nop32Exp'}), + "Call", + "llvm", + "noptype", + intrinsic_name="exp") + register_op(create_lower_func({32: 'Nop32Log'}), + "Call", + "llvm", + "noptype", + intrinsic_name="log") + register_op(create_lower_func({32: 'Nop32Sigmoid'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sigmoid") + register_op(create_lower_func({32: 'Nop32Tanh'}), + "Call", + "llvm", + "noptype", + intrinsic_name="tanh") + # This can be anything, considering the type isn't functionally correct. + register_min_func(lambda num_bits: 0, "noptype") + + +def run_ops(src_dtype, dst_dtype, rtol=1e-7, atol=1e-7): + """Run the same op, but with two different datatypes""" + def check_unary_op(op, src_dtype, dst_dtype): + t1 = relay.TensorType((5, 10, 5), src_dtype) + x = relay.var("x", t1) + z = op(x) + x_data = rs.rand(5, 10, 5).astype(t1.dtype) + + module = tvm.IRModule.from_expr(relay.Function([x], z)) + + compare(module, (x_data, ), src_dtype, dst_dtype, rtol, atol) + # print(maybe_correct_converted) + # print(correct) + + for op in [ + relay.nn.softmax, + tvm.relay.log, + tvm.relay.exp, + tvm.relay.sqrt, + tvm.relay.rsqrt, + tvm.relay.sigmoid, + tvm.relay.tanh, + relay.nn.relu, + ]: + check_unary_op(op, src_dtype, dst_dtype) + + def check_binary_op(opfunc, src_dtype, dst_dtype): + t1 = relay.TensorType((5, 10, 5), src_dtype) + t2 = relay.TensorType((5, ), src_dtype) + x = relay.var("x", t1) + y = relay.var("y", t2) + z = opfunc(x, y) + x_data = rs.rand(5, 10, 5).astype(t1.dtype) + y_data = rs.rand(5).astype(t2.dtype) + module = tvm.IRModule.from_expr(relay.Function([x, y], z)) + + compare(module, (x_data, y_data), src_dtype, dst_dtype, rtol, atol) + + for op in [ + relay.add, + relay.subtract, + relay.divide, + relay.multiply, + ]: + check_binary_op(op, src_dtype, dst_dtype) + + # we would like to test tvm_if_then_else + # but Relay.IfNode is not lowered to this intrinsic, + # so to keep our tests consistent with relay, we decide to not unit test + # Note: tvm_if_then_else is tested as part of the mobile_net model + + +def run_model(get_workload, + input_shape, + src_dtype, + dst_dtype, + num_classes, + rtol=0.0001, + atol=0.0001): + module, params = get_workload(image_shape=input_shape, + num_classes=num_classes) + + # Convert the input into the correct format. Review comment: "Convert" might not be the right word, as we're not actually converting existing data, but generating random data. ########## File path: tests/python/unittest/test_custom_datatypes_change_dtype.py ########## @@ -0,0 +1,481 @@ +# 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. +"""Utilities for changing datatypes of models.""" +import tvm +import topi.testing +import numpy as np +from numpy.random import MT19937, RandomState, SeedSequence +from tvm import relay +from tvm.relay.testing.inception_v3 import get_workload as get_inception +from tvm.relay.testing.resnet import get_workload as get_resnet +from tvm.relay.testing.mobilenet import get_workload as get_mobilenet +from tvm.target.datatype import register, register_min_func, register_op, create_lower_func, lower_ite +from nose.tools import nottest + +tgt = "llvm" +# we use a random seed to generate input_data +# to guarantee stable tests +rs = RandomState(MT19937(SeedSequence(123456789))) + +def convert_ndarray(dst_dtype, *arrays): + """Converts NDArray(s) into the specified datatype""" + def convert(array): + x = relay.var('x', shape=array.shape, dtype=str(array.dtype)) + cast = relay.Function([x], x.astype(dst_dtype)) + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + return relay.create_executor('graph').evaluate(cast)(array) + + return tuple([convert(x) for x in arrays]) + + +def change_dtype(src, dst, module, params): + module = relay.frontend.ChangeDatatype(src, dst)(module) + module = relay.transform.InferType()(module) + params = dict((p, convert_ndarray(dst, params[p])) for p in params) + return module, params + +def compare(module, input, src_dtype, dst_dtype, rtol, atol, params = {}): + ex = relay.create_executor("graph", mod=module) + + correct = ex.evaluate()(*input, **params) + + module, _ = change_dtype(src_dtype, dst_dtype, module, []) + ex = relay.create_executor("graph", mod=module) + # converts all inputs to dst_dtype + x_converted = convert_ndarray(dst_dtype, *input) + + # Vectorization is not implemented with custom datatypes + with tvm.transform.PassContext(config={"tir.disable_vectorize": True}): + maybe_correct = ex.evaluate()(*x_converted, **params) + # TODO(andrew) this only works on single output + maybe_correct_converted = convert_ndarray(src_dtype, maybe_correct)[0] + np.testing.assert_allclose(maybe_correct_converted.asnumpy(), + correct.asnumpy(), + rtol=rtol, + atol=atol) + +def setup(): + """Set up tests + + Currently, this registers some custom datatypes using the Bring Your + Own Datatypes framework. + """ + + # To use datatype operations in an external library, you should first load + # the library containing the datatype implementation: + # CDLL("libposit.so", RTLD_GLOBAL) + # In this case, the datatype library we are using is built right into TVM, + # so we do not need to explicitly load any library. + + # You can pick a code for your datatype arbitrarily, as long as it is + # greater than 128 and has not already been chosen. + + register("posites2", 131) + + register_op(create_lower_func( + { + (32, 32): "FloatToPosit32es2", + (32, 16): "FloatToPosit16es2", + (32, 8): 'FloatToPosit8es2', + }), + "Cast", "llvm", "posites2", "float") + register_op(create_lower_func( + { + (32, 32): "Posit32es2ToFloat", + (16, 32): 'Posit16es2ToFloat', + (8, 32): 'Posit8es2ToFloat', + }), + "Cast", "llvm", "float", "posites2") + register_op(create_lower_func( + { + (4, 32): 'IntToPosit32es2', + (4, 16): 'IntToPosit16es2', + (4, 8): 'IntToPosit8es2' + }), + "Cast", "llvm", "posites2", "int") + register_op(create_lower_func({ + 32: 'Posit32es2Add', + 16: 'Posit16es2Add', + 8: 'Posit8es2Add' + }), "Add", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sub', + 16: 'Posit16es2Sub', + 8: 'Posit8es2Sub' + }), "Sub", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'FloatToPosit32es2', + 16: 'FloatToPosit16es2', + 8: 'FloatToPosit8es2' + }), "FloatImm", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Mul', + 16: 'Posit16es2Mul', + 8: 'Posit8es2Mul' + }), "Mul", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Div', + 16: 'Posit16es2Div', + 8: 'Posit8es2Div' + }), "Div", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Max', + 16: 'Posit16es2Max', + 8: 'Posit8es2Max' + }), "Max", "llvm", "posites2") + register_op(create_lower_func({ + 32: 'Posit32es2Sqrt', + 16: 'Posit16es2Sqrt', + 8: 'Posit8es2Sqrt' + }), "Call", "llvm", "posites2", intrinsic_name="sqrt") + register_op(lower_ite, + "Call", + "llvm", + "posites2", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({ + 32: 'Posit32es2Exp', + 16: 'Posit16es2Exp', + 8: 'Posit8es2Exp' + }), "Call", "llvm", "posites2", intrinsic_name="exp") + register_op(create_lower_func({ + 32: 'Posit32es2Log', + 16: 'Posit16es2Log', + 8: 'Posit8es2Log' + }), "Call", "llvm", "posites2", intrinsic_name="log") + register_op(create_lower_func({ + 32: 'Posit32es2Sigmoid', + 16: 'Posit16es2Sigmoid', + 8: 'Posit8es2Sigmoid' + }), "Call", "llvm", "posites2", intrinsic_name="sigmoid") + register_op(create_lower_func({ + 32: 'Posit32es2Tanh', + 16: 'Posit16es2Tanh', + 8: 'Posit8es2Tanh' + }), "Call", "llvm", "posites2", intrinsic_name="tanh") + register_min_func(lambda num_bits: - (2 ** 2 ** 2) ** (num_bits - 2), "posites2") + + register("noptype", 132) + register_op(create_lower_func({ + (32, 32): "FloatToNop32" + }), "Cast", "llvm", "noptype", "float") + register_op(create_lower_func({ + (32, 32): 'Nop32ToFloat' + }), "Cast", "llvm", "float", "noptype") + register_op(create_lower_func({ + (4, 32): "IntToNop32" + }), "Cast", "llvm", "noptype", "int") + register_op(create_lower_func({32: 'IntToNop32'}), "Add", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sub'}), "Sub", "llvm", "noptype") + register_op(create_lower_func({32: 'FloatToNop32'}), "FloatImm", "llvm", + "noptype") + register_op(create_lower_func({32: 'Nop32Mul'}), "Mul", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Div'}), "Div", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Max'}), "Max", "llvm", "noptype") + register_op(create_lower_func({32: 'Nop32Sqrt'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sqrt") + # TODO(gus) not sure if this will work... + register_op(lower_ite, + "Call", + "llvm", + "noptype", + intrinsic_name="tvm_if_then_else") + register_op(create_lower_func({32: 'Nop32Exp'}), + "Call", + "llvm", + "noptype", + intrinsic_name="exp") + register_op(create_lower_func({32: 'Nop32Log'}), + "Call", + "llvm", + "noptype", + intrinsic_name="log") + register_op(create_lower_func({32: 'Nop32Sigmoid'}), + "Call", + "llvm", + "noptype", + intrinsic_name="sigmoid") + register_op(create_lower_func({32: 'Nop32Tanh'}), + "Call", + "llvm", + "noptype", + intrinsic_name="tanh") + # This can be anything, considering the type isn't functionally correct. + register_min_func(lambda num_bits: 0, "noptype") + + +def run_ops(src_dtype, dst_dtype, rtol=1e-7, atol=1e-7): + """Run the same op, but with two different datatypes""" + def check_unary_op(op, src_dtype, dst_dtype): + t1 = relay.TensorType((5, 10, 5), src_dtype) + x = relay.var("x", t1) + z = op(x) + x_data = rs.rand(5, 10, 5).astype(t1.dtype) + + module = tvm.IRModule.from_expr(relay.Function([x], z)) + + compare(module, (x_data, ), src_dtype, dst_dtype, rtol, atol) + # print(maybe_correct_converted) + # print(correct) + + for op in [ + relay.nn.softmax, + tvm.relay.log, + tvm.relay.exp, + tvm.relay.sqrt, + tvm.relay.rsqrt, + tvm.relay.sigmoid, + tvm.relay.tanh, + relay.nn.relu, + ]: + check_unary_op(op, src_dtype, dst_dtype) + + def check_binary_op(opfunc, src_dtype, dst_dtype): + t1 = relay.TensorType((5, 10, 5), src_dtype) + t2 = relay.TensorType((5, ), src_dtype) + x = relay.var("x", t1) + y = relay.var("y", t2) + z = opfunc(x, y) + x_data = rs.rand(5, 10, 5).astype(t1.dtype) + y_data = rs.rand(5).astype(t2.dtype) + module = tvm.IRModule.from_expr(relay.Function([x, y], z)) + + compare(module, (x_data, y_data), src_dtype, dst_dtype, rtol, atol) + + for op in [ + relay.add, + relay.subtract, + relay.divide, + relay.multiply, + ]: + check_binary_op(op, src_dtype, dst_dtype) + + # we would like to test tvm_if_then_else + # but Relay.IfNode is not lowered to this intrinsic, + # so to keep our tests consistent with relay, we decide to not unit test + # Note: tvm_if_then_else is tested as part of the mobile_net model + + +def run_model(get_workload, + input_shape, + src_dtype, + dst_dtype, + num_classes, + rtol=0.0001, + atol=0.0001): + module, params = get_workload(image_shape=input_shape, + num_classes=num_classes) + + # Convert the input into the correct format. + input = tvm.nd.array(rs.rand(*input_shape).astype(src_dtype)) + + compare(module, (input, ), src_dtype, dst_dtype, rtol, atol, params) + + # # Simplifying inference is essential right now, as batch norms (which get + # # removed) are broken with custom datatypes. + # #expr = relay.ir_pass.simplify_inference(expr) + +def run_conv2d(src_dtype, dst_dtype): + def run_test_conv2d(src_dtype, Review comment: Should we be using your `compare()` here? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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