comaniac commented on a change in pull request #8069: URL: https://github.com/apache/tvm/pull/8069#discussion_r653814924
########## File path: python/tvm/relay/transform/mixed_precision.py ########## @@ -0,0 +1,177 @@ +# 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. +# pylint: disable=line-too-long,unused-argument +"""Default behavior for ops in mixed_precision pass. Import this file to use.""" +from typing import List + +from tvm import relay +from tvm.relay.op import register_mixed_precision_conversion + +# MIXED_PRECISION_ALWAYS ops should always be done in lower precision due to the speed and memory +# savings. MIXED_PRECISION_FOLLOW ops can be done in lower precision but don't have speedups to +# justify a cast. MIXED_PRECISION_NEVER colored ops should not be done in lower precision due to +# numerical reasons. +MIXED_PRECISION_ALWAYS = 0 +MIXED_PRECISION_FOLLOW = 1 +MIXED_PRECISION_NEVER = 2 + +# Default lists inspired from TF's classifications: +# github.com/tensorflow/tensorflow/blob/v2.5.0/tensorflow/core/grappler/optimizers/auto_mixed_precision_lists.h +# They have a bias toward Nvidia Tensor Cores so modify lists per your hardware choice. +DEFAULT_ALWAYS_LIST = [ + "nn.conv1d", + "nn.conv2d", + "nn.conv3d", + "nn.conv1d_transpose", + "nn.conv2d_transpose", + "nn.conv3d_transpose", + "nn.dense", + # "nn.batch_matmul", # Handled by a special case +] +DEFAULT_FOLLOW_LIST = [ + # These ops add new data or change shape + "nn.pad", + "nn.batch_flatten", + "concatenate", + "zeros", + "split", + "squeeze", + "transpose", + "expand_dims", + "reshape", + "dyn.reshape", + "broadcast_to_like", + "dyn.broadcast_to", + "strided_slice", + "dyn.strided_slice", + "take", + "argwhere", + "where", + "tile", + "dyn.tile", + "scatter", + "full", + "dyn.full", + # Comparison + "less", + "greater", + "less_equal", + "greater_equal", + # By definition copy and cast will depend on inputs for output. + "copy", + "cast", + "cast_like", + # Simple arithmetic + "add", + "subtract", + "multiply", + "divide", + "nn.bias_add", + "nn.batch_norm", + "sum", + "mean", + "sqrt", + "shape_of", + # Simple activations + "max", + "min", + "maximum", + "minimum", + "nn.relu", + "nn.leaky_relu", + "nn.prelu", + "nn.dropout", + # Complicated activations which saturate in a narrow range + "sigmoid", + "tanh", + # Pooling operations + "nn.max_pool1d", + "nn.max_pool2d", + "nn.max_pool3d", + "nn.avg_pool1d", + "nn.avg_pool2d", + "nn.avg_pool3d", + # "nn.global_max_pool1d", # does not exist yet + "nn.global_max_pool2d", + # "nn.global_max_pool3d", # does not exist yet + # "nn.global_avg_pool1d", # does not exist yet + "nn.global_avg_pool2d", + # "nn.global_avg_pool3d", # does not exist yet + "nn.adaptive_max_pool1d", + "nn.adaptive_max_pool2d", + "nn.adaptive_max_pool3d", + "nn.adaptive_avg_pool1d", + "nn.adaptive_avg_pool2d", + "nn.adaptive_avg_pool3d", +] +DEFAULT_NEVER_LIST = [ + # In general if |f(x)| >> |x| for expected inputs then put the op here. + "exp", + "power", + "nn.cross_entropy", + "nn.cross_entropy_with_logits", + "nn.softmax", + "nn.l2_normalize", + # Error function doesn't seem to be able to be lowered into fp16 version in llvm. + # Move to follow list when it does. + "erf", +] + + +# Returns a decorator which registers for every given op, the function under FTVMMixedPrecisionConversionType +def register_func_to_op_list(list_ops): + def decorator(func): + for op_name in list_ops: + register_mixed_precision_conversion(op_name, func=func) + + return decorator + + +def get_generic_out_dtypes(call_node: relay.Call, mixed_precision_type: str) -> List[str]: + # Assume support accumulation dtypes <---> has out_dtype attr Review comment: Why this assumption? ########## File path: python/tvm/relay/transform/mixed_precision.py ########## @@ -0,0 +1,177 @@ +# 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. +# pylint: disable=line-too-long,unused-argument +"""Default behavior for ops in mixed_precision pass. Import this file to use.""" +from typing import List + +from tvm import relay +from tvm.relay.op import register_mixed_precision_conversion + +# MIXED_PRECISION_ALWAYS ops should always be done in lower precision due to the speed and memory +# savings. MIXED_PRECISION_FOLLOW ops can be done in lower precision but don't have speedups to +# justify a cast. MIXED_PRECISION_NEVER colored ops should not be done in lower precision due to +# numerical reasons. +MIXED_PRECISION_ALWAYS = 0 +MIXED_PRECISION_FOLLOW = 1 +MIXED_PRECISION_NEVER = 2 + +# Default lists inspired from TF's classifications: +# github.com/tensorflow/tensorflow/blob/v2.5.0/tensorflow/core/grappler/optimizers/auto_mixed_precision_lists.h +# They have a bias toward Nvidia Tensor Cores so modify lists per your hardware choice. +DEFAULT_ALWAYS_LIST = [ + "nn.conv1d", + "nn.conv2d", + "nn.conv3d", + "nn.conv1d_transpose", + "nn.conv2d_transpose", + "nn.conv3d_transpose", + "nn.dense", + # "nn.batch_matmul", # Handled by a special case +] +DEFAULT_FOLLOW_LIST = [ + # These ops add new data or change shape + "nn.pad", + "nn.batch_flatten", + "concatenate", + "zeros", + "split", + "squeeze", + "transpose", + "expand_dims", + "reshape", + "dyn.reshape", + "broadcast_to_like", + "dyn.broadcast_to", + "strided_slice", + "dyn.strided_slice", + "take", + "argwhere", + "where", + "tile", + "dyn.tile", + "scatter", + "full", + "dyn.full", + # Comparison + "less", + "greater", + "less_equal", + "greater_equal", + # By definition copy and cast will depend on inputs for output. + "copy", + "cast", + "cast_like", + # Simple arithmetic + "add", + "subtract", + "multiply", + "divide", + "nn.bias_add", + "nn.batch_norm", + "sum", + "mean", + "sqrt", + "shape_of", + # Simple activations + "max", + "min", + "maximum", + "minimum", + "nn.relu", + "nn.leaky_relu", + "nn.prelu", + "nn.dropout", + # Complicated activations which saturate in a narrow range + "sigmoid", + "tanh", + # Pooling operations + "nn.max_pool1d", + "nn.max_pool2d", + "nn.max_pool3d", + "nn.avg_pool1d", + "nn.avg_pool2d", + "nn.avg_pool3d", + # "nn.global_max_pool1d", # does not exist yet + "nn.global_max_pool2d", + # "nn.global_max_pool3d", # does not exist yet + # "nn.global_avg_pool1d", # does not exist yet + "nn.global_avg_pool2d", + # "nn.global_avg_pool3d", # does not exist yet + "nn.adaptive_max_pool1d", + "nn.adaptive_max_pool2d", + "nn.adaptive_max_pool3d", + "nn.adaptive_avg_pool1d", + "nn.adaptive_avg_pool2d", + "nn.adaptive_avg_pool3d", +] +DEFAULT_NEVER_LIST = [ + # In general if |f(x)| >> |x| for expected inputs then put the op here. + "exp", + "power", + "nn.cross_entropy", + "nn.cross_entropy_with_logits", + "nn.softmax", + "nn.l2_normalize", + # Error function doesn't seem to be able to be lowered into fp16 version in llvm. + # Move to follow list when it does. + "erf", +] + + +# Returns a decorator which registers for every given op, the function under FTVMMixedPrecisionConversionType +def register_func_to_op_list(list_ops): + def decorator(func): + for op_name in list_ops: + register_mixed_precision_conversion(op_name, func=func) + + return decorator + + +def get_generic_out_dtypes(call_node: relay.Call, mixed_precision_type: str) -> List[str]: + # Assume support accumulation dtypes <---> has out_dtype attr Review comment: Found it. 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