jroesch commented on code in PR #11199: URL: https://github.com/apache/tvm/pull/11199#discussion_r863737070
########## python/tvm/relay/transform/flexible_shape.py: ########## @@ -0,0 +1,273 @@ +# 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=invalid-name, dangerous-default-value +"""Relay functions for wrapping a module with flexible shape dispatch.""" +from tvm import relay + + +def override_shape(tensor_type, dim, value): + """Change a value in a tensor shape.""" + new_dims = list(tensor_type.shape) + new_dims[dim] = value + return relay.TensorType(new_dims, tensor_type.dtype) + + +def specialize_body(mod, function, dim, value, input_indices=[0], affects_output=True): + """Create a subgraph to handle specific input shapes""" + # Iterate through specified inputs and construct specialized shapes for each. + new_params = list(function.params) + data_binding = {} + dyn_data_array = [] + for inp in input_indices: + data = function.params[inp] + flex_ty = override_shape(data.type_annotation, dim, value) + dyn_data = relay.Var(data.name_hint, type_annotation=flex_ty) + new_params[inp] = dyn_data + data_binding[data] = dyn_data + dyn_data_array.append(dyn_data) + + # Create a new function body for the modified shapes. + new_body = relay.expr.bind(function.body, data_binding) + # Only change the output shape if the input shape affects it. + if affects_output: + new_ret_ty = override_shape(function.ret_type, dim, value) + else: + new_ret_ty = function.ret_type + gvar = relay.GlobalVar("main_" + str(value)) + # Add the new function to the main IRModule. + mod[gvar] = relay.Function( + new_params, new_body, new_ret_ty, function.type_params, function.attrs + ) + return gvar, [d.type_annotation for d in dyn_data_array] + + +def flexible_dispatch( + mod, dim=0, buckets=[1], auto_pad=False, pad_value=0, input_indices=[0], affects_output=True Review Comment: Same comment as above, probably worth specifying the modes in the doc string if possible to enumerate. ########## python/tvm/relay/transform/flexible_shape.py: ########## @@ -0,0 +1,273 @@ +# 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=invalid-name, dangerous-default-value +"""Relay functions for wrapping a module with flexible shape dispatch.""" +from tvm import relay + + +def override_shape(tensor_type, dim, value): + """Change a value in a tensor shape.""" + new_dims = list(tensor_type.shape) + new_dims[dim] = value + return relay.TensorType(new_dims, tensor_type.dtype) + + +def specialize_body(mod, function, dim, value, input_indices=[0], affects_output=True): + """Create a subgraph to handle specific input shapes""" + # Iterate through specified inputs and construct specialized shapes for each. + new_params = list(function.params) + data_binding = {} + dyn_data_array = [] + for inp in input_indices: + data = function.params[inp] + flex_ty = override_shape(data.type_annotation, dim, value) + dyn_data = relay.Var(data.name_hint, type_annotation=flex_ty) + new_params[inp] = dyn_data + data_binding[data] = dyn_data + dyn_data_array.append(dyn_data) + + # Create a new function body for the modified shapes. + new_body = relay.expr.bind(function.body, data_binding) + # Only change the output shape if the input shape affects it. + if affects_output: + new_ret_ty = override_shape(function.ret_type, dim, value) + else: + new_ret_ty = function.ret_type + gvar = relay.GlobalVar("main_" + str(value)) + # Add the new function to the main IRModule. + mod[gvar] = relay.Function( + new_params, new_body, new_ret_ty, function.type_params, function.attrs + ) + return gvar, [d.type_annotation for d in dyn_data_array] + + +def flexible_dispatch( + mod, dim=0, buckets=[1], auto_pad=False, pad_value=0, input_indices=[0], affects_output=True Review Comment: Same comment as above about defaults, probably worth specifying the modes in the doc string if possible to enumerate. -- This is an automated message from the Apache Git Service. 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