zxy844288792 commented on a change in pull request #8454: URL: https://github.com/apache/tvm/pull/8454#discussion_r675736343
########## File path: python/tvm/relay/frontend/tensorflow2_ops.py ########## @@ -0,0 +1,179 @@ +# 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, unused-argument, too-many-lines, len-as-condition, broad-except +"""Tensorflow2.x to relay converter ops and helper""" +import tvm +from tvm.relay.prelude import StaticTensorArrayOps, get_tensor_array_shape + +from .. import op as _op +from ..ty import Any +from .common import infer_value as _infer_value +from .common import infer_type as _infer_type +from .tensorflow_ops import _get_more_static_shape_rank + + +def _infer_type_with_prelude(val, prelude): + body = _infer_type(val, prelude.mod) + return body.checked_type + + +def _need_prelude_for_shape_inference(op): + return "TensorList" in op or "TensorArray" in op + + +def _tensorlist_reserve(): + def _impl(inputs, attr, params, prelude): + dtype_str = attr.get("element_dtype").name + elem_shape = _infer_value(inputs[0], params, prelude.mod) + elem_shape = tuple(elem_shape.asnumpy().astype("int32").flatten()) + + if elem_shape or "shape" in attr: + shape = attr["shape"] if "shape" in attr else elem_shape + static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, shape) + static_tensor_array_ops.register() + tensor_array_constructor = static_tensor_array_ops.get_global_var("tensor_array") + tensor_array = tensor_array_constructor(inputs[1]) + else: + tensor_array_constructor = prelude.get_global_var("tensor_array", dtype_str) + tensor_array = tensor_array_constructor(inputs[1]) + return tensor_array + + return _impl + + +def _tensorlist_set_item(): + def _impl(inputs, attr, params, prelude): + dtype_str = attr.get("element_dtype").name + input_ta = inputs[0] + input_ta_shape = get_tensor_array_shape(input_ta, dtype_str, prelude) + input_t_shape = _infer_type_with_prelude(inputs[2], prelude).shape + input_rank = len(input_t_shape) + + if input_ta_shape is None: + tensor_name = "tensor{}".format(input_rank) + tensor_func = prelude.get_tensor_ctor(tensor_name, dtype_str) + v = tensor_func(inputs[2]) + write_func = prelude.get_global_var("tensor_array_write", dtype_str) + out = write_func(input_ta, inputs[1], v) + else: + static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, input_ta_shape) + static_tensor_array_ops.register() + tensor_func = static_tensor_array_ops.get_ctor("tensor_constructor") + v = tensor_func(inputs[2]) + # Write tensor with more static shape + # convert shape with -1 to any() + input_ta_shape_a = [] + for dim in input_ta_shape: + if isinstance(dim, (int, tvm.tir.expr.IntImm)): + if dim < 0: + input_ta_shape_a.append(Any()) + else: + input_ta_shape_a.append(dim) + else: + input_ta_shape_a.append(dim) + actual_shape = _get_more_static_shape_rank(input_t_shape, input_ta_shape_a) + if actual_shape != input_ta_shape_a: + new_shape = [] + num_any_dim = 0 + for dim in actual_shape: + if not isinstance(dim, int): + num_any_dim += 1 + new_shape.append(dim if isinstance(dim, int) else -1) + if num_any_dim <= 1: + v = tensor_func(_op.reshape(inputs[2], new_shape)) + write_func = prelude.get_global_var_static( + "tensor_array_write", dtype_str, input_ta_shape_a + ) + out = write_func(input_ta, inputs[1], v) + return out + + return _impl + + +def _tensorlist_get_item(): + def _impl(inputs, attr, params, prelude): + dtype_str = attr["element_dtype"].name + input_shape = get_tensor_array_shape(inputs[0], dtype_str, prelude) + + if input_shape is None: + read_func = prelude.get_global_var("tensor_array_read", dtype_str) + out = read_func(inputs[0], _op.take(inputs[1], tvm.relay.const(0))) + else: + static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, input_shape) + static_tensor_array_ops.register() + read_func = static_tensor_array_ops.get_global_var("tensor_array_read") + out_tensor = read_func(inputs[0], _op.take(inputs[1], tvm.relay.const(0))) + get_data_func = static_tensor_array_ops.get_global_var("tensor_get_data") + out = get_data_func(out_tensor) + return out + + return _impl + + +def _tensorlist_stack(): + def _impl(inputs, attr, params, prelude): + dtype_str = attr["element_dtype"].name + input_ta_shape = get_tensor_array_shape(inputs[0], dtype_str, prelude) + + if input_ta_shape is None: + stack_func = prelude.get_global_var("tensor_array_stack", dtype_str) + out = stack_func(inputs[0]) + else: + static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, input_ta_shape) + static_tensor_array_ops.register() + stack_func = prelude.get_global_var_static( + "tensor_array_stack", dtype_str, input_ta_shape + ) + out_tensor = stack_func(inputs[0]) + out_shape = (Any(),) + input_ta_shape + static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, out_shape) + static_tensor_array_ops.register() + get_data_func = prelude.get_global_var_static("tensor_get_data", dtype_str, out_shape) + out = get_data_func(out_tensor) + + return out + + return _impl + + +def _tensorlist_from_tensor(): + def _impl(inputs, attr, params, prelude): + dtype_str = attr["element_dtype"].name + input_ta_shape = _infer_type_with_prelude(inputs[0], prelude).shape + + if input_ta_shape is None: + unstack_func = prelude.get_global_var("tensor_array_unstack", dtype_str) + out = unstack_func(inputs[0]) + else: + static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, input_ta_shape) + static_tensor_array_ops.register() + unstack_func = prelude.get_global_var_static( + "tensor_array_unstack", dtype_str, input_ta_shape + ) + out = unstack_func(inputs[0]) + return out + + return _impl + + +_convert_map = { + "TensorListFromTensor": _tensorlist_from_tensor(), + "TensorListGetItem": _tensorlist_get_item(), + "TensorListReserve": _tensorlist_reserve(), + "TensorListSetItem": _tensorlist_set_item(), + "TensorListStack": _tensorlist_stack(), Review comment: The reason why I chose these operators is due to the models I am working on only contain these ops, It will be ongoing efforts for adding new operator support -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
