chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name
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# ruff: noqa: E741
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"""ScatterND operator"""
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from tvm import DataTypeCode, te, tirx # hide redefinition of min and max
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from tvm.arith.analyzer import Analyzer
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from tvm.script.ir_builder import IRBuilder
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from tvm.script.ir_builder import tirx as T
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from tvm.tirx import expr
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def _verify_scatter_nd_inputs(data, indices, updates):
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analyzer = Analyzer()
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mdim = int(indices.shape[0])
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assert mdim <= len(data.shape), (
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f"The first dimension of the indices ({mdim}) must be less than or equal to "
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f"the length of the shape of the output ({len(data.shape)})."
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)
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for i in range(len(indices.shape) - 1):
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if isinstance(indices.shape[i + 1], expr.Var) or isinstance(updates.shape[i], expr.Var):
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continue
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assert analyzer.can_prove_equal(indices.shape[i + 1], updates.shape[i]), (
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f"Dimension of indices[{i + 1}] ({indices.shape[i + 1]}) must equal dimension of "
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f"updates[{i}] ({updates.shape[i]})."
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)
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for i in range(mdim, len(data.shape)):
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data_ind = i - mdim + len(indices.shape) - 1
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if isinstance(updates.shape[data_ind], expr.Var) or isinstance(data.shape[i], expr.Var):
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continue
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assert updates.shape[data_ind] == data.shape[i], (
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f"Dimension of updates[{data_ind}] ({updates.shape[data_ind]}) must equal dimension "
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f"of out_shape[{i}] ({data.shape[i]})."
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)
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assert indices.dtype.matches_code(DataTypeCode.INT, DataTypeCode.UINT), (
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f"Indices must be a tensor of integers, but its elements are {indices.dtype}."
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)
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def scatter_nd(data, indices, updates, mode):
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"""Scatter elements from a n-dimension array.
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Given updates with shape (Y_0, ..., Y_{K-1}, X_M, ..., X_{N-1}), indices with shape
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(M, Y_0, ..., Y_{K-1}), and output copied from data with shape (X_0, X_1, ..., X_{N-1}),
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scatter_nd computes
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.. code-block::
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output[indices[0, y_0, ..., y_{K-1}],
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...,
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indices[M-1, y_0, ..., y_{K-1}],
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x_M,
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...,
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x_{N-1}
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] = f(output[...], updates[y_0, ..., y_{K-1}, x_M, ..., x_{N-1}])
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where the update function f is determinted by the mode.
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Parameters
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----------
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data : tvm.te.Tensor
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The source array.
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indices : tvm.te.Tensor
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The indices of the values to extract.
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updates : tvm.te.Tensor
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The updates to apply at the Indices
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mode : string
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The update mode for the algorithm, either "update" or "add"
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If update, the update values will replace the input data
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If add, the update values will be added to the input data
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Returns
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-------
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ret : tvm.te.Tensor
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"""
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_verify_scatter_nd_inputs(data, indices, updates)
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def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr):
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# pylint: disable=invalid-name
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data = T.buffer_proxy(data_ptr)
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indices = T.buffer_proxy(indices_ptr)
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updates = T.buffer_proxy(updates_ptr)
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out = T.buffer_proxy(out_ptr)
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# We combine all the indices dimensions but the first one into a single
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# dimension so we can iterate it in single loop instead of an arbitrary
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# number of loops. We do the same thing for all the update dimensions.
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fused_indices_dimension = 1
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for i in indices_ptr.shape[1:]:
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fused_indices_dimension *= i
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fused_updates_dimension = 1
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for i in updates_ptr.shape[len(indices_ptr.shape) - 1 :]:
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fused_updates_dimension *= i
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fused_shape = 1
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for i in data_ptr.shape:
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fused_shape *= i
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with IRBuilder() as ib:
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with T.seq_scope():
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with T.serial(0, fused_shape) as i:
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out[i] = data[i]
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with T.serial(0, fused_indices_dimension) as i:
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with T.parallel(0, fused_updates_dimension) as j:
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offset = fused_updates_dimension
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index = j # This is x_M, .. x_{N-1} part of the index into out.
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# Build up the indices[0, y_0, ..], .. indices[M-1, y_0, ..] part
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# of the index into out.
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for l in reversed(range(indices_ptr.shape[0].value)):
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# indices[l, y_0, ... y_{k-1}]
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index += offset * indices[i + l * fused_indices_dimension]
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offset *= data_ptr.shape[l]
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if mode == "update":
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out[index] = updates[i * fused_updates_dimension + j]
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elif mode == "add":
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out[index] += updates[i * fused_updates_dimension + j]
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elif mode == "mul":
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out[index] *= updates[i * fused_updates_dimension + j]
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elif mode == "min":
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out[index] = tirx.min(
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out[index], updates[i * fused_updates_dimension + j]
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)
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elif mode == "max":
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out[index] = tirx.max(
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out[index], updates[i * fused_updates_dimension + j]
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)
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else:
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raise NotImplementedError(
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"scatter_nd mode not in [update, add, mul, min, max]:", mode
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)
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return ib.get()
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out_buf = tirx.decl_buffer(data.shape, data.dtype, "out_buf", layout=None)
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return te.extern(
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[data.shape],
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[data, indices, updates],
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lambda ins, outs: gen_ir(ins[0], ins[1], ins[2], outs[0]),
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dtype=data.dtype,
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out_buffers=[out_buf],
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name="scatter_nd.generic",
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tag="scatter_nd.generic",
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)
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