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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"""scatter_elements related operators"""
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import tvm
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from tvm import te, tirx
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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 .. import utils
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from ..math import cast
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from ..utils import ceil_div
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def scatter_elements(data, indices, updates, axis=0, reduction="update"):
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"""GPU implementation of scatter_elements with explicit thread bindings"""
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if not isinstance(axis, int):
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axis = utils.get_const_int(axis)
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# Prepare ranges and strides
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shape = data.shape
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if axis < 0:
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axis = len(shape) + axis
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axis_range = cast(shape[axis], indices.dtype)
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full_range = 1
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after_axis_range = 1
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for i, value in enumerate(shape, 0):
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full_range *= value
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if i > axis:
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after_axis_range *= value
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before_axis_stride = axis_range * after_axis_range
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ind_shape = indices.shape
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ind_axis_range = ind_shape[axis]
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ind_before_axis_range = 1
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ind_after_axis_range = 1
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for i, value in enumerate(ind_shape, 0):
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if i < axis:
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ind_before_axis_range *= value
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elif i > axis:
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ind_after_axis_range *= value
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ind_before_axis_stride = ind_axis_range * ind_after_axis_range
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ind_full_range_excl_axis = ind_before_axis_range * ind_after_axis_range
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def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr, reduce_func):
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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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max_threads = int(tvm.target.Target.current(allow_none=False).attrs["max_num_threads"])
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with IRBuilder() as ib:
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with T.seq_scope():
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# Init
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nthread_bx_init = cast(ceil_div(full_range, max_threads), "int32")
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tx_init = te.thread_axis("threadIdx.x")
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bx_init = te.thread_axis("blockIdx.x")
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with T.frame_scope(
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[
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T.attr(bx_init, "thread_extent", nthread_bx_init),
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T.attr(tx_init, "thread_extent", max_threads),
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]
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):
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tid = bx_init * max_threads + tx_init
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with T.If(tid < full_range):
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with T.Then():
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out[tid] = data[tid]
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# Scatter
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nthread_bx_scat = cast(ceil_div(ind_full_range_excl_axis, max_threads), "int32")
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tx_scat = te.thread_axis("threadIdx.x")
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bx_scat = te.thread_axis("blockIdx.x")
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with T.frame_scope(
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[
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T.attr(bx_scat, "thread_extent", nthread_bx_scat),
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T.attr(tx_scat, "thread_extent", max_threads),
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]
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):
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fused = bx_scat * max_threads + tx_scat
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with T.If(fused < ind_full_range_excl_axis):
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with T.Then():
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i = fused // ind_after_axis_range
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j = fused % ind_after_axis_range
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pre_index1 = i * ind_before_axis_stride + j
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pre_index2 = i * before_axis_stride + j
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with T.serial(0, ind_axis_range) as k:
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# Offset along indices or updates
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index1 = pre_index1 + k * ind_after_axis_range
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# Get index and shift to positive side if need
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k_new = indices[index1]
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shifted_index = k_new + (k_new < 0) * axis_range
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# Offset along data
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index2 = pre_index2 + shifted_index * after_axis_range
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reduce_func(out, index2, updates[index1])
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return ib.get()
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def update_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = update
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def add_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] += update
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def mul_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] *= update
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def mean_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = (dst_ptr[dst_index] + update) / 2
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def min_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = tirx.min(dst_ptr[dst_index], update)
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def max_func(dst_ptr, dst_index, update):
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dst_ptr[dst_index] = tirx.max(dst_ptr[dst_index], update)
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reduce_func = None
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if reduction == "update":
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reduce_func = update_func
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elif reduction == "add":
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reduce_func = add_func
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elif reduction == "mul":
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reduce_func = mul_func
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elif reduction == "mean":
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reduce_func = mean_func
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elif reduction == "min":
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reduce_func = min_func
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elif reduction == "max":
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reduce_func = max_func
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else:
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raise NotImplementedError(
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"scatter_elements reduction not in [update, add, mul, mean, min, max]:", reduction
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)
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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], reduce_func),
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dtype=data.dtype,
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out_buffers=[out_buf],
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name="scatter_elements.gpu",
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tag="scatter_elements.gpu",
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)
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