wehub-resource-sync
130 lines
5.8 KiB
Python
130 lines
5.8 KiB
Python
# 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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"""scatter_nd related operators"""
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import tvm
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from tvm import te, tirx # hide redefinition of min and max
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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 ..math import cast
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from ..scatter import _verify_scatter_nd_inputs
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from ..utils import ceil_div
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def scatter_nd(data, indices, updates, mode):
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"""GPU implementation of scatter_nd with explicit thread bindings."""
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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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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(fused_shape, 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 < fused_shape):
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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(fused_updates_dimension, 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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j = bx_scat * max_threads + tx_scat
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with T.If(j < fused_updates_dimension):
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with T.Then():
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with T.serial(0, fused_indices_dimension) as i:
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offset = fused_updates_dimension
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index = j # x_M, .. x_{N-1} part of the index into out.
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# Build up the indices[0, y_0, ..], ..,
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# indices[M-1, y_0, ..] part 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]:",
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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.gpu",
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tag="scatter_nd.gpu",
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)
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