forked from apache/tvm
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_nn.py
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_nn.py
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# 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=no-else-return, invalid-name, unused-argument, too-many-arguments
"""Backend compiler related feature registration"""
from __future__ import absolute_import
import topi
from topi.util import get_const_tuple
from .. import op as reg
from ..op import OpPattern, schedule_injective
from .._tensor import elemwise_shape_func
from ....api import convert
from ....hybrid import script
# relu
reg.register_schedule("nn.relu", schedule_injective)
reg.register_pattern("nn.relu", OpPattern.ELEMWISE)
# softmax
@reg.register_schedule("nn.softmax")
def schedule_softmax(_, outputs, target):
"""Schedule definition of softmax"""
with target:
return topi.generic.schedule_softmax(outputs)
reg.register_pattern("nn.softmax", OpPattern.OPAQUE)
schedule_broadcast = schedule_injective
@reg.register_schedule("nn.log_softmax")
def schedule_log_softmax(_, outputs, target):
"""Schedule definition of log_softmax"""
with target:
return topi.generic.schedule_softmax(outputs)
reg.register_pattern("nn.log_softmax", OpPattern.OPAQUE)
# dense
@reg.register_compute("nn.dense")
def compute_dense(attrs, inputs, out_type, target):
"""Compute definition of dense"""
out_dtype = attrs.out_dtype
out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
return [topi.nn.dense(inputs[0], inputs[1], None, out_dtype)]
@reg.register_schedule("nn.dense")
def schedule_dense(attrs, outputs, target):
"""Schedule definition of dense"""
with target:
return topi.generic.schedule_dense(outputs)
reg.register_pattern("nn.dense", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
@reg.register_compute('nn.fifo_buffer')
def compute_fifo_buffer(attrs, inputs, out_type, target):
return [topi.nn.fifo_buffer(inputs[0], inputs[1], axis=attrs.get_int('axis'))]
@reg.register_schedule('nn.fifo_buffer')
def schedule_fifo_buffer(attrs, outputs, target):
with target:
return topi.generic.schedule_injective(outputs)
reg.register_pattern("nn.fifo_buffer", OpPattern.OPAQUE)
# batch_matmul
@reg.register_compute("nn.batch_matmul")
def compute_batch_matmul(attrs, inputs, out_type, target):
"""Compute definition of batch_matmul"""
with target:
return [topi.nn.batch_matmul(inputs[0], inputs[1])]
@reg.register_schedule("nn.batch_matmul")
def schedule_batch_matmul(attrs, outputs, target):
"""Schedule definition of batch_matmul"""
with target:
return topi.generic.schedule_batch_matmul(outputs)
reg.register_pattern("nn.batch_matmul", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
# sparse_dense
@reg.register_compute("nn.sparse_dense")
def compute_sparse_dense(attrs, inputs, out_type, target):
"""Compute definition of sparse_dense"""
return [topi.nn.sparse_dense(inputs[0], inputs[1], inputs[2], inputs[3])]
@reg.register_schedule("nn.sparse_dense")
def schedule_sparse_dense(attrs, outputs, target):
"""Schedule definition of batch_matmul"""
with target:
return topi.generic.schedule_sparse_dense(outputs)
reg.register_pattern("nn.sparse_dense", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
# sparse_transpose
@reg.register_compute("nn.sparse_transpose")
def compute_sparse_transpose(attrs, inputs, out_type, target):
"""Compute definition of sparse_transpose"""
return topi.nn.sparse_transpose(inputs[0], inputs[1], inputs[2])
@reg.register_schedule("nn.sparse_transpose")
def schedule_sparse_transpose(attrs, outputs, target):
"""Schedule definition of batch_matmul"""
with target:
return topi.generic.schedule_sparse_transpose(outputs)
reg.register_pattern("nn.sparse_transpose", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
# conv2d
def _find_conv2d_op(op):
"""Find the op with conv2d in its tag by traversing."""
if 'conv2d' in op.tag:
return op
for tensor in op.input_tensors:
op_ = _find_conv2d_op(tensor.op)
if op_ is not None:
return op_
return None
@reg.register_compute("nn.conv2d")
def compute_conv2d(attrs, inputs, out_type, target):
"""Compute definition of conv2d"""
padding = get_const_tuple(attrs.padding)
strides = get_const_tuple(attrs.strides)
dilation = get_const_tuple(attrs.dilation)
groups = attrs.groups
layout = attrs.data_layout
kernel_layout = attrs.kernel_layout
out_dtype = attrs.out_dtype
out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
else out_dtype)
assert layout in ["NCHW", "NHWC", "NCHW4c", "HWCN"]
(dilation_h, dilation_w) = dilation
if dilation_h < 1 or dilation_w < 1:
raise ValueError("dilation should be positive value")
def _get_out_depth():
weight_shape = get_const_tuple(inputs[1].shape)
# NHWC layout
if kernel_layout.startswith("HW"):
return weight_shape[2] * weight_shape[3]
# NCHW layout.
# in ARM CPU contrib_spatial_pack schedule, we will prepack weight layout
if len(weight_shape) == 4:
return weight_shape[0] * weight_shape[1]
else:
assert len(weight_shape) == 5
C, M, _, _, VC = weight_shape
return C * VC * M
if groups == 1:
out = topi.nn.conv2d(
inputs[0], inputs[1], strides, padding,
dilation, layout, out_dtype)
elif layout == "NCHW" and _get_out_depth() == groups:
out = topi.nn.depthwise_conv2d_nchw(
inputs[0], inputs[1], strides, padding, dilation, out_dtype)
elif layout == "NHWC" and kernel_layout == "HWOI" and _get_out_depth() == groups:
out = topi.nn.depthwise_conv2d_nhwc(
inputs[0], inputs[1], strides, padding, dilation, out_dtype)
elif layout in ['NCHW', 'NCHW4c']:
out = topi.nn.group_conv2d_nchw(inputs[0], inputs[1], strides, padding, dilation, groups,
out_dtype)
else:
raise ValueError("not support arbitrary group number for now")
return [out]
@reg.register_schedule("nn.conv2d")
def schedule_conv2d(attrs, outs, target):
"""Schedule definition of conv2d"""
groups = attrs.groups
layout = attrs.data_layout
kernel_layout = attrs.kernel_layout
with target:
if groups == 1 and layout == "NCHW":
return topi.generic.schedule_conv2d_nchw(outs)
elif groups == 1 and layout == "NCHW4c":
return topi.generic.schedule_conv2d_nchw(outs)
elif groups == 1 and layout == "NHWC":
return topi.generic.schedule_conv2d_nhwc(outs)
elif groups == 1 and layout == "HWCN":
return topi.generic.schedule_conv2d_hwcn(outs)
elif groups != 1:
# collect in_channels to distinguish depthwise and group conv2d
op = _find_conv2d_op(outs[0].op)
assert op is not None
is_depthwise = 'depthwise' in op.tag
if is_depthwise:
if layout == "NCHW":
# TODO(leyuan, merrymercy, Huyuwei): fold depthwise topi into conv2d.
return topi.generic.schedule_depthwise_conv2d_nchw(outs)
if layout == "NHWC" and kernel_layout == "HWOI":
return topi.generic.schedule_depthwise_conv2d_nhwc(outs)
else:
if layout in ["NCHW", "NCHW4c"]:
return topi.generic.schedule_group_conv2d_nchw(outs)
raise ValueError("No compatible schedule")
@reg.register_alter_op_layout("nn.conv2d")
def alter_op_layout_conv2d(attrs, inputs, tinfos):
"""Alternate the layout of conv2d"""
from ... import op
return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, op)
@reg.register_legalize("nn.conv2d")
def legalize_conv2d(attrs, inputs, types):
"""Legalize conv2d op.
Parameters
----------
attrs : tvm.attrs.Attrs
Attributes of current convolution
inputs : list of tvm.relay.Expr
The args of the Relay expr to be legalized
types : list of types
List of input and output types
Returns
-------
result : tvm.relay.Expr
The legalized expr
"""
return topi.nn.conv2d_legalize(attrs, inputs, types)
reg.register_pattern("nn.conv2d", OpPattern.OUT_ELEMWISE_FUSABLE)
# conv2d_transpose
@reg.register_compute("nn.conv2d_transpose")
def compute_conv2d_transpose(attrs, inputs, out_dtype, target):
"""Compute definition of conv2d_transpose"""
padding = get_const_tuple(attrs.padding)
strides = get_const_tuple(attrs.strides)
dilation = get_const_tuple(attrs.dilation)
groups = attrs.groups
layout = attrs.data_layout
out_dtype = attrs.out_dtype
out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
else out_dtype)
assert layout == "NCHW", "only support nchw for now"
assert dilation == (1, 1), "not support dilate now"
assert groups == 1, "only support groups == 1 for now"
out = topi.nn.conv2d_transpose_nchw(
inputs[0], inputs[1], strides, padding, out_dtype)
output_padding = get_const_tuple(attrs.output_padding)
out = topi.nn.pad(out,
[0, 0, 0, 0], [0, 0, output_padding[0], output_padding[1]])
return [out]
@reg.register_compute("nn.conv3d")
def compute_conv3d(attrs, inputs, out_type, target):
"""Compute definition of conv3d"""
padding = get_const_tuple(attrs.padding)
strides = get_const_tuple(attrs.strides)
dilation = get_const_tuple(attrs.dilation)
groups = attrs.groups
layout = attrs.data_layout
out_dtype = attrs.out_dtype
out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
else out_dtype)
assert layout in ["NCDHW"]
(dilation_d, dilation_h, dilation_w) = dilation
if dilation_d < 1 or dilation_h < 1 or dilation_w < 1:
raise ValueError("dilation should be positive value")
if groups == 1:
out = topi.nn.conv3d(
inputs[0], inputs[1], strides, padding,
dilation, layout, out_dtype)
else:
raise ValueError("not support arbitrary group number for now")
return [out]
@reg.register_schedule("nn.conv3d")
def schedule_conv3d(attrs, outs, target):
"""Schedule definition of conv3d"""
groups = attrs.groups
layout = attrs.data_layout
with target:
if groups == 1 and layout == "NCDHW":
return topi.generic.schedule_conv3d_ncdhw(outs)
raise ValueError("No compatible schedule")
reg.register_pattern("nn.conv3d", OpPattern.OUT_ELEMWISE_FUSABLE)
@reg.register_schedule("nn.conv2d_transpose")
def schedule_conv2d_transpose(attrs, outs, target):
"""Schedule definition of conv2d_transpose"""
with target:
return topi.generic.schedule_conv2d_transpose_nchw(outs)
@reg.register_legalize("nn.conv2d_transpose")
def legalize_conv2d_transpose(attrs, inputs, types):
"""Legalize conv2d_transpose op.
Parameters
----------
attrs : tvm.attrs.Attrs
Attributes of current Transposed convolution
inputs : list of tvm.relay.Expr
The args of the Relay expr to be legalized
types : list of types
List of input and output types
Returns
-------
result : tvm.relay.Expr
The legalized expr
"""
return topi.nn.conv2d_transpose_legalize(attrs, inputs, types)
reg.register_pattern("nn.conv2d_transpose", OpPattern.OUT_ELEMWISE_FUSABLE)
# bias_add
reg.register_schedule("nn.bias_add", schedule_injective)
reg.register_pattern("nn.bias_add", OpPattern.BROADCAST)
# max_pool2d
@reg.register_schedule("nn.max_pool2d")
def schedule_max_pool2d(attrs, outs, target):
"""Schedule definition of max_pool2d"""
layout = attrs.layout
with target:
return topi.generic.schedule_pool(outs, layout)
reg.register_pattern("nn.max_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
# avg_pool2d
@reg.register_schedule("nn.avg_pool2d")
def schedule_avg_pool2d(attrs, outs, target):
"""Schedule definition of avg_pool2d"""
layout = attrs.layout
with target:
return topi.generic.schedule_pool(outs, layout)
reg.register_pattern("nn.avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
# max_pool2d_grad
@reg.register_schedule("nn.max_pool2d_grad")
def schedule_max_pool2d_grad(attrs, outs, target):
"""Schedule definition of max_pool2d_grad"""
with target:
return topi.generic.schedule_pool_grad(outs)
reg.register_pattern("nn.max_pool2d_grad", OpPattern.OUT_ELEMWISE_FUSABLE)
# avg_pool2d_grad
@reg.register_schedule("nn.avg_pool2d_grad")
def schedule_avg_pool2d_grad(attrs, outs, target):
"""Schedule definition of avg_pool2d_grad"""
with target:
return topi.generic.schedule_pool_grad(outs)
reg.register_pattern("nn.avg_pool2d_grad", OpPattern.OUT_ELEMWISE_FUSABLE)
# global_max_pool2d
@reg.register_schedule("nn.global_max_pool2d")
def schedule_global_max_pool2d(_, outs, target):
"""Schedule definition of global_max_pool2d"""
with target:
return topi.generic.schedule_adaptive_pool(outs)
reg.register_pattern("nn.global_max_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
# global_avg_pool2d
@reg.register_schedule("nn.global_avg_pool2d")
def schedule_global_avg_pool2d(_, outs, target):
"""Schedule definition of global_avg_pool2d"""
with target:
return topi.generic.schedule_adaptive_pool(outs)
reg.register_pattern("nn.global_avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
# leaky_relu
reg.register_schedule("nn.leaky_relu", schedule_broadcast)
reg.register_pattern("nn.leaky_relu", OpPattern.ELEMWISE)
# prelu
reg.register_schedule("nn.prelu", schedule_broadcast)
reg.register_pattern("nn.prelu", OpPattern.BROADCAST)
# flatten
reg.register_schedule("nn.batch_flatten", schedule_broadcast)
reg.register_pattern("nn.batch_flatten", OpPattern.INJECTIVE)
# lrn
@reg.register_compute("nn.lrn")
def compute_lrn(attrs, inputs, out_dtype, target):
"""Compute definition of lrn"""
assert len(inputs) == 1
return [topi.nn.lrn(inputs[0], attrs.size, attrs.axis,
attrs.alpha, attrs.beta, attrs.bias)]
@reg.register_schedule("nn.lrn")
def schedule_lrn(attrs, outs, target):
"""Schedule definition of lrn"""
with target:
return topi.generic.schedule_lrn(outs)
reg.register_pattern("nn.lrn", OpPattern.OPAQUE)
# l2_normalize
@reg.register_compute("nn.l2_normalize")
def compute_l2_normalize(attrs, inputs, out_dtype, target):
"""Compute definition of l2 normalize"""
return [topi.nn.l2_normalize(inputs[0], attrs.eps, attrs.axis)]
@reg.register_schedule("nn.l2_normalize")
def schedule_l2_normalize(attrs, outs, target):
"""Schedule definition of l2 normalize"""
with target:
return topi.generic.schedule_l2_normalize(outs)
reg.register_pattern("nn.l2_normalize", OpPattern.OUT_ELEMWISE_FUSABLE)
# upsampling
reg.register_schedule("nn.upsampling", reg.schedule_injective)
def schedule_upsampling(_, outs, target):
"""Schedule definition of upsampling"""
with target:
return topi.generic.schedule_injective(outs)
@reg.register_compute("nn.upsampling")
def compute_upsampling(attrs, inputs, out_dtype, target):
scale_h = attrs.scale_h
scale_w = attrs.scale_w
layout = attrs.layout
method = attrs.method
align_corners = attrs.align_corners
return [topi.nn.upsampling(inputs[0], scale_h, scale_w, layout, method, align_corners)]
# pad
reg.register_schedule("nn.pad", schedule_broadcast)
# mirror_pad
reg.register_schedule("nn.mirror_pad", schedule_broadcast)
@reg.register_compute("nn.mirror_pad")
def compute_mirror_pad(attrs, inputs, out_dtype, target):
pad_before, pad_after = list(zip(*attrs.pad_width))
mode = attrs.mode
out = topi.nn.mirror_pad(inputs[0], pad_before=pad_before, pad_after=pad_after, mode=mode)
return [out]
# winograd related operators
@reg.register_compute("nn.contrib_conv2d_winograd_without_weight_transform")
def compute_contrib_conv2d_winograd_without_weight_transform(attrs, inputs, out_dtype, target):
"""Compute definition of conv2d_winograd_without_weight_transform"""
# pylint: disable=assignment-from-no-return
padding = attrs.get_int_tuple("padding")
strides = attrs.get_int_tuple("strides")
dilation = attrs.get_int_tuple("dilation")
groups = attrs.get_int("groups")
data_layout = attrs.get_str("data_layout")
out_dtype = attrs.get_str("out_dtype")
tile_size = attrs.get_int("tile_size")
out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
assert dilation == (1, 1), "Do not support dilate now"
assert groups == 1, "Do not supoort arbitrary group number"
out = topi.nn.conv2d_winograd_without_weight_transform(
inputs[0], inputs[1], strides, padding, dilation, data_layout,
out_dtype, tile_size)
return [out]
@reg.register_schedule("nn.contrib_conv2d_winograd_without_weight_transform")
def schedule_contrib_conv2d_winograd_without_weight_transform(attrs, outs, target):
"""Schedule definition of conv2d_winograd_without_weight_transform"""
with target:
return topi.generic.schedule_conv2d_winograd_without_weight_transform(outs)
reg.register_pattern("nn.contrib_conv2d_winograd_without_weight_transform",
OpPattern.OUT_ELEMWISE_FUSABLE)
@reg.register_compute("nn.contrib_conv2d_winograd_weight_transform")
def compute_contrib_conv2d_winograd_weight_transform(attrs, inputs, out_dtype, target):
"""Compute definition of contrib_conv2d_winograd_weight_transform"""
out = topi.nn.conv2d_winograd_weight_transform(
inputs[0], attrs.get_int('tile_size'))
return [out]
@reg.register_schedule("nn.contrib_conv2d_winograd_weight_transform")
def schedule_contrib_conv2d_winograd_weight_transform(attrs, outs, target):
"""Schedule definition of contrib_conv2d_winograd_weight_transform"""
with target:
return topi.generic.schedule_conv2d_winograd_weight_transform(outs)
reg.register_pattern("nn.contrib_conv2d_winograd_weight_transform",
OpPattern.OUT_ELEMWISE_FUSABLE)
# winograd nnpack related operators
@reg.register_compute("nn.contrib_conv2d_winograd_nnpack_without_weight_transform")
def compute_contrib_conv2d_winograd_nnpack_without_weight_transform(
attrs, inputs, out_dtype, target):
"""Compute definition of conv2d_winograd_nnpack_without_weight_transform"""
# pylint: disable=assignment-from-no-return
padding = attrs.get_int_tuple("padding")
strides = attrs.get_int_tuple("strides")
dilation = attrs.get_int_tuple("dilation")
groups = attrs.get_int("groups")
data_layout = attrs.get_str("data_layout")
out_dtype = attrs.get_str("out_dtype")
out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
assert dilation == (1, 1), "Do not support dilate now"
assert groups == 1, "Do not supoort arbitrary group number"
# No bias
out = topi.nn.conv2d_winograd_nnpack_without_weight_transform(
inputs[0], inputs[1], None, strides, padding, dilation, data_layout,
out_dtype)
return [out]
@reg.register_schedule("nn.contrib_conv2d_winograd_nnpack_without_weight_transform")
def schedule_contrib_conv2d_winograd_nnpack_without_weight_transform(attrs, outs, target):
"""Schedule definition of conv2d_winograd_nnpack_without_weight_transform"""
with target:
return topi.generic.schedule_conv2d_winograd_nnpack_without_weight_transform(outs)
reg.register_pattern("nn.contrib_conv2d_winograd_nnpack_without_weight_transform",
OpPattern.OPAQUE)
@reg.register_compute("nn.contrib_conv2d_winograd_nnpack_weight_transform")
def compute_contrib_conv2d_winograd_nnpack_weight_transform(attrs, inputs, out_dtype, target):
"""Compute definition of contrib_conv2d_winograd_nnpack_weight_transform"""
convolution_algorithm = attrs.get_int('convolution_algorithm')
out = topi.nn.conv2d_winograd_nnpack_weight_transform(
inputs[0], convolution_algorithm, out_dtype)
return [out]
@reg.register_schedule("nn.contrib_conv2d_winograd_nnpack_weight_transform")
def schedule_contrib_conv2d_winograd_nnpack_weight_transform(attrs, outs, target):
"""Schedule definition of contrib_conv2d_winograd_nnpack_weight_transform"""
with target:
return topi.generic.schedule_conv2d_winograd_nnpack_weight_transform(outs)
reg.register_pattern("nn.contrib_conv2d_winograd_nnpack_weight_transform",
OpPattern.OPAQUE)
@reg.register_compute("nn.contrib_conv2d_NCHWc")
def compute_contrib_conv2d_NCHWc(attrs, inputs, out_dtype, target):
"""Compute definition of conv2d NCHWc"""
# pylint: disable=assignment-from-no-return
padding = attrs.get_int_tuple("padding")
strides = attrs.get_int_tuple("strides")
dilation = attrs.get_int_tuple("dilation")
data_layout = attrs.get_str("data_layout")
out_layout = attrs.get_str("out_layout")
out_dtype = attrs.get_str("out_dtype")
out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
out = topi.nn.conv2d_NCHWc(inputs[0], inputs[1], strides, padding, dilation,
data_layout, out_layout, out_dtype)
return [out]
@reg.register_schedule("nn.contrib_conv2d_NCHWc")
def schedule_contrib_conv2d_NCHWc(attrs, outs, target):
"""Schedule definition of contrib_conv2d_NCHWc"""
with target:
return topi.generic.schedule_conv2d_NCHWc(outs)
reg.register_pattern("nn.contrib_conv2d_NCHWc",
OpPattern.OUT_ELEMWISE_FUSABLE)
@reg.register_compute("nn.contrib_conv2d_NCHWc_int8")
def compute_contrib_conv2d_NCHWc_int8(attrs, inputs, out_dtype, target):
"""Compute definition of conv2d NCHWc"""
# pylint: disable=assignment-from-no-return
padding = attrs.get_int_tuple("padding")
strides = attrs.get_int_tuple("strides")
dilation = attrs.get_int_tuple("dilation")
data_layout = attrs.get_str("data_layout")
out_layout = attrs.get_str("out_layout")
out_dtype = attrs.get_str("out_dtype")
out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
out = topi.nn.conv2d_NCHWc_int8(inputs[0], inputs[1], strides, padding, dilation,
data_layout, out_layout, out_dtype)
return [out]
@reg.register_schedule("nn.contrib_conv2d_NCHWc_int8")
def schedule_contrib_conv2d_NCHWc_int8(attrs, outs, target):
"""Schedule definition of contrib_conv2d_NCHWc_int8"""
with target:
return topi.generic.schedule_conv2d_NCHWc_int8(outs)
reg.register_pattern("nn.contrib_conv2d_NCHWc_int8",
OpPattern.OUT_ELEMWISE_FUSABLE)
@reg.register_compute("nn.contrib_depthwise_conv2d_NCHWc")
def compute_contrib_depthwise_conv2d_NCHWc(attrs, inputs, out_dtype, target):
"""Compute definition of depthwise conv2d NCHWc"""
# pylint: disable=assignment-from-no-return
padding = attrs.get_int_tuple("padding")
strides = attrs.get_int_tuple("strides")
dilation = attrs.get_int_tuple("dilation")
data_layout = attrs.get_str("data_layout")
out_layout = attrs.get_str("out_layout")
out_dtype = attrs.get_str("out_dtype")
out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
out = topi.nn.depthwise_conv2d_NCHWc(inputs[0], inputs[1], strides, padding, dilation,
data_layout, out_layout, out_dtype)
return [out]
@reg.register_schedule("nn.contrib_depthwise_conv2d_NCHWc")
def schedule_contrib_depthwise_conv2d_NCHWc(attrs, outs, target):
"""Schedule definition of contrib_conv2d_NCHWc"""
with target:
return topi.generic.schedule_depthwise_conv2d_NCHWc(outs)
reg.register_pattern("nn.contrib_depthwise_conv2d_NCHWc",
OpPattern.OUT_ELEMWISE_FUSABLE)
@reg.register_compute("nn.deformable_conv2d")
def compute_deformable_conv2d(attrs, inputs, out_dtype, target):
"""Compute definition of deformable_conv2d"""
padding = get_const_tuple(attrs.padding)
strides = get_const_tuple(attrs.strides)
dilation = get_const_tuple(attrs.dilation)
deformable_groups = attrs.deformable_groups
groups = attrs.groups
out_dtype = attrs.out_dtype
out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype
with target:
out = topi.nn.deformable_conv2d_nchw(inputs[0], inputs[1], inputs[2], strides, padding,
dilation, deformable_groups, groups, out_dtype)
return [out]
@reg.register_schedule("nn.deformable_conv2d")
def schedule_deformable_conv2d(attrs, outs, target):
"""Schedule definition of deformable_conv2d"""
with target:
return topi.generic.schedule_deformable_conv2d_nchw(outs)
reg.register_pattern("nn.deformable_conv2d", OpPattern.OUT_ELEMWISE_FUSABLE)
@reg.register_compute("nn.bitpack")
def compute_bitpack(attrs, inputs, out_dtype, target):
"""Compute definition for bitpack"""
bits = attrs.bits
pack_axis = attrs.pack_axis
bit_axis = attrs.bit_axis
pack_type = attrs.pack_type
name = attrs.name
with target:
out = topi.nn.bitpack(inputs[0], bits, pack_axis, bit_axis, pack_type,
name)
return [out]
@reg.register_schedule("nn.bitpack")
def schedule_bitpack(attrs, outs, target):
with target:
return topi.generic.schedule_bitpack(outs)
reg.register_pattern("nn.bitpack", OpPattern.INJECTIVE)
@reg.register_compute("nn.bitserial_conv2d")
def compute_bitserial_conv2d(attrs, inputs, out_dtype, target):
"""Compute definition for bitserial conv2d."""
padding = get_const_tuple(attrs.padding)
strides = get_const_tuple(attrs.strides)
activation_bits = attrs.activation_bits
weight_bits = attrs.weight_bits
layout = attrs.data_layout
pack_dtype = attrs.pack_dtype
out_dtype = attrs.out_dtype
unipolar = attrs.unipolar
if layout == 'NCHW':
with target:
out = topi.nn.bitserial_conv2d_nchw(
inputs[0], inputs[1], strides, padding, activation_bits,
weight_bits, pack_dtype, out_dtype, unipolar)
elif layout == 'NHWC':
with target:
out = topi.nn.bitserial_conv2d_nhwc(
inputs[0], inputs[1], strides, padding, activation_bits,
weight_bits, pack_dtype, out_dtype, unipolar)
else:
raise ValueError("Data layout not supported.")
return [out]
@reg.register_schedule("nn.bitserial_conv2d")
def schedule_bitserial_conv2d(attrs, outs, target):
"""Schedule definition for bitserial conv2d."""
layout = attrs.data_layout
if layout == 'NCHW':
with target:
return topi.generic.schedule_bitserial_conv2d_nchw(outs)
elif layout == 'NHWC':
with target:
return topi.generic.schedule_bitserial_conv2d_nhwc(outs)
else:
raise ValueError("Data layout not supported.")
@reg.register_legalize("nn.bitserial_conv2d")
def legalize_bitserial_conv2d(attrs, inputs, types):
"""Legalize bitserial_conv2d op.
Parameters
----------
attrs : tvm.attrs.Attrs
Attributes of current convolution
inputs : list of tvm.relay.Expr
The args of the Relay expr to be legalized
types : list of types
List of input and output types
Returns
-------
result : tvm.relay.Expr
The legalized expr
"""
return topi.nn.bitserial_conv2d_legalize(attrs, inputs, types)
reg.register_pattern("nn.bitserial_conv2d", OpPattern.OUT_ELEMWISE_FUSABLE)
# bitserial_dense
@reg.register_compute("nn.bitserial_dense")
def compute_bitserial_dense(attrs, inputs, out_type, target):
"""Compute definition of bitserial_dense"""
data_bits = attrs.data_bits
weight_bits = attrs.weight_bits
pack_dtype = attrs.pack_dtype
out_dtype = attrs.out_dtype
out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
unipolar = attrs.unipolar
return [
topi.nn.bitserial_dense(
inputs[0],
inputs[1],
data_bits,
weight_bits,
pack_dtype,
out_dtype,
unipolar)
]
@reg.register_schedule("nn.bitserial_dense")
def schedule_bitserial_dense(attrs, outputs, target):
"""Schedule definition of bitserial_dense"""
with target:
return topi.generic.schedule_bitserial_dense(outputs)
reg.register_pattern("nn.bitserial_dense", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
reg.register_pattern("nn.cross_entropy", OpPattern.OPAQUE)
@reg.register_compute("nn.cross_entropy")
def compute_cross_entropy(attrs, inputs, out_dtype, target):
x, y = inputs
return [-topi.sum(topi.log(x) * y) / x.shape[0]]
reg.register_pattern("nn.cross_entropy_with_logits", OpPattern.OPAQUE)
@reg.register_compute("nn.cross_entropy_with_logits")
def compute_cross_entropy_with_logits(attrs, inputs, out_dtype, target):
x, y = inputs
return [-topi.sum(x * y) / x.shape[0]]
# shape func
@script
def _conv2d_NCHWc_shape_func(dshape, kshape, strides, padding, dilation, oc_bn):
out = output_tensor((dshape.shape[0],), "int64")
ic_chunk = dshape[1]
height = dshape[2]
width = dshape[3]
ic_bn = dshape[4]
kheight = kshape[2]
kwidth = kshape[3]
dilated_kh = (kheight - 1) * dilation[0] + 1
dilated_kw = (kwidth - 1) * dilation[1] + 1
kflatten = int64(1)
for i in const_range(kshape.shape[0]):
kflatten *= kshape[i]
oc = kflatten // (kheight * kwidth * ic_chunk * ic_bn)
oc_chunk = oc // oc_bn
out_height = (height + 2 * padding[0] - dilated_kh) // strides[0] + 1
out_width = (width + 2 * padding[1] - dilated_kw) // strides[1] + 1
out[0] = dshape[0]
out[1] = oc_chunk
out[2] = out_height
out[3] = out_width
out[4] = int64(oc_bn)
return out
@reg.register_shape_func("nn.contrib_conv2d_NCHWc", False)
def conv2d_NCHWc_shape_func(attrs, inputs, _):
"""
Shape function for contrib_conv2d_NCHWc op.
"""
strides = get_const_tuple(attrs.strides)
padding = get_const_tuple(attrs.padding)
dilation = get_const_tuple(attrs.dilation)
out_layout = attrs.out_layout
oc_bn = int(out_layout[4:-1])
return [_conv2d_NCHWc_shape_func(inputs[0], inputs[1],
convert(strides), convert(padding),
convert(dilation), convert(oc_bn))]
@script
def _pool2d_shape_func(data_shape, pool_size, strides,
padding, height_axis, width_axis):
out = output_tensor((data_shape.shape[0],), "int64")
for i in const_range(data_shape.shape[0]):
if i == height_axis:
out[i] = (data_shape[i] + padding[0] + padding[2] - pool_size[0]) // strides[0] + 1
elif i == width_axis:
out[i] = (data_shape[i] + padding[1] + padding[3] - pool_size[1]) // strides[1] + 1
else:
out[i] = data_shape[i]
return out
def pool2d_shape_func(attrs, inputs, _):
"""
Shape function for pool2d op.
"""
pool_size = get_const_tuple(attrs.pool_size)
strides = get_const_tuple(attrs.strides)
padding = get_const_tuple(attrs.padding)
layout = attrs.layout
height_axis = layout.index("H")
width_axis = layout.index("W")
if len(padding) == 1:
padding = [padding[0]] * 4
elif len(padding) == 2:
padding = [padding[0], padding[1], padding[0], padding[1]]
return [_pool2d_shape_func(inputs[0], convert(pool_size),
convert(strides), convert(padding),
convert(height_axis), convert(width_axis))]
reg.register_shape_func("nn.max_pool2d", False, pool2d_shape_func)
reg.register_shape_func("nn.avg_pool2d", False, pool2d_shape_func)
@script
def _global_pool2d_shape_func(data_shape, height_axis, width_axis):
out = output_tensor((data_shape.shape[0],), "int64")
for i in const_range(out.shape[0]):
if i == height_axis or i == width_axis:
out[i] = int64(1)
else:
out[i] = data_shape[i]
return out
def global_pool2d_shape_func(attrs, inputs, _):
"""
Shape function for global pool2d op.
"""
layout = attrs.layout
height_axis = width_axis = 1
for i, letter in enumerate(layout):
if letter == "H":
height_axis = i
if letter == "W":
width_axis = i
return [_global_pool2d_shape_func(inputs[0], convert(height_axis), convert(width_axis))]
reg.register_shape_func("nn.global_max_pool2d", False, global_pool2d_shape_func)
reg.register_shape_func("nn.global_avg_pool2d", False, global_pool2d_shape_func)
@script
def _batch_flatten_shape_func(data_shape):
out = output_tensor((2,), "int64")
out[0] = data_shape[0]
out[1] = int64(1)
for i in const_range(data_shape.shape[0] - 1):
out[1] *= data_shape[i + 1]
return out
@reg.register_shape_func("nn.batch_flatten", False)
def batch_flatten_shape_func(attrs, inputs, _):
"""
Shape function for batch_flatten op.
"""
return [_batch_flatten_shape_func(inputs[0])]
@script
def _dense_shape_func(data_shape, weight_shape):
out = output_tensor((data_shape.shape[0],), "int64")
for i in const_range(out.shape[0] - 1):
out[i] = data_shape[i]
out[out.shape[0] - 1] = weight_shape[0]
return out
@reg.register_shape_func("nn.dense", False)
def dense_shape_func(attrs, inputs, _):
"""
Shape function for dense op.
"""
ret = [_dense_shape_func(inputs[0], inputs[1])]
return ret
@script
def _pad_shape_func(data_shape, pad_width):
out = output_tensor((data_shape.shape[0],), "int64")
for i in const_range(out.shape[0]):
out[i] = data_shape[i] + pad_width[i][0] + pad_width[i][1]
return out