def test_injective_conv2d():
channels = 16
data = sym.Variable(name="data")
pool = sym.global_avg_pool2d(data=data)
weight = sym.reshape(pool, shape=[1, channels, 1, 1])
residual = sym.conv2d(data=data, kernel_size=(3,3), channels=channels, padding=(1, 1),
layout="NCHW", kernel_layout="OIHW", use_bias=False, name="conv")
net = weight * data + residual
size = 56
dtype="float32"
dshape = (1, channels, size, size)
kshape = (channels, channels, 3, 3)
oshape = dshape
shape_dict = {"data": dshape}
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(net, target, shape_dict)
# data, global_avg_pool, conv weight, conv op, fused elemwise add
assert graph.index.num_nodes == 5
data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
m = graph_runtime.create(graph, lib, ctx)
m.run(data=data, conv_weight=kernel)
# get output
out = m.get_output(0, tvm.nd.empty(oshape, dtype))
residual = topi.testing.conv2d_nchw_python(
data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME')
weight = np.mean(data.asnumpy(), axis=(2, 3))
c_np = weight[:, :, np.newaxis, np.newaxis] * data.asnumpy() + residual
tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def _alter_conv2d_layout_arm(attrs, inputs, tinfos):
"""Alter op layout for pre-computing kernel transformation"""
import nnvm.symbol as sym
copy_inputs = [s for s in inputs]
new_attrs = {k: attrs[k] for k in attrs.keys()}
# Remove attached compilation target because conv2d_NCHWc needs to create
# a conv2d_nchwc op and target is not one of conv2d's parameters.
if "target" in new_attrs:
del new_attrs["target"]
assert attrs.get_int_tuple("dilation") == (1, 1), "Does not support dilation " \
"when alter_op_layout is enabled"
strides = attrs.get_int_tuple("strides")
padding = attrs.get_int_tuple("padding")
groups = attrs.get_int('groups')
layout = attrs["layout"]
out_dtype = attrs["out_dtype"]
out_dtype = tinfos[0].dtype if out_dtype == "same" else out_dtype
if groups == 1:
# query config of this workload
workload = _conv_arg_to_workload(tinfos[0], tinfos[1], strides,
padding, layout, out_dtype)
cfg = autotvm.DispatchContext.current.query(
tvm.target.current_target(), workload)
if cfg.is_fallback: # if is fallback, clear query cache and return None
autotvm.task.clear_fallback_cache(tvm.target.current_target(),
workload)
return None
if cfg.template_key == 'direct': # packing weight tensor
new_attrs['kernel_layout'] = 'OIHW%do' % (cfg['tile_co'].size[-1])
return sym.conv2d(*copy_inputs, **new_attrs)
else: # pre-compute weight transformation in winograd
if "-device=arm_cpu" in tvm.target.current_target().options:
tile_size = 4
VC = cfg['tile_k'].size[-1]
else:
from ..mali.conv2d import _pick_tile_size
tile_size = _pick_tile_size(tinfos[0], tinfos[1])
VC = cfg['tile_bna'].val
weight = sym.contrib.conv2d_winograd_weight_transform(
copy_inputs[1], tile_size=tile_size)
CO, CI, KH, KW = get_const_tuple(tinfos[1].shape)
weight = sym.reshape(weight,
shape=(KH + tile_size - 1, KW + tile_size - 1,
CO // VC, VC, CI))
weight = sym.transpose(weight, axes=[0, 1, 2, 4, 3])
copy_inputs[1] = weight
new_attrs['tile_size'] = tile_size
return sym.contrib.conv2d_winograd_without_weight_transform(
*copy_inputs, **new_attrs)
# do nothing for depthwise convolution
return None
def nnvm_reshape(c, v, shp):
"""Implementation of reshape."""
nv = c.ref(v)
assert shp.is_constant(tuple)
if shp.value == ():
shp = (1, )
else:
shp = shp.value
return sym.reshape(nv, shape=shp)
def test_reshape():
x = sym.Variable("x", shape=(4, ))
y = sym.reshape(x, shape=(2, 2), name="y")
g, ldict = correct_layout(y, "C")
assert (ldict["x"][0] == "C")
assert (ldict["y"][0] == "__undef__")
# second pass will insert layout transform
g, ldict = correct_layout(g, "C16c")
assert (ldict["x"][0] == "C16c")
assert (ldict["x_C"][0] == "C")
assert (ldict["y"][0] == "__undef__")
def test_reshape():
x = sym.Variable("x", shape=(4,))
y = sym.reshape(x, shape=(2,2), name="y")
g, ldict = correct_layout(y, "C")
assert(ldict["x"][0] == "C")
assert(ldict["y"][0] == "__undef__")
# second pass will insert layout transform
g, ldict = correct_layout(g, "C16c")
assert(ldict["x"][0] == "C16c")
assert(ldict["x_C"][0] == "C")
assert(ldict["y"][0] == "__undef__")
def weight_prepack_conv2d(attrs, inputs, tinfos):
import ast
data = tinfos[0]
kernel = tinfos[1]
padding = ast.literal_eval(attrs['padding'])
stride = ast.literal_eval(attrs['strides'])
wkl = _get_workload(data, kernel, stride, padding, 'float32')
sch = _get_schedule_conv(wkl)
is_kernel_1x1 = isinstance(sch, AVX512Conv1x1Fwd)
ic_bn, oc_bn = sch.ic_bn, sch.oc_bn
new_attrs = {k: attrs[k] for k in attrs.keys()}
new_attrs.pop('layout', None)
kernel_sym = inputs[1]
oc, ic, h, w = get_const_tuple(tinfos[1].shape)
OC = oc // oc_bn
IC = ic // ic_bn
trans_kernel = sym.transpose(kernel_sym, axes=(1, 2, 3, 0))
trans_kernel = sym.reshape(trans_kernel, shape=(ic, h, w, OC, oc_bn))
trans_kernel = sym.transpose(trans_kernel, axes=(1, 2, 3, 4, 0))
trans_kernel = sym.reshape(trans_kernel,
shape=(h, w, OC, oc_bn, IC, ic_bn))
if is_kernel_1x1:
# (oc, ic, h, w) -> (OC, IC, ic, oc, h, w)
trans_kernel = sym.transpose(trans_kernel, axes=(2, 4, 5, 3, 0, 1))
else:
# (oc, ic, h, w) -> (OC, IC, h, w, ic, oc)
trans_kernel = sym.transpose(trans_kernel, axes=(2, 4, 0, 1, 5, 3))
if attrs.get_bool('use_bias'):
bias = inputs[2]
bias = sym.reshape(bias, shape=(OC, oc_bn))
return sym.contrib.conv2d_nchw_kernel_packed(inputs[0], trans_kernel,
bias, **new_attrs)
else:
return sym.contrib.conv2d_nchw_kernel_packed(inputs[0], trans_kernel,
**new_attrs)
def test_infer_shape():
x = sym.Variable('x', shape=(4, 2))
y = sym.add(x, x, name='add1')
y = sym.reshape(y, target=(2, 4), name="reshape1")
g = graph.create(y)
g._set_json_attr("shape_attr_key", "shape")
g = g.apply('InferShape')
jgraph = json.loads(g.apply('SaveJSON').json_attr('json'))
jnodes = jgraph['nodes']
jnode_row_ptr = jgraph['node_row_ptr']
nindex = {n['name']: i for i, n in enumerate(jnodes)}
assert g.json_attr('shape')[jnode_row_ptr[nindex["reshape1"]]] == [2, 4]
assert g.json_attr('shape')[jnode_row_ptr[nindex["add1"]]] == [4, 2]
def test_infer_shape():
x = sym.Variable('x', shape=(4, 2))
y = sym.add(x, x, name='add1')
y = sym.reshape(y, target=(2, 4), name="reshape1")
g = graph.create(y)
g._set_json_attr("shape_attr_key", "shape")
g = g.apply('InferShape')
jgraph = json.loads(g.apply('SaveJSON').json_attr('json'))
jnodes = jgraph['nodes']
jnode_row_ptr = jgraph['node_row_ptr']
nindex = {n['name']: i for i, n in enumerate(jnodes)}
assert g.json_attr('shape')[jnode_row_ptr[nindex["reshape1"]]] == [2, 4]
assert g.json_attr('shape')[jnode_row_ptr[nindex["add1"]]] == [4, 2]
def verify_reshape(dshape, oshape):
x = sym.Variable("x")
y = sym.reshape(x, shape=oshape)
y = y + 1
dtype = "float32"
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
m = graph_runtime.create(graph, lib, ctx)
# set input
data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
m.run(x=data)
out_np = data.asnumpy().reshape(oshape) + 1
out = m.get_output(0, tvm.nd.empty(out_np.shape))
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def verify_reshape(dshape, oshape):
x = sym.Variable("x")
y = sym.reshape(x, shape=oshape)
y = y + 1
dtype = "float32"
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
m = graph_runtime.create(graph, lib, ctx)
# set input
data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
m.run(x=data)
out_np = data.asnumpy().reshape(oshape) + 1
out = m.get_output(0, tvm.nd.empty(out_np.shape))
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def _alter_conv2d_layout(attrs, inputs, tinfos):
"""Alter op layout for pre-computing kernel transformation"""
import nnvm.symbol as sym
copy_inputs = [s for s in inputs]
new_attrs = {k: attrs[k] for k in attrs.keys()}
assert attrs.get_int_tuple("dilation") == (1, 1), "Does not support dilation " \
"when alter_op_layout is enabled"
strides = attrs.get_int_tuple("strides")
padding = attrs.get_int_tuple("padding")
groups = attrs.get_int('groups')
layout = attrs["layout"]
out_dtype = attrs["out_dtype"]
out_dtype = tinfos[0].dtype if out_dtype == "same" else out_dtype
if groups == 1:
# query config of this workload
workload = _conv_arg_to_workload(tinfos[0], tinfos[1], strides,
padding, layout, out_dtype)
cfg = autotvm.task.DispatchContext.current.query(
tvm.target.current_target(), workload)
if cfg.template_key == 'direct': # packing weight tensor
new_attrs['kernel_layout'] = 'OIHW%do' % (cfg['tile_co'].size[-1])
return sym.conv2d(*copy_inputs, **new_attrs)
else: # pre-compute weight transformation in winograd
tile_size = 4
weight = sym.contrib.conv2d_winograd_weight_transform(
copy_inputs[1], tile_size=tile_size)
CO, CI, KH, KW = get_const_tuple(tinfos[1].shape)
VC = cfg['tile_k'].size[-1]
weight = sym.reshape(weight,
shape=(KH + tile_size - 1, KW + tile_size - 1,
CO // VC, VC, CI))
weight = sym.transpose(weight, axes=[0, 1, 2, 4, 3])
copy_inputs[1] = weight
new_attrs['tile_size'] = tile_size
return sym.contrib.conv2d_winograd_without_weight_transform(
*copy_inputs, **new_attrs)
# do nothing for depthwise convolution
return None
def test_plan_memory():
x = sym.Variable('x', shape=(4, 2))
x2 = sym.add(x, x, name='addk')
y = sym.reshape(x2, target=(2, 4), name="reshapek")
y = sym.add(y, x2, name="add2")
y = sym.add(y, y)
g = graph.create(y)
g._set_json_attr("shape_attr_key", "shape")
g = g.apply(["InferShape", "InferType", "PlanMemory"])
jgraph = json.loads(g.apply('SaveJSON').json_attr('json'))
jnodes = jgraph['nodes']
jnode_row_ptr = jgraph['node_row_ptr']
storage_id = g.json_attr('storage_id')
nindex = {n['name']: i for i, n in enumerate(jnodes)}
assert (storage_id[jnode_row_ptr[nindex["addk"]]] !=
storage_id[jnode_row_ptr[nindex["reshapek"]]])
assert (storage_id[jnode_row_ptr[nindex["add2"]]] == storage_id[
jnode_row_ptr[nindex["reshapek"]]])
def test_plan_memory():
x = sym.Variable('x', shape=(4, 2))
x2 = sym.add(x, x, name='addk')
y = sym.reshape(x2, target=(2, 4), name="reshapek")
y = sym.add(y, x2, name="add2")
y = sym.add(y, y)
g = graph.create(y)
g._set_json_attr("shape_attr_key", "shape")
g = g.apply(["InferShape", "InferType", "PlanMemory"])
jgraph = json.loads(g.apply('SaveJSON').json_attr('json'))
jnodes = jgraph['nodes']
jnode_row_ptr = jgraph['node_row_ptr']
storage_id = g.json_attr('storage_id')
nindex = {n['name']: i for i, n in enumerate(jnodes)}
assert (storage_id[jnode_row_ptr[nindex["addk"]]] !=
storage_id[jnode_row_ptr[nindex["reshapek"]]])
assert (storage_id[jnode_row_ptr[nindex["add2"]]] ==
storage_id[jnode_row_ptr[nindex["reshapek"]]])
def test_argmax():
dshape = (204800, 2)
oshape = (1, 320, 640)
dtype = "float32"
x = sym.Variable("x", shape=dshape, dtype=dtype)
x = sym.reshape(x, shape=(1, 320, 640, 2))
x = sym.transpose(x, axes=(0, 3, 1, 2))
y = sym.argmax(x, axis=1)
target_str = "llvm"
target = tvm.target.create(target_str)
ctx = tvm.context(target_str, 0)
with nnvm.compiler.build_config(opt_level=2):
graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
m = graph_runtime.create(graph, lib, ctx)
data = np.random.uniform(size=dshape).astype(dtype)
m.run(x=data)
np_reshape = np.reshape(data, (1, 320, 640, 2))
np_transpose = np.transpose(np_reshape, axes=(0, 3, 1, 2))
np_argmax = np.argmax(np_transpose, axis=1)
out = m.get_output(0)
np.testing.assert_allclose(out.asnumpy(), np_argmax, atol=1e-5, rtol=1e-5)
def nnvm_array_reduce(c, fn, array, shape):
"""Implementation of array_reduce."""
assert fn.is_constant(Primitive)
assert shape.is_constant(tuple)
fn = fn.value
tshp = shape.value
ary = c.ref(array)
if fn == P.scalar_add:
ashp = ashape(array)
if len(tshp) < len(ashp):
ts = (1, ) * (len(ashp) - len(tshp)) + tshp
else:
ts = tshp
axis = list(i for i, t in enumerate(ts) if t == 1)
if len(axis) == 1:
axis = axis[0]
res = sym.sum(ary, axis=axis, keepdims=1)
if len(tshp) < len(ashp):
res = sym.reshape(res, shape=tshp)
return res
else:
raise NotImplementedError(f"reduce with {fn}")
def test_argmax():
dshape = (204800, 2)
oshape = (1, 320, 640)
dtype = "float32"
x = sym.Variable("x", shape=dshape, dtype=dtype)
x = sym.reshape(x, shape=(1, 320, 640, 2))
x = sym.transpose(x, axes=(0, 3, 1, 2))
y = sym.argmax(x, axis=1)
target_str = "llvm"
target = tvm.target.create(target_str)
ctx = tvm.context(target_str, 0)
with nnvm.compiler.build_config(opt_level=2):
graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
m = graph_runtime.create(graph, lib, ctx)
data = np.random.uniform(size=dshape).astype(dtype)
m.run(x=data)
np_reshape = np.reshape(data, (1, 320, 640, 2))
np_transpose = np.transpose(np_reshape, axes=(0, 3, 1, 2))
np_argmax = np.argmax(np_transpose, axis=1)
out = m.get_output(0)
np.testing.assert_allclose(out.asnumpy(), np_argmax, atol=1e-5, rtol=1e-5)
def nn(m: Model):
v_images = sym.Variable("images", shape=(BATCH_SIZE, 1, 28, 28), dtype=0)
v_true_labels = sym.Variable("true_labels",
shape=(BATCH_SIZE, 10),
dtype=0)
x = v_images
x = sym.reshape(data=x, shape=(BATCH_SIZE, 28 * 28))
x = sym.dense(data=x, units=10)
logits = x
x = -sym.elemwise_mul(v_true_labels, sym.log_softmax(x))
loss = sym.sum(x) / BATCH_SIZE
# This is not really accuracy, because we use softmax instead of hardmax
accuracy = sym.sum(v_true_labels * sym.softmax(logits)) / BATCH_SIZE
# We have to somehow list all weights (the corresponding variables are generated automatically)
weight_vars = [
v for v in loss.list_input_variables()
if v.attr('name') not in ['images', 'true_labels']
]
optimizer = SGD(learning_rate=1e-4)
update_step = optimizer.minimize(loss, var=weight_vars)
tgraph = nnvm.graph.create(sym.Group(
[loss, update_step])).apply("InferShape").apply("InferType")
fgraph = nnvm.graph.create(sym.Group(
[loss, accuracy])).apply("InferShape").apply("InferType")
m.tgraph = tgraph
m.fgraph = fgraph
m.optimizer = optimizer
m.loss = loss
return m
def _alter_conv2d_layout(attrs, inputs, tinfo):
import nnvm.symbol as sym
copy_inputs = [s for s in inputs]
new_attrs = {k: attrs[k] for k in attrs.keys()}
data, kernel = tinfo[0], tinfo[1]
batch_size, in_channel, height, width = get_const_tuple(data.shape)
groups = attrs.get_int("groups")
out_channel = attrs.get_int("channels")
padding = attrs.get_int_tuple("padding")
strides = attrs.get_int_tuple("strides")
dilation = attrs.get_int_tuple("dilation")
layout = attrs['layout']
kh, kw = attrs.get_int_tuple("kernel_size")
dtype = data.dtype
out_dtype = dtype if attrs["out_dtype"] == "same" else attrs["out_dtype"]
is_depthwise = groups == in_channel and groups == out_channel
# only optimize for NCHW
if layout != 'NCHW':
return None
if groups != 1 and not is_depthwise:
return None
dispatch_ctx = autotvm.task.DispatchContext.current
target = tvm.target.current_target()
# query schedule and fallback if necessary
workload = autotvm.task.args_to_workload(
[data, kernel, strides, padding, dilation, out_dtype], depthwise_conv2d_nchw) \
if is_depthwise else \
autotvm.task.args_to_workload(
[data, kernel, strides, padding, dilation, layout, out_dtype], conv2d)
cfg = dispatch_ctx.query(target, workload)
if cfg.is_fallback:
_get_default_config(cfg, data, kernel, strides, padding, out_dtype,
is_depthwise)
ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
new_attrs['layout'] = 'NCHW%dc' % ic_bn
new_attrs['out_layout'] = 'NCHW%dc' % oc_bn
new_data = tvm.placeholder(
(batch_size, in_channel // ic_bn, height, width, ic_bn),
dtype=data.dtype)
if is_depthwise:
# channel, channel_multiplier, kh, kw -> out_channel_chunk, kh, kw, out_channel_block
# in which out_channel = merge(channel, channel_multiplier)
kernel_sym = copy_inputs[1]
kernel_sym = sym.reshape(kernel_sym,
shape=(out_channel // oc_bn, oc_bn, kh, kw))
kernel_sym = sym.transpose(kernel_sym, axes=(0, 2, 3, 1))
copy_inputs[1] = kernel_sym
# Store altered operator's config
new_kernel = tvm.placeholder((out_channel // oc_bn, kh, kw, oc_bn),
dtype=kernel.dtype)
new_workload = autotvm.task.args_to_workload([
new_data, new_kernel, strides, padding, dilation,
new_attrs['layout'], new_attrs['out_layout'], out_dtype
], depthwise_conv2d_NCHWc)
else:
out_channel, _, kh, kw = get_const_tuple(kernel.shape)
# (oc, ic, h, w) -> (OC, IC, h, w, ic, oc)
new_attrs['kernel_layout'] = 'OIHW%di%do' % (ic_bn, oc_bn)
# Store altered operator's config
new_kernel = tvm.placeholder(
(out_channel // oc_bn, in_channel // ic_bn, kh, kw, ic_bn, oc_bn),
dtype=kernel.dtype)
new_workload = autotvm.task.args_to_workload([
new_data, new_kernel, strides, padding, dilation,
new_attrs['layout'], new_attrs['out_layout'], out_dtype
], conv2d_NCHWc)
dispatch_ctx.update(target, new_workload, cfg)
return sym.contrib.conv2d_NCHWc(*copy_inputs, **new_attrs)
def check(in_shape, tshape, out_shape):
x = sym.Variable("x", shape=in_shape)
y = sym.reshape(x, shape=tshape, name="y")
sdict = infer_shape(y)
assert(tuple(sdict["y"][0]) == tuple(out_shape))
def test_reshape():
x = sym.Variable("x")
y = sym.reshape(x, shape=(10, 20), name="y")
assert(y.list_input_names() == ["x"])
def _alter_conv2d_layout_arm(attrs, inputs, tinfos):
"""Alter op layout for pre-computing kernel transformation"""
import nnvm.symbol as sym
copy_inputs = [s for s in inputs]
new_attrs = {k: attrs[k] for k in attrs.keys()}
dilation = attrs.get_int_tuple("dilation")
strides = attrs.get_int_tuple("strides")
padding = attrs.get_int_tuple("padding")
groups = attrs.get_int('groups')
layout = attrs["layout"]
out_dtype = attrs["out_dtype"]
out_dtype = tinfos[0].dtype if out_dtype == "same" else out_dtype
if layout != 'NCHW' or groups != 1:
return None
if dilation != (1, 1):
warnings.warn("Does not support weight pre-transform for dilated convolution.")
return None
data, kernel = tinfos[0:2]
N, CI, H, W = get_const_tuple(data.shape)
CO, _, KH, KW = get_const_tuple(kernel.shape)
# query config of this workload
workload = autotvm.task.args_to_workload(
[data, kernel, strides, padding, dilation, layout, out_dtype], conv2d)
target = tvm.target.current_target()
dispatch_ctx = autotvm.DispatchContext.current
cfg = dispatch_ctx.query(target, workload)
if cfg.is_fallback: # if is fallback, clear query cache and return None
autotvm.task.clear_fallback_cache(target, workload)
return None
if cfg.template_key == 'direct': # pack weight tensor
VC = cfg['tile_co'].size[-1]
new_attrs['kernel_layout'] = 'OIHW%do' % VC
# Store the same config for the altered operator (workload)
new_data = data
new_kernel = tvm.placeholder((CO // VC, CI, KH, KW, VC), dtype=kernel.dtype)
new_workload = autotvm.task.args_to_workload(
[new_data, new_kernel, strides, padding, dilation, 'NCHW', out_dtype], conv2d)
dispatch_ctx.update(target, new_workload, cfg)
return sym.conv2d(*copy_inputs, **new_attrs)
else: # pre-compute weight transformation in winograd
if "-device=arm_cpu" in target.options:
tile_size = 4
VC = cfg['tile_k'].size[-1]
else:
from ..mali.conv2d import _pick_tile_size
tile_size = _pick_tile_size(tinfos[0], tinfos[1])
VC = cfg['tile_bna'].val
weight = sym.contrib.conv2d_winograd_weight_transform(copy_inputs[1], tile_size=tile_size)
weight = sym.reshape(weight,
shape=(KH + tile_size - 1, KW + tile_size - 1, CO // VC, VC, CI))
weight = sym.transpose(weight, axes=[0, 1, 2, 4, 3])
copy_inputs[1] = weight
new_attrs['tile_size'] = tile_size
# Store the same config for the altered operator (workload)
new_data = data
new_weight = tvm.placeholder((KH + tile_size - 1, KH + tile_size -1, CO // VC, CI, VC),
kernel.dtype)
new_workload = autotvm.task.args_to_workload(
[new_data, new_weight, strides, padding, dilation,
new_attrs['layout'], out_dtype, tile_size],
conv2d_winograd_without_weight_transform)
dispatch_ctx.update(target, new_workload, cfg)
return sym.contrib.conv2d_winograd_without_weight_transform(*copy_inputs, **new_attrs)
def check(in_shape, tshape, out_shape):
x = sym.Variable("x", shape=in_shape)
y = sym.reshape(x, shape=tshape, name="y")
sdict = infer_shape(y)
assert(tuple(sdict["y"][0]) == tuple(out_shape))
def _alter_conv2d_layout(attrs, inputs, tinfo):
import nnvm.symbol as sym
copy_inputs = [s for s in inputs]
new_attrs = {k : attrs[k] for k in attrs.keys()}
data, kernel = tinfo[0], tinfo[1]
batch_size, in_channel, height, width = get_const_tuple(data.shape)
groups = attrs.get_int("groups")
out_channel = attrs.get_int("channels")
padding = attrs.get_int_tuple("padding")
strides = attrs.get_int_tuple("strides")
dilation = attrs.get_int_tuple("dilation")
layout = attrs['layout']
kh, kw = attrs.get_int_tuple("kernel_size")
dtype = data.dtype
out_dtype = dtype if attrs["out_dtype"] == "same" else attrs["out_dtype"]
is_depthwise = groups == in_channel and groups == out_channel
# only optimize for NCHW
if layout != 'NCHW':
return None
if groups != 1 and not is_depthwise:
return None
dispatch_ctx = autotvm.task.DispatchContext.current
target = tvm.target.current_target()
# query schedule and fallback if necessary
workload = autotvm.task.args_to_workload(
[data, kernel, strides, padding, dilation, out_dtype], depthwise_conv2d_nchw) \
if is_depthwise else \
autotvm.task.args_to_workload(
[data, kernel, strides, padding, dilation, layout, out_dtype], conv2d)
cfg = dispatch_ctx.query(target, workload)
if cfg.is_fallback:
_get_default_config(cfg, data, kernel, strides, padding, out_dtype, is_depthwise)
ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
new_attrs['layout'] = 'NCHW%dc' % ic_bn
new_attrs['out_layout'] = 'NCHW%dc' % oc_bn
new_data = tvm.placeholder((batch_size, in_channel//ic_bn, height, width, ic_bn),
dtype=data.dtype)
if is_depthwise:
# channel, channel_multiplier, kh, kw -> out_channel_chunk, kh, kw, out_channel_block
# in which out_channel = merge(channel, channel_multiplier)
kernel_sym = copy_inputs[1]
kernel_sym = sym.reshape(kernel_sym, shape=(out_channel//oc_bn, oc_bn, kh, kw))
kernel_sym = sym.transpose(kernel_sym, axes=(0, 2, 3, 1))
copy_inputs[1] = kernel_sym
# Store altered operator's config
new_kernel = tvm.placeholder((out_channel//oc_bn, kh, kw, oc_bn), dtype=kernel.dtype)
new_workload = autotvm.task.args_to_workload(
[new_data, new_kernel, strides, padding, dilation, new_attrs['layout'],
new_attrs['out_layout'], out_dtype], depthwise_conv2d_NCHWc)
else:
out_channel, _, kh, kw = get_const_tuple(kernel.shape)
# (oc, ic, h, w) -> (OC, IC, h, w, ic, oc)
new_attrs['kernel_layout'] = 'OIHW%di%do' % (ic_bn, oc_bn)
# Store altered operator's config
new_kernel = tvm.placeholder((out_channel//oc_bn, in_channel//ic_bn, kh, kw, ic_bn, oc_bn),
dtype=kernel.dtype)
new_workload = autotvm.task.args_to_workload(
[new_data, new_kernel, strides, padding, dilation, new_attrs['layout'],
new_attrs['out_layout'], out_dtype], conv2d_NCHWc)
dispatch_ctx.update(target, new_workload, cfg)
return sym.contrib.conv2d_NCHWc(*copy_inputs, **new_attrs)