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提交 fb49a283 编写于 作者: M Megvii Engine Team
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refactor(mgb/dnn): refactor enum used in serializing 

GitOrigin-RevId: e57af4a59c9b4e090f3972b4d0cf01a2737f8355
上级 d69b5903
隐藏空白更改
内联 并排
Showing with 410 addition and 370 deletion
dnn/scripts/gen_flatbuffers_schema.py
浏览文件 @ fb49a283
......@@ -23,8 +23,14 @@ def _cname_to_fbname(cname):
}[cname]
def scramble_enum_member_name(name):
s = name.find('<<')
if s != -1:
name = name[0:name.find('=') + 1] + ' ' + name[s+2:]
if name in ("MIN", "MAX"):
return name + "_"
o_name = name.split(' ')[0].split('=')[0]
if o_name in ("MIN", "MAX"):
return name.replace(o_name, o_name + "_")
return name
class FlatBuffersWriter(IndentWriterBase):
......@@ -97,7 +103,8 @@ class FlatBuffersWriter(IndentWriterBase):
if e.combined:
default = e.compose_combined_enum(e.default)
else:
default = scramble_enum_member_name(str(e.members[e.default]))
default = scramble_enum_member_name(
str(e.members[e.default]).split(' ')[0].split('=')[0])
self._write("%s:%s%s = %s;", e.name_field, p.name, e.name, default)
def _resolve_const(self, v):
......@@ -124,7 +131,8 @@ class FlatBuffersWriter(IndentWriterBase):
if s.combined:
default = s.compose_combined_enum(e.get_default())
else:
default = scramble_enum_member_name(str(s.members[e.get_default()]))
default = scramble_enum_member_name(
str(s.members[e.get_default()]).split(' ')[0].split('=')[0])
self._write("%s:%s = %s;", e.name_field, enum_name, default)
def _get_fb_default(self, cppdefault):
......
dnn/scripts/gen_param_defs.py
浏览文件 @ fb49a283
......@@ -121,10 +121,12 @@ class member_defs:
def normalize_enum_value(self, value):
def normalize(v):
if isinstance(v, str):
if v not in self.members:
raise ValueError(
"enum member '{}' does not exist.".format(v))
v = self.members.index(v)
for idx, m in enumerate(self.members):
m = str(m).split(' ')[0].split('=')[0]
if v == m :
return idx
raise ValueError(
"enum member '{}' does not exist.".format(v))
assert isinstance(v, int)
return v
if self.combined:
......@@ -524,21 +526,25 @@ class SerializedDType(_ParamDefBase):
self._write_doc(e.name)
for idx, emem in enumerate(e.members):
for emem in e.members:
if e.combined:
self._write('%s = 1 << %d', emem, idx)
self._write('%s', emem)
self._write_doc(emem)
else:
self._write('%s = "%s"', emem, emem)
v = str(emem).split(' ')[0].split('=')[0]
n = int(str(emem).split('=')[1])
self._write('%s = "%s"', v, v)
self._write_doc(emem)
self._enum_member2num.append('id({}.{}):{}'.format(
qualname, emem, idx))
qualname, v, n))
for emem, emem_alias in e.member_alias:
em_a = emem_alias.split(' ')[0].split('=')[0]
if e.combined:
self._write('%s = %s', emem_alias, e.compose_combined_enum(emem))
self._write('%s = %s', em_a, e.compose_combined_enum(emem))
else:
self._write('%s = %s', emem_alias, emem)
em = str(emem).split(' ')[0].split('=')[0]
self._write('%s = %s', em_a, em)
self._unindent()
self._write('')
......@@ -546,7 +552,7 @@ class SerializedDType(_ParamDefBase):
if e.combined:
default = e.compose_combined_enum(e.default)
else:
default = "'{}'".format(e.members[e.default])
default = "'{}'".format(str(e.members[e.default]).split(' ')[0].split('=')[0])
self._cur_fields.append(self.FieldDef(
name=e.name_field,
......@@ -564,7 +570,7 @@ class SerializedDType(_ParamDefBase):
if s.combined:
default = s.compose_combined_enum(e.get_default())
else:
default = "'{}'".format(s.members[e.get_default()])
default = "'{}'".format(str(s.members[e.get_default()]).split(' ')[0].split('=')[0])
self._cur_fields.append(self.FieldDef(
name=e.name_field,
cvt='{}.convert({})'.format(qualname, e.name_field),
......@@ -700,11 +706,9 @@ class CPPWriter(IndentWriterBase):
def _on_member_enum(self, e):
self._write_doc(e.name)
self._write('enum class %s: uint32_t {', e.name, indent=1)
for idx, i in enumerate(e.members):
for i in e.members:
self._write_doc(i)
v = '{} = {}'.format(i, idx)
if e.combined:
v = '{} = 1 << {}'.format(i, idx)
v = str(i)
if i is not e.members[-1] or e.member_alias:
v += ','
self._write(v)
......@@ -712,7 +716,7 @@ class CPPWriter(IndentWriterBase):
if e.combined:
self._write('%s = %s,', alias, e.compose_combined_enum(mem))
else:
self._write('%s = %s,', alias, mem)
self._write('%s = %s,', str(alias).split(' ')[0].split('=')[0], str(mem).split(' ')[0].split('=')[0])
self._write('};', indent=-1)
self._non_static_members.append(e)
self._write('static MEGDNN_CONSTEXPR uint32_t %s_NR_MEMBER = %d;',
......@@ -720,7 +724,9 @@ class CPPWriter(IndentWriterBase):
if e.combined:
default = 'static_cast<{}>({})'.format(e.name, e.compose_combined_enum(e.default))
else:
default = '{}::{}'.format(e.name, e.members[e.default])
value = str(e.members[e.default])
value = value.split(' ')[0].split('=')[0]
default = '{}::{}'.format(e.name, value)
self._add_ctor_args(e.name, default, e.name_field)
def _on_member_enum_alias(self, e):
......@@ -732,7 +738,9 @@ class CPPWriter(IndentWriterBase):
if s.combined:
default = 'static_cast<{}>({})'.format(e.name, s.compose_combined_enum(e.default))
else:
default = '{}::{}'.format(e.name, s.members[e.get_default()])
value = str(s.members[e.get_default()])
value = value.split(' ')[0].split('=')[0]
default = '{}::{}'.format(e.name, value)
self._add_ctor_args(e.name, default, e.name_field)
def _on_member_field(self, f):
......@@ -754,11 +762,12 @@ class CPPEnumValueWriter(CPPWriter):
def _on_member_enum(self, e):
self._write_doc(e.name)
self._write('struct %s {', e.name, indent=1)
for idx, val in enumerate(e.members):
for val in e.members:
self._write_doc(val)
self._write('static const uint32_t %s = %d;', val, idx)
v = str(val)
self._write('static const uint32_t %s;', v)
for mem, alias in e.member_alias:
self._write('static const uint32_t %s = %s;', alias, mem)
self._write('static const uint32_t %s = %s;', str(alias).split(' ')[0].split('=')[0], str(mem).split(' ')[0].split('=')[0])
self._write('};', indent=-1)
def _on_member_enum_alias(self, e):
......@@ -848,9 +857,11 @@ class CPPParamJsonFuncWriter(IndentWriterBase):
members = e.src_enum.members
else:
members = e.members
for idx, i in enumerate(members):
for i in members:
v = str(i)
v = v.split(' ')[0].split('=')[0]
self._write('case %s::%s::%s: return "%s";',
self._param_name, e.name, i, i, indent=0)
self._param_name, e.name, v, v, indent=0)
self._write('default: mgb_throw(MegBrainError, "Invalid %s::%s:%%d", static_cast<int>(arg));',
self._param_name, e.name, indent=0)
self._write('}', indent=-1)
......
dnn/scripts/gen_tablegen.py
浏览文件 @ fb49a283
......@@ -89,7 +89,7 @@ class ConverterWriter(IndentWriterBase):
fullname = "::megdnn::param::{}".format(p.name)
enum_def = "MgbEnumAttr<\"{}\", \"{}\", [".format(fullname, e.name)
def format(v):
return '\"{}\"'.format(str(v))
return '\"{}\"'.format(str(v).split(' ')[0].split('=')[0])
enum_def += ','.join(format(i) for i in e.members)
if e.combined:
......@@ -110,7 +110,8 @@ class ConverterWriter(IndentWriterBase):
default_val = "static_cast<{}::{}>({})".format(
fullname, e.name, e.compose_combined_enum(e.default))
else:
default_val = "{}::{}::{}".format(fullname, e.name, e.members[e.default])
default_val = "{}::{}::{}".format(
fullname, e.name, str(e.members[e.default]).split(' ')[0].split('=')[0])
wrapped = self._wrapped_with_default_value(td_class, default_val)
......@@ -134,7 +135,8 @@ class ConverterWriter(IndentWriterBase):
default_val = "static_cast<{}::{}>({})".format(
fullname, e.name, s.compose_combined_enum(e.get_default()))
else:
default_val = "{}::{}::{}".format(fullname, e.name, s.members[e.get_default()])
default_val = "{}::{}::{}".format(fullname, e.name, str(
s.members[e.get_default()]).split(' ')[0].split('=')[0])
wrapped = self._wrapped_with_default_value(td_class, default_val)
......
dnn/scripts/opr_param_defs.py
浏览文件 @ fb49a283
......@@ -3,7 +3,7 @@ pdef('Empty')
pdef('Axis').add_fields('int32', 'axis', 0)
(pdef('Convolution', version=0, is_legacy=True).
add_enum('Mode', 'CROSS_CORRELATION', 'CONVOLUTION').
add_enum('Mode', 'CROSS_CORRELATION = 0', 'CONVOLUTION = 1').
add_fields(
'uint32',
Doc('pad_h', 'padding on one side on the first dimension'), 0,
......@@ -16,41 +16,41 @@ pdef('Axis').add_fields('int32', 'axis', 0)
'on the second dimension'), 1
).
add_enum('DataType',
Doc('FLOAT', 'input/output both float32/float16'),
'INT8x8x16',
'INT8x8x32',
Doc('FLOAT_IO16xC32', 'input/output both float16, the internal '
Doc('FLOAT = 0', 'input/output both float32/float16'),
'INT8x8x16 = 1',
'INT8x8x32 = 2',
Doc('FLOAT_IO16xC32 = 3', 'input/output both float16, the internal '
'compute is float32'),
Doc('QUINT8x8x32', 'input QuantizedAsymm8, output QuantizedS32'),
Doc('INT8x8xX', 'input int8, output specified by tensor DType'),
Doc('QUINT4x4x32', 'input QuantizedAsymm4, output QuantizedS32'),
Doc('QUINT8x8x32 = 4', 'input QuantizedAsymm8, output QuantizedS32'),
Doc('INT8x8xX = 5', 'input int8, output specified by tensor DType'),
Doc('QUINT4x4x32 = 6', 'input QuantizedAsymm4, output QuantizedS32'),
name_field='data_type').
add_enum('Sparse',
Doc('DENSE', 'dense convolution: filter shape should be '
Doc('DENSE = 0', 'dense convolution: filter shape should be '
'[oc, ic, spatial...] if format is NCHW, '
'[oc, spatial..., ic] if format is NHWC'),
Doc('GROUP', 'group convolution: filter shape should be '
Doc('GROUP = 1', 'group convolution: filter shape should be '
'[group, oc_per_group, ic_per_group, spatial...] if format is NCHW, '
'[group, oc_per_group, spatial..., ic_per_group] if format is NHWC')
).
add_enum(Doc('Format', 'convolution data/filter/output format; see '
':class:`RelayoutFormat` for more details'),
'NCHW', 'NHWC', 'NHWCD4', 'NCHW4', 'NCHW8', 'NCHW32', 'NCHW88',
'NCHW44','NCHW44_DOT',
Doc('NCHW_WINOGRAD', 'NCHW layout with weights tranformed by winograd'),
Doc('NCHW88_WINOGRAD', 'NCHW88 layout with weights tranformed by winograd'),
Doc('NCHW44_WINOGRAD', 'NCHW44 layout with weights tranformed by winograd'),
Doc('NCHW4_NCHW32', 'NCHW4_NCHW32 means input tensors are nchw4 layout, output tensor is nchw32 layout'),
Doc('NCHW32_NCHW4', 'NCHW32_NCHW4 means input tensors are nchw32 layout, output tensor is nchw4 layout'),
Doc('NCHW4_NCHW', 'NCHW4_NCHW means input tensors are nchw4 layout, output tensor is nchw layout'),
Doc('NCHW4_NHWC', 'NCHW4_NHWC means input tensors are nchw4 layout, output tensor is nhwc layout'),
Doc('NHWC_NCHW', 'NHWC_NCHW means input tensors are nhwc layout, '
'NCHW = 0', 'NHWC = 1', 'NHWCD4 = 2', 'NCHW4 = 3', 'NCHW8 = 4', 'NCHW32 = 5', 'NCHW88 = 6',
'NCHW44 = 7','NCHW44_DOT = 8',
Doc('NCHW_WINOGRAD = 9', 'NCHW layout with weights tranformed by winograd'),
Doc('NCHW88_WINOGRAD = 10', 'NCHW88 layout with weights tranformed by winograd'),
Doc('NCHW44_WINOGRAD = 11', 'NCHW44 layout with weights tranformed by winograd'),
Doc('NCHW4_NCHW32 = 12', 'NCHW4_NCHW32 means input tensors are nchw4 layout, output tensor is nchw32 layout'),
Doc('NCHW32_NCHW4 = 13', 'NCHW32_NCHW4 means input tensors are nchw32 layout, output tensor is nchw4 layout'),
Doc('NCHW4_NCHW = 14', 'NCHW4_NCHW means input tensors are nchw4 layout, output tensor is nchw layout'),
Doc('NCHW4_NHWC = 15', 'NCHW4_NHWC means input tensors are nchw4 layout, output tensor is nhwc layout'),
Doc('NHWC_NCHW = 16', 'NHWC_NCHW means input tensors are nhwc layout, '
'output tensor is nchw layout'),
Doc('NHWC_NCHW4_IC_SMALL', 'NHWC_NCHW4_IC_SMALL means input tensors are nhwc(c < 4) layout, '
Doc('NHWC_NCHW4_IC_SMALL = 17', 'NHWC_NCHW4_IC_SMALL means input tensors are nhwc(c < 4) layout, '
'output tensor is nchw4 layout, padding c=4'),
Doc('NCHW_NCHW4_IC_SMALL', 'NCHW_NCHW4_IC_SMALL means input tensors are nchw(c < 4) layout, '
Doc('NCHW_NCHW4_IC_SMALL = 18', 'NCHW_NCHW4_IC_SMALL means input tensors are nchw(c < 4) layout, '
'output tensor is nchw4 layout, padding c=4'),
Doc('CHWN4', 'CHWN4 is currently only used on Nvidia platform for fast implementation '
Doc('CHWN4 = 19', 'CHWN4 is currently only used on Nvidia platform for fast implementation '
'of convolution using CUDA/SASS. The channels are splitted to groups of 4 channels.'))
)
......@@ -72,9 +72,9 @@ pdef('Axis').add_fields('int32', 'axis', 0)
add_enum(Doc('ComputeMode', 'Specifies special computation modes, e.g. '
'different combinations of intermediate result '
'data types.'),
Doc('DEFAULT', 'No special requirements on the precision of '
Doc('DEFAULT = 0', 'No special requirements on the precision of '
'intermediate results.'),
Doc('FLOAT32', 'Use Float32 accumulator and intermediate result. '
Doc('FLOAT32 = 1', 'Use Float32 accumulator and intermediate result. '
'Only supported when input and output is Float16.'),
name_field='compute_mode')
)
......@@ -95,21 +95,21 @@ pdef('Axis').add_fields('int32', 'axis', 0)
add_enum_alias('Sparse', 'ConvolutionV0').
add_enum(Doc('Format', 'convolution data/filter/output format; see '
':class:`RelayoutFormat` for more details'),
'NCHW', 'NHWC', 'NHWCD4', 'NCHW4', 'NCHW8', 'NCHW32', 'NCHW88',
'NCHW44','NCHW44_DOT',
Doc('NCHW4_NCHW32', 'NCHW4_NCHW32 means input tensors are nchw4 layout, output tensor is nchw32 layout'),
Doc('NCHW32_NCHW4', 'NCHW32_NCHW4 means input tensors are nchw32 layout, output tensor is nchw4 layout'),
Doc('NCHW4_NCHW', 'NCHW4_NCHW means input tensors are nchw4 layout, output tensor is nchw layout'),
Doc('NCHW4_NHWC', 'NCHW4_NHWC means input tensors are nchw4 layout, output tensor is nhwc layout'),
Doc('NHWC_NCHW', 'NHWC_NCHW means input tensors are nhwc layout, '
'NCHW = 0', 'NHWC = 1', 'NHWCD4 = 2', 'NCHW4 = 3', 'NCHW8 = 4', 'NCHW32 = 5', 'NCHW88 = 6',
'NCHW44 = 7','NCHW44_DOT = 8',
Doc('NCHW4_NCHW32 = 9', 'NCHW4_NCHW32 means input tensors are nchw4 layout, output tensor is nchw32 layout'),
Doc('NCHW32_NCHW4 = 10', 'NCHW32_NCHW4 means input tensors are nchw32 layout, output tensor is nchw4 layout'),
Doc('NCHW4_NCHW = 11', 'NCHW4_NCHW means input tensors are nchw4 layout, output tensor is nchw layout'),
Doc('NCHW4_NHWC = 12', 'NCHW4_NHWC means input tensors are nchw4 layout, output tensor is nhwc layout'),
Doc('NHWC_NCHW = 13', 'NHWC_NCHW means input tensors are nhwc layout, '
'output tensor is nchw layout'),
Doc('NHWC_NCHW4_IC_SMALL', 'NHWC_NCHW4_IC_SMALL means input tensors are nhwc(c < 4) layout, '
Doc('NHWC_NCHW4_IC_SMALL = 14', 'NHWC_NCHW4_IC_SMALL means input tensors are nhwc(c < 4) layout, '
'output tensor is nchw4 layout, padding c=4'),
Doc('NCHW_NCHW4_IC_SMALL', 'NCHW_NCHW4_IC_SMALL means input tensors are nchw(c < 4) layout, '
Doc('NCHW_NCHW4_IC_SMALL = 15', 'NCHW_NCHW4_IC_SMALL means input tensors are nchw(c < 4) layout, '
'output tensor is nchw4 layout, padding c=4'),
Doc('CHWN4', 'CHWN4 is currently only used on Nvidia platform for fast implementation '
Doc('CHWN4 = 16', 'CHWN4 is currently only used on Nvidia platform for fast implementation '
'of convolution using CUDA/SASS. The channels are splitted to groups of 4 channels.'),
Doc('NCHW64', 'NCHW64 is designed for convolution implementation to utilizing TensorCore '
Doc('NCHW64 = 17', 'NCHW64 is designed for convolution implementation to utilizing TensorCore '
'instructions for 4-bit integers on Nvidia platforms')).
add_enum_alias('ComputeMode', 'ConvolutionV1',name_field='compute_mode')
)
......@@ -129,15 +129,15 @@ pdef('Axis').add_fields('int32', 'axis', 0)
)
(pdef('ConvPooling').
add_enum('Method', 'WITH_TEXTURE_OBJ', 'WITH_SHARED_MEM').
add_enum('Method', 'WITH_TEXTURE_OBJ = 0', 'WITH_SHARED_MEM = 1').
add_enum_alias('ConvMode', 'ConvolutionV0', 'Mode').
add_enum('PoolMode', 'AVERAGE', 'MAX').
add_enum('NonlineMode', 'IDENTITY', 'RELU', 'SIGMOID').
add_enum('PoolMode', 'AVERAGE = 0', 'MAX = 1').
add_enum('NonlineMode', 'IDENTITY = 0', 'RELU = 1', 'SIGMOID = 2').
add_fields('uint32', 'pool_shape_h', 1, 'pool_shape_w', 1, 'pool_stride_h', 1, 'pool_stride_w', 1, \
'pool_pad_h', 0, 'pool_pad_w', 0, 'conv_stride_h', 1, 'conv_stride_w', 1, 'conv_pad_h', 0, 'conv_pad_w', 0))
(pdef('ConvBias', 'legacy conv_bias', version=0, is_legacy=True).
add_enum('NonlineMode', 'IDENTITY', 'RELU', 'SIGMOID', 'H_SWISH').
add_enum('NonlineMode', 'IDENTITY = 0', 'RELU = 1', 'SIGMOID = 2', 'H_SWISH = 3').
add_enum_alias('Mode', 'ConvolutionV0').
add_fields('uint32', 'pad_h', 0, 'pad_w', 0, 'stride_h', 1, 'stride_w', 1))
......@@ -215,9 +215,9 @@ pdef('Axis').add_fields('int32', 'axis', 0)
)
(pdef('SeparableConv').
add_enum_alias('Mode', 'ConvolutionV0').
add_enum('BorderMode', 'BORDER_REPLICATE', 'BORDER_REFLECT',
'BORDER_REFLECT_101','BORDER_WRAP',
'BORDER_CONSTANT', 'BORDER_TRANSPARENT','BORDER_ISOLATED').
add_enum('BorderMode', 'BORDER_REPLICATE = 0', 'BORDER_REFLECT = 1',
'BORDER_REFLECT_101 = 2','BORDER_WRAP = 3',
'BORDER_CONSTANT = 4', 'BORDER_TRANSPARENT = 5','BORDER_ISOLATED = 6').
add_fields('bool', 'is_symm_kernel', 'true').
add_fields('uint32', 'pad_h', 0, 'pad_w', 0, 'stride_h', 1, 'stride_w', 1,
'ksize_h', 3, 'ksize_w', 3, 'anchor_h', 1, 'anchor_w', 1))
......@@ -233,11 +233,11 @@ pdef('Axis').add_fields('int32', 'axis', 0)
(pdef('Pooling', version=0, is_legacy=True).
add_enum(
'Mode',
Doc('MAX', 'maximum value inside pooling window'),
Doc('AVERAGE',
Doc('MAX = 0', 'maximum value inside pooling window'),
Doc('AVERAGE = 1',
'arithmetic mean of all values inside pooling window. Padding values '
'are taken into account and are viewed as zero'),
Doc('AVERAGE_COUNT_EXCLUDE_PADDING',
Doc('AVERAGE_COUNT_EXCLUDE_PADDING = 2',
'arithmetic mean of all values inside pooling window. No padding is'
'used.')
).
......@@ -273,15 +273,15 @@ pdef('Axis').add_fields('int32', 'axis', 0)
(pdef('BN').
add_enum(
'ParamDim',
Doc('DIM_11HW', 'Dim of params (Sigma, Mu) is 1 x 1 x H x W'),
Doc('DIM_1CHW', 'Dim of params (Sigma, Mu) is 1 x C x H x W'),
Doc('DIM_1C11', 'Dim of params (Sigma, Mu) is 1 x C x 1 x 1'),
Doc('DIM_11HW = 0', 'Dim of params (Sigma, Mu) is 1 x 1 x H x W'),
Doc('DIM_1CHW = 1', 'Dim of params (Sigma, Mu) is 1 x C x H x W'),
Doc('DIM_1C11 = 2', 'Dim of params (Sigma, Mu) is 1 x C x 1 x 1'),
name_field='param_dim'
).
add_enum(
'FwdMode',
Doc('TRAINING', 'Training phase.'),
Doc('INFERENCE', 'Inference phase.'),
Doc('TRAINING = 0', 'Training phase.'),
Doc('INFERENCE = 1', 'Inference phase.'),
name_field='fwd_mode'
).
add_fields('float64', 'epsilon', '1e-4f').
......@@ -293,22 +293,22 @@ pdef('Axis').add_fields('int32', 'axis', 0)
(pdef('ROIPooling').
add_enum(
'Mode',
Doc('MAX', 'maximum value inside pooling window; pooling result would '
Doc('MAX = 0', 'maximum value inside pooling window; pooling result would '
'be 0 if pooling window is empty'),
Doc('AVERAGE',
Doc('AVERAGE = 1',
'arithmetic mean of all values inside pooling window; pooling result '
'would be 0 if pooling window is empty')
).
add_fields('float32', 'scale', '1.f'))
INTERP_MODES = ['NEAREST', 'LINEAR', 'AREA', 'CUBIC', 'LANCZOS4']
BORDER_MODES = [Doc('REPLICATE', 'aaaaaa|abcdefgh|hhhhhhh'),
Doc('REFLECT', 'fedcba|abcdefgh|hgfedcb'),
Doc('REFLECT_101', 'gfedcb|abcdefgh|gfedcba'),
Doc('WRAP', 'cdefgh|abcdefgh|abcdefg'),
Doc('CONSTANT', 'iiiiii|abcdefgh|iiiiiii'),
Doc('TRANSPARENT', ''),
Doc('ISOLATED', '')]
INTERP_MODES = ['NEAREST = 0', 'LINEAR = 1', 'AREA = 2', 'CUBIC = 3', 'LANCZOS4 = 4']
BORDER_MODES = [Doc('REPLICATE = 0', 'aaaaaa|abcdefgh|hhhhhhh'),
Doc('REFLECT = 1', 'fedcba|abcdefgh|hgfedcb'),
Doc('REFLECT_101 = 2', 'gfedcb|abcdefgh|gfedcba'),
Doc('WRAP = 3', 'cdefgh|abcdefgh|abcdefg'),
Doc('CONSTANT = 4', 'iiiiii|abcdefgh|iiiiiii'),
Doc('TRANSPARENT = 5', ''),
Doc('ISOLATED = 6', '')]
(pdef('WarpPerspective', version=1, is_legacy=True).
add_enum('InterpolationMode', *INTERP_MODES,
name_field='imode', default=1,
......@@ -328,181 +328,181 @@ BORDER_MODES = [Doc('REPLICATE', 'aaaaaa|abcdefgh|hhhhhhh'),
add_fields('float32', Doc('border_val', 'used for CONSTANT bmode'), '.0f'))
pdef('SpatialTfGridGenerator').add_enum('Mode', 'AFFINE')
pdef('SpatialTfSampler').add_enum('Mode', 'BILINEAR')
pdef('SpatialTfGridGenerator').add_enum('Mode', 'AFFINE = 0')
pdef('SpatialTfSampler').add_enum('Mode', 'BILINEAR = 0')
pdef('AddUpdate').add_fields(
'float32', 'alpha', '1.f', 'beta', '1.f', 'bias', '0.f')
pdef('Elemwise').add_enum(
'Mode',
Doc('RELU', 'unary: max(x, 0)'),
Doc('ABS', 'unary: abs(x)'),
Doc('ACOS', 'unary: acos(x)'),
Doc('ASIN', 'unary: asin(x)'),
Doc('CEIL', 'unary: ceil(x)'),
Doc('COS', 'unary: cos(x)'),
Doc('EXP', 'unary: exp(x)'),
Doc('EXPM1', 'unary: numerically stable exp(x)-1'),
Doc('FLOOR', 'unary: floor(x)'),
Doc('LOG', 'unary: natural logarithm, log(x)'),
Doc('LOG1P', 'unary: numerically stable log(x+1)'),
Doc('NEGATE', 'unary: -x'),
Doc('SIGMOID', 'unary: 1/(1+exp(-x))'),
Doc('SIN', 'unary: sin(x)'),
Doc('TANH', 'unary: tanh(x)'),
Doc('ABS_GRAD', 'binary: x > 0 ? y : -y'),
Doc('ADD', 'binary: x + y'),
Doc('FLOOR_DIV', 'binary: floor(x / y)'),
Doc('MAX', 'binary: max(x, y)'),
Doc('MIN', 'binary: min(x, y)'),
Doc('MOD', 'binary: x % y or fmodf(x, y)'),
Doc('MUL', 'binary: x * y'),
Doc('POW', 'binary: pow(x, y)'),
Doc('SIGMOID_GRAD', 'binary: x * (1 - x) * y'),
Doc('SUB', 'binary: x - y'),
Doc('SWITCH_GT0', 'binary: (x > 0) * y'),
Doc('TANH_GRAD', 'binary: (1 - x * x) * y'),
Doc('TRUE_DIV', 'binary: x / y'),
Doc('LOG_SUM_EXP', 'binary: numerically stable log(exp(x) + exp(y))'),
Doc('LT', 'binary: x < y'),
Doc('LEQ', 'binary: x <= y'),
Doc('EQ', 'binary: x == y'),
Doc('SHL', 'bitwise binary: x << y. '
Doc('RELU = 0', 'unary: max(x, 0)'),
Doc('ABS = 1', 'unary: abs(x)'),
Doc('ACOS = 2', 'unary: acos(x)'),
Doc('ASIN = 3', 'unary: asin(x)'),
Doc('CEIL = 4', 'unary: ceil(x)'),
Doc('COS = 5', 'unary: cos(x)'),
Doc('EXP = 6', 'unary: exp(x)'),
Doc('EXPM1 = 7', 'unary: numerically stable exp(x)-1'),
Doc('FLOOR = 8', 'unary: floor(x)'),
Doc('LOG = 9', 'unary: natural logarithm, log(x)'),
Doc('LOG1P = 10', 'unary: numerically stable log(x+1)'),
Doc('NEGATE = 11', 'unary: -x'),
Doc('SIGMOID = 12', 'unary: 1/(1+exp(-x))'),
Doc('SIN = 13', 'unary: sin(x)'),
Doc('TANH = 14', 'unary: tanh(x)'),
Doc('ABS_GRAD = 15', 'binary: x > 0 ? y : -y'),
Doc('ADD = 16', 'binary: x + y'),
Doc('FLOOR_DIV = 17', 'binary: floor(x / y)'),
Doc('MAX = 18', 'binary: max(x, y)'),
Doc('MIN = 19', 'binary: min(x, y)'),
Doc('MOD = 20', 'binary: x % y or fmodf(x, y)'),
Doc('MUL = 21', 'binary: x * y'),
Doc('POW = 22', 'binary: pow(x, y)'),
Doc('SIGMOID_GRAD = 23', 'binary: x * (1 - x) * y'),
Doc('SUB = 24', 'binary: x - y'),
Doc('SWITCH_GT0 = 25', 'binary: (x > 0) * y'),
Doc('TANH_GRAD = 26', 'binary: (1 - x * x) * y'),
Doc('TRUE_DIV = 27', 'binary: x / y'),
Doc('LOG_SUM_EXP = 28', 'binary: numerically stable log(exp(x) + exp(y))'),
Doc('LT = 29', 'binary: x < y'),
Doc('LEQ = 30', 'binary: x <= y'),
Doc('EQ = 31', 'binary: x == y'),
Doc('SHL = 32', 'bitwise binary: x << y. '
'Note that result is undefined if y < 0 or y >= bitwidth. Logical '
'shift is performed for unsigned intergers, and arithmetic shift for '
'signed ones.'),
Doc('SHR', 'bitwise binary: x >> y; see SHL mode for more details'),
Doc('SHR = 33', 'bitwise binary: x >> y; see SHL mode for more details'),
Doc('COND_LEQ_MOV', 'ternary: x <= y ? z : 0'),
Doc('FUSE_MUL_ADD3',
Doc('COND_LEQ_MOV = 34', 'ternary: x <= y ? z : 0'),
Doc('FUSE_MUL_ADD3 = 35',
'compute ``a * b + c`` where c must either have same layout as '
'a or b, or be a scalar'),
Doc('FUSE_MUL_ADD4',
Doc('FUSE_MUL_ADD4 = 36',
'compute ``a * A + b * B`` where a and b must have equal layout, '
'and A and B must have equal layout. In the inputs ``b`` and ``B`` '
'can be swapped'),
Doc('FUSE_ADD_RELU', 'binary: max(x+y, 0)'),
Doc('FUSE_ADD_SIGMOID', 'binary: 1/(1+exp(-(x+y)))'),
Doc('FUSE_ADD_TANH', 'binary: tanh(x+y)'),
Doc('FAST_TANH', 'unary: rational approximation of tanh(x)'),
Doc('FAST_TANH_GRAD', 'binary: grad of the rational approximation of tanh(x)'),
Doc('FUSE_ADD_RELU = 37', 'binary: max(x+y, 0)'),
Doc('FUSE_ADD_SIGMOID = 38', 'binary: 1/(1+exp(-(x+y)))'),
Doc('FUSE_ADD_TANH = 39', 'binary: tanh(x+y)'),
Doc('FAST_TANH = 40', 'unary: rational approximation of tanh(x)'),
Doc('FAST_TANH_GRAD = 41', 'binary: grad of the rational approximation of tanh(x)'),
Doc('ROUND', 'unary: round(x), the nearest integer value to x, rounding '
Doc('ROUND = 42', 'unary: round(x), the nearest integer value to x, rounding '
'halfway cases away from zero. Float only.'),
Doc('RMULH', 'binary: rounded higher l bits of x * y, where l is the bit '
Doc('RMULH = 43', 'binary: rounded higher l bits of x * y, where l is the bit '
'length of x.'),
Doc('ATAN2','binary: atan2(y,x)'),
Doc('ERF', 'unary: erf(x)'),
Doc('ERFINV', 'unary: inverse function of erf(x)'),
Doc('ERFC', 'unary: erfc(x)'),
Doc('ERFCINV', 'unary: inverse function of erfc(x)'),
Doc('H_SWISH', 'unary: x * clip(x + 3, 0, 6) / 6'),
Doc('H_SWISH_GRAD', 'binary: x < -3 ? 0 : (x > 3 ? y : (2 * x + 3) / 6 * y)'),
Doc('FUSE_ADD_H_SWISH', 'binary: hswish(x+y)'),
Doc('NOT', 'unary: !x'),
Doc('AND', 'binary: x && y'),
Doc('OR', 'binary: x || y'),
Doc('XOR', 'binary: x ^ y'),
Doc('SILU', 'unary: x / (1 + exp(-x))'),
Doc('SILU_GRAD', 'binary: grad(x / (1 + exp(-x))'),
Doc('GELU', 'unary: x Phi(x)'),
Doc('GELU_GRAD', 'binary: grad(x Phi(x))'),
Doc('ATAN2 = 44','binary: atan2(y,x)'),
Doc('ERF = 45', 'unary: erf(x)'),
Doc('ERFINV = 46', 'unary: inverse function of erf(x)'),
Doc('ERFC = 47', 'unary: erfc(x)'),
Doc('ERFCINV = 48', 'unary: inverse function of erfc(x)'),
Doc('H_SWISH = 49', 'unary: x * clip(x + 3, 0, 6) / 6'),
Doc('H_SWISH_GRAD = 50', 'binary: x < -3 ? 0 : (x > 3 ? y : (2 * x + 3) / 6 * y)'),
Doc('FUSE_ADD_H_SWISH = 51', 'binary: hswish(x+y)'),
Doc('NOT = 52', 'unary: !x'),
Doc('AND = 53', 'binary: x && y'),
Doc('OR = 54', 'binary: x || y'),
Doc('XOR = 55', 'binary: x ^ y'),
Doc('SILU = 56', 'unary: x / (1 + exp(-x))'),
Doc('SILU_GRAD = 57', 'binary: grad(x / (1 + exp(-x))'),
Doc('GELU = 58', 'unary: x Phi(x)'),
Doc('GELU_GRAD = 59', 'binary: grad(x Phi(x))'),
)
pdef('ElemwiseMultiType').add_enum(
'Mode',
Doc('FUSE_MUL_ADD3_INT16x32x32x32',
Doc('FUSE_MUL_ADD3_INT16x32x32x32 = 0',
'compute ``a * b + c`` requiring that ``a`` be int16 and ``b`` and '
'``c`` int32, and the result is int32. This mode is optimized for '
'the channel-broadacsted case, i.e. ``a`` has shape (A, B, C) and '
'``b`` and ``c`` have shape (1, C, 1)'),
Doc('FUSE_MUL_ADD3_IXxF32xF32xI8',
Doc('FUSE_MUL_ADD3_IXxF32xF32xI8 = 1',
'compuate ``a * b + c`` where the inputs ``a`` is an integer type '
'``b`` and ``c`` are both ``float32``, the result is '
'``int8``. This is currently only optimized for ``(1, x)`` '
'broadcast for ``b`` and ``c``. Computation is carried in floating '
'points and results are rounded towards zero with saturated cast to '
'int.'),
Doc('ROUND_SHR_SATURATE_IXxI8xI8',
Doc('ROUND_SHR_SATURATE_IXxI8xI8 = 2',
'Compute ``a >> b``, round the result according to lower ``b`` bits '
'of ``a``` and make a saturating conversion to int8. Where ``a`` should'
' be an integer tensor and ``b`` should be an int8 scalar.'),
Doc('FUSE_ADD_RMULH_ROUND_SHR_SATURATE_INT16x16x16x8',
Doc('FUSE_ADD_RMULH_ROUND_SHR_SATURATE_INT16x16x16x8 = 3',
'Fused operation of an int16 elemwise add, an int16 rounding multiply '
'high and an int16 to int8 rounding right shift with saturation.'),
Doc('FUSE_ADD_RMULH_ROUND_SHR_SATURATE_INT32x32x32x8',
Doc('FUSE_ADD_RMULH_ROUND_SHR_SATURATE_INT32x32x32x8 = 4',
'Fused operation of an int32 elemwise add, an int32 rounding multiply '
'high and an int32 to int8 rounding right shift with saturation.'),
Doc('ROUND_SHR_SATURATE_IXxI8xI16',
Doc('ROUND_SHR_SATURATE_IXxI8xI16 = 5',
'Compute ``a >> b``, round the result according to lower ``b`` bits of '
'``a``` and make a saturating conversion to int16. Where ``a`` should'
' be an integer tensor and ``b`` should be an int8 scalar.'),
Doc('QADD', 'Fused elemwise add two quantized int8 with specified'
Doc('QADD = 6', 'Fused elemwise add two quantized int8 with specified'
'output quantized dtype'),
Doc('QFUSE_ADD_RELU', 'Fused elemwise add two quantized int8 followed'
Doc('QFUSE_ADD_RELU = 7', 'Fused elemwise add two quantized int8 followed'
' by ReLU and typecvt to specified dtype'),
Doc('QMUL', 'Fused elemwise multiply two quantized int8 with specified'
Doc('QMUL = 8', 'Fused elemwise multiply two quantized int8 with specified'
'output quantized dtype'),
Doc('QMIN', 'Fused elemwise min two quantized int8 with specified'
Doc('QMIN = 9', 'Fused elemwise min two quantized int8 with specified'
'output quantized dtype'),
Doc('QMAX', 'quantized: max(x, y), with specified output quantized dtype'),
Doc('QSUB', 'quantized: x - y'),
Doc('QTRUE_DIV', 'quantized: x / y'),
Doc('QFUSE_ADD_SIGMOID', 'quantized: sigmoid(x + y)'),
Doc('QFUSE_ADD_TANH', 'quantized: tanh(x + y)'),
Doc('QRELU', 'quantized: x > 0 ? x : 0'),
Doc('QABS', 'quantized: x > 0 ? x : -x'),
Doc('QSIGMOID', 'quantized: sigmoid(x)'),
Doc('QEXP', 'quantized: exp(x)'),
Doc('QTANH', 'quantized: tanh(x)'),
Doc('QFUSE_MUL_ADD3', 'quantized: x * y + z'),
Doc('QFAST_TANH', 'quantized: fast_tanh(x)'),
Doc('QNEGATE', 'quantized: -x'),
Doc('QACOS', 'quantized: acos(x)'),
Doc('QASIN', 'quantized: asin(x)'),
Doc('QCEIL', 'quantized: ceil(x)'),
Doc('QCOS', 'quantized: cos(x)'),
Doc('QEXPM1', 'quantized: expm1(x)'),
Doc('QFLOOR', 'quantized: floor(x)'),
Doc('QLOG', 'quantized: log(x)'),
Doc('QLOG1P', 'quantized: log1p(x)'),
Doc('QSIN', 'quantized: sin(x)'),
Doc('QROUND', 'quantized: round(x)'),
Doc('QERF', 'quantized: erf(x)'),
Doc('QERFINV', 'quantized: erfinv(x)'),
Doc('QERFC', 'quantized: erfc(x)'),
Doc('QERFCINV', 'quantized: erfcinv(x)'),
Doc('QABS_GRAD', 'quantized: abs_grad'),
Doc('QFLOOR_DIV', 'quantized floor_div'),
Doc('QMOD', 'quantized mod'),
Doc('QSIGMOID_GRAD', 'quantized sigmoid_grad'),
Doc('QSWITCH_GT0', 'quantized switch_gt0'),
Doc('QTANH_GRAD', 'quantized tanh_grad'),
Doc('QLT', 'quantized lt'),
Doc('QLEQ', 'quantized leq'),
Doc('QEQ', 'quantized eq'),
Doc('QPOW', 'quantized pow'),
Doc('QLOG_SUM_EXP', 'quantized log_sum_exp'),
Doc('QFAST_TANH_GRAD', 'quantized fast_tanh_grad'),
Doc('QATAN2', 'quantized atan2'),
Doc('QCOND_LEQ_MOV', 'quantized cond_leq_mov'),
Doc('QH_SWISH', 'quantized h_swish'),
Doc('QFUSE_ADD_H_SWISH', 'quantized h_swish(x+y)'),
Doc('QH_SWISH_GRAD', 'quantized h_swish_grad')
Doc('QMAX = 10', 'quantized: max(x, y), with specified output quantized dtype'),
Doc('QSUB = 11', 'quantized: x - y'),
Doc('QTRUE_DIV = 12', 'quantized: x / y'),
Doc('QFUSE_ADD_SIGMOID = 13', 'quantized: sigmoid(x + y)'),
Doc('QFUSE_ADD_TANH = 14', 'quantized: tanh(x + y)'),
Doc('QRELU = 15', 'quantized: x > 0 ? x : 0'),
Doc('QABS = 16', 'quantized: x > 0 ? x : -x'),
Doc('QSIGMOID = 17', 'quantized: sigmoid(x)'),
Doc('QEXP = 18', 'quantized: exp(x)'),
Doc('QTANH = 19', 'quantized: tanh(x)'),
Doc('QFUSE_MUL_ADD3 = 20', 'quantized: x * y + z'),
Doc('QFAST_TANH = 21', 'quantized: fast_tanh(x)'),
Doc('QNEGATE = 22', 'quantized: -x'),
Doc('QACOS = 23', 'quantized: acos(x)'),
Doc('QASIN = 24', 'quantized: asin(x)'),
Doc('QCEIL = 25', 'quantized: ceil(x)'),
Doc('QCOS = 26', 'quantized: cos(x)'),
Doc('QEXPM1 = 27', 'quantized: expm1(x)'),
Doc('QFLOOR = 28', 'quantized: floor(x)'),
Doc('QLOG = 29', 'quantized: log(x)'),
Doc('QLOG1P = 30', 'quantized: log1p(x)'),
Doc('QSIN = 31', 'quantized: sin(x)'),
Doc('QROUND = 32', 'quantized: round(x)'),
Doc('QERF = 33', 'quantized: erf(x)'),
Doc('QERFINV = 34', 'quantized: erfinv(x)'),
Doc('QERFC = 35', 'quantized: erfc(x)'),
Doc('QERFCINV = 36', 'quantized: erfcinv(x)'),
Doc('QABS_GRAD = 37', 'quantized: abs_grad'),
Doc('QFLOOR_DIV = 38', 'quantized floor_div'),
Doc('QMOD = 39', 'quantized mod'),
Doc('QSIGMOID_GRAD = 40', 'quantized sigmoid_grad'),
Doc('QSWITCH_GT0 = 41', 'quantized switch_gt0'),
Doc('QTANH_GRAD = 42', 'quantized tanh_grad'),
Doc('QLT = 43', 'quantized lt'),
Doc('QLEQ = 44', 'quantized leq'),
Doc('QEQ = 45', 'quantized eq'),
Doc('QPOW = 46', 'quantized pow'),
Doc('QLOG_SUM_EXP = 47', 'quantized log_sum_exp'),
Doc('QFAST_TANH_GRAD = 48', 'quantized fast_tanh_grad'),
Doc('QATAN2 = 49', 'quantized atan2'),
Doc('QCOND_LEQ_MOV = 50', 'quantized cond_leq_mov'),
Doc('QH_SWISH = 51', 'quantized h_swish'),
Doc('QFUSE_ADD_H_SWISH = 52', 'quantized h_swish(x+y)'),
Doc('QH_SWISH_GRAD = 53', 'quantized h_swish_grad')
)
pdef('PowC', 'power with constant exponent').add_fields('float32', 'exp', 0)
(pdef('DctChannelSelect', '2d discrete cosine transform', version=0, is_legacy=True).add_enum_alias('Format', 'ConvolutionV0').
add_enum('FastImpl', 'NONE', 'FIX_32_MASK').add_fields('int32', 'dct_block_size', 8))
add_enum('FastImpl', 'NONE = 0', 'FIX_32_MASK = 1').add_fields('int32', 'dct_block_size', 8))
(pdef('DctChannelSelect', '2d discrete cosine transform', version=1).add_enum_alias('Format', 'Convolution').
add_enum_alias('FastImpl', 'DctChannelSelectV0').add_fields('int32', 'dct_block_size', 8))
......@@ -510,13 +510,13 @@ pdef('PowC', 'power with constant exponent').add_fields('float32', 'exp', 0)
(pdef('MatrixMul', version=0, is_legacy=True).
add_fields('bool', 'transposeA', 'false', 'transposeB', 'false').
add_enum('DataType',
Doc('FLOAT', 'input/output both float32/float16'),
'INT8x8x16',
'INT8x8x32',
Doc('FLOAT_IO16xC32', 'input/output both float16, the internal compute is '
Doc('FLOAT = 0', 'input/output both float32/float16'),
'INT8x8x16 = 1',
'INT8x8x32 = 2',
Doc('FLOAT_IO16xC32 = 3', 'input/output both float16, the internal compute is '
'float32'),
Doc('QUINT8x8x32', 'input QuantizedAsymm8, output QuantizedS32'),
Doc('QUINT4x4x32', 'input QuantizedAsymm4, output QuantizedS32'),
Doc('QUINT8x8x32 = 4', 'input QuantizedAsymm8, output QuantizedS32'),
Doc('QUINT4x4x32 = 5', 'input QuantizedAsymm4, output QuantizedS32'),
name_field='data_type'))
(pdef('MatrixMul', version=1, is_legacy=True).
......@@ -524,9 +524,9 @@ pdef('PowC', 'power with constant exponent').add_fields('float32', 'exp', 0)
add_enum(Doc('ComputeMode', 'Specifies special computation modes, e.g. '
'different combinations of intermediate result '
'data types.'),
Doc('DEFAULT', 'No special requirements on the precision of '
Doc('DEFAULT = 0', 'No special requirements on the precision of '
'intermediate results.'),
Doc('FLOAT32', 'Use Float32 accumulator and intermediate result. '
Doc('FLOAT32 = 1', 'Use Float32 accumulator and intermediate result. '
'Only supported when input and output is Float16.'),
name_field='compute_mode'))
......@@ -534,14 +534,14 @@ pdef('PowC', 'power with constant exponent').add_fields('float32', 'exp', 0)
add_fields('bool', 'transposeA', 'false', 'transposeB', 'false').
add_enum_alias('ComputeMode', 'MatrixMulV1', name_field='compute_mode').
add_enum('Format',
Doc('DEFAULT', 'Normal matrix mul: (M, K) x (K, N) = (M, N)'),
Doc('MK4', 'Split 4 from M and K, better for neon compute:'
Doc('DEFAULT = 0', 'Normal matrix mul: (M, K) x (K, N) = (M, N)'),
Doc('MK4 = 1', 'Split 4 from M and K, better for neon compute:'
'(M/4, K/4, 4(k), 4(m)) x (K/4, N, 4(k)). if transposeA the '
'layout is (K/4, M/4, 4(k), 4(m)) x (K/4, N, 4(k))'),
Doc('MK8', 'Split 8 from M and K, better for neon compute:'
Doc('MK8 = 2', 'Split 8 from M and K, better for neon compute:'
'(M/8, K/8, 8(k), 8(m)) x (K/8, N, 8(k)). if transposeA the '
'layout is (K/8, M/8, 8(k), 8(m)) x (K/8, N, 8(k))'),
Doc('MK4_DOT', 'Split 4 from M and K, better for neon dotprod:'
Doc('MK4_DOT = 3', 'Split 4 from M and K, better for neon dotprod:'
'M/4, K/4, 4(m), 4(k)) x (K/4, N, 4(k)). if transposeA the '
'layout is (K/4, M/4, 4(m), 4(k)) x (K/4, N, 4(k))'))
)
......@@ -560,9 +560,9 @@ pdef('PowC', 'power with constant exponent').add_fields('float32', 'exp', 0)
(pdef('Reduce', 'legacy reduce', version=0, is_legacy=True).
add_enum('Mode',
'SUM',
Doc('SUM_SQR', 'sum of x * x for each element x'),
'PRODUCT', 'MIN', 'MAX').
'SUM = 0',
Doc('SUM_SQR = 1', 'sum of x * x for each element x'),
'PRODUCT = 2', 'MIN = 3', 'MAX = 4').
add_fields('int32',
Doc('axis',
'axis along which reduction is performed; if -1 is given, '
......@@ -571,16 +571,16 @@ pdef('PowC', 'power with constant exponent').add_fields('float32', 'exp', 0)
(pdef('Reduce', 'reduce along given axis', version=1, is_legacy=True).
add_enum('Mode',
'SUM',
Doc('SUM_SQR', 'sum of x * x for each element x'),
'PRODUCT', 'MIN', 'MAX', 'MEAN').
'SUM = 0',
Doc('SUM_SQR = 1', 'sum of x * x for each element x'),
'PRODUCT = 2', 'MIN = 3', 'MAX = 4', 'MEAN = 5').
add_fields('int32',
Doc('axis',
'axis along which reduction is performed; if -1 is given, '
'reduce to given target shape (only used in megbrain)'),
-1).
add_enum('DataType',
Doc('DEFAULT',
Doc('DEFAULT = 0',
'''
input/output are the same data type, and the internal computation type would be chosen by the input/output dtypes and the reduction mode.
Currently, ```DEFAULT``` mode means:
......@@ -607,26 +607,26 @@ Currently, ```DEFAULT``` mode means:
'''
),
Doc('FLOAT_IO16xC32', 'Deprecated. This was replaced by '
Doc('FLOAT_IO16xC32 = 1', 'Deprecated. This was replaced by '
'FLOAT_O16xC32, and input\'s dtype decided by actual input tensor.'),
Doc('FLOAT_O32xC32', 'compute/output both are float32'),
Doc('FLOAT_O16xC32', 'compute are float32, output float16'),
Doc('QUINT_I8xO32', 'input quint8, compute and output are qint32'),
Doc('QINT_I8xO32', 'input qint8, compute and output are qint32'),
Doc('FLOAT_O32xC32 = 2', 'compute/output both are float32'),
Doc('FLOAT_O16xC32 = 3', 'compute are float32, output float16'),
Doc('QUINT_I8xO32 = 4', 'input quint8, compute and output are qint32'),
Doc('QINT_I8xO32 = 5', 'input qint8, compute and output are qint32'),
name_field='data_type'))
(pdef('Reduce', 'reduce along given axis', version=2).
add_enum('Mode',
'SUM',
Doc('SUM_SQR', 'sum of x * x for each element x'),
'PRODUCT', 'MIN', 'MAX', 'MEAN').
'SUM = 0',
Doc('SUM_SQR = 1', 'sum of x * x for each element x'),
'PRODUCT = 2', 'MIN = 3', 'MAX = 4', 'MEAN = 5').
add_fields('int32',
Doc('axis',
'axis along which reduction is performed; if INT_MAX is given, '
'reduce to given target shape (only used in megbrain)'),
(1<<31)-1).
add_enum('DataType',
Doc('DEFAULT',
Doc('DEFAULT = 0',
'''
input/output are the same data type, and the internal computation type would be chosen by the input/output dtypes and the reduction mode.
Currently, ```DEFAULT``` mode means:
......@@ -653,12 +653,12 @@ Currently, ```DEFAULT``` mode means:
'''
),
Doc('FLOAT_IO16xC32', 'Deprecated. This was replaced by '
Doc('FLOAT_IO16xC32 = 1', 'Deprecated. This was replaced by '
'FLOAT_O16xC32, and input\'s dtype decided by actual input tensor.'),
Doc('FLOAT_O32xC32', 'compute/output both are float32'),
Doc('FLOAT_O16xC32', 'compute are float32, output float16'),
Doc('QUINT_I8xO32', 'input quint8, compute and output are qint32'),
Doc('QINT_I8xO32', 'input qint8, compute and output are qint32'),
Doc('FLOAT_O32xC32 = 2', 'compute/output both are float32'),
Doc('FLOAT_O16xC32 = 3', 'compute are float32, output float16'),
Doc('QUINT_I8xO32 = 4', 'input quint8, compute and output are qint32'),
Doc('QINT_I8xO32 = 5', 'input qint8, compute and output are qint32'),
name_field='data_type'))
(pdef('Cumsum', 'calculate accumulated sum along given axis', version=0, is_legacy=True).
......@@ -691,12 +691,12 @@ Currently, ```DEFAULT``` mode means:
(pdef('CondTake').
add_enum('Mode',
Doc('EQ', 'take if ``abs(data-val)<eps``'),
Doc('NEQ', 'take if ``abs(data-val)>=eps``'),
Doc('LT', 'take if ``data<val``'),
Doc('LEQ', 'take if ``data<=val``'),
Doc('GT', 'take if ``data>val``'),
Doc('GEQ', 'take if ``data>=val``')).
Doc('EQ = 0', 'take if ``abs(data-val)<eps``'),
Doc('NEQ = 1', 'take if ``abs(data-val)>=eps``'),
Doc('LT = 2', 'take if ``data<val``'),
Doc('LEQ = 3', 'take if ``data<=val``'),
Doc('GT = 4', 'take if ``data>val``'),
Doc('GEQ = 5', 'take if ``data>=val``')).
add_fields('float32',
Doc('val', 'the value to be compared with; note that for integer '
'data, val is also converted to int'), 0).
......@@ -704,7 +704,7 @@ Currently, ```DEFAULT``` mode means:
1e-6))
pdef('Argsort').add_enum('Order', 'ASCENDING', 'DESCENDING')
pdef('Argsort').add_enum('Order', 'ASCENDING = 0', 'DESCENDING = 1')
(pdef('IndexingRemap').
add_fields('bool',
......@@ -791,17 +791,17 @@ pdef('Sleep').add_fields('float32', Doc('time', 'time to sleep in seconds'), 0)
.add_fields('uint32', 'row_from', 0, 'row_to', 0, 'col_from', 0, 'col_to', 0))
(pdef('CvtColor')
.add_enum('Mode', 'RGB2GRAY', 'RGB2YUV', 'YUV2RGB', 'GRAY2RGB', 'RGBA2RGB',
'RGBA2BGR', 'RGBA2GRAY', 'RGB2BGR', 'BGR2GRAY', 'BGR2RGB',
Doc('YUV2GRAY_NV21', 'For historical reasons, referred to as YCC by opencv'),
'YUV2RGB_NV21', 'YUV2BGR_NV21', 'YUV2GRAY_NV12', 'YUV2RGB_NV12',
'YUV2BGR_NV12', 'YUV2GRAY_YV12', 'YUV2RGB_YV12', 'YUV2BGR_YV12',
'YUV2GRAY_YU12', 'YUV2RGB_YU12', 'YUV2BGR_YU12',
'YCrCb2RGB', 'YCrCb2BGR',
Doc('BT601_YUV2RGB_NV21', 'BT601 yuv format, referred to as YUV by opencv'),
'BT601_YUV2BGR_NV21', 'BT601_YUV2RGB_NV12', 'BT601_YUV2BGR_NV12',
'BT601_YUV2RGB_YV12', 'BT601_YUV2BGR_YV12', 'BT601_YUV2RGB_YU12',
'BT601_YUV2BGR_YU12',
.add_enum('Mode', 'RGB2GRAY = 0', 'RGB2YUV = 1', 'YUV2RGB = 2', 'GRAY2RGB = 3', 'RGBA2RGB = 4',
'RGBA2BGR = 5', 'RGBA2GRAY = 6', 'RGB2BGR = 7', 'BGR2GRAY = 8', 'BGR2RGB = 9',
Doc('YUV2GRAY_NV21 = 10', 'For historical reasons, referred to as YCC by opencv'),
'YUV2RGB_NV21 = 11', 'YUV2BGR_NV21 = 12', 'YUV2GRAY_NV12 = 13', 'YUV2RGB_NV12 = 14',
'YUV2BGR_NV12 = 15', 'YUV2GRAY_YV12 = 16', 'YUV2RGB_YV12 = 17', 'YUV2BGR_YV12 = 18',
'YUV2GRAY_YU12 = 19', 'YUV2RGB_YU12 = 20', 'YUV2BGR_YU12 = 21',
'YCrCb2RGB = 22', 'YCrCb2BGR = 23',
Doc('BT601_YUV2RGB_NV21 = 24', 'BT601 yuv format, referred to as YUV by opencv'),
'BT601_YUV2BGR_NV21 = 25', 'BT601_YUV2RGB_NV12 = 26', 'BT601_YUV2BGR_NV12 = 27',
'BT601_YUV2RGB_YV12 = 28', 'BT601_YUV2BGR_YV12 = 29', 'BT601_YUV2RGB_YU12 = 30',
'BT601_YUV2BGR_YU12 = 31',
member_alias=[('YUV2GRAY_NV21', 'BT601_YUV2GRAY_NV21'),
('YUV2GRAY_NV12', 'BT601_YUV2GRAY_NV12'),
('YUV2GRAY_YV12', 'BT601_YUV2GRAY_YV12'),
......@@ -855,7 +855,7 @@ pdef('Sleep').add_fields('float32', Doc('time', 'time to sleep in seconds'), 0)
.add_fields('float32', 'scalar', '0.f'))
(pdef('Convolution3D').
add_enum('Mode', 'CROSS_CORRELATION', 'CONVOLUTION').
add_enum('Mode', 'CROSS_CORRELATION = 0', 'CONVOLUTION = 1').
add_fields(
'uint32',
Doc('pad_d', 'padding on one side on the first dimension'), 0,
......@@ -872,32 +872,32 @@ pdef('Sleep').add_fields('float32', Doc('time', 'time to sleep in seconds'), 0)
'on the third dimension'), 1
).
add_enum('Sparse',
Doc('DENSE', 'dense convolution: filter shape should be '
Doc('DENSE = 0', 'dense convolution: filter shape should be '
'[oc, ic, spatial...] if format is NCDHW, '
'[oc, spatial..., ic] if format is NDHWC'),
Doc('GROUP', 'group convolution: filter shape should be '
Doc('GROUP = 1', 'group convolution: filter shape should be '
'[group, oc_per_group, ic_per_group, spatial...] if format is NCDHW, '
'[group, oc_per_group, spatial..., ic_per_group] if format is NDHWC')
).
add_enum('DataType',
Doc('FLOAT', 'input/output both float32/float16'),
Doc('FLOAT_IO16xC32', 'input/output both float16, the internal '
Doc('FLOAT = 0', 'input/output both float32/float16'),
Doc('FLOAT_IO16xC32 = 1', 'input/output both float16, the internal '
'compute is float32'),
name_field='data_type').
add_enum('Format', 'NCDHW', 'NDHWC')
add_enum('Format', 'NCDHW = 0', 'NDHWC = 1')
)
(pdef('Conv3DBias').
add_enum('NonlineMode', 'IDENTITY', 'RELU', 'SIGMOID').
add_enum('NonlineMode', 'IDENTITY = 0', 'RELU = 1', 'SIGMOID = 2').
add_enum_alias('Mode', 'Convolution3D').
add_fields('uint32', 'pad_d', 0, 'pad_h', 0, 'pad_w', 0,
'stride_d', 1, 'stride_h', 1, 'stride_w', 0))
(pdef('SeparableConv3D').
add_enum_alias('Mode', 'Convolution3D').
add_enum('BorderMode', 'BORDER_REPLICATE', 'BORDER_REFLECT',
'BORDER_REFLECT_101','BORDER_WRAP',
'BORDER_CONSTANT', 'BORDER_TRANSPARENT','BORDER_ISOLATED').
add_enum('BorderMode', 'BORDER_REPLICATE = 0', 'BORDER_REFLECT = 1',
'BORDER_REFLECT_101 = 2','BORDER_WRAP = 3',
'BORDER_CONSTANT = 4', 'BORDER_TRANSPARENT = 5','BORDER_ISOLATED = 6').
add_fields('bool', 'is_symm_kernel', 'true').
add_fields('uint32', 'pad_d', 0, 'pad_h', 0, 'pad_w', 0,
'stride_d', 0, 'stride_h', 1, 'stride_w', 1,
......@@ -907,11 +907,11 @@ pdef('Sleep').add_fields('float32', Doc('time', 'time to sleep in seconds'), 0)
(pdef('TopK').
add_enum(
'Mode',
Doc('KTH_ONLY', "only the value of the k'th element would be computed"),
Doc('VALUE_IDX_NOSORT',
Doc('KTH_ONLY = 0', "only the value of the k'th element would be computed"),
Doc('VALUE_IDX_NOSORT = 1',
'all the top-k values and corresponding indices would be computed; '
'no order is guaranteed'),
Doc('VALUE_IDX_SORTED',
Doc('VALUE_IDX_SORTED = 2',
'all the top-k values and corresponding indices sorted'))
)
......@@ -983,37 +983,37 @@ Note: NCHW_NCHW4_WEIGHT will auto pad oc and ic, you should remove oc in later o
(pdef('RelayoutFormat', 'Change the tensor layout format', version=0, is_legacy=True).
add_enum(
Doc('Mode', RELAYOUT_FORMAT_MODE_DOC),
'NHWC_NHWCD4',
'NHWCD4_NHWC',
'NHWC_NHWCD4I',
'NCHW_NHWCD4',
'NCHW_NHWCD4I',
'NHWCD4I_NCHW',
'NHWCD4_NCHW',
'INTER_WEIGHT_DENSE',
'INTER_WEIGHT_DENSEI',
'INTER_WEIGHT_GROUP',
'INTER_WEIGHT_GROUPI',
'INTER_WEIGHT_CHAN',
'INTER_WEIGHT_CHANI',
'INTER_WEIGHT_DENSEI_DOT',
'INTER_WEIGHT_GROUPI_DOT',
'NCHW4_CHWN4',
'CHWN4_NCHW4',
'NCHW_NCHW88_CONV_DENSE_WEIGHT',
'NCHW_NCHW88_CONV_CHAN_WEIGHT',
'NCHW_NCHW88_CONV_GROUP_WEIGHT',
'NCHW_NCHW88',
'NCHW88_NCHW',
'NCHW_NCHW4_IC_SMALL',
'NCHW_NCHW4_IC_SMALL_CONV_DENSE_WEIGHT',
'NCHW_NCHW4',
'NCHW4_NCHW',
'NCHW_NCHW4_WEIGHT',
'NCHW_NCHW64',
'NCHW64_NCHW',
'NCHW_NHWC',
'NHWC_NCHW',
'NHWC_NHWCD4 = 0',
'NHWCD4_NHWC = 1',
'NHWC_NHWCD4I = 2',
'NCHW_NHWCD4 = 3',
'NCHW_NHWCD4I = 4',
'NHWCD4I_NCHW = 5',
'NHWCD4_NCHW = 6',
'INTER_WEIGHT_DENSE = 7',
'INTER_WEIGHT_DENSEI = 8',
'INTER_WEIGHT_GROUP = 9',
'INTER_WEIGHT_GROUPI = 10',
'INTER_WEIGHT_CHAN = 11',
'INTER_WEIGHT_CHANI = 12',
'INTER_WEIGHT_DENSEI_DOT = 13',
'INTER_WEIGHT_GROUPI_DOT = 14',
'NCHW4_CHWN4 = 15',
'CHWN4_NCHW4 = 16',
'NCHW_NCHW88_CONV_DENSE_WEIGHT = 17',
'NCHW_NCHW88_CONV_CHAN_WEIGHT = 18',
'NCHW_NCHW88_CONV_GROUP_WEIGHT = 19',
'NCHW_NCHW88 = 20',
'NCHW88_NCHW = 21',
'NCHW_NCHW4_IC_SMALL = 22',
'NCHW_NCHW4_IC_SMALL_CONV_DENSE_WEIGHT = 23',
'NCHW_NCHW4 = 24',
'NCHW4_NCHW = 25',
'NCHW_NCHW4_WEIGHT = 26',
'NCHW_NCHW64 = 27',
'NCHW64_NCHW = 28',
'NCHW_NHWC = 29',
'NHWC_NCHW = 30',
)
)
......@@ -1077,7 +1077,7 @@ Note: NCHW_NCHW4_WEIGHT will auto pad oc and ic, you should remove oc in later o
(pdef('ROIAlign',version=0,is_legacy=True).
add_enum('Mode', 'MAX', 'AVERAGE', name_field='mode').
add_enum('Mode', 'MAX = 0', 'AVERAGE = 1', name_field='mode').
add_enum_alias('Format', 'ConvolutionV0').
add_fields('float32', 'spatial_scale', '1.0').
add_fields('float32', 'offset', '0.0').
......@@ -1173,9 +1173,9 @@ Note: NCHW_NCHW4_WEIGHT will auto pad oc and ic, you should remove oc in later o
pdef('Fill').add_fields('float32', 'value', '0')
PADDING_MODES = [Doc('REPLICATE', 'aaaaaa|abcdefgh|hhhhhhh'),
Doc('REFLECT', 'fedcba|abcdefgh|hgfedcb'),
Doc('CONSTANT', 'iiiiii|abcdefgh|iiiiiii')]
PADDING_MODES = [Doc('REPLICATE = 0', 'aaaaaa|abcdefgh|hhhhhhh'),
Doc('REFLECT = 1', 'fedcba|abcdefgh|hgfedcb'),
Doc('CONSTANT = 2', 'iiiiii|abcdefgh|iiiiiii')]
(pdef('Padding').
add_fields('uint32', Doc('front_offset_dim0','offset in dim 0'), 0).
add_fields('uint32', Doc('front_offset_dim1','offset in dim 1'), 0).
......
imperative/tablegen/helper.h
浏览文件 @ fb49a283
......@@ -241,14 +241,17 @@ private:
if (auto* enumAttr = llvm::dyn_cast<MgbEnumAttrMixin>(&it.attr)) {
body += formatv(" switch ({0}){{\n", "$_self." + it.name);
for (auto&& enumMember: enumAttr->getEnumMembers()) {
body += formatv(
" case {0}::{1}::{2}:\n",
getCppClassName(), enumAttr->getEnumName(), enumMember
);
body += formatv(
" props_.emplace_back(\"{0}\", \"{1}\");\n",
it.name, enumMember
);
size_t d1 = enumMember.find(' ');
size_t d2 = enumMember.find('=');
size_t d = d1 <= d2 ? d1 : d2;
body += formatv(" case {0}::{1}::{2}:\n",
getCppClassName(),
enumAttr->getEnumName(),
enumMember.substr(0, d));
body +=
formatv(" props_.emplace_back(\"{0}\", "
"\"{1}\");\n",
it.name, enumMember.substr(0, d));
body += " break;\n";
}
body += " default: break;\n";
......
imperative/tablegen/targets/cpp_class.cpp
浏览文件 @ fb49a283
......@@ -177,9 +177,13 @@ void OpDefEmitter::emit_tpl_spl() {
std::vector<std::string> case_body;
std::string ename = formatv("{0}::{1}",
op.getCppClassName(), attr->getEnumName());
llvm::for_each(attr->getEnumMembers(), [&](auto&& v){
case_body.push_back(formatv(
"case {0}::{1}: return \"{1}\";", ename, v));
llvm::for_each(attr->getEnumMembers(), [&](auto&& v) {
size_t d1 = v.find(' ');
size_t d2 = v.find('=');
size_t d = d1 <= d2 ? d1 : d2;
case_body.push_back(
formatv("case {0}::{1}: return \"{1}\";", ename,
v.substr(0, d)));
});
os << formatv(R"(
template <>
......
imperative/tablegen/targets/pybind11.cpp
浏览文件 @ fb49a283
......@@ -50,14 +50,15 @@ void OpDefEmitter::emit() {
);
std::vector<std::string> body;
for (auto&& i: attr->getEnumMembers()) {
os << formatv(
"\n .value(\"{2}\", {0}::{1}::{2})",
className, attr->getEnumName(), i
);
size_t d1 = i.find(' ');
size_t d2 = i.find('=');
size_t d = d1 <= d2 ? d1 : d2;
os << formatv("\n .value(\"{2}\", {0}::{1}::{2})",
className, attr->getEnumName(),
i.substr(0, d));
body.push_back(formatv(
"if (str == \"{2}\") return {0}::{1}::{2};",
className, attr->getEnumName(), i
));
"if (str == \"{2}\") return {0}::{1}::{2};",
className, attr->getEnumName(), i.substr(0, d)));
}
if (attr->getEnumCombinedFlag()) {
//! define operator |
......
imperative/tablegen/targets/python_c_extension.cpp
浏览文件 @ fb49a283
......@@ -102,7 +102,10 @@ void EnumAttrEmitter::emit_tpl_spl() {
&ctx);
auto quote = [&](auto&& i) -> std::string {
return formatv("\"{0}\"", i);
size_t d1 = i.find(' ');
size_t d2 = i.find('=');
size_t d = d1 <= d2 ? d1 : d2;
return formatv("\"{0}\"", i.substr(0, d));
};
os << tgfmt(R"(
template<> const char*
......@@ -110,7 +113,11 @@ $enumTpl<$opClass::$enumClass>::members[] = {$0};
)", &ctx, llvm::join(llvm::map_range(attr->getEnumMembers(), quote), ", "));
auto mem2value = [&](auto&& i) -> std::string {
return tgfmt("{normalize_enum(\"$0\"), $opClass::$enumClass::$0}", &ctx, i);
size_t d1 = i.find(' ');
size_t d2 = i.find('=');
size_t d = d1 <= d2 ? d1 : d2;
return tgfmt("{normalize_enum(\"$0\"), $opClass::$enumClass::$0}", &ctx,
i.substr(0, d));
};
os << tgfmt(R"(
template<> std::unordered_map<std::string, $opClass::$enumClass>
......@@ -192,12 +199,15 @@ os << tgfmt(R"(
auto&& members = attr->getEnumMembers();
for (size_t idx = 0; idx < members.size(); ++ idx) {
size_t d1 = members[idx].find(' ');
size_t d2 = members[idx].find('=');
size_t d = d1 <= d2 ? d1 : d2;
os << tgfmt(R"({
PyObject* inst = e_type->tp_alloc(e_type, 0);
reinterpret_cast<$enumTpl<$opClass::$enumClass>*>(inst)->value = $opClass::$enumClass::$0;
mgb_assert(PyDict_SetItemString(e_type->tp_dict, "$0", inst) >= 0);
$enumTpl<$opClass::$enumClass>::pyobj_insts[$1] = inst;
})", &ctx, members[idx], idx);
})", &ctx, members[idx].substr(0, d), idx);
}
}
......
tools/gen_header_for_bin_reduce.py
浏览文件 @ fb49a283
......@@ -136,12 +136,13 @@ class HeaderGen:
mode_list = [i.strip() for i in fin]
for i in mode_list:
i = i.split(' ')[0].split('=')[0]
if i in self._elemwise_modes:
content = '_cb({})'.format(i)
else:
content = ''
self._write_def(
'_MEGDNN_ELEMWISE_MODE_ENABLE_IMPL_{}(_cb)'.format(i), content)
'_MEGDNN_ELEMWISE_MODE_ENABLE_IMPL_{}(_cb)'.format(i.split(' ')[0].split('=')[0]), content)
self._write_def('MEGDNN_ELEMWISE_MODE_ENABLE(_mode, _cb)',
'_MEGDNN_ELEMWISE_MODE_ENABLE_IMPL_##_mode(_cb)')
......
tools/param_defs/mgb_opr_param_defs.py
浏览文件 @ fb49a283
......@@ -20,14 +20,14 @@ pdef('PersistentOutputStorage').add_fields(
(pdef('ExecutionPolicy', version=0, is_legacy=True).
add_enum('Strategy',
Doc('HEURISTIC', 'use heuristic to choose the fastest algorithm'),
Doc('HEURISTIC_REPRODUCIBLE', 'use heuristic to choose the fastest algorithm, '
Doc('HEURISTIC = 0', 'use heuristic to choose the fastest algorithm'),
Doc('HEURISTIC_REPRODUCIBLE = 1', 'use heuristic to choose the fastest algorithm, '
'and the chosen algorithm is reproducible'),
Doc('PROFILE',
Doc('PROFILE = 2',
'run possible algorithms on real device to find the best'),
Doc('PROFILE_REPRODUCIBLE',
Doc('PROFILE_REPRODUCIBLE = 3',
'the fastest of profile result that is also reproducible'),
Doc('PROFILE_HEURISTIC',
Doc('PROFILE_HEURISTIC = 4',
'use profile result and heuristic to choose the fastest algorithm')).
add_fields('uint64',
Doc('workspace_limit', 'workspace limit in bytes'),
......@@ -35,13 +35,13 @@ pdef('PersistentOutputStorage').add_fields(
(pdef('ExecutionPolicy', 'specify how to select an algorithm for an operator', version=1).
add_bit_combination_enum('Strategy',
Doc('HEURISTIC', 'use heuristic to choose the fastest algorithm'),
Doc('PROFILE',
Doc('HEURISTIC = 1 << 0', 'use heuristic to choose the fastest algorithm'),
Doc('PROFILE = 1 << 1',
'run possible algorithms on real device to find the best'),
Doc('REPRODUCIBLE',
Doc('REPRODUCIBLE = 1 << 2',
'when profile or heuristic algo selection it require the algos'
'must be reproducible'),
Doc('OPTIMIZED',
Doc('OPTIMIZED = 1 << 3',
'profile require algos are optmized to achieve fast-profile'),
default=('HEURISTIC',),
member_alias=[(('HEURISTIC', 'REPRODUCIBLE'), 'HEURISTIC_REPRODUCIBLE'),
......@@ -66,19 +66,19 @@ pdef('PersistentOutputStorage').add_fields(
(pdef('CollectiveComm', 'collective communication between multiple computing '
'nodes on localhost')
.add_enum(Doc('Mode', 'mode of collective communication'),
Doc('REDUCE_SUM', 'reduce by sum to output computing node'),
Doc('BROADCAST', 'copy input value to each output computing node'),
Doc('ALL_GATHER', 'each output comp node gets the concatenated '
Doc('REDUCE_SUM = 0', 'reduce by sum to output computing node'),
Doc('BROADCAST = 1', 'copy input value to each output computing node'),
Doc('ALL_GATHER = 2', 'each output comp node gets the concatenated '
'value of all inputs'),
Doc('REDUCE_SCATTER_SUM',
Doc('REDUCE_SCATTER_SUM = 3',
'reduce inputs by sum and each output gets one part of it'),
Doc('ALL_REDUCE_SUM', 'every output gets the sum of all inputs'),
Doc('ALL_REDUCE_MAX', 'every output gets the max of all inputs'),
Doc('ALL_REDUCE_MIN', 'every output gets the min of all inputs'),
Doc('ALL_REDUCE_PROD', 'every output gets the prod of all inputs'),
Doc('GATHER', 'concat inputs to one node'),
Doc('SCATTER', 'scatter input to each output computing node'),
Doc('ALL_TO_ALL', 'scatter inputs and gather them on each computing node'),
Doc('ALL_REDUCE_SUM = 4', 'every output gets the sum of all inputs'),
Doc('ALL_REDUCE_MAX = 5', 'every output gets the max of all inputs'),
Doc('ALL_REDUCE_MIN = 6', 'every output gets the min of all inputs'),
Doc('ALL_REDUCE_PROD = 7', 'every output gets the prod of all inputs'),
Doc('GATHER = 8', 'concat inputs to one node'),
Doc('SCATTER = 9', 'scatter input to each output computing node'),
Doc('ALL_TO_ALL = 10', 'scatter inputs and gather them on each computing node'),
name_field='mode'))
(pdef('FakeSerializedDType',
......@@ -91,13 +91,13 @@ pdef('PersistentOutputStorage').add_fields(
'evaluate a predicate and branch keys to setup ExecutionMask objects '
'with associated predicate proxy vars (PPVs)')
.add_enum(Doc('Mode', 'how to compare predicate var with branch keys'),
Doc('CASE',
Doc('CASE = 0',
'The outputs correspond to branch keys, '
'and the one which equals predicate would be activated. '
'This behaves like a case-statement in many languages.'),
Doc('CASE_FALLBACK', 'like :attr:`CASE`, but add an extra output '
Doc('CASE_FALLBACK = 1', 'like :attr:`CASE`, but add an extra output '
'that would be activated if no branch is matched'),
Doc('PIECEWISE', 'One more outputs would be produced than the '
Doc('PIECEWISE = 2', 'One more outputs would be produced than the '
'number of branch keys, representing the interval in which the '
'predicate var fits in. The intervals are defined as '
r':math:`(-\\infty, k_0), [k_0, k_1), \\ldots, '
......@@ -112,20 +112,20 @@ pdef('PersistentOutputStorage').add_fields(
(pdef('CondExecPredLogical',
'compute a logical function over a set of PPVs')
.add_enum('Mode', Doc('OR', 'logical or'),
Doc('AND', 'logical and'),
Doc('XOR', 'exclusive-or'),
Doc('NOR', 'not or(inputs)'),
Doc('NAND', 'not and(inputs)'),
Doc('XNOR', 'not xor(inputs)'))
.add_enum('Mode', Doc('OR = 0', 'logical or'),
Doc('AND = 1', 'logical and'),
Doc('XOR = 2', 'exclusive-or'),
Doc('NOR = 3', 'not or(inputs)'),
Doc('NAND = 4', 'not and(inputs)'),
Doc('XNOR = 5', 'not xor(inputs)'))
)
(pdef('CondExecMark',
'add ExecutionMask of the input PPV to this opr and readers of the '
'outputs of this opr')
.add_enum(Doc('GradMode', 'mode for computing the gradient'),
Doc('SUM', 'normal gradient mode: sum all the activated components'),
Doc('SUM_COND_OUT', 'use :attr:`CondExecMerge.SUM_COND_OUT` mode so '
Doc('SUM = 0', 'normal gradient mode: sum all the activated components'),
Doc('SUM_COND_OUT = 1', 'use :attr:`CondExecMerge.SUM_COND_OUT` mode so '
'oprs that depend on the gradient opr would not be executed '
'if the forward var is not used.'),
name_field='grad_mode')
......@@ -135,10 +135,10 @@ pdef('PersistentOutputStorage').add_fields(
execution into account, this option can be used to bypass static
inference errors. This is currently only used by automatically
generated gradient oprs."""),
Doc('SHAPE_VALUE', 'enable both shape and value inference'),
Doc('SHAPE_ONLY',
Doc('SHAPE_VALUE = 0', 'enable both shape and value inference'),
Doc('SHAPE_ONLY = 1',
'only enable shape inference (disable value inference)'),
Doc('NONE', 'disable both shape and value inference'),
Doc('NONE = 2', 'disable both shape and value inference'),
name_field='static_infer')
)
......@@ -147,17 +147,17 @@ pdef('PersistentOutputStorage').add_fields(
'number of output vars (i.e. vars per branch)'),
1)
.add_enum('Mode',
Doc('EXACT_ONE', 'copy the var whose mask is activated to the output'
Doc('EXACT_ONE = 0', 'copy the var whose mask is activated to the output'
', requiring that exactly one branch is active'),
Doc('EXACT_ONE_SAME_SHAPE', 'like :attr:`EXACT_ONE` with the '
Doc('EXACT_ONE_SAME_SHAPE = 1', 'like :attr:`EXACT_ONE` with the '
'requirement that all branches have the same shape, so shape '
'inference can be easier'),
Doc('SUM', 'sum all the active branches into output var; require '
Doc('SUM = 2', 'sum all the active branches into output var; require '
'all branches to have the same shape. Extra shape vars are '
'needed in this mod, so the outputs can be initialized to zero '
'when no input is active (and their shapes are probably '
'unknown).'),
Doc('SUM_COND_OUT', 'like :attr:`SUM` but also add an ExecutionMask'
Doc('SUM_COND_OUT = 3', 'like :attr:`SUM` but also add an ExecutionMask'
' to the readers of output vars, so they would be skipped if '
' no branch is taken')
)
......
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