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code 516 for test

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pull/1203/head
changzherui 5 years ago
parent e42631c127
commit 1e93927772
10 changed files with 39 additions and 67 deletions
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2
graphengine

@ -1 +1 @@
Subproject commit cf29b3d853b38c13d8d56181256613a11bf9eb95
Subproject commit 995b6dadc0fbbe4b80a08196886a53a18bffa60e

4
mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
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@ -32,8 +32,6 @@ namespace tbe {
static std::map<string, string> tbe_func_adapter_map = {
{"softmax", "softmax_v2"},
{"log_softmax", "log_softmax_v2"},
{"apply_momentum", "apply_momentum_d"},
{"apply_ftrl", "apply_ftrl_d"},
{"re_lu6", "relu6"},
{"re_lu6_grad", "relu6_grad"},
{"re_lu", "relu"},
@ -82,7 +80,7 @@ static std::map<string, string> tbe_func_adapter_map = {
{"batch_to_space", "batch_to_space_d"},
{"resize_bilinear", "resize_bilinear_v2_d"},
{"resize_bilinear_grad", "resize_bilinear_v2_grad"},
{"adam", "apply_adam_d"},
{"adam", "apply_adam"},
{"r_oi_align", "roi_align"},
{"r_oi_align_grad", "roi_align_grad"}};

6
mindspore/ccsrc/transform/convert.cc
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@ -206,7 +206,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
{string(kNameIOU), ADPT_DESC(Iou)},
{string(kNameGreaterEqual), ADPT_DESC(GreaterEqual)},
{string(kNameSlice), ADPT_DESC(SliceD)},
{string(kNameApplyMomentum), ADPT_DESC(ApplyMomentumD)},
{string(kNameApplyMomentum), ADPT_DESC(ApplyMomentum)},
{string(kNameMaxPool), ADPT_DESC(MaxPool)},
{string(kNameAvgPool), ADPT_DESC(AvgPool)},
{string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)},
@ -386,7 +386,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
{string(kNameDepthToSpace), ADPT_DESC(DepthToSpace)},
{string(kNameSign), ADPT_DESC(Sign)},
{string(kNameRound), ADPT_DESC(Round)},
{string(kNameApplyFtrl), ADPT_DESC(ApplyFtrlD)},
{string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)},
{string(kNameDiag), ADPT_DESC(Diag)},
{string(kNameDiagPart), ADPT_DESC(DiagPart)},
{string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)},
@ -398,7 +398,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
{string(kNameCTCLoss), ADPT_DESC(CTCLoss)}};
#ifdef ENABLE_GE
adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
adpt_map[string(kNameApplyAdam)] = ADPT_DESC(ApplyAdamD);
adpt_map[string(kNameApplyAdam)] = ADPT_DESC(ApplyAdam);
#endif
return adpt_map;
}

32
mindspore/ccsrc/transform/op_declare.cc
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@ -127,12 +127,11 @@ INPUT_MAP(Constant) = EMPTY_INPUT_MAP;
ATTR_MAP(Constant) = {{"value", ATTR_DESC(value, AnyTraits<AnyValue>())}};
OUTPUT_MAP(Constant) = {{0, OUTPUT_DESC(y)}};
// ApplyMomentumD
INPUT_MAP(ApplyMomentumD) = {
// ApplyMomentum
INPUT_MAP(ApplyMomentum) = {
{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(lr)}, {4, INPUT_DESC(grad)}, {5, INPUT_DESC(momentum)}};
ATTR_MAP(ApplyMomentumD) = {{"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits<bool>())},
{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyMomentumD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(accum)}};
ATTR_MAP(ApplyMomentum) = {{"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyMomentum) = {{0, OUTPUT_DESC(var)}};
// ScalarSummary
INPUT_MAP(Summary) = {{2, INPUT_DESC(x)}};
@ -471,16 +470,7 @@ INPUT_MAP(ApplyAdam) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(m)},
{10, INPUT_DESC(grad)}};
ATTR_MAP(ApplyAdam) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())},
{"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}};
// ApplyAdamD
INPUT_MAP(ApplyAdamD) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(m)}, {3, INPUT_DESC(v)},
{4, INPUT_DESC(beta1_power)}, {5, INPUT_DESC(beta2_power)}, {6, INPUT_DESC(lr)},
{7, INPUT_DESC(beta1)}, {8, INPUT_DESC(beta2)}, {9, INPUT_DESC(epsilon)},
{10, INPUT_DESC(grad)}};
ATTR_MAP(ApplyAdamD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())},
{"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyAdamD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(m)}, {2, OUTPUT_DESC(v)}};
OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(m)}, {2, OUTPUT_DESC(v)}};
// Relu6
INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}};
@ -1140,7 +1130,7 @@ INPUT_MAP(SparseApplyAdagradD) = {
{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(grad)}, {4, INPUT_DESC(indices)}};
ATTR_MAP(SparseApplyAdagradD) = {{"lr", ATTR_DESC(lr, AnyTraits<float>())},
{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(accum)}};
OUTPUT_MAP(SparseApplyAdagradD) = {{0, OUTPUT_DESC(var)}};
// SparseApplyFtrlD
INPUT_MAP(SparseApplyFtrlD) = {{1, INPUT_DESC(var)},
@ -1176,11 +1166,11 @@ ATTR_MAP(Round) = EMPTY_ATTR_MAP;
OUTPUT_MAP(Round) = {{0, OUTPUT_DESC(y)}};
// ApplyFtrl
INPUT_MAP(ApplyFtrlD) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(linear)},
{4, INPUT_DESC(grad)}, {5, INPUT_DESC(lr)}, {6, INPUT_DESC(l1)},
{7, INPUT_DESC(l2)}, {8, INPUT_DESC(lr_power)}};
ATTR_MAP(ApplyFtrlD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyFtrlD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(accum)}, {2, OUTPUT_DESC(linear)}};
INPUT_MAP(ApplyFtrl) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(linear)},
{4, INPUT_DESC(grad)}, {5, INPUT_DESC(lr)}, {6, INPUT_DESC(l1)},
{7, INPUT_DESC(l2)}, {8, INPUT_DESC(lr_power)}};
ATTR_MAP(ApplyFtrl) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyFtrl) = {{0, OUTPUT_DESC(var)}};
// Diag
INPUT_MAP(Diag) = {{1, INPUT_DESC(x)}};

10
mindspore/ccsrc/transform/op_declare.h
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@ -120,8 +120,6 @@ DECLARE_OP_ADAPTER(ResizeNearestNeighborV2Grad)
DECLARE_OP_USE_OUTPUT(ResizeNearestNeighborV2Grad)
DECLARE_OP_ADAPTER(ApplyAdam)
DECLARE_OP_USE_OUTPUT(ApplyAdam)
DECLARE_OP_ADAPTER(ApplyAdamD)
DECLARE_OP_USE_OUTPUT(ApplyAdamD)
DECLARE_OP_ADAPTER(Relu6)
DECLARE_OP_USE_OUTPUT(Relu6)
DECLARE_OP_ADAPTER(Relu6Grad)
@ -315,8 +313,8 @@ DECLARE_OP_ADAPTER(Assign)
DECLARE_OP_USE_OUTPUT(Assign)
DECLARE_OP_ADAPTER(Constant)
DECLARE_OP_USE_OUTPUT(Constant)
DECLARE_OP_ADAPTER(ApplyMomentumD)
DECLARE_OP_USE_OUTPUT(ApplyMomentumD)
DECLARE_OP_ADAPTER(ApplyMomentum)
DECLARE_OP_USE_OUTPUT(ApplyMomentum)
// ** Summary Operations **
DECLARE_OP_ADAPTER(Summary)
@ -446,8 +444,8 @@ DECLARE_OP_ADAPTER(LarsV2Update)
DECLARE_OP_USE_OUTPUT(LarsV2Update)
DECLARE_OP_ADAPTER(Round)
DECLARE_OP_USE_OUTPUT(Round)
DECLARE_OP_ADAPTER(ApplyFtrlD)
DECLARE_OP_USE_OUTPUT(ApplyFtrlD)
DECLARE_OP_ADAPTER(ApplyFtrl)
DECLARE_OP_USE_OUTPUT(ApplyFtrl)
DECLARE_OP_ADAPTER(SparseApplyFtrlD)
DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD)
DECLARE_OP_ADAPTER(Diag)

18
mindspore/ops/_op_impl/tbe/apply_ftrl.py
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@ -32,32 +32,30 @@ apply_ftrl_op_info = TBERegOp("ApplyFtrl") \
.input(6, "l2", False, "required", "all") \
.input(7, "lr_power", False, "required", "all") \
.output(0, "var", False, "required", "all") \
.output(1, "accum", False, "required", "all") \
.output(2, "linear", False, "required", "all") \
.dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
DataType.F16_5HD, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
DataType.F16_5HD) \
.dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ,
DataType.F16_FracZ, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
DataType.F16_FracZ) \
.dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0,
DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
DataType.F16_C1HWNCoC0) \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
DataType.F16_Default) \
.dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
DataType.F32_5HD, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
DataType.F32_5HD) \
.dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ,
DataType.F32_FracZ, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
DataType.F32_FracZ) \
.dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0,
DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
DataType.F32_C1HWNCoC0) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
DataType.F32_Default) \
.get_op_info()

17
mindspore/ops/_op_impl/tbe/apply_momentum.py
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@ -30,23 +30,22 @@ apply_momentum_op_info = TBERegOp("ApplyMomentum") \
.input(3, "grad", False, "required", "all") \
.input(4, "momentum", False, "required", "all") \
.output(0, "var", False, "required", "all") \
.output(1, "accum", False, "required", "all") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD,
DataType.F16_Default, DataType.F16_5HD, DataType.F16_5HD) \
DataType.F16_Default, DataType.F16_5HD) \
.dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0,
DataType.F16_Default, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
DataType.F16_Default, DataType.F16_C1HWNCoC0) \
.dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ,
DataType.F16_Default, DataType.F16_FracZ, DataType.F16_FracZ) \
DataType.F16_Default, DataType.F16_FracZ) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD,
DataType.F32_Default, DataType.F32_5HD, DataType.F32_5HD) \
DataType.F32_Default, DataType.F32_5HD) \
.dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0,
DataType.F32_Default, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
DataType.F32_Default, DataType.F32_C1HWNCoC0) \
.dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ,
DataType.F32_Default, DataType.F32_FracZ, DataType.F32_FracZ) \
DataType.F32_Default, DataType.F32_FracZ) \
.get_op_info()

14
mindspore/ops/operations/nn_ops.py
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@ -1465,11 +1465,8 @@ class ApplyMomentum(PrimitiveWithInfer):
def __init__(self, use_nesterov=False, use_locking=False, gradient_scale=1.0):
self.init_prim_io_names(inputs=['variable', 'accumulation', 'learning_rate', 'gradient', 'momentum'],
outputs=['output'])
self.is_tbe = context.get_context("device_target") == "Ascend"
def infer_shape(self, v_shape, a_shape, l_shape, g_shape, m_shape):
if self.is_tbe:
return v_shape, v_shape
return v_shape
def infer_dtype(self, v_dtype, a_dtype, l_dtype, g_dtype, m_dtype):
@ -1480,8 +1477,6 @@ class ApplyMomentum(PrimitiveWithInfer):
validator.check_scalar_or_tensor_type_same({"l_dtype": l_dtype}, valid_types, self.name)
validator.check_scalar_or_tensor_type_same({"g_dtype": g_dtype}, valid_types, self.name)
validator.check_scalar_or_tensor_type_same({"m_dtype": m_dtype}, valid_types, self.name)
if self.is_tbe:
return g_dtype, g_dtype
return g_dtype
@ -2622,13 +2617,13 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
validator.check('var_shape[1:]', var_shape[1:], 'grad_shape[1:]', grad_shape[1:], Rel.EQ, self.name)
validator.check_integer("indices rank", len(indices_shape), 1, Rel.EQ, self.name)
validator.check('grad_shape[0]', grad_shape[0], 'indices_shape[0]', indices_shape[0], Rel.EQ, self.name)
return var_shape, accum_shape
return var_shape
def infer_dtype(self, var_type, accum_type, grad_type, indices_type):
args = {'var': var_type, 'accum': accum_type, 'grad': grad_type}
validator.check_tensor_type_same(args, (mstype.float32,), self.name)
validator.check_tensor_type_same({'indices': indices_type}, [mstype.int32], self.name)
return var_type, accum_type
return var_type
class LARSUpdate(PrimitiveWithInfer):
@ -2737,14 +2732,11 @@ class ApplyFtrl(PrimitiveWithInfer):
self.init_prim_io_names(inputs=['var', 'accum', 'linear', 'grad', 'lr', 'l1', 'l2', 'lr_power'],
outputs=['output'])
self.use_locking = validator.check_value_type("use_locking", use_locking, [bool], self.name)
self.is_tbe = context.get_context("device_target") == "Ascend"
def infer_shape(self, var_shape, accum_shape, linear_shape, grad_shape, lr_shape, l1_shape, l2_shape,
lr_power_shape):
validator.check('var shape', var_shape, 'accum shape', accum_shape, Rel.EQ, self.name)
validator.check('var shape', var_shape, 'linear shape', linear_shape, Rel.EQ, self.name)
if self.is_tbe:
return var_shape, var_shape, var_shape
return var_shape
def infer_dtype(self, var_type, accum_type, linear_type, grad_type, lr_type, l1_type, l2_type, lr_power_type):
@ -2756,8 +2748,6 @@ class ApplyFtrl(PrimitiveWithInfer):
validator.check_scalar_or_tensor_type_same({"l1": l1_type}, valid_types, self.name)
validator.check_scalar_or_tensor_type_same({"l2": l2_type}, valid_types, self.name)
validator.check_scalar_or_tensor_type_same({"lr_power": lr_power_type}, valid_types, self.name)
if self.is_tbe:
return var_type, var_type, var_type
return var_type

2
tests/ut/python/ops/test_momentum.py
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@ -38,7 +38,7 @@ def tensor_run_opt(opt, iters, learning_rate, momentum,
gradient, variable, moment):
""" tensor_run_opt """
success = True
new_weight = opt(variable, moment, learning_rate, gradient, momentum)[0]
new_weight = opt(variable, moment, learning_rate, gradient, momentum)
success = F.depend(success, F.assign(variable, new_weight))
return success

1
tests/ut/python/ops/test_ops.py
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@ -853,7 +853,6 @@ test_case_nn_ops = [
('SparseApplyAdagrad', {
'block': P.SparseApplyAdagrad(0.5),
'desc_inputs': [[3, 3], [3, 3], [3, 3], Tensor(np.ones((3,), np.int32))],
'desc_bprop': [[3, 3], [3, 3]],
'skip': ['backward']}),
('Flatten_1', {
'block': NetForFlatten(),

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