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synchronize latest ascend software 04 Jun 2020

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pull/1873/head
yanghaoran 5 years ago
parent 39338c8627
commit 8da4c1a763
19 changed files with 86 additions and 68 deletions
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3
CMakeLists.txt
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@ -7,6 +7,9 @@ endif ()
include(${CMAKE_SOURCE_DIR}/cmake/options.cmake)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake/modules/")
if (NOT CMAKE_SYSTEM_NAME MATCHES "Windows")
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
endif ()
if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O2 -Werror -Wno-return-std-move -Wno-unused-private-field -Wno-unused-lambda-capture -Wno-sign-compare -Wno-overloaded-virtual -Wno-unneeded-internal-declaration -Wno-unused-variable -Wno-pessimizing-move -Wno-inconsistent-missing-override -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")

2
cmake/dependency_graphengine.cmake
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@ -36,6 +36,7 @@ elseif (DEFINED ENV{D_LINK_PATH})
find_library(hccl libhccl.so ${GE_LIB_PATH})
find_library(cce libcce.so ${GE_LIB_PATH})
find_library(resource libresource.so ${GE_LIB_PATH})
find_library(error_manager liberror_manager.so ${GE_LIB_PATH})
else()
# Ascend mode
if(DEFINED ENV{ASCEND_CUSTOM_PATH})
@ -54,6 +55,7 @@ else()
find_library(msprof libmsprof.so ${ASCEND_RUNTIME_PATH})
find_library(register libregister.so ${ASCEND_RUNTIME_PATH})
find_library(resource libresource.so ${ASCEND_RUNTIME_PATH})
find_library(error_manager liberror_manager.so ${ASCEND_RUNTIME_PATH})
endif()
# compile libraries from following directories

2
cmake/external_libs/gtest.cmake
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@ -1,4 +1,4 @@
set(gtest_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(gtest_CXXFLAGS "-D_FORTIFY_SOURCE=2 -D_GLIBCXX_USE_CXX11_ABI=0 -O2")
set(gtest_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(gtest
VER 1.8.0

2
cmake/external_libs/opencv.cmake
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@ -8,7 +8,7 @@ elseif (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
set(opencv_CXXFLAGS "${opencv_CXXFLAGS} -Wno-attributes -Wno-unknown-pragmas")
set(opencv_CXXFLAGS "${opencv_CXXFLAGS} -Wno-unused-value -Wno-implicit-fallthrough")
else()
set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -D_GLIBCXX_USE_CXX11_ABI=0 -O2")
set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
set(opencv_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
endif()

5
cmake/external_libs/protobuf.cmake
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@ -1,9 +1,12 @@
set(protobuf_USE_STATIC_LIBS ON)
if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
set(protobuf_CXXFLAGS "-fstack-protector-all -Wno-uninitialized -Wno-unused-parameter -fPIC -fvisibility=hidden -D_FORTIFY_SOURCE=2 -O2")
else()
elseif (${CMAKE_SYSTEM_NAME} MATCHES "Windows")
set(protobuf_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -fvisibility=hidden -D_FORTIFY_SOURCE=2 -O2")
else()
set(protobuf_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -fvisibility=hidden -D_FORTIFY_SOURCE=2 -D_GLIBCXX_USE_CXX11_ABI=0 -O2")
endif()
set(protobuf_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
set(_ms_tmp_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS})

2
graphengine

@ -1 +1 @@
Subproject commit 579dcb75a990b533f9182733a6424f2bd66f0f23
Subproject commit 9248a2fd15ffc64d9d04b40c6b2836d1c94ca0b4

4
mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
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@ -32,6 +32,8 @@ 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"},
@ -89,7 +91,7 @@ static std::map<string, string> tbe_func_adapter_map = {
{"batch_to_space_nd", "batch_to_space_nd_d"},
{"resize_bilinear", "resize_bilinear_v2_d"},
{"resize_bilinear_grad", "resize_bilinear_v2_grad"},
{"adam", "apply_adam"},
{"adam", "apply_adam_d"},
{"r_oi_align", "roi_align"},
{"r_oi_align_grad", "roi_align_grad"},
{"i_ou", "iou"},

22
mindspore/ccsrc/pre_activate/ascend/ir_fusion/momentum_lossscale_fusion.cc
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@ -32,19 +32,6 @@ bool CheckValueNodeInputOfMul(const AnfNodePtr &node) {
std::vector<size_t> mul_input_shape = AnfAlgo::GetOutputInferShape(node, 0);
return mul_input_shape.empty() || (mul_input_shape.size() == 1 && mul_input_shape[0] == 1);
}
void AddInputToOutput(const FuncGraphPtr &func_graph, const CNodePtr &old_cnode, const AnfNodePtr &new_node,
std::vector<AnfNodePtr> *new_outputs) {
MS_EXCEPTION_IF_NULL(old_cnode);
MS_EXCEPTION_IF_NULL(new_node);
MS_EXCEPTION_IF_NULL(new_outputs);
auto node_to_output = old_cnode->input(kAccumIndex + 1);
MS_EXCEPTION_IF_NULL(node_to_output);
AbstractBasePtrList abstract_list{old_cnode->abstract(), node_to_output->abstract()};
auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(abstract_list);
new_node->set_abstract(abstract_tuple);
// Create Output
CreateMultipleOutputsOfAnfNode(func_graph, new_node, kFusedMulApplyMomentumOutputNum, new_outputs);
}
} // namespace
const BaseRef MomentumLossscaleFusion::DefinePattern() const {
@ -94,14 +81,9 @@ const AnfNodePtr MomentumLossscaleFusion::Process(const FuncGraphPtr &func_graph
input_names_value[3] = "x1";
input_names_value.emplace_back("x2");
AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names_value), new_node);
new_node->set_abstract(node->abstract());
new_node->set_scope(node->scope());
// Create Outputs
std::vector<AnfNodePtr> new_outputs;
AddInputToOutput(func_graph, cnode, new_node, &new_outputs);
if (new_outputs.size() != kFusedMulApplyMomentumOutputNum) {
MS_LOG(EXCEPTION) << "Failed to create outputs of " << new_node->DebugString();
}
return new_outputs[0];
return new_node;
}
} // namespace opt
} // namespace mindspore

6
mindspore/ccsrc/transform/convert.cc
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@ -212,7 +212,7 @@ 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(ApplyMomentum)},
{string(kNameApplyMomentum), ADPT_DESC(ApplyMomentumD)},
{string(kNameMaxPool), ADPT_DESC(MaxPool)},
{string(kNameAvgPool), ADPT_DESC(AvgPool)},
{string(kNameMaxPoolWithArgmax), ADPT_DESC(MaxPoolWithArgmax)},
@ -395,7 +395,7 @@ 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(ApplyFtrl)},
{string(kNameApplyFtrl), ADPT_DESC(ApplyFtrlD)},
{string(kNameDiag), ADPT_DESC(Diag)},
{string(kNameDiagPart), ADPT_DESC(DiagPart)},
{string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)},
@ -409,7 +409,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
{string(kNameSquareSumAll), ADPT_DESC(SquareSumAll)}};
#ifdef ENABLE_GE
adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
adpt_map[string(kNameApplyAdam)] = ADPT_DESC(ApplyAdam);
adpt_map[string(kNameApplyAdam)] = ADPT_DESC(ApplyAdamD);
#endif
return adpt_map;
}

30
mindspore/ccsrc/transform/op_declare.cc
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@ -127,11 +127,12 @@ INPUT_MAP(Constant) = EMPTY_INPUT_MAP;
ATTR_MAP(Constant) = {{"value", ATTR_DESC(value, AnyTraits<AnyValue>())}};
OUTPUT_MAP(Constant) = {{0, OUTPUT_DESC(y)}};
// ApplyMomentum
INPUT_MAP(ApplyMomentum) = {
// ApplyMomentumD
INPUT_MAP(ApplyMomentumD) = {
{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(lr)}, {4, INPUT_DESC(grad)}, {5, INPUT_DESC(momentum)}};
ATTR_MAP(ApplyMomentum) = {{"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyMomentum) = {{0, OUTPUT_DESC(var)}};
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)}};
// ScalarSummary
INPUT_MAP(Summary) = {{2, INPUT_DESC(x)}};
@ -470,7 +471,16 @@ 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)}, {1, OUTPUT_DESC(m)}, {2, OUTPUT_DESC(v)}};
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)}};
// Relu6
INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}};
@ -823,7 +833,7 @@ OUTPUT_MAP(RealDiv) = {{0, OUTPUT_DESC(y)}};
// Cast
INPUT_MAP(Cast) = {{1, INPUT_DESC(x)}};
INPUT_ATTR_MAP(Cast) = {{2, ATTR_DESC(dst_type, AnyTraits<GEType>())}};
ATTR_MAP(Cast) = {{"Truncate", ATTR_DESC(truncate, AnyTraits<bool>())}};
ATTR_MAP(Cast) = EMPTY_ATTR_MAP;
OUTPUT_MAP(Cast) = {{0, OUTPUT_DESC(y)}};
// Reciprocal
@ -1194,12 +1204,12 @@ INPUT_MAP(Round) = {{1, INPUT_DESC(x)}};
ATTR_MAP(Round) = EMPTY_ATTR_MAP;
OUTPUT_MAP(Round) = {{0, OUTPUT_DESC(y)}};
// ApplyFtrl
INPUT_MAP(ApplyFtrl) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(accum)}, {3, INPUT_DESC(linear)},
// ApplyFtrlD
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(ApplyFtrl) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
OUTPUT_MAP(ApplyFtrl) = {{0, OUTPUT_DESC(var)}};
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)}};
// Diag
INPUT_MAP(Diag) = {{1, INPUT_DESC(x)}};

10
mindspore/ccsrc/transform/op_declare.h
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@ -120,6 +120,8 @@ 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)
@ -319,8 +321,8 @@ DECLARE_OP_ADAPTER(Assign)
DECLARE_OP_USE_OUTPUT(Assign)
DECLARE_OP_ADAPTER(Constant)
DECLARE_OP_USE_OUTPUT(Constant)
DECLARE_OP_ADAPTER(ApplyMomentum)
DECLARE_OP_USE_OUTPUT(ApplyMomentum)
DECLARE_OP_ADAPTER(ApplyMomentumD)
DECLARE_OP_USE_OUTPUT(ApplyMomentumD)
// ** Summary Operations **
DECLARE_OP_ADAPTER(Summary)
@ -454,8 +456,8 @@ DECLARE_OP_ADAPTER(LarsV2Update)
DECLARE_OP_USE_OUTPUT(LarsV2Update)
DECLARE_OP_ADAPTER(Round)
DECLARE_OP_USE_OUTPUT(Round)
DECLARE_OP_ADAPTER(ApplyFtrl)
DECLARE_OP_USE_OUTPUT(ApplyFtrl)
DECLARE_OP_ADAPTER(ApplyFtrlD)
DECLARE_OP_USE_OUTPUT(ApplyFtrlD)
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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@ -32,30 +32,32 @@ 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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@ -30,22 +30,23 @@ 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_Default, DataType.F16_5HD, 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_Default, DataType.F16_C1HWNCoC0, 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_Default, DataType.F16_FracZ, 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_Default, DataType.F32_5HD, 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_Default, DataType.F32_C1HWNCoC0, 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_Default, DataType.F32_FracZ, DataType.F32_FracZ) \
.get_op_info()

14
mindspore/ops/operations/nn_ops.py
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@ -1507,8 +1507,11 @@ 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):
@ -1519,6 +1522,8 @@ 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
@ -2810,13 +2815,13 @@ 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
return var_shape, accum_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
return var_type, accum_type
class ApplyProximalAdagrad(PrimitiveWithInfer):
@ -3074,11 +3079,14 @@ 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):
@ -3090,6 +3098,8 @@ 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

6
tests/st/networks/models/bert/bert_tdt_lossscale.py
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@ -199,10 +199,10 @@ def test_bert_percision():
# assertion occurs while the loss value, overflow state or loss_scale value is wrong
loss_value = np.array(callback.loss_list)
assert np.allclose(loss_value[0], 12.207198, 0, 0.000001)
assert np.allclose(loss_value[0], 12.206575, 0, 0.000001)
expect_loss_value = [12.207198, 11.980881, 11.984844, 11.879381, 11.832978, 12.411333, 12.009284,
12.621277, 12.223178, 12.427385]
expect_loss_value = [12.206575, 11.980493, 11.984225, 11.878742, 11.832555, 12.410444, 12.008799,
12.620619, 12.22254, 12.4261055]
print("loss value: {}".format(loss_value))
assert np.allclose(loss_value, expect_loss_value, 0, 0.0005)

2
tests/ut/cpp/python_input/gtest_input/pre_activate/momentum_lossscale_fusion_test.py
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@ -47,6 +47,6 @@ def test_momentum_lossscale_fusion(tag):
@fns
def after(input0, input1, input2, input3, input4):
return make_tuple(tuple_getitem(FusedMulApplyMomentum(input0, input1, input2, input3, input4, constant), 0))
return make_tuple(FusedMulApplyMomentum(input0, input1, input2, input3, input4, constant))
return fns[tag]

2
tests/ut/cpp/stub/hccl/hccl_stub.cc
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@ -103,7 +103,7 @@ hcclResult_t hcom_receive(const char *tag, void *outputPtr, u64 count, hcclDataT
/* 获取梯度参数切分方案 */
hcclResult_t hcom_get_split_strategy(const char *group, const struct model_feature *feature, u32 maxSegmentNum,
u32 *segmentNum, u32 *segmentIdx) {
u32 *segmentNum, u32 *segmentIdx, GradSplitForceMode force) {
return HCCL_SUCCESS;
}

2
tests/ut/python/ops/test_momentum.py
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@ -41,7 +41,7 @@ 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)
new_weight = opt(variable, moment, learning_rate, gradient, momentum)[0]
success = F.depend(success, F.assign(variable, new_weight))
return success

1
tests/ut/python/ops/test_ops.py
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@ -1058,6 +1058,7 @@ 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']}),
('SparseApplyFtrl', {
'block': SparseApplyFtrlNet(),

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