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Synchronize latest Ascend software suite 29 Dec 2020

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pull/10794/head
yanghaoran 4 years ago
parent ae0ea279f5
commit b1ee7d9926
7 changed files with 54 additions and 16 deletions
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2
graphengine

@ -1 +1 @@
Subproject commit c762dd5dcc207987d5b5d4ee520da3939222ec88 Subproject commit 1b4f85776269f567d11153807ae7badc91803083

57
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission_v2.cc
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@ -16,8 +16,10 @@
#include "backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission_v2.h" #include "backend/optimizer/ascend/ir_fission/dynamic_rnn_grad_fission_v2.h"
#include <vector> #include <vector>
#include <memory> #include <memory>
#include "backend/session/kernel_graph.h"
#include "backend/session/anf_runtime_algorithm.h" #include "backend/session/anf_runtime_algorithm.h"
#include "utils/trace_base.h" #include "utils/trace_base.h"
#include "utils/tensor_construct_utils.h"
namespace mindspore { namespace mindspore {
namespace opt { namespace opt {
@ -46,7 +48,7 @@ void CreateTLoopNode(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn
std::vector<size_t> output0_dims{origin_input9_shape[0], 4 * (((origin_input9_shape[1] + 15) / 16) * 16)}; std::vector<size_t> output0_dims{origin_input9_shape[0], 4 * (((origin_input9_shape[1] + 15) / 16) * 16)};
std::vector<size_t> output1_dims{input_i_shape[1], input_i_shape[2]}; std::vector<size_t> output1_dims{input_i_shape[1], input_i_shape[2]};
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32, kNumberTypeFloat32}, {output0_dims, output1_dims}, AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16, kNumberTypeFloat32}, {output0_dims, output1_dims},
basic_lstm_cell_c_state_grad.get()); basic_lstm_cell_c_state_grad.get());
AnfAlgo::SetNodeAttr("forget_bias", MakeValue(1.0f), basic_lstm_cell_c_state_grad); AnfAlgo::SetNodeAttr("forget_bias", MakeValue(1.0f), basic_lstm_cell_c_state_grad);
AnfAlgo::SetNodeAttr("activation", MakeValue("Tanh"), basic_lstm_cell_c_state_grad); AnfAlgo::SetNodeAttr("activation", MakeValue("Tanh"), basic_lstm_cell_c_state_grad);
@ -260,7 +262,7 @@ AnfNodePtr AddLSTMInputGradNode(const FuncGraphPtr &func_graph, const CNodePtr &
// Create lstm_gage_concat // Create lstm_gage_concat
auto lstm_gage_concat = func_graph->NewCNode(lstm_gage_concat_input); auto lstm_gage_concat = func_graph->NewCNode(lstm_gage_concat_input);
auto origin_input7_shape = AnfAlgo::GetOutputInferShape(origin_input7, 0); auto origin_input7_shape = AnfAlgo::GetOutputInferShape(origin_input7, 0);
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16},
{{origin_input7_shape[0], origin_input7_shape[1], 4 * origin_input7_shape[2]}}, {{origin_input7_shape[0], origin_input7_shape[1], 4 * origin_input7_shape[2]}},
lstm_gage_concat.get()); lstm_gage_concat.get());
AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(num_split_x)), lstm_gage_concat); AnfAlgo::SetNodeAttr(kAttrN, MakeValue(SizeToLong(num_split_x)), lstm_gage_concat);
@ -413,6 +415,24 @@ AnfNodePtr CreateBatchMatMul(const FuncGraphPtr &func_graph, const AnfNodePtr &l
return batch_matmul; return batch_matmul;
} }
AnfNodePtr CreateBatchMatMul2(const FuncGraphPtr &func_graph, const AnfNodePtr &lstm_input_grad,
const AnfNodePtr &node) {
MS_EXCEPTION_IF_NULL(func_graph);
// Create node
std::vector<AnfNodePtr> matmul_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimBatchMatMul->name())),
node, lstm_input_grad};
auto batch_matmul = func_graph->NewCNode(matmul_inputs);
// Set infer data type and shape
auto out_shape = {AnfAlgo::GetOutputInferShape(lstm_input_grad, 0)[0], IntToSize(1),
AnfAlgo::GetOutputInferShape(lstm_input_grad, 0)[2]};
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, batch_matmul.get());
// Set attr
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), batch_matmul);
AnfAlgo::SetNodeAttr("transpose_x1", MakeValue(false), batch_matmul);
AnfAlgo::SetNodeAttr("transpose_x2", MakeValue(false), batch_matmul);
return batch_matmul;
}
AnfNodePtr CreateDwReduceSum(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode, AnfNodePtr CreateDwReduceSum(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
const AnfNodePtr &batch_matmul) { const AnfNodePtr &batch_matmul) {
MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(func_graph);
@ -430,18 +450,38 @@ AnfNodePtr CreateDwReduceSum(const FuncGraphPtr &func_graph, const CNodePtr &dyn
return reduce_sum; return reduce_sum;
} }
AnfNodePtr CreateValueNode(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode) {
auto origin_input7 = dynamic_rnn_grad_cnode->input(8);
auto origin_input7_shape = AnfAlgo::GetOutputInferShape(origin_input7, 0);
auto t_size = origin_input7_shape[0];
auto n_size = origin_input7_shape[1];
std::vector<size_t> shape = {t_size, IntToSize(1), n_size};
std::vector<int64_t> output_shape = {SizeToLong(t_size), SizeToLong(1), SizeToLong(n_size)};
std::vector<int64_t> output_tensor = {(SizeToLong(n_size) + SizeToLong(15)) / SizeToLong(16) * SizeToLong(16) *
SizeToLong(16) * SizeToLong(t_size)};
auto tensor = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, output_tensor);
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, output_shape);
auto kernel_graph = func_graph->cast<KernelGraphPtr>();
auto value_node = kernel_graph->NewValueNode(x_abstract, tensor);
kernel_graph->AddValueNodeToGraph(value_node);
AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat32}, {shape}, value_node.get());
return value_node;
}
AnfNodePtr CreateDbReduceSum(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode, AnfNodePtr CreateDbReduceSum(const FuncGraphPtr &func_graph, const CNodePtr &dynamic_rnn_grad_cnode,
const AnfNodePtr &lstm_input_grad) { const AnfNodePtr &lstm_input_grad, const AnfNodePtr &value_node) {
MS_EXCEPTION_IF_NULL(func_graph); MS_EXCEPTION_IF_NULL(func_graph);
// Create node // Create node
auto batch_matmul = CreateBatchMatMul2(func_graph, lstm_input_grad, value_node);
std::vector<AnfNodePtr> reduce_sum_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReduceSum->name())), std::vector<AnfNodePtr> reduce_sum_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReduceSum->name())),
lstm_input_grad}; batch_matmul};
auto reduce_sum = func_graph->NewCNode(reduce_sum_inputs); auto reduce_sum = func_graph->NewCNode(reduce_sum_inputs);
// Set infer data type and shape // Set infer data type and shape
AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(dynamic_rnn_grad_cnode, 1)}, auto out_shape = {AnfAlgo::GetOutputInferShape(lstm_input_grad, 0)[2]};
{AnfAlgo::GetOutputInferShape(dynamic_rnn_grad_cnode, 1)}, reduce_sum.get()); AnfAlgo::SetOutputInferTypeAndShape({kNumberTypeFloat16}, {out_shape}, reduce_sum.get());
// Set attr // Set attr
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int64_t>{0, 1}), reduce_sum); AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(std::vector<int64_t>{0}), reduce_sum);
AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum); AnfAlgo::SetNodeAttr(kAttrKeepDims, MakeValue(false), reduce_sum);
AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum); AnfAlgo::SetNodeAttr("is_backend_insert", MakeValue(true), reduce_sum);
return reduce_sum; return reduce_sum;
@ -486,8 +526,9 @@ const AnfNodePtr DynamicRnnGradFissionV2::Process(const FuncGraphPtr &func_graph
make_tuple_inputs.emplace_back(batch_matmul); make_tuple_inputs.emplace_back(batch_matmul);
} }
auto value_node = CreateValueNode(func_graph, dynamic_rnn_grad_cnode);
// create reduce_sum_2 // create reduce_sum_2
auto db_reduce_sum = CreateDbReduceSum(func_graph, dynamic_rnn_grad_cnode, lstm_input_grad); auto db_reduce_sum = CreateDbReduceSum(func_graph, dynamic_rnn_grad_cnode, lstm_input_grad, value_node);
make_tuple_inputs.emplace_back(db_reduce_sum); make_tuple_inputs.emplace_back(db_reduce_sum);
make_tuple_inputs.insert(make_tuple_inputs.end(), new_outputs.begin(), new_outputs.end()); make_tuple_inputs.insert(make_tuple_inputs.end(), new_outputs.begin(), new_outputs.end());
auto make_tuple = func_graph->NewCNode(make_tuple_inputs); auto make_tuple = func_graph->NewCNode(make_tuple_inputs);

1
mindspore/ccsrc/transform/graph_ir/op_declare/rnn_declare.cc
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@ -124,7 +124,6 @@ ATTR_MAP(DynamicGRUV2Grad) = {{"direction", ATTR_DESC(direction, AnyTraits<std::
{"cell_clip", ATTR_DESC(cell_clip, AnyTraits<float>())}, {"cell_clip", ATTR_DESC(cell_clip, AnyTraits<float>())},
{"num_proj", ATTR_DESC(num_proj, AnyTraits<int64_t>())}, {"num_proj", ATTR_DESC(num_proj, AnyTraits<int64_t>())},
{"time_major", ATTR_DESC(time_major, AnyTraits<bool>())}, {"time_major", ATTR_DESC(time_major, AnyTraits<bool>())},
{"bias_type", ATTR_DESC(bias_type, AnyTraits<std::string>())},
{"gate_order", ATTR_DESC(gate_order, AnyTraits<std::string>())}, {"gate_order", ATTR_DESC(gate_order, AnyTraits<std::string>())},
{"reset_after", ATTR_DESC(reset_after, AnyTraits<bool>())}}; {"reset_after", ATTR_DESC(reset_after, AnyTraits<bool>())}};
OUTPUT_MAP(DynamicGRUV2Grad) = {{0, OUTPUT_DESC(dw_input)}, {1, OUTPUT_DESC(dw_hidden)}, {2, OUTPUT_DESC(db_input)}, OUTPUT_MAP(DynamicGRUV2Grad) = {{0, OUTPUT_DESC(dw_input)}, {1, OUTPUT_DESC(dw_hidden)}, {2, OUTPUT_DESC(db_input)},

2
mindspore/core/ir/tensor.cc
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@ -429,6 +429,8 @@ TensorDataPtr MakeTensorData(TypeId data_type, const ShapeVector &shape, const A
return std::make_shared<TensorDataImpl<uint64_t>>(shape, args...); return std::make_shared<TensorDataImpl<uint64_t>>(shape, args...);
case kNumberTypeFloat16: case kNumberTypeFloat16:
return std::make_shared<TensorDataImpl<float16>>(shape, args...); return std::make_shared<TensorDataImpl<float16>>(shape, args...);
case kNumberTypeFloat:
return std::make_shared<TensorDataImpl<float>>(shape, args...);
case kNumberTypeFloat32: case kNumberTypeFloat32:
return std::make_shared<TensorDataImpl<float>>(shape, args...); return std::make_shared<TensorDataImpl<float>>(shape, args...);
case kNumberTypeFloat64: case kNumberTypeFloat64:

2
mindspore/ops/_grad/grad_nn_ops.py
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@ -956,7 +956,7 @@ def get_bprop_dynamic_rnn(self):
def get_bprop_dynamic_gru_v2(self): def get_bprop_dynamic_gru_v2(self):
"""Grad definition for `DynamicGRUV2` operation.""" """Grad definition for `DynamicGRUV2` operation."""
dynamic_gru_v2_grad = G.DynamicGRUV2Grad(self.direction, self.cell_depth, self.keep_prob, self.cell_clip, dynamic_gru_v2_grad = G.DynamicGRUV2Grad(self.direction, self.cell_depth, self.keep_prob, self.cell_clip,
self.num_proj, self.time_major, 'double_bias', self.gate_order, self.num_proj, self.time_major, self.gate_order,
self.reset_after) self.reset_after)
def bprop(x, winput, whidden, binput, bhidden, seq, init_h, out, dout): def bprop(x, winput, whidden, binput, bhidden, seq, init_h, out, dout):

2
mindspore/ops/_op_impl/tbe/basic_lstm_cell_c_state_grad_v2.py
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@ -38,7 +38,7 @@ basic_lstm_cell_c_state_grad_op_info_v2 = TBERegOp("BasicLSTMCellCStateGradV2")
.output(1, "dct_1", False, "required", "all") \ .output(1, "dct_1", False, "required", "all") \
.dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ,
DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ,
DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \ DataType.F32_FracNZ, DataType.F16_FracNZ, DataType.F32_FracNZ) \
.dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ,
DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ,
DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \ DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \

4
mindspore/ops/operations/_grad_ops.py
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@ -1344,7 +1344,6 @@ class DynamicGRUV2Grad(PrimitiveWithInfer):
cell_clip (float): A float identifying the cell clip in the op. Default: -1.0. cell_clip (float): A float identifying the cell clip in the op. Default: -1.0.
num_proj (int): An integer identifying the num proj in the op. Default: 0. num_proj (int): An integer identifying the num proj in the op. Default: 0.
time_major (bool): A bool identifying the time major in the op. Default: True. time_major (bool): A bool identifying the time major in the op. Default: True.
bias_type (str): An string identifying the type of bias_type function in the op. Default to "double_bias".
gate_order (str): An string identifying the gate order in weight and bias. Default: 'rzh. gate_order (str): An string identifying the gate order in weight and bias. Default: 'rzh.
'zrh' is another option. 'zrh' is another option.
reset_after (bool): An bool identifying whether to apply reset gate after matrix multiplication. Default: True. reset_after (bool): An bool identifying whether to apply reset gate after matrix multiplication. Default: True.
@ -1402,7 +1401,6 @@ class DynamicGRUV2Grad(PrimitiveWithInfer):
cell_clip=-1.0, cell_clip=-1.0,
num_proj=0, num_proj=0,
time_major=True, time_major=True,
bias_type="double_bias",
gate_order="rzh", gate_order="rzh",
reset_after=True): reset_after=True):
self.cell_depth = validator.check_value_type("cell_depth", cell_depth, [int], self.name) self.cell_depth = validator.check_value_type("cell_depth", cell_depth, [int], self.name)
@ -1411,8 +1409,6 @@ class DynamicGRUV2Grad(PrimitiveWithInfer):
self.num_proj = validator.check_non_negative_int(num_proj, "num_proj", self.name) self.num_proj = validator.check_non_negative_int(num_proj, "num_proj", self.name)
self.time_major = validator.check_value_type("time_major", time_major, [bool], self.name) self.time_major = validator.check_value_type("time_major", time_major, [bool], self.name)
self.direction = validator.check_string(direction, ['UNIDIRECTIONAL'], "direction", self.name) self.direction = validator.check_string(direction, ['UNIDIRECTIONAL'], "direction", self.name)
self.bias_type = validator.check_string(bias_type,
['no_bias', 'single_bias', 'double_bias'], "bias_type", self.name)
self.gate_order = validator.check_string(gate_order, ['zrh', 'rzh'], "gate_order", self.name) self.gate_order = validator.check_string(gate_order, ['zrh', 'rzh'], "gate_order", self.name)
self.reset_after = validator.check_value_type("reset_after", reset_after, [bool], self.name) self.reset_after = validator.check_value_type("reset_after", reset_after, [bool], self.name)
self.add_prim_attr("io_format", "ND") self.add_prim_attr("io_format", "ND")

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