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!129 fix bert precision bug 

Merge pull request !129 from wanghua/master
This commit is contained in:
mindspore-ci-bot 2020-04-03 17:35:44 +08:00 committed by Gitee
parent e579be472f da123c5b3e
commit 9f982b513d
4 changed files with 24 additions and 16 deletions
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20
mindspore/ccsrc/device/ascend/kernel_select_ascend.cc
View File

@ -35,6 +35,7 @@ enum MatchCountPriority : int {
MATCH_COUNT_PRIORITY_BEGIN = 0,
MATCH_DTYPE_COUNT = MATCH_COUNT_PRIORITY_BEGIN,
MATCH_FORMAT_COUNT,
MATCH_SPECIAL_FORMAT_COUNT,
MATCH_5D_FORMAT_COUNT,
MATCH_OUTPUT_DTYPE_COUNT,
MATCH_COUNT_PRIORITY_END
@ -81,6 +82,12 @@ bool IsValidKernelInfo(const std::shared_ptr<CNode> &kernel_node, const kernel::
}
return true;
};
if (AnfAlgo::GetCNodeName(kernel_node) == "LayerNormBetaGammaBackprop" ||
AnfAlgo::GetCNodeName(kernel_node) == "LayerNormXBackprop") {
if (AnfAlgo::GetPrevNodeOutputFormat(kernel_node, 0) != kernel_build_info.GetInputFormat(0)) {
return true;
}
}
if (AnfAlgo::GetCNodeName(kernel_node) == prim::kPrimCast->name()) {
return AnfAlgo::GetOutputInferDataType(kernel_node, 0) == kernel_build_info.GetOutputDeviceType(0) &&
AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0) == kernel_build_info.GetInputDeviceType(0);
@ -154,7 +161,7 @@ bool PriorityChooseItem(const std::vector<int> &cur_item, std::vector<int> *best
return false;
}
}
return false;
return true;
}
void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr<CNode> &kernel_node,
@ -174,12 +181,11 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
continue;
}
}
if (input_anf_node->isa<ValueNode>()) {
if (AnfAlgo::GetOutputDeviceDataType(input_anf_node, 0) == kTypeUnknown) {
continue;
}
}
if (kernel_build_info.GetInputFormat(input_index) == AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_index)) {
if (AnfAlgo::IsFeatureMapInput(kernel_node, input_index) &&
kSpecialFormatSet.find(kernel_build_info.GetInputFormat(input_index)) != kSpecialFormatSet.end()) {
(*cur_kernelinfo_match_counts)[MATCH_SPECIAL_FORMAT_COUNT]++;
}
(*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT]++;
}
if (kernel_build_info.GetInputDeviceType(input_index) ==
@ -203,7 +209,7 @@ void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, cons
(*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT]++;
}
}
}
} // namespace
void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);

3
mindspore/ccsrc/utils/utils.h
View File

@ -195,6 +195,9 @@ const std::set<std::string> kOptOperatorSet = {
kApplyRMSPropOpName,
};
const std::set<std::string> kSpecialFormatSet = {kOpFormat_FRAC_Z, kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0,
kOpFormat_FRAC_NZ, kOpFormat_C1HWNCoC0};
static inline void ChangeFileMode(const std::string& file_name, mode_t mode) {
if (access(file_name.c_str(), F_OK) != 0) {
MS_LOG(DEBUG) << "File `" << file_name << "` does not exist.";

8
mindspore/ops/_op_impl/tbe/gelu.py
View File

@ -32,10 +32,10 @@ from mindspore.ops.op_info_register import op_info_register
{
"index": 0,
"dtype": [
"float16","float","float16","float16","float16","float16","float","float","float","float"
"float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
],
"format": [
"FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
"FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
],
"name": "x",
"need_compile": false,
@ -47,10 +47,10 @@ from mindspore.ops.op_info_register import op_info_register
{
"index": 0,
"dtype": [
"float16","float","float16","float16","float16","float16","float","float","float","float"
"float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
],
"format": [
"FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
"FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
],
"name": "y",
"need_compile": true,

9
tests/st/networks/models/bert/bert_tdt_no_lossscale.py
View File

@ -153,8 +153,7 @@ def test_bert_tdt():
batch_size = int(os.getenv('BATCH_SIZE', '16'))
config = get_config(version=version, batch_size=batch_size)
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Lamb(netwithloss.trainable_params(), decay_steps=10000, start_learning_rate=1e-4,
end_learning_rate=0.0, power=10.0, warmup_steps=0, decay_filter=lambda x: False)
optimizer = Momentum(netwithloss.trainable_params(), learning_rate=2e-5, momentum=0.9)
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
netwithgrads.set_train(True)
model = Model(netwithgrads)
@ -178,10 +177,10 @@ def test_bert_tdt():
param.default_input = weight_variable(value.asnumpy().shape)
model.train(ds.get_repeat_count(), ds, callbacks=parallel_callback, dataset_sink_mode=False)
loss_value = np.array(parallel_callback.loss_list)
expect_out = [12.191790, 11.739655, 11.523477, 11.320723, 11.113152, 11.203759, 10.841681, 10.826849,
10.616718, 10.486609]
expect_out = [12.19179, 11.965041, 11.969687, 11.97815, 11.969171, 12.603289, 12.165594,
12.824818, 12.38842, 12.604046]
logger.info("expected loss value output: {}".format(expect_out))
assert allclose(loss_value, expect_out, 0.001, 0.001)
assert allclose(loss_value, expect_out, 0.00001, 0.00001)
if __name__ == '__main__':
test_bert_tdt()