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[CodeStyle][Typos][C-59] Fix typos (Conver) (#70259)
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@rich04lin
rich04lin authored Dec 17, 2024
1 parent 29566cb commit 3e9c3cb
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Showing 4 changed files with 106 additions and 107 deletions.
1 change: 0 additions & 1 deletion _typos.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,6 @@ caculate = 'caculate'
calcualtion = 'calcualtion'
checkings = 'checkings'
childs = 'childs'
Conver = 'Conver'
convience = 'convience'
coodinate = 'coodinate'
copyed = 'copyed'
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90 changes: 45 additions & 45 deletions paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ Tensor any_decomp(const Tensor& x, const IntArray& axis, bool keepdim) {

template <typename T>
Tensor mean_decomp(const Tensor& x, const IntArray& axis, bool keepdim) {
auto x_tmp = ConverToMT<T>(x);
auto x_tmp = ConvertToMT<T>(x);

std::vector<int64_t> x_dim = x_tmp.shape();
int64_t axis_size = axis.size();
Expand Down Expand Up @@ -82,7 +82,7 @@ Tensor mean_decomp(const Tensor& x, const IntArray& axis, bool keepdim) {

Tensor res = sum_x / value;

return ConverToOrig<T>(res, x.dtype());
return ConvertToOrig<T>(res, x.dtype());
}

static void check_valid_type(const DataType& dtype) {
Expand Down Expand Up @@ -112,7 +112,7 @@ Tensor p_norm_decomp(const Tensor& x,
const float epsilon = 1.0e-12f,
const bool& keepdim = false,
const bool& asvector = false) {
auto x_tmp = ConverToMT<T>(x);
auto x_tmp = ConvertToMT<T>(x);

Tensor res;
if (porder == 0.0) {
Expand Down Expand Up @@ -146,17 +146,17 @@ Tensor p_norm_decomp(const Tensor& x,
res = elementwise_pow<T>(res, inv_porder_tensor);
}

return ConverToOrig<T>(res, x.dtype());
return ConvertToOrig<T>(res, x.dtype());
}

template <typename T>
Tensor pow_decomp(const Tensor& x, const paddle::Scalar& y) {
auto x_cast = ConverToMT<T>(x);
auto x_cast = ConvertToMT<T>(x);

check_valid_type(y.dtype());
Tensor y_full = full_scalar<T>(y, x_cast.dtype(), x_cast.place());
auto ans = elementwise_pow<T>(x_cast, y_full);
return ConverToOrig<T>(ans, x.dtype());
return ConvertToOrig<T>(ans, x.dtype());
}

template <typename T>
Expand Down Expand Up @@ -263,7 +263,7 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor> batch_norm_decomp(
bool use_global_stats,
bool trainable_statistics) {
auto org_dtype = x.dtype();
Tensor x_cast = ConverToMT<T>(x);
Tensor x_cast = ConvertToMT<T>(x);

BatchNormDecompHelper<T> decomp_help(x, scale, bias, data_layout);

Expand Down Expand Up @@ -319,7 +319,7 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor> batch_norm_decomp(
: bias.get());
}

y = ConverToOrig<T>(y, org_dtype);
y = ConvertToOrig<T>(y, org_dtype);

if (!use_run_stat) {
batch_mean_ = squeeze<T>(batch_mean, reduce_axes);
Expand All @@ -336,25 +336,25 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor> batch_norm_decomp(

template <typename T>
Tensor softmax_decomp(const Tensor& x, const int& axis) {
auto x_tmp = ConverToMT<T>(x);
auto x_tmp = ConvertToMT<T>(x);

auto max_tmp = max<T>(x_tmp, {axis}, true);
auto molecular = exp<T>(x_tmp - max_tmp);
auto res = molecular / sum<T>(molecular, {axis}, molecular.dtype(), true);

return ConverToOrig<T>(res, x.dtype());
return ConvertToOrig<T>(res, x.dtype());
}

template <typename T>
Tensor log_softmax_decomp(const Tensor& x, const int& axis) {
auto x_tmp = ConverToMT<T>(x);
auto x_tmp = ConvertToMT<T>(x);

auto max_tmp = max<T>(x_tmp, {axis}, true);
auto sub = x_tmp - max_tmp;
auto molecular = exp<T>(sub);
auto res = sub - log<T>(sum<T>(molecular, {axis}, molecular.dtype(), true));

return ConverToOrig<T>(res, x.dtype());
return ConvertToOrig<T>(res, x.dtype());
}

template <typename T>
Expand Down Expand Up @@ -411,9 +411,9 @@ Tensor stack_decomp(const std::vector<Tensor>& x, const int& axis) {

template <typename T>
Tensor silu_decomp(const Tensor& x) {
auto x_tmp = ConverToMT<T>(x);
auto x_tmp = ConvertToMT<T>(x);
auto res = x_tmp * sigmoid<T>(x_tmp);
return ConverToOrig<T>(res, x.dtype());
return ConvertToOrig<T>(res, x.dtype());
}

template <typename T>
Expand Down Expand Up @@ -541,7 +541,7 @@ std::tuple<Tensor, Tensor, Tensor> layer_norm_decomp(
int begin_norm_axis) {
std::vector<int64_t> reduce_axis;
auto org_dtype = x.dtype();
Tensor x_cast = ConverToMT<T>(x);
Tensor x_cast = ConvertToMT<T>(x);

auto x_dims = x.dims();

Expand All @@ -562,13 +562,13 @@ std::tuple<Tensor, Tensor, Tensor> layer_norm_decomp(
Tensor scale_cast;
if (scale) {
scale_cast = decomp_helper.Process<T>(scale.get(), x_cast);
scale_cast = ConverToMT<T>(scale_cast);
scale_cast = ConvertToMT<T>(scale_cast);
out = out * scale_cast;
}
Tensor bias_cast;
if (bias) {
bias_cast = decomp_helper.Process<T>(bias.get(), x_cast);
bias_cast = ConverToMT<T>(bias_cast);
bias_cast = ConvertToMT<T>(bias_cast);
out = out + bias_cast;
}
mean_ = squeeze<T>(mean_, reduce_axis);
Expand All @@ -577,7 +577,7 @@ std::tuple<Tensor, Tensor, Tensor> layer_norm_decomp(
// same as LayerNormInferMeta
// x: float32 --> out: float32, mean: float32, variance: float32
// x: float16 --> out: float16, mean: float32, variance: float32
out = ConverToOrig<T>(out, org_dtype);
out = ConvertToOrig<T>(out, org_dtype);
return std::make_tuple(out, mean_, variance);
}

Expand Down Expand Up @@ -751,7 +751,7 @@ std::tuple<Tensor, Tensor, Tensor> instance_norm_decomp(
const paddle::optional<Tensor>& bias,
float epsilon) {
auto org_dtype = x.dtype();
Tensor x_cast = ConverToMT<T>(x);
Tensor x_cast = ConvertToMT<T>(x);
const std::vector<int64_t> x_dims = x.shape();

if (has_dynamic_shape(x_dims)) {
Expand Down Expand Up @@ -790,20 +790,20 @@ std::tuple<Tensor, Tensor, Tensor> instance_norm_decomp(

if (scale) {
auto scale_cast = backend::reshape<T>(scale.get(), slice_shape_tensor);
scale_cast = ConverToMT<T>(scale_cast);
scale_cast = ConvertToMT<T>(scale_cast);
out = out * scale_cast;
}

if (bias) {
auto bias_cast = backend::reshape<T>(bias.get(), slice_shape_tensor);
bias_cast = ConverToMT<T>(bias_cast);
bias_cast = ConvertToMT<T>(bias_cast);
out = out + bias_cast;
}

std::vector<int64_t> res_shape(1, -1);
auto mean_out = reshape<T>(mean_, res_shape);
auto variance_out = reshape<T>(rsqrt_var, res_shape);
auto res = ConverToOrig<T>(out, org_dtype);
auto res = ConvertToOrig<T>(out, org_dtype);

return std::make_tuple(res, mean_out, variance_out);
}
Expand All @@ -830,20 +830,20 @@ std::tuple<Tensor, Tensor, Tensor> instance_norm_decomp(
out = reshape<T>(out, x_dims);
if (scale) {
auto scale_cast = reshape<T>(scale.get(), slice_shape);
scale_cast = ConverToMT<T>(scale_cast);
scale_cast = ConvertToMT<T>(scale_cast);
out = out * scale_cast;
}

if (bias) {
auto bias_cast = reshape<T>(bias.get(), slice_shape);
bias_cast = ConverToMT<T>(bias_cast);
bias_cast = ConvertToMT<T>(bias_cast);
out = out + bias_cast;
}

std::vector<int64_t> res_shape(1, -1);
auto mean_out = reshape<T>(mean_, res_shape);
auto variance_out = reshape<T>(rsqrt_var, res_shape);
auto res = ConverToOrig<T>(out, org_dtype);
auto res = ConvertToOrig<T>(out, org_dtype);

return std::make_tuple(res, mean_out, variance_out);
}
Expand Down Expand Up @@ -985,7 +985,7 @@ std::tuple<Tensor, Tensor, Tensor> group_norm_decomp(
}

auto org_dtype = x.dtype();
Tensor x_cast = ConverToMT<T>(x);
Tensor x_cast = ConvertToMT<T>(x);

Tensor x_dim_t;
Tensor out, mean_, var_;
Expand Down Expand Up @@ -1047,7 +1047,7 @@ std::tuple<Tensor, Tensor, Tensor> group_norm_decomp(
} else {
scale_cast = scale.get();
}
scale_cast = ConverToMT<T>(scale_cast);
scale_cast = ConvertToMT<T>(scale_cast);
out = out * scale_cast;
}
Tensor bias_cast;
Expand All @@ -1057,7 +1057,7 @@ std::tuple<Tensor, Tensor, Tensor> group_norm_decomp(
} else {
bias_cast = bias.get();
}
bias_cast = ConverToMT<T>(bias_cast);
bias_cast = ConvertToMT<T>(bias_cast);
out = out + bias_cast;
}
Tensor mean_out, var_out;
Expand All @@ -1072,20 +1072,20 @@ std::tuple<Tensor, Tensor, Tensor> group_norm_decomp(
mean_out = reshape<T>(mean_, res_shape);
var_out = reshape<T>(var_, res_shape);
}
out = ConverToOrig<T>(out, org_dtype);
out = ConvertToOrig<T>(out, org_dtype);

return std::make_tuple(out, mean_out, var_out);
}

template <typename T>
Tensor square_decomp(const Tensor& x) {
auto x_cast = ConverToMT<T>(x);
auto x_cast = ConvertToMT<T>(x);

Tensor two;
two = full_scalar<T>(2, x_cast.dtype(), x_cast.place());

auto ans = elementwise_pow<T>(x_cast, two);
return ConverToOrig<T>(ans, x.dtype());
return ConvertToOrig<T>(ans, x.dtype());
}

template <typename T>
Expand Down Expand Up @@ -1131,7 +1131,7 @@ Tensor sigmoid_cross_entropy_with_logits_decomp(

template <typename T>
Tensor mean_all_decomp(const Tensor& x) {
auto x_cast = ConverToMT<T>(x);
auto x_cast = ConvertToMT<T>(x);
auto x_shape = x.shape();

Tensor ans;
Expand All @@ -1147,7 +1147,7 @@ Tensor mean_all_decomp(const Tensor& x) {
ans = sum<T>(x_cast) / x_cast.numel();
}

return ConverToOrig<T>(ans, x.dtype());
return ConvertToOrig<T>(ans, x.dtype());
}

template <typename T>
Expand Down Expand Up @@ -1243,7 +1243,7 @@ Tensor index_sample_decomp(const Tensor& x, const Tensor& index) {

template <typename T>
Tensor elu_decomp(const Tensor& x, const float alpha) {
auto x_cast = ConverToMT<T>(x);
auto x_cast = ConvertToMT<T>(x);

Tensor zero;
Tensor tmp_res;
Expand All @@ -1258,16 +1258,16 @@ Tensor elu_decomp(const Tensor& x, const float alpha) {
tmp_res = alpha * (exp<T>(x_cast) - 1);
}
auto ans = where<T>(x_cast > zero, x_cast, tmp_res);
return ConverToOrig<T>(ans, x.dtype());
return ConvertToOrig<T>(ans, x.dtype());
}

template <typename T>
Tensor lerp_decomp(const Tensor& x, const Tensor& y, const Tensor& weight) {
Tensor x_cast = ConverToMT<T>(x);
Tensor y_cast = ConverToMT<T>(y);
Tensor weight_cast = ConverToMT<T>(weight);
Tensor x_cast = ConvertToMT<T>(x);
Tensor y_cast = ConvertToMT<T>(y);
Tensor weight_cast = ConvertToMT<T>(weight);
Tensor res = x_cast + weight_cast * (y_cast - x_cast);
return ConverToOrig<T>(res, x.dtype());
return ConvertToOrig<T>(res, x.dtype());
}

template <typename T>
Expand Down Expand Up @@ -1420,27 +1420,27 @@ Tensor eye_decomp(const paddle::Scalar& num_rows,
int32_t min_num = std::min(num_rows.to<int>(), num_columns.to<int>());
Tensor zero_tensor =
full<T>({num_rows.to<int>(), num_columns.to<int>()}, 0, dtype, place);
auto zero_tensor_cast = ConverToMT<T>(zero_tensor);
auto zero_tensor_cast = ConvertToMT<T>(zero_tensor);
Tensor diag_one = unsqueeze<T>(full<T>({min_num}, 1, dtype, place), {1});
auto diag_one_cast = ConverToMT<T>(diag_one);
auto diag_one_cast = ConvertToMT<T>(diag_one);

auto start = full<T>({1}, 0, dtype, place);
auto stop = full<T>({1}, min_num, dtype, place);
auto step = full<T>({1}, 1, dtype, place);
Tensor index = unsqueeze<T>(
backend::arange<T>(start, stop, step, DataType::INT32, place), {1});

auto index_cast = ConverToMT<T>(index);
auto index_cast = ConvertToMT<T>(index);
Tensor res = put_along_axis<T>(zero_tensor_cast, index, diag_one_cast, 1);

return ConverToOrig<T>(res, dtype);
return ConvertToOrig<T>(res, dtype);
}

template <typename T>
Tensor diag_decomp(const Tensor& x,
const int& offset = 0,
const float& padding_value = 0.0) {
Tensor cast_x = ConverToMT<T>(x);
Tensor cast_x = ConvertToMT<T>(x);
int64_t rank = cast_x.dims().size();
Tensor res;
if (rank == 1) {
Expand Down Expand Up @@ -1482,7 +1482,7 @@ Tensor diag_decomp(const Tensor& x,
backend::arange<T>(start, end, stride, DataType::INT64, cast_x.place());
res = take_along_axis<T>(x_flat, indices, 0);
}
return ConverToOrig<T>(res, x.dtype());
return ConvertToOrig<T>(res, x.dtype());
}

} // namespace details
Expand Down
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