8000 Add assertion to align with cuda by shiyang-weng · Pull Request #153233 · pytorch/pytorch · GitHub
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Add assertion to align with cuda #153233

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Add assertion to align with cuda #153233

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2a93f9f
Add assertion to align with cuda
shiyang-weng May 9, 2025
136ab31
use TORCH_CHECK_VALUE instead of TORCH_CHECK
shiyang-weng May 12, 2025
5fd22bf
Merge branch 'main' into wengshiy/add_bn_assert
shiyang-weng May 23, 2025
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5 changes: 5 additions & 0 deletions aten/src/ATen/native/Normalization.cpp
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Expand Up @@ -770,6 +770,11 @@ std::tuple<Tensor, Tensor> batch_norm_update_stats_cpu(

std::tuple<Tensor&, Tensor&, Tensor&> batch_norm_cpu_out(const Tensor& self, const std::optional<Tensor>& weight_opt, const std::optional<Tensor>& bias_opt, const std::optional<Tensor>& running_mean_opt, const std::optional<Tensor>& running_var_opt,
bool train, double momentum, double eps, Tensor& out, Tensor& save_mean, Tensor& save_var) {
const bool has_running_mean = (running_mean_opt.has_value() && running_mean_opt->defined());
const bool has_running_var = (running_var_opt.has_value() && running_var_opt->defined());
TORCH_CHECK_VALUE(has_running_mean == has_running_var,
"running_mean and running_var must either both be None or neither be None");

// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> weight_maybe_owned = at::borrow_from_optional_tensor(weight_opt);
const Tensor& weight = *weight_maybe_owned;
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3 changes: 2 additions & 1 deletion aten/src/ATen/native/cuda/Normalization.cu
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Expand Up @@ -435,7 +435,8 @@ void batch_norm_calc_invstd(const Tensor& out_invstd, const Tensor& running_var,
std::tuple<Tensor&, Tensor&, Tensor&> batch_norm_cuda_out(const Tensor& self, const std::optional<Tensor>& weight_opt, const std::optional<Tensor>& bias_opt, const std::optional<Tensor>& running_mean_opt, const std::optional<Tensor>& running_var_opt, bool train, double momentum, double epsilon, Tensor& output, Tensor& save_mean, Tensor& save_invstd) {
const bool has_running_mean = (running_mean_opt.has_value() && running_mean_opt->defined());
const bool has_running_var = (running_var_opt.has_value() && running_var_opt->defined());
TORCH_CHECK(has_running_mean == has_running_var);
TORCH_CHECK_VALUE(has_running_mean == has_running_var,
"running_mean and running_var must either both be None or neither be None");

if (train) {
batch_norm_mean_var(self, save_mean, save_invstd);
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9 changes: 6 additions & 3 deletions aten/src/ATen/native/mps/operations/Normalization.mm
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Expand Up @@ -103,7 +103,8 @@ static void get_shapes(MPSShape* input_shape_readonly,

const bool has_running_mean = (running_mean_opt.has_value() && running_mean_opt->defined());
const bool has_running_var = (running_var_opt.has_value() && running_var_opt->defined());
TORCH_CHECK(has_running_mean == has_running_var);
TORCH_CHECK_VALUE(has_running_mean == has_running_var,
"running_mean and running_var must either both be None or neither be None");

const bool has_weight = (weight_opt.has_value() && weight_opt->defined());
const bool has_bias = (bias_opt.has_value() && bias_opt->defined());
Expand Down Expand Up @@ -587,10 +588,12 @@ Check if running mean exists (maybe do this check before making graph)

const bool has_running_mean = (running_mean_opt.has_value() && running_mean_opt->defined());
const bool has_running_var = (running_var_opt.has_value() && running_var_opt->defined());
TORCH_CHECK(has_running_mean == has_running_var);
TORCH_CHECK_VALUE(has_running_mean == has_running_var,
"running_mean and running_var must either both be None or neither be None");
const bool has_save_mean = (save_mean_opt.has_value() && save_mean_opt->defined());
const bool has_save_var = (save_var_opt.has_value() && save_var_opt->defined());
TORCH_CHECK(has_save_mean == has_save_var);
TORCH_CHECK_VALUE(has_save_mean == has_save_var,
"save_mean and save_var must either both be None or neither be None");

const bool has_weight = (weight_opt.has_value() && weight_opt->defined());

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