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[Others] add PADDLE_ENFORCE #5288

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Merged
merged 4 commits into from
Nov 28, 2025
Merged

[Others] add PADDLE_ENFORCE #5288

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9a7f40f
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zhoutianzi666 Nov 28, 2025
d7872a1
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zhoutianzi666 Nov 28, 2025
b84d473
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zhoutianzi666 Nov 28, 2025
8d97cc4
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zhoutianzi666 Nov 28, 2025
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8 changes: 4 additions & 4 deletions custom_ops/gpu_ops/moe/ep_moe_expert_dispatch.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -964,7 +964,7 @@ std::vector<paddle::Tensor> EPMoeExpertDispatchFP8(
} else {
token_rows = input_dims[0];
}
const int num_rows = token_rows;

const int hidden_size = input.dims()[input_dims.size() - 1];
const int num_experts_per_rank = num_experts_per_rank_tensor.dims()[0];

Expand All @@ -988,9 +988,9 @@ std::vector<paddle::Tensor> EPMoeExpertDispatchFP8(
auto dst_weights = GetEmptyTensor(
{token_nums_feed_to_ffn}, paddle::DataType::FLOAT32, place);
auto dst_indices = GetEmptyTensor(
{num_rows, num_experts_per_rank}, paddle::DataType::INT32, place);
{token_rows, num_experts_per_rank}, paddle::DataType::INT32, place);
auto permute_indices_per_token = paddle::full(
{num_experts_per_rank, num_rows}, -1, paddle::DataType::INT32, place);
{num_experts_per_rank, token_rows}, -1, paddle::DataType::INT32, place);
auto cumsum_idx_gpu =
paddle::full({num_experts_per_rank}, 0, paddle::DataType::INT32, place);

Expand All @@ -1001,7 +1001,7 @@ std::vector<paddle::Tensor> EPMoeExpertDispatchFP8(
num_experts_per_rank_tensor,
num_experts_per_rank_padded_tensor,
moe_topk,
num_rows,
token_rows,
-1,
-1,
hidden_size,
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5 changes: 5 additions & 0 deletions custom_ops/gpu_ops/per_token_quant_fp8.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -232,6 +232,11 @@ std::vector<paddle::Tensor> PerTokenQuantPadding(paddle::Tensor &input,
auto input_dim = input.dims();
const int token_num = input_dim[0];
const int hidden_size = input_dim[1];

PADDLE_ENFORCE(block_size == 128, "now only support block_size = 128");
PADDLE_ENFORCE(hidden_size % 128 == 0,
"hidden_size must be divisible by 128");

const int hidden_size_scale = hidden_size / block_size;
auto quanted_x = GetEmptyTensor(
{token_num, hidden_size}, paddle::DataType::FLOAT8_E4M3FN, input.place());
Expand Down
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