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refactor rl get_name_mappings_to_training (#2847)
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* refactor rl get_name_mappings_to_training

* fix tp>1

* change variable name(ffn1->up_gate_proj/ffn2->down_proj)

* change variable name(linear_weight->weight/linear_bias->bias)

* add rl names mapping for vl

* fix ernie 0.3B error

* fix develop code

* fix
This commit is contained in:
Yuanle Liu
2025-07-15 22:31:42 +08:00
committed by GitHub
parent e7bcbbab52
commit 61b3997b85
47 changed files with 1591 additions and 1629 deletions
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54
custom_ops/gpu_ops/cpp_extensions.cc
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@@ -116,11 +116,11 @@ PreCacheLenConcat(const paddle::Tensor &seq_lens_decoder,
paddle::Tensor FusedExpertMoeFunc(
const paddle::Tensor &input, const paddle::Tensor &gate_weight,
const paddle::Tensor &ffn1_weight, const paddle::Tensor &ffn2_weight,
const paddle::optional<paddle::Tensor> &ffn1_bias,
const paddle::optional<paddle::Tensor> &ffn1_scale,
const paddle::optional<paddle::Tensor> &ffn2_bias,
const paddle::optional<paddle::Tensor> &ffn2_scale,
const paddle::Tensor &up_gate_proj_weight, const paddle::Tensor &down_proj_weight,
const paddle::optional<paddle::Tensor> &up_gate_proj_bias,
const paddle::optional<paddle::Tensor> &up_gate_proj_scale,
const paddle::optional<paddle::Tensor> &down_proj_bias,
const paddle::optional<paddle::Tensor> &down_proj_scale,
const std::string &quant_method, const int moe_topk,
const bool norm_topk_prob, const bool group_moe);
@@ -149,7 +149,7 @@ MoERedundantTopKSelectKernel(const paddle::Tensor &gating_logits,
std::vector<paddle::Tensor>
EPMoeExpertDispatch(const paddle::Tensor &input, const paddle::Tensor &topk_ids,
const paddle::Tensor &topk_weights,
const paddle::optional<paddle::Tensor> &ffn1_in_scale,
const paddle::optional<paddle::Tensor> &up_gate_proj_in_scale,
const std::vector<int> &token_nums_per_expert,
const int token_nums_this_rank,
const std::string &moe_quant_type);
@@ -173,7 +173,7 @@ std::vector<paddle::Tensor> EPMoeExpertCombine(
const paddle::Tensor &ffn_out, const paddle::Tensor &expert_scales_float,
const paddle::Tensor &permute_indices_per_token,
const paddle::Tensor &top_k_indices,
const paddle::optional<paddle::Tensor> &ffn2_bias,
const paddle::optional<paddle::Tensor> &down_proj_bias,
const bool norm_topk_prob, const float routed_scaling_factor);
std::vector<std::vector<int>> GetExpertTokenNum(const paddle::Tensor &topk_ids,
@@ -182,35 +182,35 @@ std::vector<std::vector<int>> GetExpertTokenNum(const paddle::Tensor &topk_ids,
paddle::Tensor MoeExpertFFNFunc(
const paddle::Tensor& permute_input,
const paddle::Tensor& tokens_expert_prefix_sum,
const paddle::Tensor& ffn1_weight, const paddle::Tensor& ffn2_weight,
const paddle::optional<paddle::Tensor>& ffn1_bias,
const paddle::optional<paddle::Tensor>& ffn1_scale,
const paddle::optional<paddle::Tensor>& ffn2_scale,
const paddle::optional<paddle::Tensor>& ffn2_in_scale,
const paddle::Tensor& up_gate_proj_weight, const paddle::Tensor& down_proj_weight,
const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
const paddle::optional<paddle::Tensor>& down_proj_scale,
const paddle::optional<paddle::Tensor>& down_proj_in_scale,
const paddle::optional<paddle::Tensor>& expert_idx_per_token,
const std::string& quant_method, const bool used_in_ep_low_latency);
paddle::Tensor MoeExpertFFNWint2Func(
const paddle::Tensor& permute_input,
const paddle::Tensor& tokens_expert_prefix_sum,
const paddle::Tensor& ffn1_weight,
const paddle::Tensor& ffn2_weight,
const paddle::optional<paddle::Tensor>& ffn1_bias,
const paddle::optional<paddle::Tensor>& ffn1_scale,
const paddle::optional<paddle::Tensor>& ffn2_scale,
const paddle::optional<paddle::Tensor>& ffn1_local_scale,
const paddle::optional<paddle::Tensor>& ffn1_code_scale,
const paddle::optional<paddle::Tensor>& ffn1_code_zp,
const paddle::optional<paddle::Tensor>& ffn2_local_scale,
const paddle::optional<paddle::Tensor>& ffn2_code_scale,
const paddle::optional<paddle::Tensor>& ffn2_code_zp,
const paddle::Tensor& up_gate_proj_weight,
const paddle::Tensor& down_proj_weight,
const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
const paddle::optional<paddle::Tensor>& down_proj_scale,
const paddle::optional<paddle::Tensor>& up_gate_proj_local_scale,
const paddle::optional<paddle::Tensor>& up_gate_proj_code_scale,
const paddle::optional<paddle::Tensor>& up_gate_proj_code_zp,
const paddle::optional<paddle::Tensor>& down_proj_local_scale,
const paddle::optional<paddle::Tensor>& down_proj_code_scale,
const paddle::optional<paddle::Tensor>& down_proj_code_zp,
const bool used_in_ep_low_latency);
paddle::Tensor MoeExpertReduceFunc(
const paddle::Tensor &ffn_out, const paddle::Tensor &top_k_weight,
const paddle::Tensor &permute_indices_per_token,
const paddle::Tensor &top_k_indices,
const paddle::optional<paddle::Tensor> &ffn2_bias,
const paddle::optional<paddle::Tensor> &down_proj_bias,
const bool norm_topk_prob, const float routed_scaling_factor);
void InitKVSignalPerQuery(const paddle::Tensor &seq_lens_encoder_tensor,
@@ -816,7 +816,7 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
* ep_moe_dispatch
*/
m.def("ep_moe_expert_dispatch", &EPMoeExpertDispatch, py::arg("input"),
py::arg("topk_ids"), py::arg("topk_weights"), py::arg("ffn1_in_scale"),
py::arg("topk_ids"), py::arg("topk_weights"), py::arg("up_gate_proj_in_scale"),
py::arg("token_nums_per_expert"), py::arg("token_nums_this_rank"),
py::arg("moe_quant_type"), "ep moe export dispatch function");
@@ -824,7 +824,7 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
m.def("ep_moe_expert_combine", &EPMoeExpertCombine, py::arg("ffn_out"),
py::arg("expert_scales_float"), py::arg("permute_indices_per_token"),
py::arg("top_k_indices"), py::arg("ffn2_bias"),
py::arg("top_k_indices"), py::arg("down_proj_bias"),
py::arg("norm_topk_prob"), py::arg("routed_scaling_factor"),
"ep moe export combine function");
@@ -866,7 +866,7 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
*/
m.def("moe_expert_reduce", &MoeExpertReduceFunc, py::arg("ffn_out"),
py::arg("top_k_weight"), py::arg("permute_indices_per_token"),
py::arg("top_k_indices"), py::arg("ffn2_bias"),
py::arg("top_k_indices"), py::arg("down_proj_bias"),
py::arg("norm_topk_prob"), py::arg("routed_scaling_factor"),
"moe export reduce function");