feat: clean up module for inference use only (#501)

Author: guocuimi

src/layers/attention/attention.h

@@ -10,7 +10,7 @@
 
 namespace llm {
 
-class AttentionImpl : public llm::nn::Module {
+class AttentionImpl : public Module {
  public:
   AttentionImpl(int64_t n_heads,
                 int64_t n_kv_heads,

src/layers/embedding.h

@@ -16,16 +16,14 @@ namespace llm {
 // This module is often used to store word embeddings and retrieve them using
 // indices.
 
-class EmbeddingImpl : public llm::nn::Module {
+class EmbeddingImpl : public Module {
  public:
   EmbeddingImpl(int64_t num_embeddings,
                 int64_t embedding_dim,
                 const torch::TensorOptions& options) {
     // register the weight parameter
     weight_ = register_parameter(
-        "weight",
-        torch::empty({num_embeddings, embedding_dim}, options),
-        /*requires_grad=*/false);
+        "weight", torch::empty({num_embeddings, embedding_dim}, options));
   }
 
   // The input to the module is a list of indices, and the output is the
@@ -68,7 +66,7 @@ class EmbeddingImpl : public llm::nn::Module {
 LLM_MODULE(Embedding);
 
 // Embedding parallelized in the embedding dimension.
-class ParallelEmbeddingImpl : public llm::nn::Module {
+class ParallelEmbeddingImpl : public Module {
  public:
   ParallelEmbeddingImpl(int64_t num_embeddings,
                         int64_t embedding_dim,
@@ -84,8 +82,7 @@ class ParallelEmbeddingImpl : public llm::nn::Module {
     // register the weight parameter
     weight_ = register_parameter(
         "weight",
-        torch::empty({num_embeddings, embedding_dim_per_partition}, options),
-        /*requires_grad=*/false);
+        torch::empty({num_embeddings, embedding_dim_per_partition}, options));
   }
 
   // The input to the module is a list of indices, and the output is the
@@ -139,7 +136,7 @@ class ParallelEmbeddingImpl : public llm::nn::Module {
 LLM_MODULE(ParallelEmbedding);
 
 // Embedding parallelized in the vocabulary dimension
-class VocabParallelEmbeddingImpl : public llm::nn::Module {
+class VocabParallelEmbeddingImpl : public Module {
  public:
   VocabParallelEmbeddingImpl(int64_t num_embeddings,
                              int64_t embedding_dim,
@@ -154,8 +151,7 @@ class VocabParallelEmbeddingImpl : public llm::nn::Module {
     // register the weight parameter
     weight_ = register_parameter(
         "weight",
-        torch::empty({num_embeddings_per_partition, embedding_dim}, options),
-        /*requires_grad=*/false);
+        torch::empty({num_embeddings_per_partition, embedding_dim}, options));
   }
 
   // The input to the module is a list of indices, and the output is the

src/layers/fused_linear.h

@@ -12,7 +12,7 @@
 
 namespace llm {
 
-class FusedColumnParallelLinearImpl : public llm::nn::Module {
+class FusedColumnParallelLinearImpl : public Module {
  public:
   FusedColumnParallelLinearImpl(int64_t in_features,
                                 const std::vector<int64_t>& out_features,

src/layers/linear.h

@@ -16,7 +16,7 @@ using TensorTransform = std::function<torch::Tensor(const torch::Tensor&)>;
 // an interface for parallel linear layer.
 // all linear classes should inherit from this class and implement the forward
 // function.
-class ParallelLinearImpl : public llm::nn::Module {
+class ParallelLinearImpl : public Module {
  public:
   ~ParallelLinearImpl() override = default;
 
@@ -39,10 +39,10 @@ class ParallelLinearImpl : public llm::nn::Module {
   }
 };
 
-class ColumnParallelLinear : public llm::nn::ModuleHolder<ParallelLinearImpl> {
+class ColumnParallelLinear : public ModuleHolder<ParallelLinearImpl> {
  public:
-  using llm::nn::ModuleHolder<ParallelLinearImpl>::ModuleHolder;
-  using Impl __attribute__((__unused__)) = ParallelLinearImpl;
+  using ModuleHolder<ParallelLinearImpl>::ModuleHolder;
+  using Impl [[maybe_unused]] = ParallelLinearImpl;
 
   // construct a rotary positional embedding.
   // chose right implementation based on the args.
@@ -62,10 +62,10 @@ class ColumnParallelLinear : public llm::nn::ModuleHolder<ParallelLinearImpl> {
                        const torch::TensorOptions& options);
 };
 
-class RowParallelLinear : public llm::nn::ModuleHolder<ParallelLinearImpl> {
+class RowParallelLinear : public ModuleHolder<ParallelLinearImpl> {
  public:
-  using llm::nn::ModuleHolder<ParallelLinearImpl>::ModuleHolder;
-  using Impl __attribute__((__unused__)) = ParallelLinearImpl;
+  using ModuleHolder<ParallelLinearImpl>::ModuleHolder;
+  using Impl [[maybe_unused]] = ParallelLinearImpl;
 
   // construct a rotary positional embedding.
   // chose right implementation based on the args.

src/layers/linear_impl.cpp

@@ -28,14 +28,11 @@ ColumnParallelLinearImpl::ColumnParallelLinearImpl(
   // we allocate the transpose.
   weight_ = register_parameter(
       "weight",
-      torch::empty({out_features_per_partition, in_features}, options),
-      /*requires_grad=*/false);
+      torch::empty({out_features_per_partition, in_features}, options));
 
   if (bias) {
-    bias_ =
-        register_parameter("bias",
-                           torch::empty({out_features_per_partition}, options),
-                           /*requires_grad=*/false);
+    bias_ = register_parameter(
+        "bias", torch::empty({out_features_per_partition}, options));
   }
 }
 
@@ -104,13 +101,10 @@ RowParallelLinearImpl::RowParallelLinearImpl(
   // Allocate the transpose since linear performs XA^T.
   weight_ = register_parameter(
       "weight",
-      torch::empty({out_features, in_features_per_partition}, options),
-      /*requires_grad=*/false);
+      torch::empty({out_features, in_features_per_partition}, options));
 
   if (bias) {
-    bias_ = register_parameter("bias",
-                               torch::empty({out_features}, options),
-                               /*requires_grad=*/false);
+    bias_ = register_parameter("bias", torch::empty({out_features}, options));
   }
 }
 

src/layers/normalization.h

@@ -65,7 +65,7 @@ inline torch::Tensor layer_norm(torch::Tensor input,
 // apply layer normalization over a mini-batch of inputs as described in
 // the paper `Layer Normalization`: https://arxiv.org/abs/1607.06450
 // x = ((x - mean(x)) / sqrt(std(x) + eps)) * weight + bias
-class LayerNormImpl : public llm::nn::Module {
+class LayerNormImpl : public Module {
  public:
   // dim: the dim over which the mean and std are calculated separately.
   // eps: a value added to the denominator for numerical stability.
@@ -75,13 +75,11 @@ class LayerNormImpl : public llm::nn::Module {
                 const torch::TensorOptions& options)
       : eps_(eps) {
     normalized_shape_ = {dim};
-    weight_ = register_parameter("weight",
-                                 torch::empty(normalized_shape_, options),
-                                 /*requires_grad=*/false);
+    weight_ =
+        register_parameter("weight", torch::empty(normalized_shape_, options));
     if (bias) {
-      bias_ = register_parameter("bias",
-                                 torch::zeros(normalized_shape_, options),
-                                 /*requires_grad=*/false);
+      bias_ =
+          register_parameter("bias", torch::zeros(normalized_shape_, options));
     }
   }
 
@@ -145,13 +143,11 @@ class LayerNormImpl : public llm::nn::Module {
 LLM_MODULE(LayerNorm);
 
 // Root mean square normalization
-class RMSNormImpl : public llm::nn::Module {
+class RMSNormImpl : public Module {
  public:
   RMSNormImpl(int64_t dim, float eps, const torch::TensorOptions& options)
       : eps_(eps) {
-    weight_ = register_parameter("weight",
-                                 torch::empty({dim}, options),
-                                 /*requires_grad=*/false);
+    weight_ = register_parameter("weight", torch::empty({dim}, options));
   }
 
   torch::Tensor forward(const torch::Tensor& input) {
@@ -195,13 +191,11 @@ class RMSNormImpl : public llm::nn::Module {
 };
 LLM_MODULE(RMSNorm);
 
-class GemmaRMSNormImpl : public llm::nn::Module {
+class GemmaRMSNormImpl : public Module {
  public:
   GemmaRMSNormImpl(int64_t dim, float eps, const torch::TensorOptions& options)
       : eps_(eps) {
-    weight_ = register_parameter("weight",
-                                 torch::empty({dim}, options),
-                                 /*requires_grad=*/false);
+    weight_ = register_parameter("weight", torch::empty({dim}, options));
   }
 
   torch::Tensor forward(const torch::Tensor& input) {
@@ -246,15 +240,13 @@ class GemmaRMSNormImpl : public llm::nn::Module {
 LLM_MODULE(GemmaRMSNorm);
 
 // Root mean square normalization
-class RMSNormResidualImpl : public llm::nn::Module {
+class RMSNormResidualImpl : public Module {
  public:
   RMSNormResidualImpl(int64_t dim,
                       float eps,
                       const torch::TensorOptions& options)
       : eps_(eps) {
-    weight_ = register_parameter("weight",
-                                 torch::empty({dim}, options),
-                                 /*requires_grad=*/false);
+    weight_ = register_parameter("weight", torch::empty({dim}, options));
   }
 
   torch::Tensor forward(const torch::Tensor& input, torch::Tensor& residual) {

src/layers/pos_embedding.h

@@ -41,11 +41,11 @@ class RotaryEmbeddingImpl : public torch::nn::Module {
 
 // RotaryEmbedding is a wrapper class that chooses the right rotary positional
 // embedding implementation based on the args.
-// Similar to TORCH_MODULE(RotaryEmbedding) except of the explicit constructor.
+// Similar to LLM_MODULE(RotaryEmbedding) except of the explicit constructor.
 class RotaryEmbedding : public torch::nn::ModuleHolder<RotaryEmbeddingImpl> {
  public:
   using torch::nn::ModuleHolder<RotaryEmbeddingImpl>::ModuleHolder;
-  using Impl __attribute__((__unused__)) = RotaryEmbeddingImpl;
+  using Impl [[maybe_unused]] = RotaryEmbeddingImpl;
 
   // construct a rotary positional embedding.
   // chose right implementation based on the args.

src/layers/qkv_linear.h

@@ -6,13 +6,15 @@
 #include "fused_linear.h"
 #include "model_loader/state_dict.h"
 #include "model_parallel/parallel_args.h"
+#include "module/module.h"
+#include "module/module_holder.h"
 #include "quantization/quant_args.h"
 
 namespace llm {
 
 // a thin wrapper to handle state_dict loading for QKV with
 // support of MQA/GQA
-class QKVColumnParallelLinearImpl : public llm::nn::Module {
+class QKVColumnParallelLinearImpl : public Module {
  public:
   QKVColumnParallelLinearImpl(int64_t hidden_size,
                               int64_t n_heads,

src/models/aquila.h

@@ -16,12 +16,12 @@
 #include "models/parameters.h"
 #include "module/module.h"
 #include "module/module_holder.h"
-#include "module/modulelist.h"
+#include "module/module_list.h"
 
 // Aquila model compatible with huggingface weights
 namespace llm::hf {
 
-class AquilaMLPImpl : public llm::nn::Module {
+class AquilaMLPImpl : public Module {
  public:
   AquilaMLPImpl(const ModelArgs& args,
                 const QuantArgs& quant_args,
@@ -82,7 +82,7 @@ class AquilaMLPImpl : public llm::nn::Module {
 };
 LLM_MODULE(AquilaMLP);
 
-class AquilaAttentionImpl : public llm::nn::Module {
+class AquilaAttentionImpl : public Module {
  public:
   AquilaAttentionImpl(const ModelArgs& args,
                       const QuantArgs& quant_args,
@@ -169,7 +169,7 @@ class AquilaAttentionImpl : public llm::nn::Module {
 };
 LLM_MODULE(AquilaAttention);
 
-class AquilaDecoderLayerImpl : public llm::nn::Module {
+class AquilaDecoderLayerImpl : public Module {
  public:
   AquilaDecoderLayerImpl(const ModelArgs& args,
                          const QuantArgs& quant_args,
@@ -229,7 +229,7 @@ class AquilaDecoderLayerImpl : public llm::nn::Module {
 };
 LLM_MODULE(AquilaDecoderLayer);
 
-class AquilaModelImpl : public llm::nn::Module {
+class AquilaModelImpl : public Module {
  public:
   AquilaModelImpl(const ModelArgs& args,
                   const QuantArgs& quant_args,
@@ -244,7 +244,7 @@ class AquilaModelImpl : public llm::nn::Module {
     handler_ = AttentionHandler::create_handler_with_rope(
         args, /*interleaved=*/false, options);
 
-    blocks_ = register_module("layers", llm::nn::ModuleList());
+    blocks_ = register_module("layers", ModuleList());
     layers_.reserve(args.n_layers());
     for (int32_t i = 0; i < args.n_layers(); i++) {
       auto block = AquilaDecoderLayer(
@@ -298,15 +298,15 @@ class AquilaModelImpl : public llm::nn::Module {
   // attention handler
   std::unique_ptr<AttentionHandler> handler_{nullptr};
 
-  llm::nn::ModuleList blocks_{nullptr};
+  ModuleList blocks_{nullptr};
   // hold same data but different type as blocks_ to avoid type cast
   std::vector<AquilaDecoderLayer> layers_;
 
   RMSNorm norm_{nullptr};
 };
 LLM_MODULE(AquilaModel);
 
-class AquilaForCausalLMImpl : public llm::nn::Module {
+class AquilaForCausalLMImpl : public Module {
  public:
   AquilaForCausalLMImpl(const ModelArgs& args,
                         const QuantArgs& quant_args,

src/models/baichuan.h

@@ -18,7 +18,7 @@
 #include "models/parameters.h"
 #include "module/module.h"
 #include "module/module_holder.h"
-#include "module/modulelist.h"
+#include "module/module_list.h"
 
 // Baichuan model compatible with huggingface weights
 
@@ -31,7 +31,7 @@ enum class BaichuanType : uint8_t {
   Baichuan2_13B,
 };
 
-class BaichuanMLPImpl : public llm::nn::Module {
+class BaichuanMLPImpl : public Module {
  public:
   BaichuanMLPImpl(const ModelArgs& args,
                   const QuantArgs& quant_args,
@@ -92,7 +92,7 @@ class BaichuanMLPImpl : public llm::nn::Module {
 };
 LLM_MODULE(BaichuanMLP);
 
-class BaichuanAttentionImpl : public llm::nn::Module {
+class BaichuanAttentionImpl : public Module {
  public:
   BaichuanAttentionImpl(const ModelArgs& args,
                         const QuantArgs& quant_args,
@@ -179,7 +179,7 @@ class BaichuanAttentionImpl : public llm::nn::Module {
 };
 LLM_MODULE(BaichuanAttention);
 
-class BaichuanDecoderLayerImpl : public llm::nn::Module {
+class BaichuanDecoderLayerImpl : public Module {
  public:
   BaichuanDecoderLayerImpl(const ModelArgs& args,
                            const QuantArgs& quant_args,
@@ -247,7 +247,7 @@ class BaichuanDecoderLayerImpl : public llm::nn::Module {
 };
 LLM_MODULE(BaichuanDecoderLayer);
 
-class BaichuanModelImpl : public llm::nn::Module {
+class BaichuanModelImpl : public Module {
  public:
   BaichuanModelImpl(const ModelArgs& args,
                     const QuantArgs& quant_args,
@@ -271,7 +271,7 @@ class BaichuanModelImpl : public llm::nn::Module {
           args, alibi_slopes, options);
     }
 
-    blocks_ = register_module("layers", llm::nn::ModuleList());
+    blocks_ = register_module("layers", ModuleList());
     layers_.reserve(args.n_layers());
     for (int32_t i = 0; i < args.n_layers(); i++) {
       auto block = BaichuanDecoderLayer(args,
@@ -366,7 +366,7 @@ class BaichuanModelImpl : public llm::nn::Module {
   std::unique_ptr<AttentionHandler> handler_{nullptr};
 
   // parameter members, must be registered
-  llm::nn::ModuleList blocks_{nullptr};
+  ModuleList blocks_{nullptr};
   // hold same data but different type as blocks_ to avoid type cast
   std::vector<BaichuanDecoderLayer> layers_;
 
@@ -375,7 +375,7 @@ class BaichuanModelImpl : public llm::nn::Module {
 };
 LLM_MODULE(BaichuanModel);
 
-class BaichuanForCausalLMImpl : public llm::nn::Module {
+class BaichuanForCausalLMImpl : public Module {
  public:
   BaichuanForCausalLMImpl(const ModelArgs& args,
                           const QuantArgs& quant_args,