From 4342b650880b2361a2022c1be6f01857c15f341c Mon Sep 17 00:00:00 2001 From: hypan <936187425@qq.com> Date: Mon, 15 Apr 2024 22:53:59 -0400 Subject: [PATCH 01/11] [feat]add mixtral.h --- src/models/huggingface/moe.h | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 src/models/huggingface/moe.h diff --git a/src/models/huggingface/moe.h b/src/models/huggingface/moe.h new file mode 100644 index 00000000..43e5a281 --- /dev/null +++ b/src/models/huggingface/moe.h @@ -0,0 +1,2 @@ +#pragma once +#include From 4e88395cca8cc8f581b40c975de470988c687f72 Mon Sep 17 00:00:00 2001 From: hypan <936187425@qq.com> Date: Thu, 18 Apr 2024 10:37:14 -0400 Subject: [PATCH 02/11] [feat] add some classes in mixtral.h --- src/models/huggingface/mixtral.h | 299 +++++++++++++++++++++++++++++++ src/models/huggingface/moe.h | 2 - src/models/model_registry.cpp | 1 + 3 files changed, 300 insertions(+), 2 deletions(-) create mode 100644 src/models/huggingface/mixtral.h delete mode 100644 src/models/huggingface/moe.h diff --git a/src/models/huggingface/mixtral.h b/src/models/huggingface/mixtral.h new file mode 100644 index 00000000..6850fb21 --- /dev/null +++ b/src/models/huggingface/mixtral.h @@ -0,0 +1,299 @@ +#pragma once +#include + +#include "chat_template/coded_chat_template.h" +#include "layers/activation.h" +#include "layers/attention/attention.h" +#include "layers/attention/handler.h" +#include "layers/embedding.h" +#include "layers/linear.h" +#include "layers/normalization.h" +#include "memory/kv_cache.h" +#include "models/model_args.h" +#include "models/model_registry.h" +#include "models/parameters.h" + +namespace llm::hf { + +class MixtralMoEImpl : public torch::nn::Module { + public: + MixtralMoEImpl(const ModelArgs& args, + const QuantArgs& quant_args, + const ParallelArgs& parallel_args, + const torch::TensorOptions& options) {} + torch::Tensor forward() {} +}; +TORCH_MODULE(MixtralMoE); + +class MixtralAttentionImpl : public torch::nn::Module { + public: + MixtralAttentionImpl(const ModelArgs& args, + const QuantArgs& quant_args, + const ParallelArgs& parallel_args, + const torch::TensorOptions& options, + AttentionHandler* handler) { + const int32_t world_size = parallel_args.world_size(); + const int64_t hidden_size = args.hidden_size(); + const int64_t n_heads = args.n_heads(); + const int64_t head_dim = args.head_dim(); + const int64_t n_kv_heads = args.n_kv_heads().value_or(n_heads); + const int64_t n_local_heads = n_heads / world_size; + const int64_t n_local_kv_heads = n_kv_heads / world_size; + + // size for q, k, v + qkv_sizes_ = {n_local_heads * head_dim, + n_local_kv_heads * head_dim, + n_local_kv_heads * head_dim}; + + // register submodules + qkv_proj_ = register_module( + "qkv_proj", + ColumnParallelLinear(hidden_size, + (n_heads + 2 * n_kv_heads) * head_dim, + /*bias=*/false, + /*gather_output=*/false, + quant_args, + parallel_args, + options)); + + o_proj_ = register_module("o_proj", + RowParallelLinear(n_heads * head_dim, + hidden_size, + /*bias=*/false, + /*input_is_parallelized=*/true, + quant_args, + parallel_args, + options)); + + // initialize attention + atten_ = register_module( + "atten", Attention(n_local_heads, n_local_kv_heads, head_dim, handler)); + } + torch::Tensor forward(torch::Tensor x, + torch::Tensor positions, + KVCache& kv_cache, + const InputParameters& input_params) { + // (num_tokens, dim) x (dim, n_local_heads * head_dim) + // => (num_tokens, n_local_heads * head_dim) + auto qkv = qkv_proj_(x).split(/*split_size=*/qkv_sizes_, /*dim=*/-1); + DCHECK_EQ(qkv.size(), 3); + + // calculate attention, + // output: (num_tokens, n_local_heads*head_dim) + auto output = + atten_(qkv[0], qkv[1], qkv[2], positions, kv_cache, input_params); + return o_proj_(output); + } + + private: + // parameter members, must be registered + ColumnParallelLinear qkv_proj_{nullptr}; + + RowParallelLinear o_proj_{nullptr}; + + // module members without parameters + Attention atten_{nullptr}; + + // size for q, k, v + std::vector qkv_sizes_; +}; +TORCH_MODULE(MixtralAttention); + +class MixtraDecoderLayerImpl : public torch::nn::Module { + public: + MixtraDecoderLayerImpl(const ModelArgs& args, + const QuantArgs& quant_args, + const ParallelArgs& parallel_args, + const torch::TensorOptions& options, + AttentionHandler* handler) { + // register submodules + self_attn_ = register_module( + "self_attn", + MixtralAttention(args, quant_args, parallel_args, options, handler)); + + moe_ = register_module( + "moe", MixtralMoE(args, quant_args, parallel_args, options)); + + input_layernorm_ = register_module( + "input_layernorm", + RMSNormResidual(args.hidden_size(), args.rms_norm_eps(), options)); + + post_attention_layernorm_ = register_module( + "post_attention_layernorm", + RMSNormResidual(args.hidden_size(), args.rms_norm_eps(), options)); + } + + torch::Tensor forward(torch::Tensor x, + torch::Tensor positions, + KVCache& kv_cache, + const InputParameters& input_params, + torch::Tensor& residual) { + auto hidden_states = input_layernorm_(x, residual); + + hidden_states = + self_attn_(hidden_states, positions, kv_cache, input_params); + + // fully connected + hidden_states = post_attention_layernorm_(hidden_states, residual); + + return moe_(hidden_states); + } + + private: + MixtralAttention self_attn_{nullptr}; + + MixtralMoE moe_{nullptr}; + + RMSNormResidual input_layernorm_{nullptr}; + + RMSNormResidual post_attention_layernorm_{nullptr}; +}; +TORCH_MODULE(MixtraDecoderLayer); + +class MixtralModelImpl : public torch::nn::Module { + public: + MixtralModelImpl(const ModelArgs& args, + const QuantArgs& quant_args, + const ParallelArgs& parallel_args, + const torch::TensorOptions& options) { + // TODO: If we have implemented the lora, the vocab_size should be + // processed. + embed_tokens_ = register_module( + "embed_tokens", + ParallelEmbedding( + args.vocab_size(), args.hidden_size(), parallel_args, options)); + + handler_ = AttentionHandler::create_handler_with_rope( + args, /*interleaved=*/false, options); + + blocks_ = register_module("layers", torch::nn::ModuleList()); + layers_.reserve(args.n_layers()); + for (int32_t i = 0; i < args.n_layers(); i++) { + auto block = MixtralDecoderLayer( + args, quant_args, parallel_args, options, handler_.get()); + layers_.push_back(block); + blocks_->push_back(block); + } + + norm_ = register_module( + "norm", + RMSNormResidual(args.hidden_size(), args.rms_norm_eps(), options)); + } + + torch::Tensor forward(torch::Tensor tokens, + torch::Tensor positions, + std::vector& kv_caches, + const InputParameters& input_params) { + hidden_states = embed_tokens(tokens); + + torch::Tensor residual; + for (int32_t i = 0; i < modelArgs_.n_layers(); i++) { + auto& layer = layers_[i]; + h = layer(h, positions, kv_caches[i], input_params, residual); + } + + return norm_(h, residual); + } + + private: + // parameter members, must be registered + // embedding module + ParallelEmbedding embed_tokens_{nullptr}; + + RMSNormResidual norm_{nullptr}; + + // attention handler + std::unique_ptr handler_{nullptr}; + + torch::nn::ModuleList blocks_{nullptr}; + // hold same data but different type as blocks_ to avoid type cast + std::vector layers_{nullptr}; +}; +TORCH_MODULE(MixtralModel); + +class MixtralForCausalLMImpl : public torch::nn::Module { + public: + MixtralForCausalLMImpl(const ModelArgs& args, + const QuantArgs& quant_args, + const ParallelArgs& parallel_args, + const torch::TensorOptions& options) { + model_ = register_module( + "model", MixtralModel(args, quant_args, parallel_args, options)); + + // TODO: we can need the lora flag in the future + lm_head_ = register_module("lm_head", + ColumnParallelLinear(args.hidden_size(), + args.vocab_size(), + /*bias=*/false, + /*gather_output=*/true, + parallel_args, + options)); + } + + torch::Tensor forward(const torch::Tensor& tokens, + const torch::Tensor& positions, + std::vector& kv_caches, + const InputParameters& input_params) { + return model_(tokens, positions, kv_caches, input_params); + } + + torch::Tensor logits(const torch::Tensor& hidden_states, + const torch::Tensor& selected_idxes) { + // select tokens if provided + auto h = hidden_states; + if (seleted_idxes.defined()) { + h = h.index_select(/*dim=*/0, seleted_idxes); + } + return lm_head_(h); + } + + void load_state_dict(const StateDict& state_dict) { + model_->load_state_dict(state_dict.select("model.")); + + lm_head_->load_state_dict(state_dict.select("lm_head.")); + } + + void verify_loaded_weights() const { + model_->verify_loaded_weights("model."); + lm_head_->verify_loaded_weights("lm_head."); + } + + private: + MixtralModel model_{nullptr}; + + ColumnParallelLinear lm_head_{nullptr}; +}; +TORCH_MODULE(MixtralForCausalLM); + +// register the model to make it available +REGISTER_CAUSAL_MODEL(mixtral, MixtralForCasualLM); + +REGISTER_MODEL_ARGS(mixtral, [&] { + // example config from huggingface + // https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/blob/main/config.json + LOAD_ARG_OR(model_type, "model_type", "mixtral"); + LOAD_ARG_OR(bos_token_id, "bos_token_id", 1); + LOAD_ARG_OR(eos_token_id, "eos_token_id", 2); + LOAD_ARG_OR(hidden_size, "hidden_size", 4096); + LOAD_ARG_OR(intermediate_size, "intermediate_size", 14336); + LOAD_ARG_OR(max_position_embeddings, "max_position_embeddings", 32768); + LOAD_ARG_OR(n_heads, "num_attention_heads", 32); + // LOAD_ARG_OR(n_experts_per_tok,"num_experts_per_tok",2) + LOAD_ARG_OR(n_layers, "num_hidden_layers", 32); + LOAD_ARG_OR(n_kv_heads, "num_key_value_heads", 8); + // LOAD_ARG_OR(n_local_experts,"num_local_experts",8); + // LOAD_ARG_OR(out_router_logits,"output_router_logits",false); + LOAD_ARG_OR(rms_norm_eps, "rms_norm_eps", 1e-5); + LOAD_ARG_OR(rope_theta, "rope_theta", 10000.0f); + // LOAD_ARG_OR(router_aux_loss_coef,"router_aux_loss_coef",0.02); + LOAD_ARG_OR(dtype, "torch_dtype", "bfloat16"); + LOAD_ARG_OR(vocab_size, "vocab_size", 32000); + + LOAD_ARG_OR(hidden_act, "hidden_activation", "silu"); + + LOAD_ARG_OR_FUNC(head_dim, "head_dim", [&] { + return args->hidden_size() / args->n_heads(); + }); +}); + +} // namespace llm::hf \ No newline at end of file diff --git a/src/models/huggingface/moe.h b/src/models/huggingface/moe.h deleted file mode 100644 index 43e5a281..00000000 --- a/src/models/huggingface/moe.h +++ /dev/null @@ -1,2 +0,0 @@ -#pragma once -#include diff --git a/src/models/model_registry.cpp b/src/models/model_registry.cpp index 6f09d5b0..67c20a78 100644 --- a/src/models/model_registry.cpp +++ b/src/models/model_registry.cpp @@ -14,6 +14,7 @@ #include "huggingface/internlm.h" // IWYU pragma: keep #include "huggingface/llama.h" // IWYU pragma: keep #include "huggingface/mistral.h" // IWYU pragma: keep +#include "huggingface/mixtral.h" // IWYU pragma: keep #include "huggingface/mpt.h" // IWYU pragma: keep #include "huggingface/phi.h" // IWYU pragma: keep #include "huggingface/qwen.h" // IWYU pragma: keep From 71727861d7e6ab2b1fff7e2bbba074d991a33aea Mon Sep 17 00:00:00 2001 From: hypan <936187425@qq.com> Date: Fri, 19 Apr 2024 01:48:47 -0400 Subject: [PATCH 03/11] [feat] construct modules from mixtral model except Mixtral moe impl --- src/models/huggingface/mixtral.h | 87 ++++++++++++++++++++++++++------ src/models/model_args.h | 14 +++++ 2 files changed, 85 insertions(+), 16 deletions(-) diff --git a/src/models/huggingface/mixtral.h b/src/models/huggingface/mixtral.h index 6850fb21..1e09adbe 100644 --- a/src/models/huggingface/mixtral.h +++ b/src/models/huggingface/mixtral.h @@ -21,7 +21,11 @@ class MixtralMoEImpl : public torch::nn::Module { const QuantArgs& quant_args, const ParallelArgs& parallel_args, const torch::TensorOptions& options) {} - torch::Tensor forward() {} + torch::Tensor forward(torch::Tensor x) { return torch::Tensor(); } + + void load_state_dict(const StateDict& state_dict) {} + + void verify_loaded_weights(const std::string& prefix) const {} }; TORCH_MODULE(MixtralMoE); @@ -85,6 +89,17 @@ class MixtralAttentionImpl : public torch::nn::Module { return o_proj_(output); } + void load_state_dict(const StateDict& state_dict) { + // call each submodule's load_state_dict function + qkv_proj_->load_state_dict(state_dict, {"q_proj.", "k_proj.", "v_proj."}); + o_proj_->load_state_dict(state_dict.select("o_proj.")); + } + + void verify_loaded_weights(const std::string& prefix) const { + qkv_proj_->verify_loaded_weights(prefix + "[q_proj,k_proj,v_proj]."); + o_proj_->verify_loaded_weights(prefix + "o_proj."); + } + private: // parameter members, must be registered ColumnParallelLinear qkv_proj_{nullptr}; @@ -99,13 +114,13 @@ class MixtralAttentionImpl : public torch::nn::Module { }; TORCH_MODULE(MixtralAttention); -class MixtraDecoderLayerImpl : public torch::nn::Module { +class MixtralDecoderLayerImpl : public torch::nn::Module { public: - MixtraDecoderLayerImpl(const ModelArgs& args, - const QuantArgs& quant_args, - const ParallelArgs& parallel_args, - const torch::TensorOptions& options, - AttentionHandler* handler) { + MixtralDecoderLayerImpl(const ModelArgs& args, + const QuantArgs& quant_args, + const ParallelArgs& parallel_args, + const torch::TensorOptions& options, + AttentionHandler* handler) { // register submodules self_attn_ = register_module( "self_attn", @@ -139,6 +154,22 @@ class MixtraDecoderLayerImpl : public torch::nn::Module { return moe_(hidden_states); } + void load_state_dict(const StateDict& state_dict) { + self_attn_->load_state_dict(state_dict.select("self_attn.")); + input_layernorm_->load_state_dict(state_dict.select("input_layernorm.")); + post_attention_layernorm_->load_state_dict( + state_dict.select("post_attention_layernorm.")); + moe_->load_state_dict(state_dict.select("block_sparse_moe.")); + } + + void verify_loaded_weights(const std::string& prefix) const { + self_attn_->verify_loaded_weights(prefix + "self_attn."); + input_layernorm_->verify_loaded_weights(prefix + "input_layernorm."); + post_attention_layernorm_->verify_loaded_weights( + prefix + "post_attention_layernorm."); + moe_->verify_loaded_weights(prefix + "block_sparse_moe."); + } + private: MixtralAttention self_attn_{nullptr}; @@ -148,7 +179,7 @@ class MixtraDecoderLayerImpl : public torch::nn::Module { RMSNormResidual post_attention_layernorm_{nullptr}; }; -TORCH_MODULE(MixtraDecoderLayer); +TORCH_MODULE(MixtralDecoderLayer); class MixtralModelImpl : public torch::nn::Module { public: @@ -156,6 +187,8 @@ class MixtralModelImpl : public torch::nn::Module { const QuantArgs& quant_args, const ParallelArgs& parallel_args, const torch::TensorOptions& options) { + modelArgs_ = args; + // TODO: If we have implemented the lora, the vocab_size should be // processed. embed_tokens_ = register_module( @@ -184,7 +217,7 @@ class MixtralModelImpl : public torch::nn::Module { torch::Tensor positions, std::vector& kv_caches, const InputParameters& input_params) { - hidden_states = embed_tokens(tokens); + auto h = embed_tokens_(tokens); torch::Tensor residual; for (int32_t i = 0; i < modelArgs_.n_layers(); i++) { @@ -195,7 +228,29 @@ class MixtralModelImpl : public torch::nn::Module { return norm_(h, residual); } + void load_state_dict(const StateDict& state_dict) { + embed_tokens_->load_state_dict(state_dict.select("embed_tokens.weight")); + + for (int i = 0; i < layers_.size(); i++) { + layers_[i]->load_state_dict( + state_dict.select("layers." + std::to_string(i) + ".")); + } + norm_->load_state_dict(state_dict.select("norm.weight")); + } + + void verify_loaded_weights(const std::string& prefix) const { + embed_tokens_->verify_loaded_weights(prefix + "embed_tokens.weight"); + + for (int i = 0; i < layers_.size(); i++) { + layers_[i]->verify_loaded_weights(prefix + "layers." + std::to_string(i) + + "."); + } + + norm_->verify_loaded_weights(prefix + "norm.weight"); + } + private: + ModelArgs modelArgs_; // parameter members, must be registered // embedding module ParallelEmbedding embed_tokens_{nullptr}; @@ -241,8 +296,8 @@ class MixtralForCausalLMImpl : public torch::nn::Module { const torch::Tensor& selected_idxes) { // select tokens if provided auto h = hidden_states; - if (seleted_idxes.defined()) { - h = h.index_select(/*dim=*/0, seleted_idxes); + if (selected_idxes.defined()) { + h = h.index_select(/*dim=*/0, selected_idxes); } return lm_head_(h); } @@ -266,7 +321,7 @@ class MixtralForCausalLMImpl : public torch::nn::Module { TORCH_MODULE(MixtralForCausalLM); // register the model to make it available -REGISTER_CAUSAL_MODEL(mixtral, MixtralForCasualLM); +REGISTER_CAUSAL_MODEL(mixtral, MixtralForCausalLM); REGISTER_MODEL_ARGS(mixtral, [&] { // example config from huggingface @@ -278,14 +333,14 @@ REGISTER_MODEL_ARGS(mixtral, [&] { LOAD_ARG_OR(intermediate_size, "intermediate_size", 14336); LOAD_ARG_OR(max_position_embeddings, "max_position_embeddings", 32768); LOAD_ARG_OR(n_heads, "num_attention_heads", 32); - // LOAD_ARG_OR(n_experts_per_tok,"num_experts_per_tok",2) + LOAD_ARG_OR(n_experts_per_tok, "num_experts_per_tok", 2); LOAD_ARG_OR(n_layers, "num_hidden_layers", 32); LOAD_ARG_OR(n_kv_heads, "num_key_value_heads", 8); - // LOAD_ARG_OR(n_local_experts,"num_local_experts",8); - // LOAD_ARG_OR(out_router_logits,"output_router_logits",false); + LOAD_ARG_OR(n_local_experts, "num_local_experts", 8); + LOAD_ARG_OR(out_router_logits, "output_router_logits", false); LOAD_ARG_OR(rms_norm_eps, "rms_norm_eps", 1e-5); LOAD_ARG_OR(rope_theta, "rope_theta", 10000.0f); - // LOAD_ARG_OR(router_aux_loss_coef,"router_aux_loss_coef",0.02); + LOAD_ARG_OR(router_aux_loss_coef, "router_aux_loss_coef", 0.02); LOAD_ARG_OR(dtype, "torch_dtype", "bfloat16"); LOAD_ARG_OR(vocab_size, "vocab_size", 32000); diff --git a/src/models/model_args.h b/src/models/model_args.h index ddbd45e5..5cdb698e 100644 --- a/src/models/model_args.h +++ b/src/models/model_args.h @@ -103,6 +103,16 @@ struct ModelArgs { // Stop token ids for decoding. DEFINE_ARG(std::unordered_set, stop_token_ids); + + // The number of experts per tok in MoE + DEFINE_ARG(int, n_experts_per_tok); + + // The number of experts in MoE + DEFINE_ARG(int, n_local_experts); + + DEFINE_ARG(bool, out_router_logits); + + DEFINE_ARG(float, router_aux_loss_coef); }; inline std::ostream& operator<<(std::ostream& os, const ModelArgs& args) { @@ -134,6 +144,10 @@ inline std::ostream& operator<<(std::ostream& os, const ModelArgs& args) { os << ", linear_bias: " << args.linear_bias(); os << ", qkv_bias: " << args.qkv_bias(); os << ", residual_post_layernorm: " << args.residual_post_layernorm(); + os << ", n_experts_per_tok: " << args.n_experts_per_tok(); + os << ", n_local_experts: " << args.n_local_experts(); + os << ", out_router_logits: " << args.out_router_logits(); + os << ", router_aux_loss_coef: " << args.router_aux_loss_coef(); os << "]"; return os; } From c13ebd30b04eca44da3261d46bd64f0cdf0def53 Mon Sep 17 00:00:00 2001 From: hypan <936187425@qq.com> Date: Fri, 26 Apr 2024 05:13:12 -0400 Subject: [PATCH 04/11] [feat] add the replicated_linear --- src/layers/linear_impl.cpp | 40 +++++++++++++ src/layers/linear_impl.h | 33 ++++++++++ src/models/huggingface/mixtral.h | 100 ++++++++++++++++++++++++++++++- src/models/model_args.h | 4 +- 4 files changed, 173 insertions(+), 4 deletions(-) diff --git a/src/layers/linear_impl.cpp b/src/layers/linear_impl.cpp index 822c65a3..5f861faf 100644 --- a/src/layers/linear_impl.cpp +++ b/src/layers/linear_impl.cpp @@ -257,4 +257,44 @@ void RowParallelLinearImpl::load_state_dict(const StateDict& state_dict) { } } +ReplicatedLinearImpl::ReplicatedLinearImpl(int64_t in_features, + int64_t out_features, + bool bias, + bool skip_bias_add, + const QuantArgs& quant_args, + const torch::TensorOptions& options) + : skip_bias_add(skip_bias_add) { + weight_ = register_parameter( + "weight", torch::empty({out_features, in_features}, options)); + if (bias) { + bias_ = register_parameter("bias", torch::empty({out_features}, options)); + } +} + +torch::Tensor ReplicatedLinearImpl::forward(torch::Tensor input, + torch::Tensor& output_bias) { + torch::Tensor bias = (!skip_bias_add) ? bias_ : torch::Tensor(); + namespace F = torch::nn::functional; + auto output = F::linear(input, weight_, bias); + output_bias = (skip_bias_add) ? bias_ : torch::Tensor(); +} + +// load the weight from the checkpoint +void ReplicatedLinearImpl::load_state_dict(const StateDict& state_dict) { + if (weight_.defined()) { + const auto weight = state_dict.get_tensor("weight"); + CHECK_EQ(weight_.sizes(), weight.sizes()) + << "weight size mismatch for" << name(); + weight_.copy_(weight); + weight_is_loaded_ = true; + } + if (bias_.defined()) { + const auto bias = state_dict.get_tensor("bias"); + CHECK_EQ(bias.sizes(), bias_.sizes()) + << "weight size mismatch for" << name(); + bias_.copy_(bias); + bias_is_loaded_ = true; + } +} + } // namespace llm diff --git a/src/layers/linear_impl.h b/src/layers/linear_impl.h index 50cd7990..11d35cbf 100644 --- a/src/layers/linear_impl.h +++ b/src/layers/linear_impl.h @@ -135,4 +135,37 @@ class RowParallelLinearImpl : public ParallelLinearImpl { // parallel args ParallelArgs parallel_args_; }; + +// Replicated linear layer for moe model +class ReplicatedLinearImpl : public torch::nn::Module { + public: + ReplicatedLinearImpl(int64_t in_features, + int64_t out_features, + bool bias, + bool skip_bias_add, + const QuantArgs& quant_args, + const torch::TensorOptions& options); + torch::Tensor forward(torch::Tensor input, torch::Tensor& output_bias); + void load_state_dict(const StateDict& state_dict); + + void verify_loaded_weights(const std::string& prefix = "") { + CHECK(weight_is_loaded_) + << "weight is not loaded for " << prefix + "weight"; + CHECK(!bias_.defined() || bias_is_loaded_) + << "bias is not loaded for " << prefix + "bias"; + }; + + private: + // parameter members, must be registered + // we allocate the transpose since linear performs XA^T. + // A^T: [out_features, in_features_per_partition] + torch::Tensor weight_{nullptr}; + torch::Tensor bias_{nullptr}; + + bool skip_bias_add = false; + // whether the weight is loaded + bool weight_is_loaded_ = false; + bool bias_is_loaded_ = false; +}; +TORCH_MODULE(ReplicatedLinear); } // namespace llm diff --git a/src/models/huggingface/mixtral.h b/src/models/huggingface/mixtral.h index 1e09adbe..c61a2076 100644 --- a/src/models/huggingface/mixtral.h +++ b/src/models/huggingface/mixtral.h @@ -7,6 +7,7 @@ #include "layers/attention/handler.h" #include "layers/embedding.h" #include "layers/linear.h" +#include "layers/linear_impl.h" #include "layers/normalization.h" #include "memory/kv_cache.h" #include "models/model_args.h" @@ -20,12 +21,107 @@ class MixtralMoEImpl : public torch::nn::Module { MixtralMoEImpl(const ModelArgs& args, const QuantArgs& quant_args, const ParallelArgs& parallel_args, - const torch::TensorOptions& options) {} - torch::Tensor forward(torch::Tensor x) { return torch::Tensor(); } + const torch::TensorOptions& options) { + args_ = args; + gate_ = register_module("gate", + ReplicatedLinear(args_.hidden_size(), + args_.n_local_experts(), + /*bias*/ false, + /*skip_bias_add*/ false, + quant_args, + options)); + w1_ = register_parameter( + "weight", torch::empty({}, options), /*required_grad*/ false); + w2_ = register_parameter( + "weight", torch::empty({}, options), /*required_grad*/ false); + } + + torch::Tensor forward(torch::Tensor hidden_states) { + auto sizes = hidden_states.sizes(); + auto num_token = sizes[0]; + auto hidden_size = sizes[1]; + hidden_states = hidden_states.view({-1, hidden_size}); + torch::Tensor out_bias; + auto router_logits = gate_(hidden_states, out_bias); + // router_logits: (num_tokens, n_experts) + auto final_hidden_states = fused_moe(hidden_states, + w1_, + w2_, + router_logits, + args_.n_experts_per_tok(), + true, + true); + /* + if self.tp_size > 1: # 张量并行的做法 + final_hidden_states = tensor_model_parallel_all_reduce( + final_hidden_states) + */ + return final_hidden_states.view({num_token, hidden_size}); + } + + // TODO: + /* + This function computes a Mixture of Experts (MoE) layer using two sets of + weights, w1 and w2, and top-k gating mechanis + Parameters: + - hidden_states (torch.Tensor): The input tensor to the MoE layer. + [hidden_size,hidden_dim] + - w1 (torch.Tensor): The first set of expert weights. + [n_expert,,hidden_size] + - w2 (torch.Tensor): The second set of expert weights. + - gating_output (torch.Tensor): The output of the gating operation + (before softmax). [n_tokens,n_expert] + - topk (int): The number of top-k experts to select. + - renormalize (bool): If True, renormalize the top-k weights to sum to 1. + - inplace (bool): If True, perform the operation in-place. + Defaults to False. + */ + torch::Tensor fused_moe(torch::Tensor hidden_states, + torch::Tensor w1, + torch::Tensor w2, + torch::Tensor gating_output, + int topk, + bool renormalize, + bool inplace) { + // match the number of tokens + DCHECK_EQ(hidden_states.sizes()[0], gating_output.sizes()[0]); + // match the number of hidden_size + DCHECK_EQ(hidden_states.sizes()[1], w1.sizes()[2]); + // match the number of experts + DCHECK_EQ(gating_output.sizes()[1], w1.sizes()[0]); + // be sure that hidden_states/w1/w2 are contiguous + DCHECK_EQ(hidden_states.is_contiguous(), true); + DCHECK_EQ(w1.is_contiguous(), true); + DCHECK_EQ(w2.is_contiguous(), true); + + auto M = hidden_states.sizes()[0]; + auto E = w1.sizes()[0]; + auto N = w1.sizes()[1]; + + // topk_output = softmax(topk(gating_output)) + // gating_output:[num_tokens,n_experts] + auto router_weight = torch::softmax(gating_output, -1, torch::kFloat32); + // top_weights,topk_indices: [num_tokens,topk] + auto [topk_weights, topk_indices] = torch::topk(router_weight, topk, -1); + + if (renormalize){ + topk_weights = topk_weights / topk_weights.sum(-1,true); + } + // TODO aggregation layer + + return torch::Tensor(); + } void load_state_dict(const StateDict& state_dict) {} void verify_loaded_weights(const std::string& prefix) const {} + + private: + ModelArgs args_; + torch::Tensor w1_; + torch::Tensor w2_; + + ReplicatedLinear gate_{nullptr}; }; TORCH_MODULE(MixtralMoE); diff --git a/src/models/model_args.h b/src/models/model_args.h index 5cdb698e..3b873093 100644 --- a/src/models/model_args.h +++ b/src/models/model_args.h @@ -104,11 +104,11 @@ struct ModelArgs { // Stop token ids for decoding. DEFINE_ARG(std::unordered_set, stop_token_ids); - // The number of experts per tok in MoE + // The number of experts per tok in MoE used in topk DEFINE_ARG(int, n_experts_per_tok); // The number of experts in MoE - DEFINE_ARG(int, n_local_experts); + DEFINE_ARG(int64_t, n_local_experts); DEFINE_ARG(bool, out_router_logits); From 13da555645e9740a7f4615004617536fdb3f0d0f Mon Sep 17 00:00:00 2001 From: hypan <936187425@qq.com> Date: Fri, 26 Apr 2024 05:17:32 -0400 Subject: [PATCH 05/11] [format] --- src/models/huggingface/mixtral.h | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/models/huggingface/mixtral.h b/src/models/huggingface/mixtral.h index c61a2076..c6a47042 100644 --- a/src/models/huggingface/mixtral.h +++ b/src/models/huggingface/mixtral.h @@ -102,13 +102,13 @@ class MixtralMoEImpl : public torch::nn::Module { // gating_output:[num_tokens,n_experts] auto router_weight = torch::softmax(gating_output, -1, torch::kFloat32); // top_weights,topk_indices: [num_tokens,topk] - auto [topk_weights, topk_indices] = torch::topk(router_weight, topk, -1); - - if (renormalize){ - topk_weights = topk_weights / topk_weights.sum(-1,true); - } + auto [topk_weights, topk_indices] = torch::topk(router_weight, topk, -1); + + if (renormalize) { + topk_weights = topk_weights / topk_weights.sum(-1, true); + } // TODO aggregation layer - + return torch::Tensor(); } From bdb529cf72cf556d9765234a6d7d36c211e73dfd Mon Sep 17 00:00:00 2001 From: hypan <936187425@qq.com> Date: Mon, 13 May 2024 08:39:03 -0400 Subject: [PATCH 06/11] [refactor] add the wrapper of fused_moe_layer add the fused_moe_kernel --- gongfeng-copilot-vscode-latest (2).vsix | Bin 0 -> 3725638 bytes src/kernels/fused_moe_kernels.cu | 21 +++++ src/kernels/fused_moe_kernels.h | 14 ++++ src/layers/fused_moe.cpp | 58 ++++++++++++++ src/layers/fused_moe.h | 38 +++++++++ src/models/huggingface/mixtral.h | 98 ++++++------------------ 6 files changed, 155 insertions(+), 74 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