v0.1.4 ready to release

Co-authored-by: Zhekai Zhang <sxtyzhangzk@gmail.com>
Co-authored-by: Muyang Li <lmxyy1999@foxmail.com>
Co-authored-by: Yujun Lin <16437040+synxlin@users.noreply.github.com>

src/FluxModel.cpp

@@ -607,14 +607,22 @@ std::tuple<Tensor, Tensor> JointTransformerBlock::forward(Tensor hidden_states,
     return { hidden_states, encoder_hidden_states };
 }
 
-FluxModel::FluxModel(bool use_fp4, Tensor::ScalarType dtype, Device device) {
+FluxModel::FluxModel(bool use_fp4, bool offload, Tensor::ScalarType dtype, Device device) : offload(offload) {
     for (int i = 0; i < 19; i++) {
         transformer_blocks.push_back(std::make_unique<JointTransformerBlock>(3072, 24, 3072, false, use_fp4, dtype, device));
         registerChildren(*transformer_blocks.back(), format("transformer_blocks.{}", i));
+        if (offload && i > 0) { // don't offload first block
+            transformer_blocks.back()->setLazyLoad(true);
+            transformer_blocks.back()->releaseLazyParams();
+        }
     }
     for (int i = 0; i < 38; i++) {
         single_transformer_blocks.push_back(std::make_unique<FluxSingleTransformerBlock>(3072, 24, 3072, 4, use_fp4, dtype, Device::cuda()));
         registerChildren(*single_transformer_blocks.back(), format("single_transformer_blocks.{}", i));
+        if (offload) {
+            single_transformer_blocks.back()->setLazyLoad(true);
+            single_transformer_blocks.back()->releaseLazyParams();
+        }
     }
 }
 
@@ -626,22 +634,51 @@ Tensor FluxModel::forward(Tensor hidden_states, Tensor encoder_hidden_states, Te
     const int txt_tokens = encoder_hidden_states.shape[1];
     const int img_tokens = hidden_states.shape[1];
 
-    for (auto &&block : transformer_blocks) {
-        std::tie(hidden_states, encoder_hidden_states) = block->forward(hidden_states, encoder_hidden_states, temb, rotary_emb_img, rotary_emb_context, 0.0f);
-    }
+    const int numLayers = transformer_blocks.size() + single_transformer_blocks.size();
 
-    // txt first, same as diffusers
-    Tensor concat = Tensor::allocate({batch_size, txt_tokens + img_tokens, 3072}, dtype, device);
-    for (int i = 0; i < batch_size; i++) {
-        concat.slice(0, i, i + 1).slice(1, 0, txt_tokens).copy_(encoder_hidden_states);
-        concat.slice(0, i, i + 1).slice(1, txt_tokens, txt_tokens + img_tokens).copy_(hidden_states);
-    }
-    hidden_states = concat;
-    encoder_hidden_states = {};
+    Tensor concat;
 
-    for (auto &&block : single_transformer_blocks) {
-        hidden_states = block->forward(hidden_states, temb, rotary_emb_single);
-    }
+    auto compute = [&](int layer) {
+        if (size_t(layer) < transformer_blocks.size()) {
+            auto &block = transformer_blocks.at(layer);
+            std::tie(hidden_states, encoder_hidden_states) = block->forward(hidden_states, encoder_hidden_states, temb, rotary_emb_img, rotary_emb_context, 0.0f);
+        } else {
+            if (size_t(layer) == transformer_blocks.size()) {
+                // txt first, same as diffusers
+                concat = Tensor::allocate({batch_size, txt_tokens + img_tokens, 3072}, dtype, device);
+                for (int i = 0; i < batch_size; i++) {
+                    concat.slice(0, i, i + 1).slice(1, 0, txt_tokens).copy_(encoder_hidden_states);
+                    concat.slice(0, i, i + 1).slice(1, txt_tokens, txt_tokens + img_tokens).copy_(hidden_states);
+                }
+                hidden_states = concat;
+                encoder_hidden_states = {};
+            }
+
+            auto &block = single_transformer_blocks.at(layer - transformer_blocks.size());
+            hidden_states = block->forward(hidden_states, temb, rotary_emb_single);
+        }
+    };
+    auto load = [&](int layer) {
+        if (size_t(layer) < transformer_blocks.size()) {
+            auto &block = transformer_blocks.at(layer);
+            block->loadLazyParams();
+        } else {
+            auto &block = single_transformer_blocks.at(layer - transformer_blocks.size());
+            block->loadLazyParams();
+        }
+    };
+    auto unload = [&](int layer) {
+        if (size_t(layer) < transformer_blocks.size()) {
+            auto &block = transformer_blocks.at(layer);
+            block->releaseLazyParams();
+        } else {
+            auto &block = single_transformer_blocks.at(layer - transformer_blocks.size());
+            block->releaseLazyParams();
+        }
+    };
+
+    LayerOffloadHelper helper(this->offload, numLayers, compute, load, unload);
+    helper.run();
 
     return hidden_states;
 }
\ No newline at end of file

src/FluxModel.h

@@ -128,10 +128,13 @@ private:
 
 class FluxModel : public Module {
 public:
-    FluxModel(bool use_fp4, Tensor::ScalarType dtype, Device device);
+    FluxModel(bool use_fp4, bool offload, Tensor::ScalarType dtype, Device device);
     Tensor forward(Tensor hidden_states, Tensor encoder_hidden_states, Tensor temb, Tensor rotary_emb_img, Tensor rotary_emb_context, Tensor rotary_emb_single);
 
 public:
     std::vector<std::unique_ptr<JointTransformerBlock>> transformer_blocks;
     std::vector<std::unique_ptr<FluxSingleTransformerBlock>> single_transformer_blocks;
+
+private:
+    bool offload;
 };
\ No newline at end of file

src/Linear.cpp

@@ -16,7 +16,7 @@ GEMM_F16::GEMM_F16(int in_features, int out_features, bool use_bias, Tensor::Sca
     this->bias = use_bias ? Tensor::allocate({out_features}, dtype, device) : Tensor{};
 
     registerParams
-        (weight, "weight")
+        (weight, "weight", ParamFlags::LazyLoad)
         (bias, "bias")
     ;
 }
@@ -27,7 +27,7 @@ Tensor GEMM_F16::forward(Tensor x) {
 }
 
 GEMV_AWQ::GEMV_AWQ(int in_features, int out_features, bool use_bias, Tensor::ScalarType dtype, Device device) : 
-    in_features(in_features), out_features(out_features), group_size(64), lora_rank(0), lora_scale(1.0f)
+    in_features(in_features), out_features(out_features), group_size(64), lora_rank(0), lora_scale(1.0f), device(device)
 {
     this->qweight = Tensor::allocate({out_features / 4, ceilDiv(in_features, 8) * 4}, Tensor::INT32, device);
     this->wscales = Tensor::allocate({ceilDiv(in_features, group_size), out_features}, dtype, device);
@@ -39,7 +39,7 @@ GEMV_AWQ::GEMV_AWQ(int in_features, int out_features, bool use_bias, Tensor::Sca
     this->lora_up = Tensor::allocate({out_features, lora_rank}, dtype, device, true);
 
     registerParams
-        (qweight, "qweight")
+        (qweight, "qweight", ParamFlags::LazyLoad)
         (wscales, "wscales")
         (wzeros, "wzeros")
         (bias, "bias")
@@ -52,7 +52,7 @@ void GEMV_AWQ::loadParam(std::string key, Tensor &dst, Tensor src) {
     if (key == "lora_down" || key == "lora_up") {
         assert(src.ndims() == 2);
         if (dst.shape.dataExtent != src.shape.dataExtent) {
-            dst = src.copy(this->qweight.device());
+            dst = src.copy(this->device);
             if (key == "lora_down") {
                 const int new_rank = dst.shape[0];
                 this->lora_rank = new_rank;
@@ -100,7 +100,7 @@ GEMM_W4A4::GEMM_W4A4(int in_features, int out_features, bool bias, bool use_fp4,
     in_features(in_features), out_features(out_features), 
     in_features_pad(ceilDiv(in_features, 128) * 128), out_features_pad(ceilDiv(out_features, 128) * 128),
     use_fp4(use_fp4),
-    lora_rank(0), dtype(dtype)
+    lora_rank(0), dtype(dtype), device(device)
 {
     this->qweight = Tensor::allocate({out_features_pad, in_features_pad / 2}, Tensor::INT8, device, true);
     if (use_fp4) {
@@ -124,7 +124,7 @@ GEMM_W4A4::GEMM_W4A4(int in_features, int out_features, bool bias, bool use_fp4,
     this->wcscales = Tensor::allocate({0}, dtype, device, true);
 
     registerParams
-        (qweight, "qweight")
+        (qweight, "qweight", ParamFlags::LazyLoad)
         (wscales, "wscales")
         (this->bias, "bias")
         (lora_down, "lora_down", ParamFlags::Optional)
@@ -143,7 +143,7 @@ void GEMM_W4A4::loadParam(std::string key, Tensor &dst, Tensor src) {
     if (key == "lora_down" || key == "lora_up") {
         assert(src.ndims() == 2);
         if (dst.shape.dataExtent != src.shape.dataExtent) {
-            dst = src.copy(this->qweight.device());
+            dst = src.copy(this->device);
             this->lora_rank = dst.shape[1];
             this->lora_scales.resize(ceilDiv(this->lora_rank, 16), 1.0f);
         } else {
@@ -152,7 +152,7 @@ void GEMM_W4A4::loadParam(std::string key, Tensor &dst, Tensor src) {
     } else if (key == "wcscales") {
         assert(src.ndims() == 1);
         assert(src.shape[0] == out_features_pad);
-        dst = src.copy(this->qweight.device());
+        dst = src.copy(this->device);
     } else if (key == "wtscale") {
         assert(src.numel() == 1);
         if (src.dtype() == Tensor::BF16) {
@@ -242,15 +242,15 @@ std::variant<Tensor, GEMM_W4A4::QuantizedActivation> GEMM_W4A4::forward_quant(Qu
         // shape[-1] = out_features;
         auto shape = TensorShape(qact.actShape.dataExtent);
         shape[-1] = out_features;
-        out = Tensor::allocate(shape, dtype, qweight.device());
+        out = Tensor::allocate(shape, dtype, device);
     } else {
-        qout.act = Tensor::allocate({M, out_features_pad / 2}, Tensor::INT8, qweight.device());
+        qout.act = Tensor::allocate({M, out_features_pad / 2}, Tensor::INT8, device);
         if (use_fp4) {
-            qout.ascales = Tensor::allocate({out_features_pad / 16, M}, Tensor::FP8_E4M3, qweight.device());
+            qout.ascales = Tensor::allocate({out_features_pad / 16, M}, Tensor::FP8_E4M3, device);
         } else {
-            qout.ascales = Tensor::allocate({out_features_pad / 64, M}, dtype, qweight.device());
+            qout.ascales = Tensor::allocate({out_features_pad / 64, M}, dtype, device);
         }
-        qout.lora_act = Tensor::allocate({M, lora_rank}, Tensor::FP32, qweight.device());
+        qout.lora_act = Tensor::allocate({M, lora_rank}, Tensor::FP32, device);
         qout.is_unsigned = !use_fp4;
         qout.actShape = qact.actShape;
 
@@ -363,13 +363,13 @@ GEMM_W4A4::QuantizedActivation GEMM_W4A4::quantize(Tensor x, bool fuse_glu) {
     // shape[-1] = in_features / 2;
 
     QuantizedActivation qact;
-    qact.act = Tensor::allocate({M, in_features_pad / 2}, Tensor::INT8, qweight.device());
+    qact.act = Tensor::allocate({M, in_features_pad / 2}, Tensor::INT8, device);
     if (use_fp4) {
-        qact.ascales = Tensor::allocate({in_features_pad / 16, M}, Tensor::FP8_E4M3, qweight.device());
+        qact.ascales = Tensor::allocate({in_features_pad / 16, M}, Tensor::FP8_E4M3, device);
     } else {
-        qact.ascales = Tensor::allocate({in_features_pad / 64, M}, dtype, qweight.device());
+        qact.ascales = Tensor::allocate({in_features_pad / 64, M}, dtype, device);
     }
-    qact.lora_act = Tensor::allocate({M, lora_rank}, Tensor::FP32, qweight.device());
+    qact.lora_act = Tensor::allocate({M, lora_rank}, Tensor::FP32, device);
     qact.is_unsigned = false;
     qact.actShape = x.shape.dataExtent;
 
@@ -420,7 +420,7 @@ GEMM_W8A8::GEMM_W8A8(int in_features, int out_features, bool bias, Tensor::Scala
     this->bias = bias ? Tensor::allocate({out_features}, dtype, device, true) : Tensor{};
 
     registerParams
-        (qweight, "qweight")
+        (qweight, "qweight", ParamFlags::LazyLoad)
         (wscales, "wscales")
         (this->bias, "bias")
     ;

src/Linear.h

@@ -36,6 +36,7 @@ public:
     int lora_rank;
     float lora_scale;
 
+    const Device device;
 public:
     Tensor qweight;
     Tensor wscales;
@@ -86,6 +87,7 @@ public:
     std::vector<float> lora_scales; // every 16 ranks share a scale
 
     const Tensor::ScalarType dtype;
+    const Device device;
 
 protected:
     virtual void loadParam(std::string key, Tensor &dst, Tensor src) override;

src/Serialization.h

@@ -44,6 +44,7 @@ private:
     class MMapImpl;
     class MMapImplMio;
     class MMapImplPrivate;
+    class MMapImplRead;
 
     struct TensorInfo {
         TensorShape shape;
@@ -54,4 +55,6 @@ private:
     };
     std::map<std::string, TensorInfo> tensors;
     std::unique_ptr<MMapImpl> mapped;
+
+    bool hostRegistered, memoryPinned;
 };
\ No newline at end of file

src/Tensor.h

@@ -85,14 +85,15 @@ public:
         if (size == 0) {
             this->ptr = nullptr;
         }
-        checkCUDA(cudaMallocAsync(&this->ptr, size, 0));    // use default stream to sync with all other streams
+        // TODO: buffer used in multiple streams?
+        checkCUDA(cudaMallocAsync(&this->ptr, size, getCurrentCUDAStream()));
     }
     virtual ~BufferCUDA() {
         if (this->size == 0) {
             assert(!this->ptr);
             return;
         }
-        checkCUDA(cudaFreeAsync(this->ptr, 0));
+        checkCUDA(cudaFreeAsync(this->ptr, getCurrentCUDAStream()));
     }
     virtual bool isAsyncBuffer() override { 
         return true;
@@ -361,7 +362,7 @@ public:
 
     Tensor &zero_() {
         assert(this->is_contiguous());
-        checkCUDA(cudaMemset(data_ptr<char>() + shape.offset * scalar_size(), 0, shape.size() * scalar_size()));
+        checkCUDA(cudaMemsetAsync(data_ptr<char>() + shape.offset * scalar_size(), 0, shape.size() * scalar_size(), getCurrentCUDAStream()));
         return *this;
     }
     Tensor &copy_(Tensor other) {

src/kernels/awq/gemv_awq.cu

@@ -307,7 +307,7 @@ Tensor gemv_awq(
             return;
         }
         if constexpr (M > 0) {
-            gemv_kernel<half_t, N_PER_BLOCK, M, BLOCK_SIZE, GROUP_SIZE><<<num_blocks, num_threads>>>(
+            gemv_kernel<half_t, N_PER_BLOCK, M, BLOCK_SIZE, GROUP_SIZE><<<num_blocks, num_threads, 0, getCurrentCUDAStream()>>>(
                 in_feats, kernel, scaling_factors, zeros, out_feats, k, n
             );
             checkCUDA(cudaGetLastError());