Add cpu adagrad

csrc/adagrad/cpu_adagrad.cpp

@@ -0,0 +1,227 @@
+#include "cpu_adagrad.h"
+#include <cuda_runtime_api.h>
+#include <math.h>
+#include <omp.h>
+#include <torch/extension.h>
+#include <iostream>
+#include <memory>
+#include <type_traits>
+#include <unordered_map>
+#include "cublas_v2.h"
+#include "cuda.h"
+#include "curand.h"
+#include "custom_cuda_layers.h"
+
+static std::unordered_map<int, std::shared_ptr<void>> s_optimizers;
+
+// C++ interface
+
+void Adagrad_Optimizer::Step_1(float* _params,
+                               float* grads,
+                               float* _exp_avg_sq,
+                               size_t _param_size,
+                               __half* dev_params,
+                               bool half_precision)
+{
+    size_t rounded_size = 0;
+#if defined(__AVX512__) or defined(__AVX256__)
+    Step_AVX<1>(
+        &rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
+#endif
+    if (_param_size > rounded_size) {
+        float step_size = -1 * _alpha;
+        __half* grads_cast_h;
+        __half* params_cast_h;
+        if (half_precision) {
+            grads_cast_h = reinterpret_cast<__half*>(grads);
+            params_cast_h = reinterpret_cast<__half*>(_params);
+        }
+        for (size_t t = rounded_size; t < _param_size; t += TILE) {
+            size_t copy_size = TILE;
+            if ((t + TILE) > _param_size) copy_size = _param_size - t;
+            size_t offset = copy_size + t;
+            if ((t / TILE) >= 2) { cudaStreamSynchronize(_streams[_buf_index]); }
+#pragma omp parallel for
+            for (size_t k = t; k < offset; k++) {
+                float grad = half_precision ? (float)grads_cast_h[k] : grads[k];
+                float param = half_precision ? (float)params_cast_h[k] : _params[k];
+                float momentum = grads[k];
+                float variance = _exp_avg_sq[k];
+                if (_weight_decay > 0) { grad = param * _weight_decay + grad; }
+
+                variance += grad * grad;
+
+                grad = sqrt(variance);
+                grad += _eps;
+                grad = momentum / grad;
+                param = grad * step_size + param;
+                if (dev_params) _doubled_buffer[_buf_index][k - t] = param;
+
+                if (half_precision)
+                    params_cast_h[k] = (__half)param;
+                else
+                    _params[k] = param;
+                // STORE UPDATE TERM TO GRAD'S MEMORY
+                grads[k] = grad * step_size;
+                _exp_avg_sq[k] = variance;
+            }
+            if (dev_params) {
+                launch_param_update(
+                    _doubled_buffer[_buf_index], dev_params + t, (copy_size), _streams[_buf_index]);
+                _buf_index = !_buf_index;
+            }
+        }
+    }
+}
+
+void Adagrad_Optimizer::Step_4(float* _params,
+                               float* grads,
+                               float* _exp_avg_sq,
+                               size_t _param_size,
+                               __half* dev_params,
+                               bool half_precision)
+{
+    size_t rounded_size = 0;
+#if defined(__AVX512__) or defined(__AVX256__)
+    Step_AVX<4>(
+        &rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
+#endif
+    if (_param_size > rounded_size)
+        Step_1((_params + rounded_size),
+               (grads + rounded_size),
+               (_exp_avg_sq + rounded_size),
+               (_param_size - rounded_size),
+               (dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
+               half_precision);
+}
+
+int create_adagrad_optimizer(int optimizer_id,
+                             float alpha = 1e-2,
+                             float eps = 1e-8,
+                             float weight_decay = 0,
+                             bool should_log = false)
+{
+    auto opt = std::make_shared<Adagrad_Optimizer>(alpha, eps, weight_decay);
+
+    s_optimizers[optimizer_id] = opt;
+
+    if (should_log) {
+        std::string avx_type = "";
+#if defined(__AVX512__)
+        avx_type = "AVX512";
+#else
+#if defined(__AVX256__)
+        avx_type = "AVX2";
+#else
+        avx_type = "scalar";
+#endif
+#endif
+
+        printf("Adagrad Optimizer #%d is created with %s arithmetic capability.\n",
+               optimizer_id,
+               avx_type.c_str());
+        printf("Config: alpha=%f, weight_decay=%f\n", alpha, weight_decay);
+    }
+
+    return 0;
+}
+
+void Adagrad_Optimizer::Step_8(float* _params,
+                               float* grads,
+                               float* _exp_avg_sq,
+                               size_t _param_size,
+                               __half* dev_params,
+                               bool half_precision)
+{
+    size_t rounded_size = 0;
+#if defined(__AVX512__) or defined(__AVX256__)
+    Step_AVX<8>(
+        &rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
+#endif
+    if (_param_size > rounded_size)
+        Step_4((_params + rounded_size),
+               (grads + rounded_size),
+               (_exp_avg_sq + rounded_size),
+               (_param_size - rounded_size),
+               (dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
+               half_precision);
+}
+
+int ds_adagrad_step(int optimizer_id,
+                    size_t step,
+                    float lr,
+                    float epsilon,
+                    float weight_decay,
+                    torch::Tensor& params,
+                    torch::Tensor& grads,
+                    torch::Tensor& exp_avg_sq)
+{
+    auto params_c = params.contiguous();
+    auto grads_c = grads.contiguous();
+    auto exp_avg_sq_c = exp_avg_sq.contiguous();
+
+    float* params_ptr = (float*)params_c.data_ptr();
+    float* grads_ptr = (float*)grads_c.data_ptr();
+    float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
+
+    std::shared_ptr<Adagrad_Optimizer> opt =
+        std::static_pointer_cast<Adagrad_Optimizer>(s_optimizers[optimizer_id]);
+    opt->IncrementStep(step);
+    opt->update_state(lr, epsilon, weight_decay);
+    opt->Step_8(params_ptr, grads_ptr, exp_avg_sq_ptr, params_c.size(0));
+
+    opt->SynchronizeStreams();
+    return 0;
+}
+
+int ds_adagrad_step_plus_copy(int optimizer_id,
+                              size_t step,
+                              float lr,
+                              float epsilon,
+                              float weight_decay,
+                              torch::Tensor& params,
+                              torch::Tensor& grads,
+                              torch::Tensor& exp_avg_sq,
+                              torch::Tensor& gpu_params)
+{
+    auto params_c = params.contiguous();
+    auto gpu_params_c = gpu_params.contiguous();
+    auto exp_avg_sq_c = exp_avg_sq.contiguous();
+    auto grads_c = grads.contiguous();
+
+    float* params_ptr = (float*)params_c.data_ptr();
+    float* grads_ptr = (float*)grads_c.data_ptr();
+    __half* gpu_params_ptr = (__half*)gpu_params_c.data_ptr();
+    float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
+
+    std::shared_ptr<Adagrad_Optimizer> opt =
+        std::static_pointer_cast<Adagrad_Optimizer>(s_optimizers[optimizer_id]);
+    opt->IncrementStep(step);
+    opt->update_state(lr, epsilon, weight_decay);
+    opt->Step_8(params_ptr,
+                grads_ptr,
+                exp_avg_sq_ptr,
+                params_c.size(0),
+                gpu_params_ptr,
+                (params.options().dtype() == at::kHalf));
+
+    opt->SynchronizeStreams();
+    return 0;
+}
+
+int destroy_adagrad_optimizer(int optimizer_id)
+{
+    s_optimizers.erase(optimizer_id);
+
+    return 0;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("adagrad_update", &ds_adagrad_step, "DeepSpeed CPU Adagrad update (C++)");
+    m.def("adagrad_update_copy",
+          &ds_adagrad_step_plus_copy,
+          "DeepSpeed CPU Adagrad update and param copy (C++)");
+    m.def("create_adagrad", &create_adagrad_optimizer, "DeepSpeed CPU Adagrad (C++)");
+    m.def("destroy_adagrad", &destroy_adagrad_optimizer, "DeepSpeed CPU Adagrad destroy (C++)");
+}