Upload apex-master/csrc/multi_tensor_lamb_mp.cu with huggingface_hub

apex-master/csrc/multi_tensor_lamb_mp.cu

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+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/Exceptions.h>
+// Another possibility:
+// #include <torch/all.h>
+
+#include <assert.h>
+
+#include "type_shim.h"
+#include "multi_tensor_apply.cuh"
+
+#define BLOCK_SIZE 512
+#define ILP 4
+
+template<typename T>
+__device__ __forceinline__ bool is_aligned(T* p){
+  return ((uint64_t)p) % (ILP*sizeof(T)) == 0;
+}
+
+template<typename T>
+__device__ __forceinline__ void load_store(T* dst, T* src, int dst_offset, int src_offset){
+  typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
+  ((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
+}
+
+typedef enum{
+  MOMENT_MODE_0   =0, // L2 regularization mode
+  MOMENT_MODE_1   =1  // Decoupled weight decay mode
+} adamMode_t;
+
+std::tuple<at::Tensor, at::Tensor> multi_tensor_l2norm_mp_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  at::optional<bool> per_tensor_python);
+
+using MATH_T = float;
+
+template<typename T, typename param_t>
+struct LAMBStage1Functor
+{
+   __device__ __forceinline__ void operator()(
+    int chunk_size,
+    volatile int* noop_gmem,
+    TensorListMetadata<4>& tl,
+    const float beta1,
+    const float beta2,
+    const float beta3,
+    const int* step_ptr,
+    const int bias_correction,
+    const float epsilon,
+    adamMode_t mode,
+    const float decay,
+    const float* global_grad_norm,
+    const float* max_global_grad_norm,
+    const float* found_inf,
+    const float* inv_scale)
+  {
+    if (*noop_gmem) {
+      return;
+    }
+
+    float beta1_correction = 1.0f;
+    float beta2_correction = 1.0f;
+    if (bias_correction == 1) {
+      int step = *step_ptr;
+      beta1_correction = 1 - std::pow(beta1, step);
+      beta2_correction = 1 - std::pow(beta2, step);
+    }
+
+    int tensor_loc = tl.block_to_tensor[blockIdx.x];
+    int chunk_idx = tl.block_to_chunk[blockIdx.x];
+    int n = tl.sizes[tensor_loc];
+
+    float clipped_global_grad_norm = (*global_grad_norm) > (*max_global_grad_norm) ? (*global_grad_norm) / (*max_global_grad_norm) : 1.0f;
+
+    T* g = (T*)tl.addresses[0][tensor_loc];
+    g += chunk_idx*chunk_size;
+
+    param_t* p = (param_t*)tl.addresses[1][tensor_loc];
+    p += chunk_idx*chunk_size;
+
+    param_t* m = (param_t*)tl.addresses[2][tensor_loc];
+    m += chunk_idx*chunk_size;
+
+    param_t* v = (param_t*)tl.addresses[3][tensor_loc];
+    v += chunk_idx*chunk_size;
+
+    n -= chunk_idx*chunk_size;
+
+    MATH_T r_g[ILP];
+    MATH_T r_p[ILP];
+    MATH_T r_m[ILP];
+    MATH_T r_v[ILP];
+    // to make things simple, we put aligned case in a different code path
+    if(n % ILP == 0 &&
+       chunk_size % ILP == 0 &&
+       is_aligned(g) &&
+       is_aligned(p) &&
+       is_aligned(m) &&
+       is_aligned(v))
+    {
+      T l_g[ILP];
+      param_t l_p[ILP];
+      param_t l_m[ILP];
+      param_t l_v[ILP];
+      for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
+      {
+        // load
+        load_store(l_g, g, 0, i_start);
+        if (decay != 0)
+          load_store(l_p, p, 0, i_start);
+        load_store(l_m, m, 0, i_start);
+        load_store(l_v, v, 0, i_start);
+        // unpack
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          r_g[ii] = l_g[ii] * (*inv_scale);
+          if (decay == 0) {
+            r_p[ii] = MATH_T(0);
+          }
+          else {
+            r_p[ii] = l_p[ii];
+          }
+          r_m[ii] = l_m[ii];
+          r_v[ii] = l_v[ii];
+        }
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          if (mode == MOMENT_MODE_0) {
+            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
+            // L2 on scaled grad
+            scaled_grad = scaled_grad + decay*r_p[ii];
+            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
+            r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
+            MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
+            MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
+            MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
+            r_p[ii] = next_m_unbiased / denom;
+          }
+          else {
+            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
+            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
+            r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
+            MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
+            MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
+            MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
+            r_p[ii] = (next_m_unbiased/denom) + (decay*r_p[ii]);
+          }
+        }
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          l_p[ii] = r_p[ii];
+          // Difference from APEX's LAMB kernel. `g` and `p` can be different dtypes.
+          l_g[ii] = r_p[ii];
+          l_m[ii] = r_m[ii];
+          l_v[ii] = r_v[ii];
+        }
+        // store
+        load_store(g, l_g, i_start, 0);
+        load_store(m, l_m, i_start, 0);
+        load_store(v, l_v, i_start, 0);
+      }
+    }
+    else
+    {
+      // see note in multi_tensor_scale_kernel.cu
+      for(int i_start = 0;
+          i_start < n && i_start < chunk_size;
+          i_start += blockDim.x*ILP)
+      {
+        MATH_T r_g[ILP];
+        MATH_T r_p[ILP];
+        MATH_T r_m[ILP];
+        MATH_T r_v[ILP];
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          int i = i_start + threadIdx.x + ii*blockDim.x;
+          if(i < n && i < chunk_size)
+          {
+            r_g[ii] = g[i] * (*inv_scale);
+            // special ?optimization? for lamb stage 1
+            if (decay == 0) {
+              r_p[ii] = MATH_T(0);
+            }
+            else {
+              r_p[ii] = p[i];
+            }
+            r_m[ii] = m[i];
+            r_v[ii] = v[i];
+          } else {
+            r_g[ii] = MATH_T(0);
+            r_p[ii] = MATH_T(0);
+            r_m[ii] = MATH_T(0);
+            r_v[ii] = MATH_T(0);
+          }
+        }
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          if (mode == MOMENT_MODE_0) {
+            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
+            // L2 on scaled grad
+            scaled_grad = scaled_grad + decay*r_p[ii];
+            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
+            r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
+            MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
+            MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
+            MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
+            r_p[ii] = next_m_unbiased / denom;
+          }
+          else {
+            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
+            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
+            r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
+            MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
+            MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
+            MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
+            r_p[ii] = (next_m_unbiased/denom) + (decay*r_p[ii]);
+          }
+        }
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          int i = i_start + threadIdx.x + ii*blockDim.x;
+          if(i < n && i < chunk_size)
+          {
+            g[i] = r_p[ii];
+            m[i] = r_m[ii];
+            v[i] = r_v[ii];
+          }
+        }
+      }
+    }
+  }
+};
+
+// Step 2 reads in 'update' value and per-tensor param_norm and update_norm.
+// It computes new parameter value.
+// N == 2: FP32 params, no master params
+// N == 3: FP16 params, FP32 master params.
+template<typename T, int N, typename param_t>
+struct LAMBStage2Functor
+{
+  static_assert((N == 2 && std::is_same<T, param_t>::value) || (N == 3 && std::is_same<param_t, float>::value), "");
+   __device__ __forceinline__ void operator()(
+    int chunk_size,
+    volatile int* noop_gmem,
+    TensorListMetadata<N>& tl,
+    const float* per_tensor_param_norm,
+    const float* per_tensor_update_norm,
+    const float* learning_rate,
+    const float decay,
+    bool use_nvlamb)
+  {
+    if (*noop_gmem) {
+      return;
+    }
+
+    int tensor_loc = tl.block_to_tensor[blockIdx.x];
+    int tensor_num = tl.start_tensor_this_launch + tensor_loc;
+    int chunk_idx = tl.block_to_chunk[blockIdx.x];
+    int n = tl.sizes[tensor_loc];
+
+    MATH_T ratio = *learning_rate;
+    // nvlamb: apply adaptive learning rate to all parameters
+    // otherwise, only apply to those with non-zero weight decay
+    if (use_nvlamb || (decay != 0.0))
+    {
+      float param_norm = per_tensor_param_norm[tensor_num];
+      float update_norm = per_tensor_update_norm[tensor_num];
+      ratio = (update_norm != 0.0f && param_norm != 0.0f) ? *learning_rate * (param_norm / update_norm) : *learning_rate;
+    }
+
+    T* update = (T*)tl.addresses[0][tensor_loc];
+    update += chunk_idx*chunk_size;
+
+    param_t* p = (param_t*)tl.addresses[1][tensor_loc];
+    p += chunk_idx*chunk_size;
+
+    T* out_p;
+    if (N == 3) {
+      out_p = (T*)tl.addresses[2][tensor_loc];
+      out_p += chunk_idx*chunk_size;
+    }
+
+    n -= chunk_idx*chunk_size;
+
+    // to make things simple, we put aligned case in a different code path
+    bool can_use_aligned_path = n % ILP == 0 && chunk_size % ILP == 0 && is_aligned(p) && is_aligned(update);
+    if (N == 3) {
+      can_use_aligned_path = can_use_aligned_path && is_aligned(out_p);
+    }
+    if(can_use_aligned_path)
+    {
+      param_t r_p[ILP];
+      T r_update[ILP];
+      T r_out_p[ILP];
+      for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
+      {
+        // load
+        load_store(r_p, p, 0, i_start);
+        load_store(r_update, update, 0, i_start);
+        if (N == 3) {
+          load_store(r_out_p, out_p, 0, i_start);
+        }
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          r_p[ii] = static_cast<MATH_T>(r_p[ii]) - (ratio * static_cast<MATH_T>(r_update[ii]));
+          if (N == 3) {
+            r_out_p[ii] = r_p[ii];
+          }
+        }
+        load_store(p, r_p, i_start, 0);
+        if (N == 3) {
+          load_store(out_p, r_out_p, i_start, 0);
+        }
+      }
+    }
+    else
+    {
+      for(int i_start = 0;
+          i_start < n && i_start < chunk_size;
+          i_start += blockDim.x*ILP)
+      {
+        MATH_T r_p[ILP];
+        MATH_T r_update[ILP];
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          int i = i_start + threadIdx.x + ii*blockDim.x;
+          if(i < n && i < chunk_size)
+          {
+            r_p[ii] = p[i];
+            r_update[ii] = update[i];
+          }
+        }
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          r_p[ii] = r_p[ii] - (ratio * r_update[ii]);
+        }
+#pragma unroll
+        for(int ii = 0; ii < ILP; ii++)
+        {
+          int i = i_start + threadIdx.x + ii*blockDim.x;
+          if(i < n && i < chunk_size)
+          {
+            p[i] = r_p[ii];
+            if (N == 3) {
+              out_p[i] = r_p[ii];
+            }
+          }
+        }
+      }
+    }
+  }
+};
+
+
+void multi_tensor_lamb_mp_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  at::Tensor lr,
+  const float beta1,
+  const float beta2,
+  const float epsilon,
+  at::Tensor step,
+  const int bias_correction,
+  const float weight_decay,
+  const int grad_averaging,
+  const int mode,
+  at::Tensor global_grad_norm,
+  at::Tensor max_grad_norm,
+  at::optional<bool> use_nvlamb_python,
+  at::Tensor found_inf,
+  at::Tensor inv_scale)
+{
+  // n_tensors == 5: FP16 model params & FP32 master params
+  // n_tensors == 4: FP32 model params & NO FP32 master params
+  const auto n_tensors = tensor_lists.size();
+  assert(n_tensors == 4 || n_tensors == 5);
+  using namespace at;
+
+  bool use_nvlamb = use_nvlamb_python.has_value() ? use_nvlamb_python.value() : false;
+
+  // note(mkozuki): move bias handling below to functor
+  // Handle bias correction mode
+  // float bias_correction1 = 1.0f, bias_correction2 = 1.0f;
+  // if (bias_correction == 1) {
+  //   bias_correction1 = 1 - std::pow(beta1, step);
+  //   bias_correction2 = 1 - std::pow(beta2, step);
+  // }
+
+  // Handle grad averaging mode
+  float beta3 = 1.0f;
+  if (grad_averaging == 1) beta3 = 1 - beta1;
+
+  std::vector<std::vector<at::Tensor>> stage1_tensor_lists(tensor_lists.begin(), tensor_lists.begin() + 4);
+  std::vector<std::vector<at::Tensor>> grad_list(tensor_lists.begin(), tensor_lists.begin()+1);
+  std::vector<std::vector<at::Tensor>> param_list(tensor_lists.begin()+1, tensor_lists.begin()+2);
+
+  // Compute per tensor param norm
+  auto param_norm_tuple = multi_tensor_l2norm_mp_cuda(chunk_size, noop_flag, param_list, true);
+
+  // We now in-place modify grad to store update before compute its norm
+  // Generally this is not a issue since people modify grad in step() method all the time
+  // We can also grab list of empty tensor to avoid this, but I'd like to save space/cpu code
+  if (n_tensors == 4) {
+    DISPATCH_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(), 0, "lamb_stage_1",
+        multi_tensor_apply<4>(
+          BLOCK_SIZE,
+          chunk_size,
+          noop_flag,
+          stage1_tensor_lists,
+          LAMBStage1Functor<scalar_t_0, scalar_t_0>(),
+          beta1,
+          beta2,
+          beta3, // 1-beta1 or 1 depends on averaging mode
+          // bias_correction1,
+          // bias_correction2,
+          step.data_ptr<int>(),
+          bias_correction,
+          epsilon,
+          (adamMode_t) mode,
+          weight_decay,
+          global_grad_norm.data_ptr<float>(),
+          max_grad_norm.data_ptr<float>(),
+          found_inf.data_ptr<float>(),
+          inv_scale.data_ptr<float>()); )
+  } else {
+    DISPATCH_FLOAT_HALF_AND_BFLOAT(tensor_lists[0][0].scalar_type(), 0, "lamb_stage_1",
+        multi_tensor_apply<4>(
+          BLOCK_SIZE,
+          chunk_size,
+          noop_flag,
+          stage1_tensor_lists,
+          LAMBStage1Functor<scalar_t_0, float>(),
+          beta1,
+          beta2,
+          beta3, // 1-beta1 or 1 depends on averaging mode
+          // bias_correction1,
+          // bias_correction2,
+          step.data_ptr<int>(),
+          bias_correction,
+          epsilon,
+          (adamMode_t) mode,
+          weight_decay,
+          global_grad_norm.data_ptr<float>(),
+          max_grad_norm.data_ptr<float>(),
+          found_inf.data_ptr<float>(),
+          inv_scale.data_ptr<float>()); )
+  }
+
+  // Compute update norms
+  auto update_norm_tuple = multi_tensor_l2norm_mp_cuda(chunk_size, noop_flag, grad_list, true);
+
+  std::vector<std::vector<at::Tensor>> grad_param_list(tensor_lists.begin(), tensor_lists.begin()+2);
+  if (n_tensors == 4) {
+    DISPATCH_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(), 0, "lamb_stage_2",
+        multi_tensor_apply<2>(
+          BLOCK_SIZE,
+          chunk_size,
+          noop_flag,
+          grad_param_list,
+          LAMBStage2Functor<scalar_t_0, 2, scalar_t_0>(),
+          std::get<1>(param_norm_tuple).data_ptr<float>(),
+          std::get<1>(update_norm_tuple).data_ptr<float>(),
+          lr.data_ptr<float>(),
+      weight_decay,
+      use_nvlamb); )
+  } else {
+    grad_param_list.push_back(tensor_lists[4]);
+    DISPATCH_FLOAT_HALF_AND_BFLOAT(tensor_lists[0][0].scalar_type(), 0, "lamb_stage_2",
+        multi_tensor_apply<3>(
+          BLOCK_SIZE,
+          chunk_size,
+          noop_flag,
+          grad_param_list,
+          LAMBStage2Functor<scalar_t_0, 3, float>(),
+          std::get<1>(param_norm_tuple).data_ptr<float>(),
+          std::get<1>(update_norm_tuple).data_ptr<float>(),
+          lr.data_ptr<float>(),
+      weight_decay,
+      use_nvlamb); )
+  }
+  AT_CUDA_CHECK(cudaGetLastError());
+
+}