add back legacy lamb code for backward comptibility now

csrc/multi_tensor_lamb_stage_1.cu

+#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
+
+// Step 1 computes the 'update' value of regular Adam optimizer.
+template<typename GRAD_T, typename T, typename UPD_T>
+struct LAMBStage1Functor
+{
+   __device__ __forceinline__ void operator()(
+    int chunk_size,
+    volatile int* noop_gmem,
+    TensorListMetadata<5>& tl,
+    const float* per_tensor_decay,
+    const float beta1,
+    const float beta2,
+    const float beta1_correction,
+    const float beta2_correction,
+    const float epsilon,
+    const float clipped_global_grad_norm)
+  {
+    // I'd like this kernel to propagate infs/nans.
+    // if(*noop_gmem == 1)
+    //   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];
+
+    float decay = per_tensor_decay[tensor_num];
+
+    GRAD_T* g = (GRAD_T*)tl.addresses[0][tensor_loc];
+    g += chunk_idx*chunk_size;
+
+    T* p = (T*)tl.addresses[1][tensor_loc];
+    p += chunk_idx*chunk_size;
+
+    T* m = (T*)tl.addresses[2][tensor_loc];
+    m += chunk_idx*chunk_size;
+
+    T* v = (T*)tl.addresses[3][tensor_loc];
+    v += chunk_idx*chunk_size;
+
+    UPD_T* update = (UPD_T*)tl.addresses[4][tensor_loc];
+    update += chunk_idx*chunk_size;
+
+    n -= chunk_idx*chunk_size;
+
+    // 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)
+    {
+      GRAD_T r_g[ILP];
+      T r_p[ILP];
+      T r_m[ILP];
+      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];
+          r_p[ii] = p[i];
+          r_m[ii] = m[i];
+          r_v[ii] = v[i];
+        } else {
+          r_g[ii] = GRAD_T(0);
+          r_p[ii] = T(0);
+          r_m[ii] = T(0);
+          r_v[ii] = T(0);
+        }
+      }
+#pragma unroll
+      for(int ii = 0; ii < ILP; ii++)
+      {
+        T scaled_grad = r_g[ii] / clipped_global_grad_norm;
+        r_m[ii] = r_m[ii] * beta1 + (1-beta1) * scaled_grad;
+        r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
+        T next_m_unbiased = r_m[ii] / beta1_correction;
+        T next_v_unbiased = r_v[ii] / beta2_correction;
+        T denom = std::sqrt(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)
+        {
+          update[i] = (UPD_T)r_p[ii];
+          m[i] = r_m[ii];
+          v[i] = r_v[ii];
+        }
+      }
+    }
+  }
+};
+
+void multi_tensor_lamb_stage1_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  at::Tensor per_tensor_decay,
+  const int step,
+  const float beta1,
+  const float beta2,
+  const float epsilon,
+  const float global_grad_norm,
+  const float max_global_grad_norm)
+{
+  using namespace at;
+
+  float clipped_global_grad_norm = global_grad_norm > max_global_grad_norm ? global_grad_norm / max_global_grad_norm : 1.0f;
+  float next_step = float(step+1);
+  float beta1_correction = 1.0f - std::pow(beta1, next_step);
+  float beta2_correction = 1.0f - std::pow(beta2, next_step);
+  DISPATCH_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(), 0, "lamb_stage_1",
+    DISPATCH_FLOAT_AND_HALF(tensor_lists[1][0].scalar_type(), 1, "lamb_stage_1",
+      DISPATCH_FLOAT_AND_HALF(tensor_lists[4][0].scalar_type(), 2, "lamb_stage_1",
+        multi_tensor_apply<5>(
+          BLOCK_SIZE,
+          chunk_size,
+          noop_flag,
+          tensor_lists,
+          LAMBStage1Functor<scalar_t_0, scalar_t_1, scalar_t_2>(),
+          per_tensor_decay.data<float>(),
+          beta1,
+          beta2,
+          beta1_correction,
+          beta2_correction,
+          epsilon,
+          clipped_global_grad_norm); )))
+
+  AT_CUDA_CHECK(cudaGetLastError());
+
+  // AT_CUDA_CHECK(cudaDeviceSynchronize());
+}

csrc/multi_tensor_lamb_stage_2.cu

+#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
+
+// Step 2 reads in 'update' value and per-tensor param_norm and update_norm.
+// It computes new parameter value.
+template<typename T, typename UPD_T>
+struct LAMBStage2Functor
+{
+   __device__ __forceinline__ void operator()(
+    int chunk_size,
+    volatile int* noop_gmem,
+    TensorListMetadata<2>& tl,
+    const float* per_tensor_param_norm,
+    const float* per_tensor_update_norm,
+    const float learning_rate)
+  {
+    // I'd like this kernel to propagate infs/nans.
+    // if(*noop_gmem == 1)
+    //   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];
+
+    float param_norm = per_tensor_param_norm[tensor_num];
+    float update_norm = per_tensor_update_norm[tensor_num];
+    T ratio = (update_norm != 0.0f && param_norm != 0.0f) ? learning_rate * (param_norm / update_norm) : learning_rate;
+
+    T* p = (T*)tl.addresses[0][tensor_loc];
+    p += chunk_idx*chunk_size;
+
+    UPD_T* update = (UPD_T*)tl.addresses[1][tensor_loc];
+    update += chunk_idx*chunk_size;
+
+    n -= chunk_idx*chunk_size;
+
+    for(int i_start = 0;
+            i_start < n && i_start < chunk_size;
+            i_start += blockDim.x*ILP)
+    {
+      T r_p[ILP];
+      UPD_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*(T)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];
+        }
+      }
+    }
+  }
+};
+
+void multi_tensor_lamb_stage2_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  at::Tensor per_tensor_param_norm,
+  at::Tensor per_tensor_update_norm,
+  const float learning_rate)
+{
+  using namespace at;
+
+  DISPATCH_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(), 0, "lamb_stage_2",
+    DISPATCH_FLOAT_AND_HALF(tensor_lists[1][0].scalar_type(), 1, "lamb_stage_2",
+      multi_tensor_apply<2>(
+        BLOCK_SIZE,
+        chunk_size,
+        noop_flag,
+        tensor_lists,
+        LAMBStage2Functor<scalar_t_0, scalar_t_1>(),
+        per_tensor_param_norm.data<float>(),
+        per_tensor_update_norm.data<float>(),
+        learning_rate); ))
+
+  AT_CUDA_CHECK(cudaGetLastError());
+
+  // AT_CUDA_CHECK(cudaDeviceSynchronize());
+}

setup.py

@@ -89,6 +89,8 @@ if "--cuda_ext" in sys.argv:
                                    'csrc/multi_tensor_scale_kernel.cu',
                                    'csrc/multi_tensor_axpby_kernel.cu',
                                    'csrc/multi_tensor_l2norm_kernel.cu',
+                                   'csrc/multi_tensor_lamb_stage_1.cu',
+                                   'csrc/multi_tensor_lamb_stage_2.cu',
                                    'csrc/multi_tensor_adam.cu',
                                    'csrc/multi_tensor_novograd.cu',
                                    'csrc/multi_tensor_lamb.cu'],