Fixing merge conflict in setup.py

apex/optimizers/fused_adam.py

@@ -2,6 +2,8 @@ import types
 import torch
 import importlib
 
+from ..multi_tensor_apply import multi_tensor_applier
+
 class FusedAdam(torch.optim.Optimizer):
 
     """Implements Adam algorithm. Currently GPU-only.  Requires Apex to be installed via
@@ -25,6 +27,8 @@ class FusedAdam(torch.optim.Optimizer):
             adds eps to the bias-corrected second moment estimate before
             evaluating square root instead of adding it to the square root of
             second moment estimate as in the original paper. (default: False)
+        use_mt (boolean, optional): use multi tensor apply for lower launch
+            latency. (default: False)
 
     .. _Adam\: A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
@@ -35,10 +39,18 @@ class FusedAdam(torch.optim.Optimizer):
     def __init__(self, params,
                  lr=1e-3, bias_correction = True,
                  betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt = False,
-                 weight_decay=0., max_grad_norm=0., amsgrad=False):
+                 weight_decay=0., max_grad_norm=0., amsgrad=False, use_mt=False):
         global fused_adam_cuda
         fused_adam_cuda = importlib.import_module("fused_adam_cuda")
 
+        self._use_multi_tensor = False
+        if use_mt:
+            if not multi_tensor_applier.available:
+                print("Warning:  multi_tensor_applier is unavailable")
+            else:
+                self._use_multi_tensor = True
+                self._overflow_buf = torch.cuda.IntTensor([0])
+
         if amsgrad:
             raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
         defaults = dict(lr=lr, bias_correction=bias_correction,
@@ -105,6 +117,12 @@ class FusedAdam(torch.optim.Optimizer):
 
             bias_correction = 1 if group['bias_correction'] else 0
 
+            if self._use_multi_tensor:
+                if output_params:
+                    tensorlists = [[],[],[],[],[]]
+                else:
+                    tensorlists = [[],[],[],[]]
+
             for p, grad, output_param in zip(group['params'], grads_this_group, output_params_this_group):
                 #note: p.grad should not ever be set for correct operation of mixed precision optimizer that sometimes sends None gradients
                 if p.grad is None and grad is None:
@@ -130,18 +148,43 @@ class FusedAdam(torch.optim.Optimizer):
                 state['step'] += 1
 
                 out_p = torch.tensor([], dtype = torch.float) if output_param is None else output_param
-                fused_adam_cuda.adam(p.data,
-                                     out_p,
-                                     exp_avg,
-                                     exp_avg_sq,
-                                     grad,
-                                     group['lr'],
-                                     beta1,
-                                     beta2,
-                                     group['eps'],
-                                     combined_scale,
-                                     state['step'],
-                                     self.eps_mode,
-                                     bias_correction,
-                                     group['weight_decay'])
+                if self._use_multi_tensor:
+                    pl = [p.data, exp_avg, exp_avg_sq, grad]
+                    if output_param is not None:
+                        pl.append(out_p)
+
+                    for tl, t in zip(tensorlists, pl):
+                        tl.append(t)
+                else:
+                    fused_adam_cuda.adam(p.data,
+                                         out_p,
+                                         exp_avg,
+                                         exp_avg_sq,
+                                         grad,
+                                         group['lr'],
+                                         beta1,
+                                         beta2,
+                                         group['eps'],
+                                         combined_scale,
+                                         state['step'],
+                                         self.eps_mode,
+                                         bias_correction,
+                                         group['weight_decay'])
+
+            if self._use_multi_tensor:
+                multi_tensor_applier(
+                    fused_adam_cuda.adam_mt,
+                    self._overflow_buf,
+                    tensorlists,
+                    group['lr'],
+                    beta1,
+                    beta2,
+                    group['eps'],
+                    combined_scale,
+                    state['step'],
+                    self.eps_mode,
+                    bias_correction,
+                    group['weight_decay'])
+
         return loss
+

csrc/fused_adam_cuda.cpp

@@ -3,6 +3,9 @@
 // CUDA forward declaration
 void fused_adam_cuda(at::Tensor & p, at::Tensor & p_copy, at::Tensor & m, at::Tensor & v, at::Tensor & g, float lr, float beta1, float beta2, float eps, float grad_scale, int step, int mode, int bias_correction, float decay);
 
+void fused_adam_cuda_mt(int chunk_size, at::Tensor noop_flag, std::vector<std::vector<at::Tensor>> tensor_lists, float lr, float beta1, float beta2, float eps, float grad_scale, int step, int mode, int bias_correction, float decay);
+
+
 #define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
 #define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
 #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
@@ -25,4 +28,5 @@ void adam(at::Tensor & p, at::Tensor & p_copy, at::Tensor & m, at::Tensor & v, a
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         m.def("adam", &adam, "Adam optimized CUDA implementation.");
+        m.def("adam_mt", &fused_adam_cuda_mt, "Multi tensor Adam optimized CUDA implementation.");
 }

csrc/fused_adam_cuda_kernel.cu

@@ -9,6 +9,10 @@
 #include "ATen/Type.h"
 #include "ATen/AccumulateType.h"
 #include <THC/THCGeneral.h>
+#include "multi_tensor_apply.cuh"
+
+#define BLOCK_SIZE 512
+#define ILP 4
 
 #include "type_shim.h"
 
@@ -55,6 +59,93 @@ __global__ void adam_cuda_kernel(
         }
 }
 
+template <int DEPTH, typename T, typename GRAD_T>
+struct AdamFunctor
+{
+    __device__ __forceinline__ void operator()(
+        int chunk_size,
+        volatile int* noop_gmem,
+        TensorList<DEPTH>& tl,
+        const float b1,
+        const float b2,
+        const float eps,
+        const float grad_scale,
+        const float step_size,
+        adamMode_t mode,
+        const float decay)
+    {
+        int tensor_loc = tl.block_to_tensor[blockIdx.x];
+        int chunk_idx = tl.block_to_chunk[blockIdx.x];
+        int n = tl.sizes[tensor_loc];
+
+        T* p = (T *)tl.addresses[0][tensor_loc];
+        p += chunk_idx*chunk_size;
+        T* m = (T *)tl.addresses[1][tensor_loc];
+        m += chunk_idx*chunk_size;
+        T* v = (T *)tl.addresses[2][tensor_loc];
+        v += chunk_idx*chunk_size;
+        GRAD_T* g = (GRAD_T *)tl.addresses[3][tensor_loc];
+        g += chunk_idx*chunk_size;
+        GRAD_T* p_copy = NULL;
+        if (DEPTH == 5) {
+            p_copy = (GRAD_T *)tl.addresses[4][tensor_loc];
+            p_copy += chunk_idx*chunk_size;
+        }
+
+        n -= chunk_idx*chunk_size;
+
+        T incoming_p[ILP];
+        T incoming_m[ILP];
+        T incoming_v[ILP];
+        T incoming_g[ILP];
+        
+        for(int i_start = 0;
+            i_start < n && i_start < chunk_size;
+            i_start += blockDim.x*ILP) {
+            
+            #pragma unroll
+            for(int ii = 0; ii < ILP; ii++) {
+                incoming_p[ii] = 0;
+                incoming_m[ii] = 0;
+                incoming_v[ii] = 0;
+                incoming_g[ii] = 0;
+                
+                int i = i_start + threadIdx.x + ii*blockDim.x;
+                if (i < n && i < chunk_size) {
+                    incoming_p[ii] = p[i];
+                    incoming_m[ii] = m[i];
+                    incoming_v[ii] = v[i];
+                    incoming_g[ii] = static_cast<T>(g[i]);
+                }
+            }
+
+            // note for clarification to future michael:
+            // From a pure memory dependency perspective, there's likely no point unrolling
+            // the write loop, since writes just fire off once their LDGs arrive.
+            // Put another way, the STGs are dependent on the LDGs, but not on each other.
+            // There is still compute ILP benefit from unrolling the loop though.
+            #pragma unroll
+            for(int ii = 0; ii < ILP; ii++) {
+                int j = i_start + threadIdx.x + ii*blockDim.x;
+
+                if(j < n && j < chunk_size) {
+                    T scaled_grad = incoming_g[ii]/grad_scale;
+                    m[j] = b1*incoming_m[ii] + (1-b1)*scaled_grad;
+                    v[j] = b2*incoming_v[ii] + (1-b2)*scaled_grad*scaled_grad;
+                    float denom;
+                    if (mode == ADAM_MODE_0)
+                        denom = sqrtf(v[j] + eps);
+                    else // Mode 1
+                        denom = sqrtf(v[j]) + eps;
+                    float update = (m[j]/denom) + (decay*incoming_p[ii]);
+                    p[j] = incoming_p[ii] - (step_size*update);
+                    if (DEPTH == 5)  p_copy[j] = (GRAD_T) p[j];
+                }
+            }
+        }
+    }
+};
+
 void fused_adam_cuda(
         at::Tensor & p,
         at::Tensor & p_copy,
@@ -135,3 +226,110 @@ void fused_adam_cuda(
       THCudaCheck(cudaGetLastError());
 
 }
+
+void fused_adam_cuda_mt(
+    int chunk_size,
+    at::Tensor noop_flag,
+    std::vector<std::vector<at::Tensor>> tensor_lists, // p, m, v, g, p_copy
+    float lr,
+    float beta1,
+    float beta2,
+    float eps,
+    float grad_scale,
+    int step,
+    int mode,
+    int bias_correction,
+    float decay) {
+
+    //Constants
+    float step_size = 0;
+    if (bias_correction == 1) {
+        const float bias_correction1 = 1 - std::pow(beta1, step);
+        const float bias_correction2 = 1 - std::pow(beta2, step);
+        step_size = lr * std::sqrt(bias_correction2)/bias_correction1;
+    }
+    else {
+        step_size = lr;
+    }
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+    size_t tl_sz = tensor_lists.size();
+    AT_ASSERTM(tl_sz == 4 || tl_sz == 5, "expected tensor lists of size 4 or 5");
+
+    if (tensor_lists[3][0].type().scalarType() == at::ScalarType::Half) {
+//alher values should be fp32 for half gradients
+        AT_ASSERTM(tensor_lists[0][0].type().scalarType() == at::ScalarType::Float, "expected parameter to be of float type");
+//dich is done on the gradient type
+        if (tl_sz == 5) {
+            AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
+                using accscalar_t = at::acc_type<scalar_t, true>;
+                multi_tensor_apply<5>(
+                    BLOCK_SIZE,
+                    chunk_size,
+                    noop_flag,
+                    tensor_lists,
+                    AdamFunctor<5, accscalar_t, scalar_t>(),
+                    beta1,
+                    beta2,
+                    eps,
+                    grad_scale,
+                    step_size,
+                    (adamMode_t) mode,
+                    decay);
+            }));
+        } else {
+            AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
+                using accscalar_t = at::acc_type<scalar_t, true>;
+                multi_tensor_apply<4>(
+                    BLOCK_SIZE,
+                    chunk_size,
+                    noop_flag,
+                    tensor_lists,
+                    AdamFunctor<4, accscalar_t, scalar_t>(),
+                    beta1,
+                    beta2,
+                    eps,
+                    grad_scale,
+                    step_size,
+                    (adamMode_t) mode,
+                    decay);
+            }));
+        }
+    } else {
+        if (tl_sz == 5) { 
+            AT_DISPATCH_FLOATING_TYPES(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
+                multi_tensor_apply<5>(
+                    BLOCK_SIZE,
+                    chunk_size,
+                    noop_flag,
+                    tensor_lists,
+                    AdamFunctor<5, scalar_t, scalar_t>(),
+                    beta1,
+                    beta2,
+                    eps,
+                    grad_scale,
+                    step_size,
+                    (adamMode_t) mode,
+                    decay);
+            }));
+        } else {
+            AT_DISPATCH_FLOATING_TYPES(tensor_lists[3][0].type(), "adam_cuda_mt_kernel", ([&] {
+                multi_tensor_apply<4>(
+                    BLOCK_SIZE,
+                    chunk_size,
+                    noop_flag,
+                    tensor_lists,
+                    AdamFunctor<4, scalar_t, scalar_t>(),
+                    beta1,
+                    beta2,
+                    eps,
+                    grad_scale,
+                    step_size,
+                    (adamMode_t) mode,
+                    decay);
+            }));
+        }
+    }
+    THCudaCheck(cudaGetLastError());
+}
+

tests/L0/run_mixed_adam/test_mixed_adam.py

@@ -15,15 +15,18 @@ class TestFusedAdam(unittest.TestCase):
     def tearDown(self):
         pass
 
-    def gen_param_optim(self, tensors, adam_option):
+    def gen_param_optim(self, tensors, ref_adam_option, tst_adam_option=None):
         ref_param = []
         tst_param = []
         for tensor in tensors:
             ref_param.append(torch.nn.Parameter(tensor.clone()))
             tst_param.append(torch.nn.Parameter(tensor.clone()))
 
-        ref_optim = torch.optim.Adam(ref_param, **adam_option)
-        tst_optim = apex.optimizers.FusedAdam(tst_param, **adam_option)
+        ref_optim = torch.optim.Adam(ref_param, **ref_adam_option)
+        if tst_adam_option:
+            tst_optim = apex.optimizers.FusedAdam(tst_param, **tst_adam_option)
+        else:
+            tst_optim = apex.optimizers.FusedAdam(tst_param, **ref_adam_option)
        
         return (ref_param, tst_param, ref_optim, tst_optim)
 
@@ -42,8 +45,8 @@ class TestFusedAdam(unittest.TestCase):
     def get_max_diff(self, ref_param, tst_param):
         max_abs_diff = max_rel_diff = 0
         for p_ref, p_tst in zip(ref_param, tst_param):
-            max_abs_diff_p = (p_ref - p_tst).abs().max().item()
-            max_rel_diff_p = ((p_ref - p_tst) / p_ref).abs().max().item()
+            max_abs_diff_p = (p_ref - p_tst.type(p_ref.type())).abs().max().item()
+            max_rel_diff_p = ((p_ref - p_tst.type(p_ref.type())) / p_ref).abs().max().item()
 
             if max_abs_diff_p > max_abs_diff:  max_abs_diff = max_abs_diff_p
             if max_rel_diff_p > max_rel_diff:  max_rel_diff = max_rel_diff_p
@@ -173,6 +176,34 @@ class TestFusedAdam(unittest.TestCase):
             self.assertLessEqual(max_abs_diff, self.max_abs_diff)
             self.assertLessEqual(max_rel_diff, self.max_rel_diff)
 
+    def test_multi_tensor(self):
+        sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]
+        ref_adam_option = {'lr':5e-4, 'betas':(0.9, 0.999), 'eps':1e-08,
+            'weight_decay':0, 'amsgrad':False}
+        tst_adam_option = dict(ref_adam_option, **{'use_mt':True})
+
+        tensors = []
+        fp16_params = []
+        for size in sizes:
+            tensors.append(torch.rand(size, dtype=torch.float, device='cuda'))
+            fp16_params.append(torch.nn.Parameter(tensors[-1].clone().half()))
+
+        ref_param, tst_param, ref_optim, tst_optim = \
+            self.gen_param_optim(tensors, ref_adam_option, tst_adam_option)
+
+        for i in range(self.iters):
+            half_grads = self.gen_mixed_grad(ref_param, tst_param)
+            ref_optim.step()
+            tst_optim.step(grads=half_grads, output_params=fp16_params)
+
+            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
+            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
+            self.assertLessEqual(max_rel_diff, self.max_rel_diff)
+
+            max_abs_diff, max_rel_diff = self.get_max_diff(tst_param, \
+                fp16_params)
+            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
+            self.assertLessEqual(max_rel_diff, self.max_rel_diff)
 
 if __name__ == '__main__':
     script_path = os.path.dirname(os.path.realpath(__file__))