Multi-tensor axpby kernel for more flexible unscaling (groundwork for p…

…ytorch#163 and pytorch#179 fix)

csrc/amp_C_frontend.cpp

@@ -6,7 +6,16 @@ void multi_tensor_scale_cuda(
   std::vector<std::vector<at::Tensor>> tensor_lists,
   float scale);
 
+void multi_tensor_axpby_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  float a,
+  float b);
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("multi_tensor_scale", &multi_tensor_scale_cuda,
         "Fused overflow check + scale for a list of contiguous tensors");
+  m.def("multi_tensor_axpby", &multi_tensor_axpby_cuda,
+        "out = a*x + b*y for a list of contiguous tensors");
 }

csrc/multi_tensor_axpby_kernel.cu

@@ -0,0 +1,110 @@
+#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 x_t, typename y_t, typename out_t>
+struct AxpbyFunctor
+{
+   __device__ __forceinline__ void operator()(
+    int chunk_size,
+    volatile int* noop_gmem,
+    TensorListMetadata<3>& tl,
+    float a,
+    float b)
+  {
+    // I'd like this kernel to propagate infs/nans.
+    // if(*noop_gmem == 1)
+    //   return;
+
+    int tensor_loc = tl.block_to_tensor[blockIdx.x];
+    int chunk_idx = tl.block_to_chunk[blockIdx.x];
+    int n = tl.sizes[tensor_loc];
+
+    x_t* x = (x_t*)tl.addresses[0][tensor_loc];
+    x += chunk_idx*chunk_size;
+
+    y_t* y = (y_t*)tl.addresses[1][tensor_loc];
+    y += chunk_idx*chunk_size;
+
+    out_t* out = (out_t*)tl.addresses[2][tensor_loc];
+    out += chunk_idx*chunk_size;
+
+    n -= chunk_idx*chunk_size;
+
+    // Non-divergent exit condition for __syncthreads, not necessary here
+    float xs[ILP];
+    float ys[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++)
+      {
+        xs[ii] = 0;
+        ys[ii] = 0;
+        int i = i_start + threadIdx.x + ii*blockDim.x;
+        if(i < n && i < chunk_size)
+        {
+          xs[ii] = static_cast<float>(x[i]);
+          ys[ii] = static_cast<float>(y[i]);
+        }
+      }
+
+      // see note in multi_tensor_scale_kernel.cu
+      #pragma unroll
+      for(int ii = 0; ii < ILP; ii++)
+      {
+        int i = i_start + threadIdx.x + ii*blockDim.x;
+        if(i < n && i < chunk_size)
+          if(isfinite(xs[ii]) && isfinite(ys[ii]))
+            out[i] = static_cast<out_t>(a*xs[ii] + b*ys[ii]);
+          else
+          {
+            out[i] = static_cast<out_t>(a*xs[ii] + b*ys[ii]);
+            *noop_gmem = 1; // Blindly fire off a write.  These will race but that's ok.
+          }
+      }
+    }
+  }
+};
+
+void multi_tensor_axpby_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  float a,
+  float b)
+{
+  using namespace at;
+  // The output (downscaled) type is always float.
+  // If build times suffer, think about where to put this dispatch,
+  // and what logic should be moved out of multi_tensor_apply.
+
+  DISPATCH_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(), 0, "multi_tensor_axpby_cuda",
+    DISPATCH_FLOAT_AND_HALF(tensor_lists[1][0].scalar_type(), 1, "multi_tensor_axpby_cuda",
+      DISPATCH_FLOAT_AND_HALF(tensor_lists[2][0].scalar_type(), 2, "multi_tensor_axpby_cuda",
+           multi_tensor_apply<3>(
+             BLOCK_SIZE,
+             chunk_size,
+             noop_flag,
+             tensor_lists,
+             AxpbyFunctor<scalar_t_0, scalar_t_1, scalar_t_2>(),
+             a,
+             b); )))
+
+  AT_CUDA_CHECK(cudaGetLastError());
+
+  // AT_CUDA_CHECK(cudaDeviceSynchronize());
+}

csrc/type_shim.h

@@ -12,3 +12,22 @@ struct TypeShim
   // Enable dispatch switch statements to take *this directly for  post-3aeb78
   operator at::ScalarType(){ return payload.scalarType(); };
 };
+
+#define DISPATCH_FLOAT_AND_HALF(TYPE, LEVEL, NAME, ...) \
+  switch(TYPE) \
+  { \
+    case at::ScalarType::Float: \
+    { \
+      using scalar_t_##LEVEL = float; \
+      __VA_ARGS__; \
+      break; \
+    } \
+    case at::ScalarType::Half: \
+    { \
+      using scalar_t_##LEVEL = at::Half; \
+      __VA_ARGS__; \
+      break; \
+    } \
+    default: \
+      AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'");  \
+  }

setup.py

@@ -48,10 +48,12 @@
         ext_modules.append(
             CUDAExtension(name='amp_C',
                           sources=['csrc/amp_C_frontend.cpp',
-                                   'csrc/multi_tensor_scale_kernel.cu'],
+                                   'csrc/multi_tensor_scale_kernel.cu',
+                                   'csrc/multi_tensor_axpby_kernel.cu'],
                           extra_compile_args={'cxx': ['-O3'],
                                               'nvcc':['-lineinfo',
                                                       '-O3',
+                                                      # '--resource-usage',
                                                       '--use_fast_math']}))
         ext_modules.append(
             CUDAExtension(name='fused_adam_cuda',

tests/L0/run_amp/test_multi_tensor_axpby.py

@@ -0,0 +1,128 @@
+import unittest
+
+import functools as ft
+import itertools as it
+
+from apex import amp
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from utils import common_init, HALF, FLOAT,\
+    ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
+
+try:
+  import amp_C
+  from amp_C import multi_tensor_axpby
+  from apex.multi_tensor_apply import MultiTensorApply
+  disabled = False
+except ImportError as err:
+  print("amp_C fused kernels unavailable, disabling TestMultiTensorApply.  ImportError was ", err)
+  disabled = True
+
+
+class TestMultiTensorAxpby(unittest.TestCase):
+
+    def setUp(self):
+        common_init(self)
+
+        self.a = 2.0
+        self.b = 8.0
+        self.xval = 4.0
+        self.yval = 16.0
+        self.overflow_buf = torch.cuda.IntTensor(1).zero_()
+        self.ref = torch.cuda.FloatTensor([136.0])
+
+    def tearDown(self):
+        pass
+
+    # The tensor creation here is written for convenience, not speed.
+    def axpby(self, sizea, sizeb, applier, repeat_tensors,
+              x_type, y_type, out_type, inplace=False):
+        self.overflow_buf.zero_()
+        t1 = torch.cuda.FloatTensor(sizea).fill_(1.0)
+        t2 = torch.cuda.FloatTensor(sizeb).fill_(1.0)
+
+        y_list = []
+        for i in range(repeat_tensors):
+            y_list += [t1.clone().to(y_type)*self.yval, t2.clone().to(y_type)*self.yval]
+
+        x_list = [x.clone().to(x_type)*(self.xval/self.yval) for x in y_list]
+
+        if inplace:
+            out_list = y_list
+        else:
+            out_list = [out.clone().to(out_type)*3.0 for out in y_list]
+
+        applier(multi_tensor_axpby, self.overflow_buf, [x_list, y_list, out_list], self.a, self.b)
+
+        self.assertTrue(all([torch.allclose(out, self.ref.to(out_type)) for out in out_list]),
+                        msg="{} {} {} {} {} {} {}".format(sizea, sizeb, repeat_tensors,
+                        x_type, y_type, out_type, inplace))
+        self.assertTrue(self.overflow_buf.item() == 0,
+                        msg="{} {} {} {} {} {} {}".format(sizea, sizeb, repeat_tensors,
+                        x_type, y_type, out_type, inplace))
+
+    # def find_inf(self, sizea, sizeb, applier, repeat_tensors, in_type, out_type, t, ind, val, inplace=False):
+    #     self.overflow_buf.zero_()
+    #     a = torch.cuda.FloatTensor(sizea).fill_(self.scale)
+    #     b = torch.cuda.FloatTensor(sizeb).fill_(self.scale)
+
+    #     out_list = []
+    #     for i in range(repeat_tensors):
+    #         out_list += [a.clone().to(out_type), b.clone().to(out_type)]
+
+    #     if inplace:
+    #         in_list = out_list
+    #     else:
+    #         in_list = [out.clone().to(in_type) for out in out_list]
+
+    #     applier(multi_tensor_scale, self.overflow_buf, [in_list, out_list], 1./self.scale)
+
+    #     self.overflow_buf.zero_()
+    #     in_list[t][ind] = val
+    #     applier(multi_tensor_scale, self.overflow_buf, [in_list, out_list], 1./self.scale)
+    #     self.assertTrue(self.overflow_buf.item())
+
+    @unittest.skipIf(disabled, "amp_C is unavailable")
+    def test_fuzz(self):
+        input_size_pairs = (
+            (7777*77, 555*555),
+            (777, 555),
+            (555, 2048*32+1),
+            (2048*32+1, 555),
+            (555, 2048*32),
+            (2048*32, 555),
+            (33333, 555),
+            (555, 33333))
+        appliers = (
+            MultiTensorApply(2048*32),
+            MultiTensorApply(333),
+            MultiTensorApply(33333))
+        repeat_tensors = (
+            1,
+            55)
+
+        for sizea, sizeb in input_size_pairs:
+          for applier in appliers:
+            for repeat in repeat_tensors:
+              for x_type in (torch.float32, torch.float16):
+                for y_type in (torch.float32, torch.float16):
+                  for out_type in (torch.float32, torch.float16):
+                    for inplace in (True, False):
+                      if inplace is True and (y_type is not out_type):
+                        continue
+                      else:
+                        self.axpby(sizea, sizeb, applier, repeat,
+                                   x_type, y_type, out_type, inplace=inplace)
+                      # self.find_inf(sizea, sizeb, applier, repeat, in_type, out_type,
+                      #               0, 0, float('nan'), inplace=inplace)
+                      # self.find_inf(sizea, sizeb, applier, repeat, in_type, out_type,
+                      #               2*repeat-1, sizeb-1, float('inf'), inplace=inplace)
+                      # self.find_inf(sizea, sizeb, applier, repeat, in_type, out_type,
+                      #              2*(repeat//2), sizea//2, float('inf'), inplace=inplace)
+
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/L0/run_amp/test_multi_tensor_scale.py

@@ -24,12 +24,11 @@
 class TestMultiTensorScale(unittest.TestCase):
 
     def setUp(self):
+        common_init(self)
         self.scale = 4.0
         self.overflow_buf = torch.cuda.IntTensor(1).zero_()
         self.ref = torch.cuda.FloatTensor([1.0])
 
-        common_init(self)
-
     def tearDown(self):
         pass