Support non-contiguous NestedTensors for elementwise ops (#87888)

Enables benchmarking of math path of sdp kernel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87888
Approved by: https://github.com/drisspg

aten/src/ATen/native/native_functions.yaml

@@ -1881,6 +1881,7 @@
   dispatch:
     SparseCPU, SparseCUDA: div_sparse
     ZeroTensor: div_zerotensor
+    NestedTensorCPU, NestedTensorCUDA: NestedTensor_div_Tensor
   tags: canonical
 
 - func: div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
@@ -1928,6 +1929,7 @@
   variants: function, method
   dispatch:
     CompositeExplicitAutograd: div
+    NestedTensorCPU, NestedTensorCUDA: NestedTensor_div_Scalar
   tags: canonical
 
 - func: div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)

aten/src/ATen/native/nested/NestedTensorMath.cpp

@@ -500,10 +500,21 @@ get_elementwise_nested_tensor_impl(
       op_name,
       " does not support broadcasting when given a NestedTensor");
   TORCH_CHECK(
-      nested_tensor_impl_is_contiguous(self_ptr) &&
-          nested_tensor_impl_is_contiguous(other_ptr),
+      at::equal(
+          self_ptr->get_nested_stride_tensor(),
+          other_ptr->get_nested_stride_tensor()),
       op_name,
-      " does not support non-contiguous NestedTensor inputs");
+      " requires strides to match when given NestedTensors");
+  auto self_offsets = self_ptr->get_storage_offsets();
+  auto other_offsets = other_ptr->get_storage_offsets();
+  bool offsets_match = true;
+  for (size_t i = 0; i < self_offsets.size(); i++) {
+    offsets_match = offsets_match && (self_offsets[i] == other_offsets[i]);
+  }
+  TORCH_CHECK(
+      offsets_match,
+      op_name,
+      " requires offsets to match when given NestedTensors");
   return std::make_pair(self_ptr, other_ptr);
 }
 
@@ -517,16 +528,20 @@ Tensor NestedTensor_elementwise_Tensor(
   if (!self.is_nested() && self.dim() == 0 && self.numel() == 1) {
     auto other_impl = get_nested_tensor_impl(other);
     return wrap_buffer(
-      f(self, other_impl->get_buffer()),
-      other_impl->get_nested_size_tensor().clone()
+      f(self, other_impl->get_unsafe_storage_as_tensor()),
+      other_impl->get_nested_size_tensor().clone(),
+      other_impl->get_nested_stride_tensor().clone(),
+      other_impl->get_storage_offsets()
     );
   }
   // other is a scalar
   if (!other.is_nested() && other.dim() == 0 && other.numel() == 1) {
     auto self_impl = get_nested_tensor_impl(self);
     return wrap_buffer(
-      f(self_impl->get_buffer(), other),
-      self_impl->get_nested_size_tensor().clone()
+      f(self_impl->get_unsafe_storage_as_tensor(), other),
+      self_impl->get_nested_size_tensor().clone(),
+      self_impl->get_nested_stride_tensor().clone(),
+      self_impl->get_storage_offsets()
     );
   }
   NestedTensorImpl* self_impl = nullptr;
@@ -535,13 +550,12 @@ Tensor NestedTensor_elementwise_Tensor(
       get_elementwise_nested_tensor_impl(self, other, op_name);
   TORCH_INTERNAL_ASSERT_DEBUG_ONLY(self_impl);
   TORCH_INTERNAL_ASSERT_DEBUG_ONLY(other_impl);
-  const auto& nt_self = *self_impl;
-  const auto& nt_other = *other_impl;
-  const auto& self_sizes = nt_self.get_nested_size_tensor();
   return wrap_buffer(
-      f(nt_self.get_buffer().reshape({-1}),
-        nt_other.get_buffer().reshape({-1})),
-      self_sizes);
+      f(self_impl->get_unsafe_storage_as_tensor(),
+        other_impl->get_unsafe_storage_as_tensor()),
+      self_impl->get_nested_size_tensor(),
+      self_impl->get_nested_stride_tensor(),
+      self_impl->get_storage_offsets());
 }
 
 Tensor NestedTensor_add_Tensor(
@@ -566,6 +580,18 @@ Tensor NestedTensor_mul_Scalar(const Tensor& self, const Scalar& other) {
   return NestedTensor_mul_Tensor(self, wrapped_scalar_tensor(other));
 }
 
+Tensor NestedTensor_div_Tensor(const Tensor& self, const Tensor& other) {
+  return NestedTensor_elementwise_Tensor(
+      self, other, "div", [](const Tensor& b1, const Tensor& b2) {
+        return at::div(b1, b2);
+      });
+}
+
+// Only usable on the C++ side; scalars are converted to tensors coming from Python.
+Tensor NestedTensor_div_Scalar(const Tensor& self, const Scalar& other) {
+  return NestedTensor_div_Tensor(self, wrapped_scalar_tensor(other));
+}
+
 template <typename Func>
 Tensor& NestedTensor_elementwise__Tensor(
     Tensor& self,

aten/src/ATen/native/nested/NestedTensorUtils.h

@@ -1,7 +1,7 @@
 #pragma once
 
-#include <ATen/core/Tensor.h>
 #include <ATen/NestedTensorImpl.h>
+#include <ATen/core/Tensor.h>
 #include <c10/core/DispatchKeySet.h>
 #include <c10/core/TensorImpl.h>
 #include <c10/macros/Macros.h>
@@ -50,6 +50,18 @@ inline at::Tensor wrap_buffer(
       std::move(offsets));
 }
 
+inline at::Tensor wrap_buffer(
+    at::Tensor buffer,
+    at::Tensor nested_size_tensor,
+    at::Tensor nested_stride_tensor,
+    const std::vector<int64_t>& offsets) {
+  std::vector<int64_t> offsets_copy(offsets);
+  return wrap_buffer(buffer,
+                     nested_size_tensor,
+                     nested_stride_tensor,
+                     std::move(offsets_copy));
+}
+
 inline at::Tensor get_buffer(const at::Tensor& tensor) {
   return get_nested_tensor_impl(tensor)->get_buffer();
 }
@@ -119,7 +131,6 @@ inline std::vector<IntArrayRef> NestedTensor_get_sizes(
   return sizes;
 }
 
-
 TORCH_API std::vector<int64_t> NestedTensor_get_max_size(
     const NestedTensorImpl& nt);
 
@@ -161,17 +172,18 @@ inline std::vector<IntArrayRef> NestedTensor_get_strides(
 inline void check_numel_equals_buffer_size(const at::Tensor& self) {
   auto self_impl = get_nested_tensor_impl(self);
   TORCH_CHECK(
-      self.numel() == self_impl -> get_buffer_size(),
+      self.numel() == self_impl->get_buffer_size(),
       "Number of elements in nested tensor must match number of elements in buffer.");
 }
 
 inline void check_numel_equals_buffer_size(const NestedTensorImpl* self_ptr) {
   TORCH_CHECK(
-      self_ptr-> numel() == self_ptr -> get_buffer_size(),
+      self_ptr->numel() == self_ptr->get_buffer_size(),
       "Number of elements in nested tensor must match number of elements in buffer.");
 }
 //  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-// Data structures and functions for generically applying a function on a nested tensor.
+// Data structures and functions for generically applying a function on a nested
+// tensor.
 namespace impl {
 
 template <typename T>

aten/src/ATen/native/transformers/attention.cpp

@@ -703,7 +703,7 @@ std::tuple<Tensor, Tensor> _scaled_dot_product_attention_math(
     auto attn_mask = attn_mask_;
     // Naive, composite implementation defined here.
     const auto embed_size = query_.size(-1);
-    const auto query = query_ * (1. / ::sqrt(static_cast<double>(embed_size)));
+    const auto query = query_ / ::sqrt(static_cast<double>(embed_size));
     if (is_causal) {
         TORCH_CHECK(!attn_mask.has_value(),
                 "_scaled_dot_product_attention: Explicit attn_mask should not be set when is_causal=True");

benchmarks/transformer/sdp.py

@@ -7,6 +7,8 @@ import random
 
 import warnings
 warnings.filterwarnings("ignore")
+
+
 class CompositeMHA(torch.nn.Module):
     def __init__(self, num_heads, in_proj_weight, in_proj_bias, out_proj):
         super().__init__()
@@ -90,8 +92,8 @@ def benchmark_torch_function(iters, f, *args, **kwargs):
     return (start_event.elapsed_time(end_event) * 1.0e-3) / iters
 
 
-def run_timing(iters, batch_size, embed_dimension, num_heads, max_sequence_len, pad_percentage, writer):
-    with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=True):
+def run_timing(iters, batch_size, embed_dimension, num_heads, max_sequence_len, pad_percentage, enable_math, enable_flash, writer):
+    with torch.backends.cuda.sdp_kernel(enable_math=enable_math, enable_flash=enable_flash):
         with torch.inference_mode():
             dropout_p = 0.0
             mask = None
@@ -122,6 +124,8 @@ def run_timing(iters, batch_size, embed_dimension, num_heads, max_sequence_len,
             results["cp_time"] = cp_time
             results["speedup"] = pt_time / cp_time
             results["dtype"] = str(x.dtype)
+            results["enable_math"] = str(enable_math)
+            results["enable_flash"] = str(enable_flash)
             writer.writerow(results)
 
 
@@ -131,15 +135,22 @@ def main():
     np.random.seed(seed)
     torch.manual_seed(seed)
 
-    headers = ["max_sequence_len", "num_heads", "embed_dimension", "pt_time", "cp_time", "speedup", "dtype"]
+    headers = ["max_sequence_len", "num_heads", "embed_dimension", "pt_time",
+               "cp_time", "speedup", "dtype", "enable_math", "enable_flash"]
     writer = csv.DictWriter(sys.stdout, headers)
     writer.writeheader()
 
     batch_size = 64
     pad_percentage = 0.5
 
-    for num_heads, max_seq_len in itertools.product([2, 4, 8, 16, 32], [64, 128, 256]):
-        run_timing(iters, batch_size, 1024, num_heads, max_seq_len, pad_percentage, writer)
+    for (enable_math, enable_flash) in [(False, True), (True, False), (True, True)]:
+        for num_heads, max_seq_len in itertools.product([2, 4, 8, 16, 32], [64, 128, 256]):
+            run_timing(iters, batch_size, 1024, num_heads, max_seq_len,
+                       pad_percentage, enable_math, enable_flash, writer)
+            run_timing(iters, batch_size, 1024, num_heads, max_seq_len,
+                       pad_percentage, enable_math, enable_flash, writer)
+            run_timing(iters, batch_size, 1024, num_heads, max_seq_len,
+                       pad_percentage, enable_math, enable_flash, writer)
 
 
 if __name__ == "__main__":

test/test_nestedtensor.py

@@ -24,6 +24,8 @@ from torch.testing._internal.common_utils import (
 
 # Tests are ported from pytorch/nestedtensor.
 # This makes porting as_nested_tensor easier in the future.
+
+
 def _iter_constructors():
     # yield as_nested_tensor
     yield torch.nested.nested_tensor
@@ -33,6 +35,8 @@ def _iter_constructors():
 # an output nested tensor consists of
 # * `len(ragged_sizes)` matrices
 # * matrices[i].shape == (20, ragged_sizes[i])
+
+
 def random_nt_noncontiguous_pair(ragged_sizes, device="cpu", dtype=torch.float16):
     xs = []
     for size in ragged_sizes:
@@ -49,6 +53,8 @@ def random_nt_noncontiguous_pair(ragged_sizes, device="cpu", dtype=torch.float16
 
 # Helper functions to pad a noncontiguous nested tensor
 # can be replaced once to_padded_tensor supports noncontiguous memory
+
+
 def noncontiguous_to_padded_tensor(input, shape=None):
     tensors = input.unbind()
     ntensors = len(tensors)
@@ -72,6 +78,8 @@ def noncontiguous_to_padded_tensor(input, shape=None):
     return result
 
 # Helper function to generate a random nested tensor
+
+
 def random_nt(device, dtype, num_tensors, max_dims, min_dims=None):
     if min_dims is None:
         min_dims = tuple([0] * len(max_dims))
@@ -83,6 +91,7 @@ def random_nt(device, dtype, num_tensors, max_dims, min_dims=None):
         ts1.append(t1)
     return torch.nested.nested_tensor(ts1, device=device, dtype=dtype)
 
+
 class TestNestedTensor(TestCase):
 
     @torch.inference_mode()
@@ -478,7 +487,6 @@ class TestNestedTensorDeviceType(TestCase):
         self.assertEqual(a.grad, torch.ones(2, 4, device=device, dtype=dtype))
         self.assertEqual(b.grad, torch.ones(5, 4, device=device, dtype=dtype))
 
-
     @dtypes(torch.float, torch.float16, torch.double)
     def test_unbind_noncontiguous(self, device, dtype):
         nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7), device, dtype)
@@ -857,6 +865,39 @@ class TestNestedTensorDeviceType(TestCase):
             lambda: vector.mul(nt1)
         )
 
+    @dtypes(torch.float, torch.float16)
+    @skipMeta
+    @torch.inference_mode()
+    def test_nested_tensor_div(self, device, dtype):
+        nt, nt2 = self.random_nt_pair(device, dtype, 4, (4, 4))
+        scale = 4.0
+        ref = torch.nested.nested_tensor([t / scale for t in nt.unbind()])
+        out = nt / 4.0
+        self.assertEqual(ref, out)
+        ref_transposed = ref.transpose(1, 2)
+        out = nt.transpose(1, 2) / 4.0
+        self.assertEqual(ref_transposed, out)
+
+        ref = torch.nested.nested_tensor([t / t2 for (t, t2) in zip(nt.unbind(), nt2.unbind())])
+        out = nt / nt2
+        self.assertEqual(ref, out)
+
+        out = nt.transpose(1, 2) / nt2.transpose(1, 2)
+        self.assertEqual(ref.transpose(1, 2), out)
+
+        nt_transpose_copy = torch.nested.nested_tensor([t.transpose(0, 1) for t in nt.unbind()])
+
+        self.assertRaisesRegex(
+            RuntimeError, "div requires strides to match when given NestedTensors",
+            lambda: nt_transpose_copy.transpose(1, 2) / nt2)
+
+        nt = torch.nested.nested_tensor([torch.randn(i, 4) for i in [3, 4, 5]], device=device, dtype=dtype)
+        nt_chunks = nt.chunk(2, -1)
+        self.assertRaisesRegex(
+            RuntimeError, "div requires offsets to match when given NestedTensors",
+            lambda: nt_chunks[0] / nt_chunks[1])
+
+
     @dtypes(torch.float, torch.float16)
     @skipMeta
     @torch.inference_mode()
@@ -1732,7 +1773,6 @@ class TestNestedTensorAutograd(TestCase):
         return torch.nested.as_nested_tensor([torch.randn(1, 2, requires_grad=requires_grad),
                                               torch.randn(7, 8, requires_grad=requires_grad)])
 
-
     def _create_nested_tensor_from_mask(self, requires_grad=False):
         data = torch.randn(2, 3, 4, requires_grad=requires_grad)
         mask = torch.ones_like(data[:, :, 0]).bool()
@@ -1772,7 +1812,6 @@ class TestNestedTensorAutograd(TestCase):
         self.assertEqual(a.grad, None)
         self.assertEqual(b.grad, None)
 
-
     def test_set_requires_grad_from_list(self):
         nt = self._create_nested_tensor_from_list()
         nt.requires_grad_()
@@ -2139,6 +2178,7 @@ class TestNestedTensorAutograd(TestCase):
         expected_grad = torch.nested.nested_tensor([grad_x0, torch.zeros((3, 4))])
         self.assertEqual(nt.grad, expected_grad)
 
+
 instantiate_device_type_tests(TestNestedTensorDeviceType, globals())
 
 if __name__ == '__main__':