[PyTorch] Expose function to bulk-allocate tensors backed by the same buffer

transformer_engine/pytorch/csrc/extensions.h

@@ -309,6 +309,16 @@ std::vector<py::object> rmsnorm_fwd(const py::handle &input, const py::handle &w
                                     py::object ln_out, py::handle quantizer, DType otype,
                                     const int sm_margin, const bool zero_centered_gamma);
 
+/***************************************************************************************************
+ * Memory allocation
+ **************************************************************************************************/
+
+// Allocates tensors all backed by a single contiguous buffer.
+std::vector<at::Tensor> bulk_allocate(const std::vector<std::vector<size_t>> &shapes,
+                                      const std::vector<at::ScalarType> &dtypes,
+                                      std::optional<c10::Device> device = std::nullopt,
+                                      std::optional<std::vector<size_t>> alignments = std::nullopt);
+
 /***************************************************************************************************
  * Cast
  **************************************************************************************************/

transformer_engine/pytorch/csrc/extensions/allocate.cpp

@@ -0,0 +1,86 @@
+/*************************************************************************
+ * Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ *
+ * See LICENSE for license information.
+ ************************************************************************/
+
+#include <memory>
+#include <vector>
+
+#include "../extensions.h"
+
+namespace transformer_engine {
+namespace pytorch {
+
+std::vector<at::Tensor> bulk_allocate(const std::vector<std::vector<size_t>> &shapes,
+                                      const std::vector<at::ScalarType> &dtypes,
+                                      std::optional<at::Device> device,
+                                      std::optional<std::vector<size_t>> alignments) {
+  // Check shapes and dtypes
+  const size_t n = shapes.size();
+  NVTE_CHECK(dtypes.size() == n, "Got ", shapes.size(), " shapes and ", dtypes.size(), " dtypes.");
+  NVTE_CHECK(!alignments || alignments->size() == n, "Got ", shapes.size(), " shapes and ",
+             alignments->size(), " alignments.");
+
+  // Return immediately if no tensors are needed
+  if (n == 0) return {};
+
+  // Set defaults for optional arguments
+  if (!device) {
+    device = at::Device(at::kCUDA);
+  }
+  if (!alignments) {
+    alignments = std::vector<size_t>{};
+    alignments->reserve(n);
+    for (const auto &dtype : dtypes) {
+      alignments->push_back(c10::elementSize(dtype));
+    }
+  }
+
+  // Compute offsets in base buffer
+  std::vector<size_t> byte_sizes(n);
+  std::vector<size_t> offsets(n);
+  size_t base_byte_size = 0;
+  size_t base_alignment = 1;
+  for (size_t i = 0; i < n; ++i) {
+    byte_sizes[i] = product(shapes[i]) * at::elementSize(dtypes[i]);
+    offsets[i] = roundup(base_byte_size, (*alignments)[i]);
+    base_byte_size = offsets[i] + byte_sizes[i];
+    base_alignment = std::max(base_alignment, (*alignments)[i]);
+  }
+  if (base_alignment > 1) {
+    // Pad in case data pointer is not aligned
+    base_byte_size += base_alignment;
+  }
+
+  // Allocate base buffer
+  auto base_buffer = std::make_shared<at::Tensor>(
+      at::empty({static_cast<int64_t>(base_byte_size)}, at::device(*device).dtype(torch::kUInt8)));
+  uint8_t *base_ptr = base_buffer->data_ptr<uint8_t>();
+  base_ptr =
+      reinterpret_cast<uint8_t *>(roundup(reinterpret_cast<uintptr_t>(base_ptr), base_alignment));
+
+  // Create views into base buffer
+  std::vector<at::Tensor> out;
+  out.reserve(n);
+  std::vector<int64_t> shape_int64;
+  for (size_t i = 0; i < n; ++i) {
+    shape_int64.assign(shapes[i].begin(), shapes[i].end());
+    if (byte_sizes[i] == 0) {
+      // Work around problems with from_blob when constructing an
+      // empty tensor. Passing a null pointer fails because it checks
+      // that the pointer is on GPU. Passing a non-null pointer can
+      // cause bugs in TE kernels.
+      out.emplace_back(at::empty(shape_int64, at::device(*device).dtype(dtypes[i])));
+    } else {
+      // Construct tensor with custom deleter to keep base buffer alive
+      out.emplace_back(at::from_blob(
+          base_ptr + offsets[i], shape_int64, [base_buffer](void *) {},
+          at::device(*device).dtype(dtypes[i])));
+    }
+  }
+  return out;
+}
+
+}  // namespace pytorch
+}  // namespace transformer_engine