Add option to enforce alignment constraint when planning memory (#8003)

Summary:

Add ability to enforce start alignment in Cadence Memory Planner

Reviewed By: zonglinpeng

Differential Revision: D68762973

Author: Eashan Garg

backends/cadence/aot/compiler.py

@@ -264,6 +264,7 @@ def export_to_executorch_gen_etrecord(
     alloc_graph_output: bool = True,
     memory_config: Optional[MemoryConfig] = None,
     dump_graphs: bool = False,
+    mem_alignment: int = 1,
 ) -> ExecutorchProgramManager:
     cadence_passes = get_cadence_passes(opt_level)
     edge_prog_manager = export_to_edge(model, inputs, dump_graphs)
@@ -290,6 +291,7 @@ def export_to_executorch_gen_etrecord(
         mem_algo=mem_algo,
         alloc_graph_input=alloc_graph_input,
         alloc_graph_output=alloc_graph_output,
+        mem_alignment=mem_alignment,
     )
 
     # Get executorch program after Cadence specific passes

backends/cadence/aot/memory_planning.py

@@ -9,6 +9,7 @@
 import collections
 import itertools
 import logging
+import math
 import typing
 from functools import partial
 from typing import Iterable, List, Optional, Tuple
@@ -39,6 +40,10 @@ def get_size(memory_config: MemoryConfig, exir_id: int) -> int:
     return memory_config.memory_sizes[exir_id - 1]
 
 
+def get_aligned_offset(pre_aligned_offset: int, alignment: int) -> int:
+    return int(math.ceil(pre_aligned_offset / alignment) * alignment)
+
+
 def collect_specs_from_graph_module(
     graph_module: torch.fx.GraphModule,
     alloc_graph_input: bool,
@@ -95,7 +100,7 @@ def overlap(spec: TensorSpec) -> Optional[TensorSpec]:
         return None
 
     def memory_available(spec: TensorSpec) -> bool:
-        return spec.mem_offset + spec.allocated_memory <= get_size(
+        return get_aligned_offset(spec.mem_offset + spec.allocated_memory, alignment) <= get_size(
             memory_config, spec.mem_id
         )
 
@@ -116,7 +121,7 @@ def memory_available(spec: TensorSpec) -> bool:
                 continue
             spec.mem_offset = 0
             while memory_available(spec) and (overlapped := overlap(spec)):
-                spec.mem_offset = overlapped.mem_offset + overlapped.allocated_memory
+                spec.mem_offset = get_aligned_offset(overlapped.mem_offset + overlapped.allocated_memory, alignment)
             if memory_available(spec):
                 allocated_buffers[spec.mem_id].append(spec)
                 bufsizes[spec.mem_id] = max(
@@ -202,11 +207,11 @@ def greedy_by_size_for_offset_calculation_with_hierarchy(
                     # calculation of gap incorrect. Moving it out will make the algorithm degenerate
                     # to the naive one, reusing 0 tensor. The paper may have a typo here.
                     prev_offset = max(
-                        allocated_spec.mem_offset + allocated_spec.allocated_memory,
+                        get_aligned_offset(allocated_spec.mem_offset + allocated_spec.allocated_memory, alignment),
                         prev_offset,
                     )
             if spec.mem_offset is None:
-                if prev_offset + spec.allocated_memory > get_size(
+                if get_aligned_offset(prev_offset + spec.allocated_memory, alignment) > get_size(
                     memory_config, spec.mem_id
                 ):
                     continue
@@ -423,6 +428,7 @@ def __init__(
                 ]
             ]
         ] = None,
+        mem_alignment: int = 1,
     ) -> None:
         self._init_mem_algos()
 
@@ -433,6 +439,9 @@ def __init__(
         self.alloc_graph_output = alloc_graph_output
         self.additional_constraint_gen_passes = additional_constraint_gen_passes
 
+        assert mem_alignment > 0, "mem_alignment must be positive"
+        self.mem_alignment = mem_alignment
+
     def _init_mem_algos(self) -> None:
         self.available_mem_algos = [
             position_based_greedy_with_hierarchy,
@@ -459,6 +468,7 @@ def __call__(self, graph_module: torch.fx.GraphModule) -> PassResult:
             allow_lifetime_and_storage_overlap=(self.opt_level >= 2),
             alloc_graph_input=self.alloc_graph_input,
             alloc_graph_output=self.alloc_graph_output,
+            alignment=self.mem_alignment,
         )
         mem_planning(graph_module)
 

backends/cadence/aot/tests/test_memory_passes.py

@@ -1,5 +1,7 @@
 # (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
 
+# pyre-strict
+
 import logging
 import math
 import unittest
@@ -13,10 +15,11 @@
 from executorch.exir import memory
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.tests.models import MultiLayerPerceptron
+from executorch.exir.memory_planning import collect_specs_from_nodes
 
 
 class TestMemPlanningPasses(unittest.TestCase):
-    def test_calculate_peak_memory_pass(self):
+    def test_calculate_peak_memory_pass(self) -> None:
         class PeakMemoryTestModel(torch.nn.Module):
             def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
                 super().__init__()
@@ -30,7 +33,7 @@ def forward(self, x: torch.Tensor):
                 x = self.linear2(x)
                 return x
 
-        def calculate_aligned_num_bytes(num: int, alignment: int = 16):
+        def calculate_aligned_num_bytes(num: int, alignment: int = 16) -> int:
             return math.ceil(num / alignment) * alignment
 
         # model 1
@@ -84,7 +87,7 @@ def calculate_aligned_num_bytes(num: int, alignment: int = 16):
         )  # Align data on a 16 byte boundary
         self.assertEqual(peak_usage, expected_peak_usage)
 
-    def test_zero_memory_pass(self):
+    def test_zero_memory_pass(self) -> None:
         class ZeroMem(torch.nn.Module):
             def forward(self, x):
                 return x[:, 2::3, ...]
@@ -186,7 +189,7 @@ def _verify_select_nop_memory_alloc(self, node: torch.fx.Node) -> None:
             f"{spec=} {arg_spec=}",
         )
 
-    def verify_nop_memory_alloc(self, graph_module):
+    def verify_nop_memory_alloc(self, graph_module: torch.fx.GraphModule) -> None:
         for node in graph_module.graph.find_nodes(
             op="call_function", target=torch.ops.aten._cat_nop.out
         ):
@@ -202,7 +205,7 @@ def verify_nop_memory_alloc(self, graph_module):
         ):
             self._verify_select_nop_memory_alloc(node)
 
-    def test_optimize_cat_on_placeholders(self):
+    def test_optimize_cat_on_placeholders(self) -> None:
         class Cat(torch.nn.Module):
             def forward(self, x, y):
                 return torch.ops.aten.cat((x, y))
@@ -226,7 +229,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_cat_outermost(self):
+    def test_optimize_cat_outermost(self) -> None:
         class OptimizeCatFeasible1(torch.nn.Module):
             def forward(self, x, y):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -253,7 +256,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_cat_non_outermost(self):
+    def test_optimize_cat_non_outermost(self) -> None:
         class OptimizeCatFeasible2(torch.nn.Module):
             def forward(self, x, y):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -280,7 +283,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_no_optimize_cat_non_outermost(self):
+    def test_no_optimize_cat_non_outermost(self) -> None:
         class OptimizeCatInfeasible1(torch.nn.Module):
             def forward(self, x, y):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -306,7 +309,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_no_optimize_cat_non_outermost1(self):
+    def test_no_optimize_cat_non_outermost1(self) -> None:
         class OptimizeCatInfeasible2(torch.nn.Module):
             def forward(self, x, y):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -333,7 +336,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_cat_with_slice(self):
+    def test_optimize_cat_with_slice(self) -> None:
         class OptimizeCatSliceFeasible(torch.nn.Module):
             def forward(self, x):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -362,7 +365,7 @@ def forward(self, x):
         self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_cat_with_slice_infeasible(self):
+    def test_optimize_cat_with_slice_infeasible(self) -> None:
         class OptimizeCatSliceInfeasible(torch.nn.Module):
             def forward(self, x, y):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -388,7 +391,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_slice_Tensor(self):
+    def test_optimize_slice_Tensor(self) -> None:
         class SliceTensor(torch.nn.Module):
             def forward(self, x, y, z):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -450,7 +453,7 @@ def forward(self, x, y, z):
         )
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_select_Tensor(self):
+    def test_optimize_select_Tensor(self) -> None:
         class SelectTensor(torch.nn.Module):
             def forward(self, x, y, z):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -517,7 +520,7 @@ def forward(self, x, y, z):
 
     # TODO: Test fails due to memory planning
     @unittest.expectedFailure
-    def test_optimize_cat_with_param(self):
+    def test_optimize_cat_with_param(self) -> None:
         class CatWithPadding(torch.nn.Module):
             def __init__(self, padding_shape):
                 super().__init__()
@@ -545,7 +548,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, exir_ops.edge.aten.cat.default), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_cat_then_slice_on_mutable_buffer(self):
+    def test_optimize_cat_then_slice_on_mutable_buffer(self) -> None:
         class CatWithPadding(torch.nn.Module):
             def __init__(self, padding_shape):
                 super().__init__()
@@ -570,7 +573,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 1)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_optimize_cat_with_view(self):
+    def test_optimize_cat_with_view(self) -> None:
         class CatViewFeasible(torch.nn.Module):
             def forward(self, x, y):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -597,7 +600,7 @@ def forward(self, x, y):
         self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 0)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_no_optimize_cat_with_repeated_args(self):
+    def test_no_optimize_cat_with_repeated_args(self) -> None:
         class CatViewInfeasible(torch.nn.Module):
             def forward(self, x):
                 x1 = torch.add(x, 2.4, 3.1)
@@ -621,7 +624,7 @@ def forward(self, x):
         self.assertEqual(count_node(graph_module, torch.ops.aten._cat_nop.out), 0)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_no_optimize_cat_with_placeholder(self):
+    def test_no_optimize_cat_with_placeholder(self) -> None:
         class CatViewInfeasible(torch.nn.Module):
             def forward(self, x, y):
                 # Repeat will be decomposed into a cat. The cat cannot be optimized
@@ -739,7 +742,7 @@ def forward(self, x) -> torch.Tensor:
         self.assertEqual(count_node(graph_module, torch.ops.aten.cat.out), 0)
         self.verify_nop_memory_alloc(graph_module)
 
-    def test_view_for_unallocated_output(self):
+    def test_view_for_unallocated_output(self) -> None:
         class Model(torch.nn.Module):
             def __init__(self, padding_shape):
                 super().__init__()
@@ -762,3 +765,41 @@ def forward(self, x, y):
         )
         self.assertEqual(count_node(graph_module, memory.view), 1)
         self.verify_nop_memory_alloc(graph_module)
+
+
+    def test_start_alignment_constraints(self) -> None:
+        class Model(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x: torch.Tensor, y: torch.Tensor):
+                add_0 = torch.add(x, y)
+                add_1 = torch.add(x, add_0)
+                add_2 = torch.add(add_0, add_1)
+                add_3 = torch.add(add_1, add_2)
+                return add_3
+
+        model = Model()
+        inputs = (torch.randn(4, 17), torch.randn(4, 17))
+        for mem_algo in range(0, 2):
+            graph_module = (
+                compiler.export_to_executorch_gen_etrecord(
+                    model,
+                    inputs,
+                    opt_level=1,
+                    mem_algo=mem_algo,
+                    alloc_graph_input=False,
+                    alloc_graph_output=False,
+                    mem_alignment=37,
+                )
+                .exported_program()
+                .graph_module
+            )
+            # Assert that all memory allocations are aligned to 32B start address
+            for spec in collect_specs_from_nodes(
+                graph_module.graph.nodes,
+                ignore_graph_input=True,
+                ignore_graph_output=True,
+            ):
+                if spec and spec.mem_offset:
+                    self.assertEqual(spec.mem_offset % 37, 0)