Arm backend: Update VelaIO handling

VelaIO is always 6D.
- Update AOT handling of metadata from Vela.
- Adds unittest to trigger 5D cases.
- Updates EthosUBackend to read IO as 6D arrays.

Signed-off-by: Oscar Andersson <[email protected]>
Change-Id: I8d7d3a44ac84e5bb14fa27e7b7765c3b7a8ee483

Author: oscarandersson8218

backends/arm/arm_vela.py

@@ -25,17 +25,19 @@
 # per-io structs to simplify runtime use.
 def vela_bin_pack_io(prefix, data):
     vela_input_shapes = data[prefix + "_shape"]
+    # Vela input/output shape is fixed to 6D
+    vela_io_shape_dims = 6
 
     ios = struct.pack("<i", len(vela_input_shapes))
     for i in range(len(vela_input_shapes)):
         io_shape = vela_input_shapes[i]
         io_elem_size = data[prefix + "_elem_size"][i]
         io_offset = data[prefix + "_offset"][i]
         io_region = data[prefix + "_region"][i]
-        assert len(io_shape) <= 4
-        inp_pad = io_shape.tolist() + [0] * (4 - len(io_shape))
+        assert len(io_shape) == vela_io_shape_dims
+        inp_pad = io_shape.tolist()
         io_struct = struct.pack(
-            "<iiiiiii", *inp_pad, io_elem_size, io_offset, io_region
+            "<iiiiiiiii", *inp_pad, io_elem_size, io_offset, io_region
         )
         ios += io_struct
     return ios

backends/arm/requirements-arm-ethos-u.txt

@@ -3,4 +3,4 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-ethos-u-vela @ git+https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela@d37febc1715edf0d236c2ff555739a8a9aadcf9a
+ethos-u-vela @ git+https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela@9a43a1bf26bfc7588358d7e6e6bb2613b4981a34

backends/arm/runtime/EthosUBackend.cpp

@@ -383,8 +383,8 @@ class EthosUBackend final : public ::executorch::runtime::BackendInterface {
       *tensor_count = *tensor_count * tensor.size(i);
     }
 
-    // The VelaIO type has a shape of fixed size 4
-    for (int i = 0; i < 4; i++) {
+    // The VelaIO type has a shape of fixed size 6
+    for (int i = 0; i < shapeDim; i++) {
       *io_count = *io_count * io->shape[i];
     }
   }

backends/arm/runtime/VelaBinStream.h

@@ -34,9 +34,11 @@ typedef struct {
   char data[]; // block.name specific format data
 } VelaBinBlock;
 
+constexpr int shapeDim = 6; // Number of dimensions in VelaIO
+
 // A Vela input or output descriptor in the binary stream
 typedef struct {
-  int shape[4]; // Up to 4D shape of input or output
+  int shape[shapeDim]; // Shape of input or output
   int elem_size; // Element sizeof in bytes
   int offset; // Offset in bytes within SRAM working data
   int region; // Scratch region this belongs to

backends/arm/test/ops/test_squeeze.py

@@ -29,6 +29,7 @@ class SqueezeDim(torch.nn.Module):
         "squeeze3d_dim_neg_2": lambda: (torch.randn(1, 1, 5), -2),
         "squeeze4d_dim_pos_3": lambda: (torch.randn(1, 2, 3, 1), 3),
         "squeeze4d_dim_neg_2": lambda: (torch.randn(1, 5, 1, 5), -2),
+        "squeeze5d_dim_neg_2": lambda: (torch.randn(1, 1, 5, 1, 5), -2),
     }
 
     def forward(self, x: torch.Tensor, dim: int):
@@ -40,6 +41,7 @@ class SqueezeDims(torch.nn.Module):
         "squeeze3d_dims_0_1": lambda: (torch.randn(1, 1, 5), (0, 1)),
         "squeeze4d_dims_0_neg_1": lambda: (torch.randn(1, 5, 5, 1), (0, -1)),
         "squeeze4d_dims_0_neg_2": lambda: (torch.randn(1, 5, 1, 5), (0, -2)),
+        "squeeze5d_dims_0_neg_2": lambda: (torch.randn(1, 1, 5, 1, 5), (0, -2)),
     }
 
     def forward(self, x: torch.Tensor, dims: tuple[int]):
@@ -51,6 +53,7 @@ class Squeeze(torch.nn.Module):
         "squeeze3d": lambda: (torch.randn(1, 1, 5),),
         "squeeze4d_dims": lambda: (torch.randn(1, 5, 5, 1),),
         "squeeze3d_dims_mix": lambda: (torch.randn(1, 5, 1, 5),),
+        "squeeze4d_dims_mix": lambda: (torch.randn(1, 1, 5, 1, 5),),
     }
 
     def forward(self, x: torch.Tensor):

backends/arm/test/ops/test_unflatten.py

@@ -9,6 +9,8 @@
 import torch
 from executorch.backends.arm.test import common
 from executorch.backends.arm.test.tester.test_pipeline import (
+    EthosU55PipelineINT,
+    EthosU85PipelineINT,
     TosaPipelineFP,
     TosaPipelineINT,
     VgfPipeline,
@@ -30,8 +32,10 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return torch.unflatten(x, self.dim, self.sizes)
 
     test_data: dict[str, test_data_t] = {
-        "randn_4d": (lambda: (Unflatten(1, (2, 2)), (torch.randn(3, 4, 5, 1),))),
-        "rand_3d": (lambda: (Unflatten(1, (-1, 2)), (torch.rand(3, 4, 4),))),
+        "rand_3d_batch3": (lambda: (Unflatten(1, (-1, 2)), (torch.rand(3, 4, 4),))),
+        "rand_3d_batch1": (lambda: (Unflatten(1, (-1, 2)), (torch.rand(1, 4, 4),))),
+        "randn_4d_dim1": (lambda: (Unflatten(1, (2, 2)), (torch.randn(3, 4, 5, 1),))),
+        "randn_4d_dim3": (lambda: (Unflatten(3, (2, 2)), (torch.randn(1, 1, 5, 4),))),
     }
 
 
@@ -49,7 +53,33 @@ def test_unflatten_int_tosa_FP(test_data: test_data_t):
 @common.parametrize("test_data", Unflatten.test_data)
 def test_unflatten_int_tosa_INT(test_data: test_data_t):
     module, inputs = test_data()
-    pipeline = TosaPipelineINT[input_t](
+    pipeline = TosaPipelineINT[input_t](module, inputs, Unflatten.aten_op)
+    pipeline.run()
+
+
+xfails = {
+    "rand_3d_batch3": "Batch size > 1 currently not supported for FVP tests",
+    "randn_4d_dim1": "Batch size > 1 currently not supported for FVP tests",
+}
+
+
+@common.parametrize("test_data", Unflatten.test_data, xfails=xfails, strict=False)
+@common.XfailIfNoCorstone300
+def test_unflatten_int_u55_INT(test_data: test_data_t):
+    module, inputs = test_data()
+    pipeline = EthosU55PipelineINT[input_t](
+        module,
+        inputs,
+        Unflatten.aten_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Unflatten.test_data, xfails=xfails, strict=False)
+@common.XfailIfNoCorstone320
+def test_unflatten_int_u85_INT(test_data: test_data_t):
+    module, inputs = test_data()
+    pipeline = EthosU85PipelineINT[input_t](
         module,
         inputs,
         Unflatten.aten_op,

backends/arm/test/ops/test_unsqueeze.py

@@ -25,7 +25,7 @@
 
 
 class Unsqueeze(torch.nn.Module):
-    shapes: list[int | Sequence[int]] = [5, (5, 5), (5, 4), (5, 4, 3)]
+    shapes: list[int | Sequence[int]] = [5, (5, 5), (5, 4), (5, 4, 3), (1, 5, 4, 3)]
     test_parameters = {}
     for n in shapes:
         test_parameters[f"rand_{n}"] = (torch.randn(n),)

backends/arm/test/ops/test_view.py

@@ -51,6 +51,10 @@ class View(torch.nn.Module):
         "rand_4d_4_3": lambda: (torch.rand(5, 10, 1, 1), (1, 25, 2)),
         "rand_4d_4_2": lambda: (torch.rand(2, 50, 1, 1), (1, 100)),
         "rand_4d_2_4_same": lambda: (torch.rand(2, 3, 2, 3), (2, 3, 3, 2)),
+        "rand_4d_5d": lambda: (torch.rand(1, 3, 4, 5), (1, 1, 4, 5, -1)),
+        "rand_5d_5d": lambda: (torch.rand(1, 1, 4, 5, 6), (1, 1, 4, -1, 6)),
+        "rand_5d_3d": lambda: (torch.rand(1, 1, 4, 5, 6), (2, 3, -1)),
+        "rand_3d_5d": lambda: (torch.rand(4, 5, 6), (1, 1, 2, -1, 3)),
     }
 
     rank_product_too_large = {