Update to SDK 3.3.0

.devcontainer.dockerfile

@@ -1,4 +1,4 @@
-FROM graphcore/pytorch:3.2.0-ubuntu-20.04
+FROM graphcore/pytorch:3.3.0-ubuntu-20.04
 
 RUN apt-get update \
     && apt-get install -y \

.github/workflows/ci.yaml

@@ -13,7 +13,7 @@ concurrency:
 jobs:
   ci:
     runs-on: ubuntu-latest
-    container: graphcore/pytorch:3.2.0-ubuntu-20.04
+    container: graphcore/pytorch:3.3.0-ubuntu-20.04
     timeout-minutes: 10
     steps:
       - uses: actions/checkout@v3
@@ -23,7 +23,10 @@ jobs:
           apt-get install -y clang-format
           pip install -r requirements-dev.txt
       - name: Run CI
-        run: ./dev ci
+        run: |
+          source /opt/setup-bash-env.sh
+          ./dev ci
+        shell: bash
       - name: Publish documentation
         if: ${{github.ref == 'refs/heads/main'}}
         uses: Cecilapp/GitHub-Pages-deploy@v3

README.md

@@ -8,7 +8,7 @@ A collection of addons to [PopTorch](https://github.com/graphcore/poptorch), wit
 See [documentation](https://graphcore-research.github.io/poptorch-experimental-addons).
 
 ```bash
-# Tested on Poplar SDK 3.2.0+1277, Ubuntu 20.04, Python 3.8
+# Tested on Poplar SDK 3.3.0+7857, Ubuntu 20.04, Python 3.8
 pip install git+https://github.com/graphcore-research/poptorch-experimental-addons
 
 # Run an example

poptorch_experimental_addons/_impl/core.py

@@ -57,7 +57,7 @@ def autograd_proxy(fwd: Tensor, proxy: Tensor) -> Tensor:
             example_outputs=[fwd],
         )
     else:
-        y = _AutogradProxy.apply(fwd, proxy)
+        y = _AutogradProxy.apply(fwd, proxy)  # type:ignore[no-untyped-call]
     return y
 
 

requirements-dev.txt

@@ -3,6 +3,7 @@ black==23.1.0
 flake8==6.0.0
 isort==5.12.0
 mypy==1.0.1
+numpy>=1.24.4
 pdoc3==0.10.0
 pytest==7.2.1
 pytest-cov==4.0.0

tests/test_collectives.py

@@ -16,8 +16,6 @@
 
 from . import utils
 
-assert_close = torch.testing.assert_close  # type:ignore[attr-defined]
-
 
 def set_seed(seed: int) -> None:
     torch.manual_seed(seed)
@@ -136,4 +134,4 @@ def test_collective(op: Callable[[torch.Tensor, int], torch.Tensor]) -> None:
     num_ipus = 2
     actual = run_collective(X, op, num_ipus)
     expected = simulate_collective(X, _op_mapping[op], num_ipus)
-    list(map(assert_close, actual, expected))
+    list(map(torch.testing.assert_close, actual, expected))

tests/test_core.py

@@ -9,8 +9,6 @@
 
 import poptorch_experimental_addons as pea
 
-assert_close = torch.testing.assert_close  # type:ignore[attr-defined]
-
 
 def run_forward_and_backward(
     fn: Callable[..., Tensor],
@@ -57,8 +55,8 @@ def test_autograd_proxy(device: str) -> None:
         patterns=dict(AutogradProxyOpPattern=True),
         device=device,
     )
-    assert_close(outputs["loss"], torch.tensor(6.0))
-    assert_close(outputs["grad_x"], torch.tensor(3.0))
+    torch.testing.assert_close(outputs["loss"], torch.tensor(6.0))
+    torch.testing.assert_close(outputs["grad_x"], torch.tensor(3.0))
 
 
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float16])
@@ -87,19 +85,19 @@ def test_distance_matrix(p: int, dtype: torch.dtype) -> None:
     atol = {torch.float32: 1e-5, torch.float16: 2e-3}[dtype]
     rtol = {torch.float32: 2e-6, torch.float16: 2e-3}[dtype]
 
-    assert_close(
+    torch.testing.assert_close(
         output_ipu["output"],
         output_torch["output"],
         rtol=rtol,
         atol=atol,
     )
-    assert_close(
+    torch.testing.assert_close(
         output_ipu["grad_tensor1"],
         output_torch["grad_tensor1"],
         rtol=rtol,
         atol=atol,
     )
-    assert_close(
+    torch.testing.assert_close(
         output_ipu["grad_tensor2"],
         output_torch["grad_tensor2"],
         rtol=rtol,

tests/test_sharded_matmul.py

@@ -16,8 +16,6 @@
 
 from . import utils
 
-assert_close = torch.testing.assert_close  # type:ignore[attr-defined]
-
 
 class Sharding(Enum):
     Replicated = 1
@@ -265,7 +263,7 @@ def test_sharded_matmul(op: Callable[[Any], torch.Tensor]) -> None:
     num_ipus = 2
     actual = run_sharded_matmul(X, Y, op, num_ipus)
     expected = simulate_sharded_matmul(X, Y, op, num_ipus)
-    list(map(assert_close, actual, expected))
+    list(map(torch.testing.assert_close, actual, expected))
 
 
 @pytest.mark.parametrize("op", list(_op_mapping.keys()))
@@ -281,7 +279,7 @@ def test_simulator(op: Callable[[Any], torch.Tensor]) -> None:
         mode=SimulatorMode.Base,
     )
     out_sim, _ = simulator()
-    assert_close(out_sim, out_base)
+    torch.testing.assert_close(out_sim, out_base)
 
 
 if __name__ == "__main__":

tests/test_sparse.py

@@ -11,8 +11,6 @@
 
 import poptorch_experimental_addons as pea
 
-assert_close = torch.testing.assert_close  # type:ignore[attr-defined]
-
 
 @dataclass
 class Problem:
@@ -83,7 +81,7 @@ def test_coo_methods(shape: Tuple[int], block_size: int, seed: int) -> None:
     assert torch.sum(dense != 0) == nnz_blocks * block_size ** len(shape)
 
     if len(shape) == 2:
-        assert_close(
+        torch.testing.assert_close(
             pea.sparse.block_coo_to_dense(pea.sparse.block_coo_transpose(array)),
             dense.T,
         )
@@ -141,7 +139,7 @@ def test_block_coo_spmm(
         )(problem.dense)
 
         for name, output in outputs.items():
-            assert_close(
+            torch.testing.assert_close(
                 output.float(),
                 problem.expected_output,
                 rtol=0,
@@ -167,7 +165,7 @@ def test_high_level_api() -> None:
     expected_output = dense_in @ pea.sparse.block_coo_to_dense(sparse).T
 
     def check(output: Tensor) -> None:
-        assert_close(output, expected_output, atol=1e-5, rtol=0)
+        torch.testing.assert_close(output, expected_output, atol=1e-5, rtol=0)
 
     check(pea.sparse.StaticSparseLinear(sparse)(dense_in))
     check((pea.sparse.StaticSparseMatrix(sparse) @ dense_in.T).T)