add a new unit test for cuda ops

.github/workflows/nv-inference.yml

@@ -61,4 +61,4 @@ jobs:
           if [[ -d ./torch-extensions ]]; then rm -rf ./torch-extensions; fi
           cd tests
           EXPECTED_TORCH=$(pip index versions torch | grep -oP -m1 "^\s*LATEST.*\s\K\d+\.\d+")
-          TRANSFORMERS_CACHE=/blob/transformers_cache/ TORCH_EXTENSIONS_DIR=./torch-extensions pytest --color=yes --durations=0 --forked -n 4 --verbose -m 'inference' unit/ --torch_ver=$EXPECTED_TORCH --cuda_ver="11.3"
+          TRANSFORMERS_CACHE=/blob/transformers_cache/ TORCH_EXTENSIONS_DIR=./torch-extensions pytest --color=yes --durations=0 -n 4 --verbose -m 'inference' unit/ --torch_ver=$EXPECTED_TORCH --cuda_ver="11.3"

tests/unit/ops/transformer/inference/test_bias_gelu.py

@@ -0,0 +1,61 @@
+import pytest
+import torch
+import deepspeed
+from deepspeed.ops.op_builder import InferenceBuilder
+
+if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
+    pytest.skip("Inference ops are not available on this system",
+                allow_module_level=True)
+
+inference_module = None
+torch_minor_version = None
+
+
+def allclose(x, y):
+    assert x.dtype == y.dtype
+    rtol, atol = {torch.float32: (5e-4, 5e-5), torch.float16: (3e-2, 2e-3)}[x.dtype]
+    return torch.allclose(x, y, rtol=rtol, atol=atol)
+
+
+def version_appropriate_gelu(activations):
+    global torch_minor_version
+    if torch_minor_version is None:
+        torch_minor_version = int(torch.__version__.split('.')[1])
+    # If torch version = 1.12
+    if torch_minor_version < 12:
+        return torch.nn.functional.gelu(activations)
+    else:
+        return torch.nn.functional.gelu(activations, approximate='tanh')
+
+
+def run_bias_gelu_reference(activations, bias):
+    # Expected behavior is that of casting to float32 internally and using the tanh approximation
+    return version_appropriate_gelu(
+        activations.to(torch.float32) + bias.to(torch.float32)).to(activations.dtype)
+
+
+def run_bias_gelu_ds(activations, bias):
+    global inference_module
+    if inference_module is None:
+        inference_module = InferenceBuilder().load()
+    if activations.dtype == torch.float16:
+        return inference_module.bias_gelu_fp16(activations, bias)
+    else:
+        return inference_module.bias_gelu_fp32(activations, bias)
+
+
+@pytest.mark.inference
+@pytest.mark.parametrize("batch", [1, 2])
+@pytest.mark.parametrize("sequence", [1, 128, 255])
+@pytest.mark.parametrize("channels", [512, 1232, 4096])
+@pytest.mark.parametrize("dtype", [torch.float16, torch.float32])
+def test_bias_gelu(batch, sequence, channels, dtype):
+    activations_ds = torch.randn((batch, sequence, channels), dtype=dtype, device='cuda')
+    bias_ds = torch.randn((channels), dtype=dtype, device='cuda')
+
+    activations_ref = activations_ds.clone().detach()
+    bias_ref = bias_ds.clone().detach()
+
+    ds_out = run_bias_gelu_ds(activations_ds, bias_ds)
+    ref_out = run_bias_gelu_reference(activations_ref, bias_ref)
+    assert (allclose(ds_out, ref_out))