torch.allclose opinfo (pytorch#68023)

Summary: Pull Request resolved: pytorch#68023

Test Plan: Imported from OSS

Reviewed By: mruberry

Differential Revision: D32295811

Pulled By: george-qi

fbshipit-source-id: 3253104a5a9655d8ba7bbba6620038ed6d6669f1

torch/testing/_internal/common_methods_invocations.py

@@ -6774,6 +6774,26 @@ def sample_inputs_pixel_unshuffle(op_info, device, dtype, requires_grad, **kwarg
         for downscale_factor in (1, 3)
     ]
 
+def sample_inputs_allclose(op_info, device, dtype, requires_grad, **kwargs):
+    samples = []
+    sample_shapes = [(), (S), (S, S, S)]
+    atols = [1e-2, 1e-16]
+    rtols = [1e-1, 0.5]
+    eps = 1e-8
+    for s, rtol, atol in product(sample_shapes, rtols, atols):
+        # close sample
+        t = make_tensor(s, device=device, dtype=dtype, requires_grad=requires_grad)
+        close = (t + atol).detach().requires_grad_(requires_grad)
+        close_sample = SampleInput(t, args=(close,), kwargs=dict(rtol=rtol, atol=atol))
+        samples.append(close_sample)
+
+        # random sample
+        a = make_tensor(s, device=device, dtype=dtype, requires_grad=requires_grad)
+        b = make_tensor(s, device=device, dtype=dtype, requires_grad=requires_grad)
+        r_sample = SampleInput(a, args=(b,), kwargs=dict(rtol=rtol, atol=atol))
+        samples.append(r_sample)
+
+    return samples
 
 foreach_unary_op_db: List[OpInfo] = [
     ForeachFuncInfo('exp'),
@@ -7657,6 +7677,16 @@ def ref_pairwise_distance(input1, input2):
                                     device_type='cuda', dtypes=[torch.cfloat],
                                     active_if=IS_WINDOWS),
                    )),
+    OpInfo('allclose',
+           dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16),
+           ref=np.allclose,
+           supports_autograd=False,
+           supports_forward_ad=False,
+           sample_inputs_func=sample_inputs_allclose,
+           skips=(
+               DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),
+           ),
+           supports_out=False),
     OpInfo('broadcast_to',
            dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16),
            supports_out=False,