[torch_xla2] Enable nn.functional.cosine_embedding_loss (#8368)

experimental/torch_xla2/test/test_ops.py

@@ -37,29 +37,22 @@
     "nn.functional.conv_transpose1d",
     "nn.functional.conv_transpose2d",
     "nn.functional.conv_transpose3d",
-    "nn.functional.cosine_embedding_loss",
     "nn.functional.ctc_loss",
     "nn.functional.dropout2d",
     "nn.functional.dropout3d",
     "nn.functional.dropout",
     "nn.functional.embedding_bag",
     "nn.functional.fractional_max_pool2d",
     "nn.functional.fractional_max_pool3d",
-    "nn.functional.group_norm",
-    "nn.functional.hinge_embedding_loss",
     "nn.functional.interpolate",
-    "nn.functional.margin_ranking_loss",
     "nn.functional.max_pool1d",
     "nn.functional.max_pool2d",
     "nn.functional.max_pool3d",
     "nn.functional.multi_head_attention_forward",
-    "nn.functional.multi_margin_loss",
     "nn.functional.multilabel_margin_loss",
     "nn.functional.pairwise_distance",
     "nn.functional.poisson_nll_loss",
     "nn.functional.rrelu",
-    "nn.functional.triplet_margin_loss",
-    "nn.functional.triplet_margin_with_distance_loss",
     "nn.functional.upsample_nearest",
     "nonzero",
     "nonzero_static",

experimental/torch_xla2/torch_xla2/ops/jaten.py

@@ -43,6 +43,7 @@
   torch.ops.aten.relu_: torch.ops.aten.relu,
   # squeeze_ is expected to change tensor's shape. So replace with new value 
   torch.ops.aten.squeeze_: (torch.ops.aten.squeeze, True),
+  torch.ops.aten.sqrt_: torch.ops.aten.sqrt,
   torch.ops.aten.clamp_: torch.ops.aten.clamp,
   torch.ops.aten.clamp_min_: torch.ops.aten.clamp_min,
   torch.ops.aten.sigmoid_: torch.ops.aten.sigmoid,
@@ -112,7 +113,11 @@ def _aten_add(x, y, *, alpha=1):
 
   assert x.dtype == y.dtype, (x.dtype, y.dtype)
   """
-  return x + y * alpha
+  res = x + y * alpha
+  if isinstance(x, float) or isinstance(y, float):
+    new_dtype = mappings.t2j_dtype(torch.get_default_dtype())
+    res = res.astype(new_dtype)
+  return res
 
 
 @op(torch.ops.aten.copy_, is_jax_function=False)
@@ -169,6 +174,16 @@ def _aten_cauchy_(x, median=0, sigma=1):
   return x.at[:].set(samples)
 
 
+@op(torch.ops.aten.atleast_2d)
+def _aten_atleast_2d(inputs):
+  return jnp.atleast_2d(inputs)
+
+
+@op(torch.ops.aten.atleast_1d)
+def _aten_atleast_1d(inputs):
+  return jnp.atleast_1d(inputs)
+
+
 # aten.complex
 @op(torch.ops.aten.complex)
 def _aten_complex(real, imag):
@@ -281,6 +296,10 @@ def _aten_mul(x, y):
   res = x * y
   if isinstance(x, float) or isinstance(y, float):
     res = res.astype(new_dtype)
+  else:
+    if (not isinstance(x, int)) and (not isinstance(y, int)):
+      if x.dtype == np.dtype(np.float64) or y.dtype == np.dtype(np.float64):
+        res = res.astype(new_dtype)
   return res
 
 
@@ -1284,6 +1303,9 @@ def _aten_native_group_norm(input, weight, bias, N, C, HxW, group, eps=1e-5):
 
   input_shape = input.shape
 
+  if 0 in input_shape:
+    return input, input, input
+
   # Reshape for group-wise normalization
   reshaped_input = jnp.reshape(input, (1, N * group, -1))