Misc fixes

torchax/torchax/ops/jaten.py

@@ -55,6 +55,7 @@
   torch.ops.aten.log_normal_: torch.ops.aten.log_normal,
   torch.ops.aten.scatter_add_: torch.ops.aten.scatter_add,
   torch.ops.aten.scatter_reduce_.two: torch.ops.aten.scatter_reduce,
+  torch.ops.aten.scatter_: torch.ops.aten.scatter,
 }
 
 # Note: tuple comparisons work intuitively, e.g. `_jax_version >= (0, 4, 32)`.
@@ -440,6 +441,15 @@ def _aten_resize_as_(x, y):
 def repeat_interleave(repeats, dim=0):
   return jnp.repeat(jnp.arange(repeats.shape[dim]), repeats)
 
+@op(torch.ops.aten.repeat_interleave.self_int)
+@op(torch.ops.aten.repeat_interleave.self_Tensor)
+def repeat_interleave(self, repeats, dim=0):
+  total_repeat_length = None
+  if isinstance(repeats, int):
+    total_repeat_length = self.shape[dim] * repeats
+    repeats = np.array([repeats] * self.shape[dim])
+  return jnp.repeat(self, repeats, dim, total_repeat_length=total_repeat_length)
+
 
 # aten.upsample_bilinear2d
 @op(torch.ops.aten.upsample_bilinear2d)
@@ -462,6 +472,7 @@ def _aten_stack(tensors, dim=0):
 
 @op(torch.ops.aten._softmax)
 @op(torch.ops.aten.softmax)
+@op(torch.ops.aten.softmax.int)
 def _aten_softmax(x, dim, halftofloat = False):
   if x.shape == ():
     return jax.nn.softmax(x.reshape([1]), axis=0).reshape([])
@@ -933,6 +944,11 @@ def _aten_native_layer_norm(
     norm_x += bias
   return norm_x, mean, rstd
 
+
+@op(torch.ops.aten.matmul)
+def _aten_matmul(x, y):
+  return x @ y
+
 
 # - func: addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
 @op(torch.ops.aten.addmm)
@@ -1742,10 +1758,9 @@ def _aten_atan(self):
   return res
 
 
-# aten.scatter_reduce
-@op(torch.ops.aten.scatter)
 @op(torch.ops.aten.scatter_reduce)
-def _aten_scatter_reduce(input, dim, index, src, reduce, *, include_self=True):
+@op(torch.ops.aten.scatter)
+def _aten_scatter_reduce(input, dim, index, src, reduce=None, *, include_self=True):
   if not isinstance(src, jnp.ndarray):
     src = jnp.array(src, dtype=input.dtype)
   input_indexes, source_indexes = _scatter_index(dim, index)
@@ -1781,7 +1796,7 @@ def _aten_scatter_reduce(input, dim, index, src, reduce, *, include_self=True):
   elif reduce == "amin":
     return input.at[input_indexes].min(src[source_indexes])
   else:
-    raise RuntimeError("Unknown reduction type: ", reduce)
+    return input.at[input_indexes].set(src[source_indexes])
 
 
 # aten.acos

torchax/torchax/ops/jtorch.py

@@ -122,7 +122,8 @@ def _sdpa_reference(query, key, value, attn_mask=None, dropout_p=0.0,
     attn_weight = query @ key.transpose(-2, -1) * scale_factor
     attn_weight += attn_bias
     attn_weight = torch.softmax(attn_weight, dim=-1)
-    attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
+    if dropout_p > 0:
+      attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
     return attn_weight @ value
 
 
@@ -210,6 +211,7 @@ def pad(tensor, pad, mode="constant", value=None):
 
 
 @register_function(torch.nn.functional.scaled_dot_product_attention, is_jax_function=False, needs_env=True)
+@register_function(torch.ops.aten.scaled_dot_product_attention, is_jax_function=False, needs_env=True)
 def scaled_dot_product_attention(
    query, key, value, attn_mask=None,
    dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False, env=None) -> torch.Tensor: