Add test that exports to MLIR a small sharded Llama model (nod-ai#220)

Add test that exports to MLIR a small sharded Llama model

The decode step requires exporting in non-strict torch mode due to
pytorch/pytorch#135061

This export required to extend the registration functionality of our custom
tensor types by provinding `flatten_with_keys_fn`. This is also required
to bump the PyTorch version to >=2.4 for other export tests.

The export to MLIR fails with
TypeError: Unsupported torch type conversion for
!torch.vtensor<[3,1,7],complex<f32>>
Needs further debugging.
Detailed error
[here](https://github.com/user-attachments/files/17163454/sharded-llama-export-to-mlir-failure.txt).

sharktank/sharktank/ops/sharded_impls.py

@@ -10,7 +10,6 @@
 import itertools
 from numbers import Number
 import math
-import numpy as np
 
 from ..types import (
     AnyTensor,
@@ -365,6 +364,14 @@ def elementwise_binary_replicated_lhs_unsharded_rhs(
     return elementwise(operator, x, y_replicated, *args, **kwargs)
 
 
+@elementwise.override(Tensor, ReplicatedTensor)
+def elementwise_binary_replicated_lhs_unsharded_rhs(
+    operator, x: Tensor, y: ReplicatedTensor, *args, **kwargs
+):
+    x_replicated = reshard_like(x, like=y)
+    return elementwise(operator, x_replicated, y, *args, **kwargs)
+
+
 # Embedding Lookup
 @embedding_lookup.override(ReplicatedTensor, ReplicatedTensor)
 def embedding_lookup_default(

sharktank/sharktank/types/tensors.py

@@ -25,7 +25,7 @@
 
 import torch
 from torch import Tensor
-from torch.utils._pytree import register_pytree_node
+from torch.utils._pytree import register_pytree_node, SequenceKey
 from ..utils.math import ceildiv
 from shark_turbine.aot import (
     ExternalTensorTrait,
@@ -1286,10 +1286,16 @@ def unflatten_defult_primitive_tensor(
     return DefaultPrimitiveTensor(data=values_as_list[0], name=ctx["name"])
 
 
+def flatten_with_keys_default_primitive_tensor(t: DefaultPrimitiveTensor):
+    values, context = flatten_default_primitive_tensor(t)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
 register_pytree_node(
     DefaultPrimitiveTensor,
     flatten_fn=flatten_default_primitive_tensor,
     unflatten_fn=unflatten_defult_primitive_tensor,
+    flatten_with_keys_fn=flatten_with_keys_default_primitive_tensor,
 )
 
 
@@ -1307,10 +1313,16 @@ def unflatten_split_primitive_tensor(
     )
 
 
+def flatten_with_keys_split_primitive_tensor(t: SplitPrimitiveTensor):
+    values, context = flatten_split_primitive_tensor(t)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
 register_pytree_node(
     SplitPrimitiveTensor,
     flatten_fn=flatten_split_primitive_tensor,
     unflatten_fn=unflatten_split_primitive_tensor,
+    flatten_with_keys_fn=flatten_with_keys_split_primitive_tensor,
 )
 
 
@@ -1326,8 +1338,14 @@ def unflatten_replicated_tensor(
     return ReplicatedTensor(ts=list(values), name=ctx["name"])
 
 
+def flatten_with_keys_replicated_tensor(t: ReplicatedTensor):
+    values, context = flatten_replicated_tensor(t)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
 register_pytree_node(
     ReplicatedTensor,
     flatten_fn=flatten_replicated_tensor,
     unflatten_fn=unflatten_replicated_tensor,
+    flatten_with_keys_fn=flatten_with_keys_replicated_tensor,
 )