(torchx-fb/named_resources) implement @named_resource decorator to autogenerate aliases and fractionals, migrate zion_4s_80g to use it. (#1121)

Summary:

Context: D82556202

```
named_resource(aliases=["zion4s_80g"], fractionals=powers_of_two_gpus)
def zion_4s_80g(fractional: float = 1.0) -> Resource:
   ...
```

Registers the named resources:

1. `zion_4s_80g` and the alias `zion4s_80g`
1. `zion_4s_80g_1 == zion_4s_80g(fractional=EIGHTH)`
1. `zion_4s_80g_2 == zion_4s_80g(fractional=QUARTER)`
1. `zion_4s_80g_4 == zion_4s_80g(fractional=HALF)`
1. `zion_4s_80g_8 == zion_4s_80g(fractional=WHOLE)`

so that we don't have to manually do this.

The auto-generated fractionals will return `True` when:

```
from torchx.specs.fb.resource import is_fractional
from torchx.specs import get_named_resource

assert is_fractional(get_named_resource("zion_4s_80g_1")) == True
assert is_fractional(get_named_resource("zion_4s_80g_2")) == True
assert is_fractional(get_named_resource("zion_4s_80g_4")) == True
assert is_fractional(get_named_resource("zion_4s_80g_8")) == True

assert is_fractional(get_named_resource("zion_4s_80g")) == False
```

NOTE:  each of the fractional types (including WHOLE) will have `resource.capabilities[IS_FRACTIONAL] == True` so that we can use it as a way to determine whether a resource originates from a fractional named resource.

Reviewed By: ajauhri, highker

Differential Revision: D82594325

Author: kiukchung

torchx/specs/__init__.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python3
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
 #
@@ -52,14 +51,19 @@
 
 from torchx.util.modules import import_attr
 
-AWS_NAMED_RESOURCES: Mapping[str, Callable[[], Resource]] = import_attr(
+GiB: int = 1024
+
+ResourceFactory = Callable[[], Resource]
+
+AWS_NAMED_RESOURCES: Mapping[str, ResourceFactory] = import_attr(
     "torchx.specs.named_resources_aws", "NAMED_RESOURCES", default={}
 )
-GENERIC_NAMED_RESOURCES: Mapping[str, Callable[[], Resource]] = import_attr(
+GENERIC_NAMED_RESOURCES: Mapping[str, ResourceFactory] = import_attr(
     "torchx.specs.named_resources_generic", "NAMED_RESOURCES", default={}
 )
-
-GiB: int = 1024
+FB_NAMED_RESOURCES: Mapping[str, ResourceFactory] = import_attr(
+    "torchx.specs.fb.named_resources", "NAMED_RESOURCES", default={}
+)
 
 
 def _load_named_resources() -> Dict[str, Callable[[], Resource]]:
@@ -69,6 +73,7 @@ def _load_named_resources() -> Dict[str, Callable[[], Resource]]:
     for name, resource in {
         **GENERIC_NAMED_RESOURCES,
         **AWS_NAMED_RESOURCES,
+        **FB_NAMED_RESOURCES,
         **resource_methods,
     }.items():
         materialized_resources[name] = resource

torchx/specs/api.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python3
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
 #
@@ -83,6 +82,8 @@ class Resource:
         memMB: MB of ram
         capabilities: additional hardware specs (interpreted by scheduler)
         devices: a list of named devices with their quantities
+        tags: metadata tags for the resource (not interpreted by schedulers)
+          used to add non-functional information about resources (e.g. whether it is an alias of another resource)
 
     Note: you should prefer to use named_resources instead of specifying the raw
     resource requirement directly.
@@ -93,6 +94,7 @@ class Resource:
     memMB: int
     capabilities: Dict[str, Any] = field(default_factory=dict)
     devices: Dict[str, int] = field(default_factory=dict)
+    tags: Dict[str, object] = field(default_factory=dict)
 
     @staticmethod
     def copy(original: "Resource", **capabilities: Any) -> "Resource":
@@ -101,6 +103,7 @@ def copy(original: "Resource", **capabilities: Any) -> "Resource":
         are present in the original resource and as parameter, the one from parameter
         will be used.
         """
+
         res_capabilities = dict(original.capabilities)
         res_capabilities.update(capabilities)
         return Resource(