[Executorch][llm] Enable local global attention in export_llama script

Added a new option of --local_global_attention that takes in pattern of sizes to determine which layers are using local sliding window attention.
For example, [0, 256, 256, 0, 256, 256] can be used for 6 layers transformer. Or you can also use [0, 256, 256] as pattern you want
to repeat.

Differential Revision: [D73891423](https://our.internmc.facebook.com/intern/diff/D73891423/)

[ghstack-poisoned]

Author: kimishpatel

examples/models/llama/export_llama_lib.py

@@ -62,6 +62,7 @@
 from .source_transformation.custom_kv_cache import (
     replace_kv_cache_with_custom_kv_cache,
     replace_kv_cache_with_quantized_kv_cache,
+    replace_kv_cache_with_ring_kv_cache,
 )
 
 from .source_transformation.quantize import (
@@ -147,6 +148,23 @@ def build_model(
     return export_llama(args)
 
 
+def parse_list_of_ints(s):
+    import ast
+
+    try:
+        parsed = ast.literal_eval(s)
+        if isinstance(parsed, list) and all(isinstance(i, int) for i in parsed):
+            print(parsed)
+            return parsed
+        raise argparse.ArgumentTypeError(
+            "Must be a list of integers, e.g., [0, 16, 0, 16]"
+        )
+    except Exception:
+        raise argparse.ArgumentTypeError(
+            "Must be a list of integers, e.g., [0, 16, 0, 16]"
+        )
+
+
 def build_args_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
     parser.add_argument("-o", "--output-dir", default=".", help="output directory")
@@ -357,6 +375,13 @@ def build_args_parser() -> argparse.ArgumentParser:
         help="maximum length of context for model to remember",
     )
 
+    parser.add_argument(
+        "--local_global_attention",
+        type=parse_list_of_ints,
+        default=None,
+        help="List of integers specifying local and global attention pattern, e.g., [0, 16, 0, 16].",
+    )
+
     parser.add_argument("-2", "--fairseq2", action="store_true")
     parser.add_argument("-v", "--verbose", action="store_true")
     parser.add_argument(
@@ -1297,6 +1322,14 @@ def _get_source_transforms(  # noqa
     if args.vulkan:
         transforms.append(replace_with_vulkan_rotary_emb)
 
+    if args.local_global_attention:
+        transforms.append(
+            partial(
+                replace_kv_cache_with_ring_kv_cache,
+                layer_sizes=args.local_global_attention,
+            )
+        )
+
     return transforms
 
 

examples/models/llama/source_transformation/custom_kv_cache.py

@@ -519,8 +519,17 @@ def replace_kv_cache_with_ring_kv_cache(module, layer_sizes):
     # This is needed to ensure that custom ops are registered
     from executorch.extension.llm.custom_ops import custom_ops  # noqa: F401
 
+    assert len(module.layers) > len(
+        layer_sizes
+    ), f"Length of layer sizes {len(layer_sizes)} must match the number of layers in the module {len(module.layers)}."
+    multiplier = len(module.layers) // len(layer_sizes)
+    modulo = len(module.layers) % len(layer_sizes)
+    assert (
+        modulo == 0
+    ), f"num layers specified must be multiple of model layers in order to specify pattern. pattern: {layer_sizes} model's num layers {len(module.layers)}"
+    layer_sizes = layer_sizes * multiplier
     logging.info(
-        "Replacing kv cache with ring kv cache. This modifies the model in place."
+        f"Applying local sliding window attention with following pattern {layer_sizes}."
     )
     assert len(layer_sizes) == len(
         module.layers
@@ -534,4 +543,8 @@ def replace_kv_cache_with_ring_kv_cache(module, layer_sizes):
         ), f"Transfomer block must have attention module. Transformer block {transformer_block}"
         attention = transformer_block.attention
         _replace_kv_cache_with_ring_kv_cache(attention, sliding_window_size)
+        # if attention's sdpa is custom sdpa then we have to make sure
+        # it is not doing causal attention
+        if "SDPACustom" in attention.SDPA.__class__.__name__:
+            attention.SDPA.use_attention_mask = True
     return module