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

Pull Request resolved: #10612

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.
ghstack-source-id: 283404674
@exported-using-ghexport

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

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 (
@@ -153,6 +154,23 @@ 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")
@@ -363,6 +381,15 @@ 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] to specify that every other layer is sliding window of 16."
+        " [0, 16, 32] pattern specifes 2nd and 3rd layer has sliding window of 16 and 32 respecitvely. "
+        " [16] pattern specifies all layers have sliding window of 16.",
+    )
+
     parser.add_argument("-2", "--fairseq2", action="store_true")
     parser.add_argument("-v", "--verbose", action="store_true")
     parser.add_argument(
@@ -1440,6 +1467,14 @@ class Args:
     if vulkan:
         transforms.append(replace_with_vulkan_rotary_emb)
 
+    if getattr(args, "local_global_attention", None) is not None:
+        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

@@ -555,8 +555,17 @@ 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
@@ -570,4 +579,8 @@ 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

examples/models/llama/tests/TARGETS

@@ -85,3 +85,19 @@ python_unittest(
         "//executorch/examples/models/llama:sdpa",
     ],
 )
+
+python_unittest(
+    name = "test_export_llama_lib",
+    srcs = [
+        "test_export_llama_lib.py",
+    ],
+    preload_deps = [
+        "//executorch/extension/llm/custom_ops:custom_ops_aot_lib",
+    ],
+    deps = [
+        "//caffe2:torch",
+        "//executorch/examples/models/llama:export_library",
+        "//executorch/examples/models/llama:llama_transformer",
+        "//executorch/extension/pybindings:portable_lib",
+    ],
+)