[Executorch][llama] Rename update_quantized_cache to update_cache

examples/models/llama/source_transformation/quantized_kv_cache.py

@@ -151,22 +151,14 @@ def update(self, input_pos, k_val, v_val):
             # instead of quantizing on their own.
             # But until this opting for code simplicity
             start_pos = input_pos[0].item()
-            _ = torch.ops.llama.update_quantized_cache(
-                quantized_k_val, self.k_cache, start_pos
-            )
-            _ = torch.ops.llama.update_quantized_cache(
-                k_scales, self.k_cache_scales, start_pos
-            )
-            _ = torch.ops.llama.update_quantized_cache(
+            _ = torch.ops.llama.update_cache(quantized_k_val, self.k_cache, start_pos)
+            _ = torch.ops.llama.update_cache(k_scales, self.k_cache_scales, start_pos)
+            _ = torch.ops.llama.update_cache(
                 k_zero_points, self.k_cache_zero_points, start_pos
             )
-            _ = torch.ops.llama.update_quantized_cache(
-                quantized_v_val, self.v_cache, start_pos
-            )
-            _ = torch.ops.llama.update_quantized_cache(
-                v_scales, self.v_cache_scales, start_pos
-            )
-            _ = torch.ops.llama.update_quantized_cache(
+            _ = torch.ops.llama.update_cache(quantized_v_val, self.v_cache, start_pos)
+            _ = torch.ops.llama.update_cache(v_scales, self.v_cache_scales, start_pos)
+            _ = torch.ops.llama.update_cache(
                 v_zero_points, self.v_cache_zero_points, start_pos
             )
 
@@ -205,8 +197,8 @@ def update(self, input_pos, k_val, v_val):
                 v_out[:, :, input_pos] = v_val
         else:
             start_pos = input_pos[0].item()
-            _ = torch.ops.llama.update_quantized_cache(k_val, k_out, start_pos)
-            _ = torch.ops.llama.update_quantized_cache(v_val, v_out, start_pos)
+            _ = torch.ops.llama.update_cache(k_val, k_out, start_pos)
+            _ = torch.ops.llama.update_cache(v_val, v_out, start_pos)
 
         return k_out, v_out
 

extension/llm/custom_ops/TARGETS

@@ -23,9 +23,9 @@ runtime.python_test(
 )
 
 runtime.python_test(
-    name = "test_update_quantized_cache",
+    name = "test_update_cache",
     srcs = [
-        "test_update_quantized_cache.py",
+        "test_update_cache.py",
     ],
     preload_deps = [
         ":custom_ops_aot_lib",

extension/llm/custom_ops/op_sdpa_aot.cpp

@@ -9,7 +9,7 @@
 #include <executorch/extension/aten_util/make_aten_functor_from_et_functor.h>
 #include <executorch/extension/kernel_util/make_boxed_from_unboxed_functor.h>
 #include <executorch/extension/llm/custom_ops/op_sdpa.h>
-#include <executorch/extension/llm/custom_ops/op_update_quantized_cache.h>
+#include <executorch/extension/llm/custom_ops/op_update_cache.h>
 
 #include <torch/library.h>
 
@@ -127,22 +127,22 @@ at::Tensor custom_sdpa_aten(
   return output;
 }
 
-Tensor& update_quantized_cache_out_no_context(
+Tensor& update_cache_out_no_context(
     const Tensor& value,
     Tensor& cache,
     const int64_t start_pos,
     Tensor& output) {
   exec_aten::RuntimeContext context{};
-  return torch::executor::native::update_quantized_cache_out(
+  return torch::executor::native::update_cache_out(
       context, value, cache, start_pos, output);
 }
 
-at::Tensor update_quantized_cache_aten(
+at::Tensor update_cache_aten(
     const at::Tensor& value,
     at::Tensor& cache,
     const int64_t start_pos) {
   auto output = at::empty({1});
-  WRAP_TO_ATEN(update_quantized_cache_out_no_context, 3)
+  WRAP_TO_ATEN(update_cache_out_no_context, 3)
   (value, cache, start_pos, output);
   return output;
 }
@@ -169,10 +169,10 @@ TORCH_LIBRARY_FRAGMENT(llama, m) {
       "Tensor? attn_mask=None, float drpout_p=0.0, bool is_causal=False, "
       "float? scale=None, *, Tensor(a!) out) -> Tensor(a!)");
   m.def(
-      "update_quantized_cache(Tensor value, Tensor(a!) cache, "
+      "update_cache(Tensor value, Tensor(a!) cache, "
       "SymInt start_pos) -> Tensor");
   m.def(
-      "update_quantized_cache.out(Tensor value, Tensor(a!) cache, "
+      "update_cache.out(Tensor value, Tensor(a!) cache, "
       "SymInt start_pos, *, Tensor(b!) out) -> Tensor(b!)");
 }
 
@@ -188,11 +188,8 @@ TORCH_LIBRARY_IMPL(llama, CompositeExplicitAutograd, m) {
   m.impl(
       "custom_sdpa.out",
       WRAP_TO_ATEN(torch::executor::native::custom_sdpa_out_no_context, 8));
+  m.impl("update_cache", torch::executor::native::update_cache_aten);
   m.impl(
-      "update_quantized_cache",
-      torch::executor::native::update_quantized_cache_aten);
-  m.impl(
-      "update_quantized_cache.out",
-      WRAP_TO_ATEN(
-          torch::executor::native::update_quantized_cache_out_no_context, 3));
+      "update_cache.out",
+      WRAP_TO_ATEN(torch::executor::native::update_cache_out_no_context, 3));
 }

extension/llm/custom_ops/op_update_quantized_cache.cpp → extension/llm/custom_ops/op_update_cache.cpp

@@ -6,7 +6,7 @@
  * LICENSE file in the root directory of this source tree.
  */
 
-#include <executorch/extension/llm/custom_ops/op_update_quantized_cache.h>
+#include <executorch/extension/llm/custom_ops/op_update_cache.h>
 
 #include <executorch/runtime/core/exec_aten/util/dim_order_util.h>
 // @lint-ignore CLANGTIDY facebook-unused-include-check
@@ -60,7 +60,7 @@ bool validate_cache_params(
 }
 } // anonymous namespace
 
-Tensor& update_quantized_cache_out(
+Tensor& update_cache_out(
     RuntimeContext& ctx,
     const Tensor& value,
     Tensor& cache,
@@ -139,5 +139,5 @@ Tensor& update_quantized_cache_out(
 // In later diffs will rename this to update_cache.
 EXECUTORCH_LIBRARY(
     llama,
-    "update_quantized_cache.out",
-    torch::executor::native::update_quantized_cache_out);
+    "update_cache.out",
+    torch::executor::native::update_cache_out);

extension/llm/custom_ops/op_update_quantized_cache.h → extension/llm/custom_ops/op_update_cache.h

@@ -15,7 +15,7 @@ namespace executor {
 
 namespace native {
 
-Tensor& update_quantized_cache_out(
+Tensor& update_cache_out(
     RuntimeContext& ctx,
     const Tensor& value,
     Tensor& cache,

extension/llm/custom_ops/sdpa_with_kv_cache.py

@@ -203,8 +203,8 @@ def _validate_update_cache_params(
     ), f"Start position  + length = {start_pos + seq_len} must be less than sequence length {cache.size(1)}"
 
 
-@impl(custom_ops_lib, "update_quantized_cache", "Meta")
-def update_quantized_cache_meta(
+@impl(custom_ops_lib, "update_cache", "Meta")
+def update_cache_meta(
     value,
     cache,
     start_pos,

extension/llm/custom_ops/targets.bzl

@@ -22,13 +22,13 @@ def define_common_targets():
                 "op_fallback.cpp",
                 "op_fast_hadamard_transform.cpp",
                 "op_sdpa.cpp",
-                "op_update_quantized_cache.cpp",
+                "op_update_cache.cpp",
             ],
             exported_headers = [
                 "op_fallback.h",
                 "op_fast_hadamard_transform.h",
                 "op_sdpa.h",
-                "op_update_quantized_cache.h",
+                "op_update_cache.h",
             ],
             preprocessor_flags = get_vec_preprocessor_flags(),
             exported_deps = [

extension/llm/custom_ops/test_update_quantized_cache.py → extension/llm/custom_ops/test_update_cache.py

@@ -67,17 +67,13 @@ def _update_and_validate(
         self._update_k(start_pos, k, k_scales, k_zero_points)
         self._update_v(start_pos, v, v_scales, v_zero_points)
 
-        torch.ops.llama.update_quantized_cache(k, k_cache, start_pos)
-        torch.ops.llama.update_quantized_cache(k_scales, k_scales_cache, start_pos)
-        torch.ops.llama.update_quantized_cache(
-            k_zero_points, k_zero_points_cache, start_pos
-        )
+        torch.ops.llama.update_cache(k, k_cache, start_pos)
+        torch.ops.llama.update_cache(k_scales, k_scales_cache, start_pos)
+        torch.ops.llama.update_cache(k_zero_points, k_zero_points_cache, start_pos)
 
-        torch.ops.llama.update_quantized_cache(v, v_cache, start_pos)
-        torch.ops.llama.update_quantized_cache(v_scales, v_scales_cache, start_pos)
-        torch.ops.llama.update_quantized_cache(
-            v_zero_points, v_zero_points_cache, start_pos
-        )
+        torch.ops.llama.update_cache(v, v_cache, start_pos)
+        torch.ops.llama.update_cache(v_scales, v_scales_cache, start_pos)
+        torch.ops.llama.update_cache(v_zero_points, v_zero_points_cache, start_pos)
 
         self.assertTrue(torch.allclose(k_cache, self.quantized_k_cache))
         self.assertTrue(torch.allclose(v_cache, self.quantized_v_cache))