[ExecuTorch][Llama] Split custom sdpa op and kv cache

Summary:
This enables us to do more easier module swap with model definitions
from torchtune

Test Plan:
CI

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Subscribers:

Tasks:

Tags:

ghstack-source-id: 9db180e
Pull Request resolved: pytorch/executorch#7412

Author: kimishpatel

examples/models/llama/export_llama_lib.py

@@ -56,6 +56,7 @@
     get_quant_weight_transform,
 )
 from .source_transformation.quantized_kv_cache import (
+    replace_kv_cache_with_custom_kv_cache,
     replace_kv_cache_with_quantized_kv_cache,
 )
 from .source_transformation.rms_norm import replace_rms_norm_with_native_rms_norm
@@ -1058,6 +1059,7 @@ def _get_source_transforms(  # noqa
         transforms.append(materialze_broadcast_of_rope_freq_cis)
 
     if args.use_sdpa_with_kv_cache:
+        transforms.append(replace_kv_cache_with_custom_kv_cache)
         transforms.append(replace_sdpa_with_custom_op)
 
     if args.quantize_kv_cache:

examples/models/llama/source_transformation/quantized_kv_cache.py

@@ -6,6 +6,7 @@
 
 import logging
 from enum import Enum
+from typing import Tuple
 
 import torch
 import torch.nn as nn
@@ -44,7 +45,6 @@ def __init__(
             QuantizedCacheType.AffineSymmetric,
             QuantizedCacheType.AffineAsymmetric,
         ):
-
             raise ValueError(
                 f"Only affine symmetric and asymmetric cache types are supported: got {cache_type}"
             )
@@ -81,10 +81,11 @@ def __init__(
             )
 
     def _quantize(self, value):
-        scales, zero_points = (
-            torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default(
-                value, self.quantized_cache_dtype
-            )
+        (
+            scales,
+            zero_points,
+        ) = torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default(
+            value, self.quantized_cache_dtype
         )
         quantized_value = torch.ops.quantized_decomposed.quantize_per_token(
             value,
@@ -262,3 +263,71 @@ def replace_kv_cache_with_quantized_kv_cache(module):
         else:
             replace_kv_cache_with_quantized_kv_cache(child)
     return module
+
+
+class CustomKVCache(nn.Module):
+    def __init__(
+        self,
+        max_batch_size: int,
+        max_seq_length: int,
+        n_heads: int,
+        head_dim: int,
+        dtype=torch.float32,
+    ):
+        super().__init__()
+        self.max_seq_length = max_seq_length
+        cache_shape = (max_batch_size, max_seq_length, n_heads, head_dim)
+
+        self.max_batch_size = max_batch_size
+        self.n_heads = n_heads
+        self.head_dim = head_dim
+        self.register_buffer(
+            "k_cache", torch.zeros(cache_shape, dtype=dtype, device="cpu")
+        )
+        self.register_buffer(
+            "v_cache", torch.zeros(cache_shape, dtype=dtype, device="cpu")
+        )
+
+    def update(
+        self, input_pos: torch.Tensor, k_val: torch.Tensor, v_val: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # input_pos: [S], k_val: [B, S, H, D]
+        start_pos = input_pos[0].item()
+        _ = torch.ops.llama.update_cache(k_val, self.k_cache, start_pos)
+        _ = torch.ops.llama.update_cache(v_val, self.v_cache, start_pos)
+        return self.k_cache, self.v_cache
+
+
+def replace_kv_cache_with_custom_kv_cache(module):
+    r"""
+    Replace KVCache with CustomKVCache. This modifies the model in place.
+    At the moment custom kv cache only supports cache with shape
+    [B, S, H, D] as opposed to [B, H, S, D]
+    This is because the custom op treats second dim as sequence dim.
+    Future work: support [B, H, S, D]
+    """
+    logging.warning(
+        "Replacing KVCache with CustomKVCache. This modifies the model in place."
+    )
+    for name, child in module.named_children():
+        if isinstance(child, KVCache):
+            cache_shape = child.k_cache.shape
+            cache_dtype = child.k_cache.dtype
+            assert (
+                child.is_transposed is False
+            ), "CustomKVCache does not support transposed cache"
+            max_batch_size, max_seq_length, n_heads, head_dim = cache_shape
+            setattr(
+                module,
+                name,
+                CustomKVCache(
+                    max_batch_size,
+                    max_seq_length,
+                    n_heads,
+                    head_dim,
+                    dtype=cache_dtype,
+                ),
+            )
+        else:
+            replace_kv_cache_with_custom_kv_cache(child)
+    return module

examples/models/llama/source_transformation/sdpa.py

@@ -56,33 +56,16 @@ def forward(
 
         k_cache = self.kv_cache.k_cache
         v_cache = self.kv_cache.v_cache
-        if hasattr(self.kv_cache, "quantized_cache_dtype"):
-            # updated quantize cache, scale and zero points
-            # returns dequantized kv cache
-            # Not most optimal. Optimizations to follow next
-            k_cache, v_cache = self.kv_cache.update(input_pos, k, v)
-            output = torch.ops.llama.custom_sdpa(
-                q,
-                k_cache,
-                v_cache,
-                input_pos[0].item(),
-                None,  # Attention mask
-                0,  # dropout probability. Ignored by the code
-                True,  # is_causal
-            )
-        else:
-            output = torch.ops.llama.sdpa_with_kv_cache(
-                q,
-                k,
-                v,
-                k_cache,
-                v_cache,
-                input_pos[0].item(),
-                seqlen,
-                None,  # Attention mask
-                0,  # dropout probability. Ignored by the code
-                True,  # is_causal
-            )
+        k_cache, v_cache = self.kv_cache.update(input_pos, k, v)
+        output = torch.ops.llama.custom_sdpa(
+            q,
+            k_cache,
+            v_cache,
+            input_pos[0].item(),
+            None,  # Attention mask
+            0,  # dropout probability. Ignored by the code
+            True,  # is_causal
+        )
         return output.view(bsz, seqlen, self.dim).to(dtype=input_dtype)
 
 
@@ -106,7 +89,6 @@ def replace_sdpa_with_custom_op(module: torch.nn.Module) -> torch.nn.Module:
 
 
 class SDPASimple(torch.nn.Module):
-
     def __init__(
         self,
         kv_cache: KVCache,
@@ -166,7 +148,6 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
 
 
 class SDPAFlex(torch.nn.Module):
-
     def __init__(
         self,
         kv_cache: KVCache,

examples/models/llama/source_transformation/test_sdpa_with_quantized_kv_cache.py

@@ -11,6 +11,7 @@
 from executorch.examples.models.llama.llama_transformer import KVCache
 
 from executorch.examples.models.llama.source_transformation.quantized_kv_cache import (
+    CustomKVCache,
     QuantizedCacheType,
     QuantizedKVCache,
 )
@@ -19,7 +20,6 @@
 
 
 class SDPAWithQuantizedKVCacheTest(unittest.TestCase):
-
     def _init_cache(self):
         self.kv_cache = KVCache(
             self.max_batch_size,
@@ -33,6 +33,19 @@ def _init_cache(self):
         self.quantized_kv_cache = QuantizedKVCache.from_float(
             self.kv_cache, QuantizedCacheType.AffineAsymmetric
         )
+        # Need this because first test actually has seq_len > 1
+        # and vanilla kvcache cannot handle seq_len > 1, due to
+        # how input_pos encoding works in the current stack.
+        # This needs fixing by making sure rest of the stack including
+        # custom ops or other backends can work with input_pos
+        # as a sequence of token positions
+        self.custom_kv_cache = CustomKVCache(
+            self.max_batch_size,
+            self.max_seq_len,
+            self.n_kv_heads,
+            self.head_dim,
+            dtype=self.dtype,
+        )
 
     def _init_kv(self):
         kv_shape = (1, self.seq_len, self.n_kv_heads, self.head_dim)
@@ -59,7 +72,7 @@ def test_simple(self, is_dynamic_shape=False):
         self.seq_len = 3
         self._init_cache()
         q, k, v = self._init_kv()
-        self.float_sdpa = SDPACustom(self.kv_cache, self.dim)
+        self.float_sdpa = SDPACustom(self.custom_kv_cache, self.dim)
         self.quantized_sdpa = SDPACustom(self.quantized_kv_cache, self.dim)
         float_out = self.float_sdpa(input_pos, q, k, v, 1, self.seq_len, None)
         quantized_out = self.quantized_sdpa(input_pos, q, k, v, 1, self.seq_len, None)