Changes to split kv cache and sdpa

Summary:

+ Make all the backend specific kvcache and sdpa implementation abide by
  the new API

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 369434c4d64e6d4500ecfea03b0fd99945b30461
Pull Request resolved: #7413

examples/models/llama/llama_transformer.py

@@ -232,22 +232,16 @@ def __init__(
         max_seq_length: int,
         n_heads: int,
         head_dim: int,
-        transpose_cache: bool,
         enable_dynamic_shape: bool,
         dtype=torch.float32,
     ):
         super().__init__()
         self.max_seq_length = max_seq_length
-        self.is_transposed = transpose_cache
-        if transpose_cache:
-            cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
-        else:
-            cache_shape = (max_batch_size, max_seq_length, n_heads, head_dim)
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
 
         self.max_batch_size = max_batch_size
         self.n_heads = n_heads
         self.head_dim = head_dim
-        self.transpose_cache = transpose_cache
         self.enable_dynamic_shape = enable_dynamic_shape
         self.register_buffer(
             "k_cache", torch.zeros(cache_shape, dtype=dtype, device="cpu")
@@ -259,12 +253,12 @@ def __init__(
     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, H, S, D] or [B, S, H, D] depending on transpose_cache
+        # input_pos: [S], k_val: [B, H, S, D]
         if self.enable_dynamic_shape:
             start_pos = input_pos[0].item()
             torch._check_is_size(start_pos)
             torch._check(start_pos < self.max_seq_length)
-            dim_to_slice = 2 if self.transpose_cache else 1
+            dim_to_slice = 2
             seq_length = k_val.size(dim_to_slice)
             # Replace the entry in the cache for this token
             # The following lines are equivalent to:
@@ -283,28 +277,22 @@ def update(
         else:
             k_out = self.k_cache
             v_out = self.v_cache
-            if self.transpose_cache:
-                k_out[:, :, input_pos] = k_val
-                v_out[:, :, input_pos] = v_val
-            else:
-                k_out[:, input_pos] = k_val
-                v_out[:, input_pos] = v_val
+            k_out[:, :, input_pos] = k_val
+            v_out[:, :, input_pos] = v_val
 
             return k_out, v_out
 
 
 class SDPA(nn.Module):
     def __init__(
         self,
-        kv_cache: KVCache,
         dim: int,
         head_dim: int,
         n_rep: int,
         max_seq_len: int,
         enable_dynamic_shape: bool,
     ):
         super().__init__()
-        self.kv_cache = kv_cache
         self.dim = dim
         self.head_dim = head_dim
         self.n_rep = n_rep
@@ -314,18 +302,16 @@ def __init__(
     def forward(
         self,
         input_pos: torch.Tensor,
-        q: torch.Tensor,  # Already have rotary embeddings. (bs, seqlen, n_local_heads, head_dim)
-        k: torch.Tensor,  # Already have rotary embeddings. (bs, seqlen, n_local_kv_heads, head_dim)
-        v: torch.Tensor,  # (bs, seqlen, n_local_kv_heads, head_dim)
+        q: torch.Tensor,  # Already have rotary embeddings. (bs, n_local_heads, seqlen, head_dim)
+        k: torch.Tensor,  # Already have rotary embeddings. (bs, n_local_kv_heads, seqlen, head_dim)
+        v: torch.Tensor,  # (bs, n_local_kv_heads, seqlen, head_dim)
         bsz,
         seqlen,
         mask: torch.Tensor,
     ) -> torch.Tensor:
-        q = q.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
-        k = k.transpose(1, 2)
-        v = v.transpose(1, 2)
 
-        k, v = self.kv_cache.update(input_pos, k, v)
+        # TODO(kimishpatel): Move this slicing logic to Attention block so that
+        # SDPA does not have to take input_pos as arg
         if self.enable_dynamic_shape:
             start_pos = input_pos[-1].item()
             torch._check_is_size(start_pos)
@@ -336,6 +322,8 @@ def forward(
         else:
             attn_mask = mask[None, None, input_pos]
 
+        # TODO(kimishpatel): This should not be necessary because scaled_dot_product_attention
+        # can natively support GQA now. But needs enable_gqa=True
         k = k.repeat_interleave(self.n_rep, dim=1)
         v = v.repeat_interleave(self.n_rep, dim=1)
         y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=0.0)
@@ -383,11 +371,9 @@ def __init__(self, args: ModelArgs, layer_id: int, rope: Rope):
                 args.max_seq_len,
                 self.n_kv_heads,
                 self.head_dim,
-                not args.use_sdpa_with_kv_cache_op,  # if we are using the custom op don't transpose the cache. Expect untransposed q k v
                 args.enable_dynamic_shape,
             )
             self.SDPA = SDPA(
-                kv_cache=self.kv_cache,
                 dim=self.n_local_heads * self.head_dim,
                 head_dim=self.head_dim,
                 n_rep=self.n_rep,
@@ -414,15 +400,16 @@ def forward(
         # RoPE relative positional embeddings
         q, k = self.rope.forward(q, k, freqs_cos, freqs_sin)
 
+        q = q.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
         if self.use_kv_cache:
             assert input_pos is not None
+            k, v = self.kv_cache.update(input_pos, k, v)
             output = self.SDPA(input_pos, q, k, v, bsz, seqlen, self.mask)
             return self.wo(output)
 
-        q = q.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
-        k = k.transpose(1, 2)
-        v = v.transpose(1, 2)
-
         # grouped multiquery attention: expand out keys and values
         k = k.repeat_interleave(self.n_rep, dim=1)
         v = v.repeat_interleave(self.n_rep, dim=1)

examples/models/llama/source_transformation/quantized_kv_cache.py

@@ -37,8 +37,6 @@ def __init__(
         n_heads,
         head_dim,
         cache_type: QuantizedCacheType = QuantizedCacheType.AffineSymmetric,
-        tranposed=False,
-        enable_dynamic_shape=False,
     ):
         super().__init__()
         if cache_type not in (
@@ -52,14 +50,8 @@ def __init__(
         # For now supporting int8 only
         self.quantized_cache_dtype = torch.int8
         self.cache_fp_type = torch.float32
-        self.is_transposed = tranposed
-        self.enable_dynamic_shape = enable_dynamic_shape
-        if self.is_transposed:
-            cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
-            scale_shape = (max_batch_size, n_heads, max_seq_length, 1)
-        else:
-            cache_shape = (max_batch_size, max_seq_length, n_heads, head_dim)
-            scale_shape = (max_batch_size, max_seq_length, n_heads, 1)
+        cache_shape = (max_batch_size, max_seq_length, n_heads, head_dim)
+        scale_shape = (max_batch_size, max_seq_length, n_heads, 1)
         self.register_buffer(
             "k_cache", torch.zeros(cache_shape, dtype=self.quantized_cache_dtype)
         )
@@ -98,71 +90,37 @@ def _quantize(self, value):
         return quantized_value, scales, zero_points
 
     def update(self, input_pos, k_val, v_val):
+        """
+        k_val, v_val: [B, H, S, D]
+        return: [B, H, S, D]
+        However the storage is [B, S, H, D] so we incur transpose in, transpose out
+        This shall be removed by subsequent post-export graph pass
+        """
+        k_val = k_val.transpose(1, 2)
+        v_val = v_val.transpose(1, 2)
         # quantize current k_val and store it in the cache
         quantized_k_val, k_scales, k_zero_points = self._quantize(k_val)
 
         quantized_v_val, v_scales, v_zero_points = self._quantize(v_val)
 
-        if self.is_transposed:
-            # We cannot use update_cache op at the moment
-            # if the cache is transposed
-            # Also note that we shold not need separate paths
-            # for dynamic shape vs !
-            # Only reason it is done this way is to accommodate
-            # for lowering pains of backends that work better
-            # with index_put op.
-            if self.enable_dynamic_shape:
-                start_pos = input_pos[0].item()
-                torch._check_is_size(start_pos)
-                dim_to_slice = 2 if self.is_transposed else 1
-                torch._check(start_pos < self.k_cache.size(dim_to_slice))
-                seq_length = k_val.size(dim_to_slice)
-                narrowed_k = self.k_cache.narrow(dim_to_slice, start_pos, seq_length)
-                narrowed_k_scales = self.k_cache_scales.narrow(
-                    dim_to_slice, start_pos, seq_length
-                )
-                narrowed_k_zp = self.k_cache_zero_points.narrow(
-                    dim_to_slice, start_pos, seq_length
-                )
-                narrowed_k.copy_(quantized_k_val)
-                narrowed_k_scales.copy_(k_scales)
-                narrowed_k_zp.copy_(k_zero_points)
-                narrowed_v = self.v_cache.narrow(dim_to_slice, start_pos, seq_length)
-                narrowed_v_scales = self.v_cache_scales.narrow(
-                    dim_to_slice, start_pos, seq_length
-                )
-                narrowed_v_zp = self.v_cache_zero_points.narrow(
-                    dim_to_slice, start_pos, seq_length
-                )
-                narrowed_v.copy_(quantized_v_val)
-                narrowed_v_scales.copy_(v_scales)
-                narrowed_v_zp.copy_(v_zero_points)
-            else:
-                self.k_cache[:, :, input_pos] = quantized_k_val
-                self.k_cache_scales[:, :, input_pos] = k_scales
-                self.k_cache_zero_points[:, :, input_pos] = k_zero_points
-                self.v_cache[:, :, input_pos] = quantized_v_val
-                self.v_cache_scales[:, :, input_pos] = v_scales
-                self.v_cache_zero_points[:, :, input_pos] = v_zero_points
-        else:
-            # Right now using custom ops on this path.
-            # In future we can update custom op to handle transposed cache
-            # as well.
-            # Note that we may have to revert this change if other ET
-            # backends such as QNN want to use quantized cache, with dynamic shape,
-            # instead of quantizing on their own.
-            # But until this opting for code simplicity
-            start_pos = input_pos[0].item()
-            _ = 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_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
-            )
+        # Right now using custom ops on this path.
+        # In future we can update custom op to handle transposed cache
+        # as well.
+        # Note that we may have to revert this change if other ET
+        # backends such as QNN want to use quantized cache, with dynamic shape,
+        # instead of quantizing on their own.
+        # But until this opting for code simplicity
+        start_pos = input_pos[0].item()
+        _ = 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_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
+        )
 
         k_out = torch.ops.quantized_decomposed.dequantize_per_token(
             self.k_cache,
@@ -183,42 +141,24 @@ def update(self, input_pos, k_val, v_val):
             self.cache_fp_type,
         )
 
-        if self.is_transposed:
-            if self.enable_dynamic_shape:
-                start_pos = input_pos[0].item()
-                torch._check_is_size(start_pos)
-                dim_to_slice = 2 if self.is_transposed else 1
-                torch._check(start_pos < self.k_cache.size(dim_to_slice))
-                seq_length = k_val.size(dim_to_slice)
-                narrowed_k = k_out.narrow(dim_to_slice, start_pos, seq_length)
-                narrowed_k.copy_(k_val)
-                narrowed_v = v_out.narrow(dim_to_slice, start_pos, seq_length)
-                narrowed_v.copy_(v_val)
-            else:
-                k_out[:, :, input_pos] = k_val
-                v_out[:, :, input_pos] = v_val
-        else:
-            start_pos = input_pos[0].item()
-            _ = torch.ops.llama.update_cache(k_val, k_out, start_pos)
-            _ = torch.ops.llama.update_cache(v_val, v_out, start_pos)
+        start_pos = input_pos[0].item()
+        _ = 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
+        return k_out.transpose(1, 2), v_out.transpose(1, 2)
 
     @classmethod
     def from_float(cls, kv_cache, cache_type: QuantizedCacheType):
-        cache_shape = kv_cache.k_cache.shape
-        if kv_cache.is_transposed:
-            max_batch_size, n_heads, max_seq_length, head_dim = cache_shape
-        else:
-            max_batch_size, max_seq_length, n_heads, head_dim = cache_shape
+        max_batch_size, n_heads, max_seq_length, head_dim = kv_cache.k_cache.shape
+        if isinstance(kv_cache, CustomKVCache):
+            # If replacing custom kv cache, then the shape is [B, S, H, D]
+            max_batch_size, max_seq_length, n_heads, head_dim = kv_cache.k_cache.shape
         return cls(
             max_batch_size,
             max_seq_length,
             n_heads,
             head_dim,
             cache_type,
-            kv_cache.is_transposed,
-            kv_cache.enable_dynamic_shape,
         )
 
 
@@ -254,7 +194,7 @@ def replace_kv_cache_with_quantized_kv_cache(module):
         "Replacing KVCache with QuantizedKVCache. This modifies the model in place."
     )
     for name, child in module.named_children():
-        if isinstance(child, KVCache):
+        if isinstance(child, KVCache) or isinstance(child, CustomKVCache):
             setattr(
                 module,
                 name,
@@ -291,11 +231,13 @@ def __init__(
     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]
+        # input_pos: [S], k_val: [B, H, S, D]
+        k_val = k_val.transpose(1, 2)
+        v_val = v_val.transpose(1, 2)
         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
+        return self.k_cache.transpose(1, 2), self.v_cache.transpose(1, 2)
 
 
 def replace_kv_cache_with_custom_kv_cache(module):
@@ -313,10 +255,7 @@ def replace_kv_cache_with_custom_kv_cache(module):
         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
+            max_batch_size, n_heads, max_seq_length, head_dim = cache_shape
             setattr(
                 module,
                 name,