move rope related logic together

Right now, rope related code scatters around a few different places in `llama_transformer`. It makes it hard to make changes to rope related things.

This PR moves all rope related logic into its own module.

Differential Revision: [D65173598](https://our.internmc.facebook.com/intern/diff/D65173598/)

[ghstack-poisoned]

Author: helunwencser

examples/models/llama/llama_transformer.py

@@ -143,6 +143,69 @@ def __post_init__(self):
             self.hidden_dim = find_multiple(hidden_dim, multiple_of)
 
 
+class Rope(torch.nn.Module):
+    def __init__(self, params: ModelArgs):
+        super().__init__()
+        self.params = params
+        if self.params.use_hf_rope:
+            self.precompute_freqs_cis = hf_precompute_freqs_cis
+        else:
+            self.precompute_freqs_cis = partial(
+                precompute_freqs_cis, use_scaled=self.params.use_scaled_rope
+            )
+        freqs_cos, freqs_sin = self.precompute_freqs_cis(
+            self.params.dim // self.params.n_heads,
+            (
+                self.params.max_seq_len  # Normal llama2.
+                if self.params.ffn_dim_multiplier is None
+                else self.params.max_seq_len * 2  # Sharded checkpoint.
+            ),
+            self.params.rope_freq_base,
+        )
+        self.register_buffer("freqs_cos", freqs_cos, persistent=False)
+        self.register_buffer("freqs_sin", freqs_sin, persistent=False)
+        if self.params.use_hf_rope:
+            self.apply_rotary_emb = hf_apply_rotary_emb
+        else:
+            self.apply_rotary_emb = RotaryEmbedding()
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        seq_len: int,
+        input_pos: Optional[torch.Tensor] = None,
+    ):
+        if self.params.use_kv_cache:
+            assert (
+                input_pos is not None
+            ), "input_pos must be provided when use_kv_cache is True"
+
+            if self.params.enable_dynamic_shape:
+                # when KV cache is used, seqlen is most likely 1. We want to slice from the start_pos.
+                input_pos_item = input_pos[-1].item()
+                torch._check_is_size(input_pos_item)
+                torch._check(input_pos_item < self.params.max_seq_len)
+                # pyre-ignore: Incompatible parameter type [6]: torch.narrow does expect int or Tensor
+                freqs_cos = self.freqs_cos.narrow(0, input_pos_item, seq_len)
+                # pyre-ignore: Incompatible parameter type [6]
+                freqs_sin = self.freqs_sin.narrow(0, input_pos_item, seq_len)
+            else:
+                # When not using dynamic shape, use of the .item results in
+                # symints, due to querying the data from tensor.
+                # this path avoids that for mps backend, although probably mps backend
+                # can support dynamic shape?
+                freqs_cos = self.freqs_cos[input_pos]
+                freqs_sin = self.freqs_sin[input_pos]
+
+        else:
+            assert input_pos is None, "input_pos is unused when use_kv_cache is False"
+            freqs_cos = self.freqs_cos[:seq_len]
+            freqs_sin = self.freqs_sin[:seq_len]
+        q, k = self.apply_rotary_emb(q, k, freqs_cos, freqs_sin)
+        return q, k
+
+
 class KVCache(nn.Module):
     def __init__(
         self,
@@ -262,7 +325,7 @@ def forward(
 
 
 class Attention(nn.Module):
-    def __init__(self, args: ModelArgs, layer_id: int):
+    def __init__(self, args: ModelArgs, layer_id: int, rope: Rope):
         super().__init__()
         self.use_kv_cache = args.use_kv_cache
         self.n_heads = args.n_heads
@@ -284,6 +347,8 @@ def __init__(self, args: ModelArgs, layer_id: int):
 
         self.layer_id = layer_id
 
+        self.rope = rope
+
         causal_mask = torch.tril(
             torch.ones(
                 self.max_seq_len,
@@ -300,7 +365,7 @@ def __init__(self, args: ModelArgs, layer_id: int):
                 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 dont transpose the cache. Expect untransposed q k v
+                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(
@@ -311,16 +376,10 @@ def __init__(self, args: ModelArgs, layer_id: int):
                 max_seq_len=self.max_seq_len,
                 enable_dynamic_shape=args.enable_dynamic_shape,
             )
-        if args.use_hf_rope:
-            self.apply_rotary_emb = hf_apply_rotary_emb
-        else:
-            self.apply_rotary_emb = RotaryEmbedding()
 
     def forward(
         self,
         x: torch.Tensor,
-        freqs_cos: torch.Tensor,
-        freqs_sin: torch.Tensor,
         input_pos: Optional[torch.Tensor] = None,
     ):
         bsz, seqlen, _ = x.shape
@@ -333,7 +392,7 @@ def forward(
         v = v.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
 
         # RoPE relative positional embeddings
-        q, k = self.apply_rotary_emb(q, k, freqs_cos, freqs_sin)
+        q, k = self.rope.forward(q, k, seqlen, input_pos)
 
         if self.use_kv_cache:
             assert input_pos is not None
@@ -421,24 +480,22 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class TransformerBlock(nn.Module):
-    def __init__(self, layer_id: int, args: ModelArgs):
+    def __init__(self, layer_id: int, args: ModelArgs, rope: Rope):
         super().__init__()
         self.use_kv_cache = args.use_kv_cache
         self.n_heads = args.n_heads
         self.dim = args.dim
         self.head_dim = args.dim // args.n_heads
-        self.attention = Attention(args, layer_id)
+        self.attention = Attention(args, layer_id, rope)
         if args.moe:
             self.block_sparse_moe = MOEFeedForward(args)
         else:
             self.feed_forward = FeedForward(args)
         self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
         self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
 
-    def forward(self, x, freqs_cos, freqs_sin, input_pos=None):  # x: 1xN
-        h = self.attention.forward(
-            self.attention_norm(x), freqs_cos, freqs_sin, input_pos
-        )
+    def forward(self, x, input_pos=None):  # x: 1xN
+        h = self.attention.forward(self.attention_norm(x), input_pos)
 
         h = x + h
         if hasattr(self, "block_sparse_moe"):
@@ -456,33 +513,17 @@ def __init__(self, params: ModelArgs):
         self.n_layers = params.n_layers
 
         self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
+        self.rope = Rope(params)
         self.layers = torch.nn.ModuleList()
         for layer_id in range(params.n_layers):
-            self.layers.append(TransformerBlock(layer_id, params))
+            self.layers.append(TransformerBlock(layer_id, params, self.rope))
         self.norm = RMSNorm(params.dim, eps=params.norm_eps)
         self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
         self.use_kv_cache = params.use_kv_cache
         self.generate_full_logits = params.generate_full_logits
         self.max_seq_len = params.max_seq_len
         self.input_prune_map = params.input_prune_map
         self.output_prune_map = params.output_prune_map
-        if params.use_hf_rope:
-            self.precompute_freqs_cis = hf_precompute_freqs_cis
-        else:
-            self.precompute_freqs_cis = partial(
-                precompute_freqs_cis, use_scaled=params.use_scaled_rope
-            )
-        freqs_cos, freqs_sin = self.precompute_freqs_cis(
-            params.dim // params.n_heads,
-            (
-                params.max_seq_len  # Normal llama2.
-                if params.ffn_dim_multiplier is None
-                else params.max_seq_len * 2  # Sharded checkpoint.
-            ),
-            params.rope_freq_base,
-        )
-        self.register_buffer("freqs_cos", freqs_cos, persistent=False)
-        self.register_buffer("freqs_sin", freqs_sin, persistent=False)
 
     def forward(
         self,
@@ -498,42 +539,9 @@ def forward(
             )
         if tokens is not None and h is None:
             h = self.tok_embeddings(tokens)
-        seqlen = h.shape[1]
-
-        if self.use_kv_cache:
-            assert (
-                input_pos is not None
-            ), "input_pos must be provided when use_kv_cache is True"
-
-            if self.params.enable_dynamic_shape:
-                # when KV cache is used, seqlen is most likely 1. We want to slice from the start_pos.
-                input_pos_item = input_pos[-1].item()
-                torch._check_is_size(input_pos_item)
-                torch._check(input_pos_item < self.params.max_seq_len)
-                # pyre-ignore: Incompatible parameter type [6]: torch.narrow does expect int or Tensor
-                freqs_cos = self.freqs_cos.narrow(0, input_pos_item, seqlen)
-                # pyre-ignore: Incompatible parameter type [6]
-                freqs_sin = self.freqs_sin.narrow(0, input_pos_item, seqlen)
-            else:
-                # When not using dynamic shape, use of the .item results in
-                # symints, due to querying the data from tensor.
-                # this path avoids that for mps backend, although probably mps backend
-                # can support dynamic shape?
-                freqs_cos = self.freqs_cos[input_pos]
-                freqs_sin = self.freqs_sin[input_pos]
-
-        else:
-            assert input_pos is None, "input_pos is unused when use_kv_cache is False"
-            freqs_cos = self.freqs_cos[:seqlen]
-            freqs_sin = self.freqs_sin[:seqlen]
 
         for layer in self.layers:
-            h = layer(
-                h,
-                freqs_cos,
-                freqs_sin,
-                input_pos,
-            )
+            h = layer(h, input_pos)
 
         if not self.generate_full_logits:
             # Only the last logit is used for the new generated token