[Executorch][llama] Make RoPE freq calculation broadcast for per head

Pull Request resolved: #2353

This is a workaround, may not be even worth landing, to avoid broadcasting
semantics in the mul op and for that matter any binary op. Current
implementation of oiptimized ops doesnt handle broadcasting and falls back to
portable op implementation.

This diff also fixes an issue where (as seen in llama) two tensors of binary op
are not broadcasting, but they have different # of dims, which results in
invocation of unoptimized path. e.g. a = [1, 1, 2048], b = [2048], out = [1, 1,
2048].

In llama case this is optimized path when generating one token at a time. Not
so during pre-fill

Making optimized op handle broadcasting, and support vectorization, is not
hard, but may take some time.
ghstack-source-id: 218755098
@exported-using-ghexport

Differential Revision: [D54766067](https://our.internmc.facebook.com/intern/diff/D54766067/)

Author: kimishpatel

examples/models/llama2/llama_transformer.py

@@ -109,22 +109,28 @@ def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
     )
 
 
-def precompute_freqs_cis(dim: int, end: int, theta: float):
+def precompute_freqs_cis(dim: int, n_heads: int, end: int, theta: float):
     freqs = 1.0 / (
         theta ** (torch.arange(0, dim, 2, device="cpu")[: (dim // 2)].float() / dim)
     )
     t = torch.arange(end, device=freqs.device)  # pyre-ignore
     freqs = torch.outer(t, freqs).float()  # pyre-ignore
     freqs_cos = torch.cos(freqs)
     freqs_sin = torch.sin(freqs)
+    freqs_cos = freqs_cos.view(end, 1, dim // 2)
+    freqs_cos = freqs_cos.expand(end, n_heads, dim // 2).contiguous()
+    freqs_sin = freqs_sin.view(end, 1, dim // 2)
+    freqs_sin = freqs_sin.expand(end, n_heads, dim // 2).contiguous()
     return freqs_cos, freqs_sin
 
 
 def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
     ndim = x.ndim
     assert 0 <= 1 < ndim
-    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
-    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+    assert freqs_cis.shape == (x.shape[1], x.shape[2], x.shape[-1])
+    shape = [
+        d if (i == 1 or i == 2 or i == ndim - 1) else 1 for i, d in enumerate(x.shape)
+    ]
     return freqs_cis.view(shape)
 
 
@@ -416,6 +422,7 @@ def __init__(self, params: ModelArgs):
 
         freqs_cos, freqs_sin = precompute_freqs_cis(
             params.dim // params.n_heads,
+            params.n_heads,
             params.max_seq_len,
             params.rope_freq_base,
         )

kernels/optimized/cpu/op_mul.cpp

@@ -20,6 +20,29 @@ namespace native {
 using Tensor = exec_aten::Tensor;
 using ScalarType = exec_aten::ScalarType;
 
+namespace {
+
+// Move to generic util as this is applicable to all binary ops
+bool can_use_optimized_path(
+    const Tensor& a,
+    const Tensor& b,
+    const Tensor& out) {
+  ScalarType a_type = a.scalar_type();
+  ScalarType b_type = b.scalar_type();
+  ScalarType out_type = out.scalar_type();
+
+  bool can_use_optimized_path = true;
+  can_use_optimized_path =
+      can_use_optimized_path && ((a_type == b_type) && (a_type == out_type));
+  can_use_optimized_path = can_use_optimized_path &&
+      (a_type != ScalarType::Half && b_type != ScalarType::Half);
+  can_use_optimized_path = can_use_optimized_path &&
+      (a.sizes().equals(b.sizes()) ||
+       (a.numel() == b.numel() && a.numel() == out.numel()));
+  return can_use_optimized_path;
+}
+} // namespace
+
 Tensor& opt_mul_out(
     RuntimeContext& ctx,
     const Tensor& a,
@@ -31,8 +54,7 @@ Tensor& opt_mul_out(
   ScalarType b_type = b.scalar_type();
   ScalarType out_type = out.scalar_type();
 
-  if (a_type == b_type && a_type == out_type && a.sizes().equals(b.sizes()) &&
-      a_type != ScalarType::Half) {
+  if (can_use_optimized_path(a, b, out)) {
     // Resize for dynamic shape
     auto error = resize_tensor(out, a.sizes());
     ET_KERNEL_CHECK_MSG(