[BugFix] Implement RoPE for GPT-J (#941)

tests/kernels/test_pos_encoding.py

@@ -7,49 +7,64 @@ import torch.nn.functional as F
 
 from vllm import pos_encoding_ops
 
+IS_NEOX_STYLE = [True, False]
 DTYPES = [torch.half, torch.bfloat16, torch.float]
 HEAD_SIZES = [64, 80, 96, 112, 128, 256]
 ROTARY_DIMS = [None, 32]  # None means rotary dim == head size
 NUM_HEADS = [7, 12, 40, 52]  # Arbitrary values for testing
-NUM_TOKENS = [7, 83, 2048]  # Arbitrary values for testing
+NUM_TOKENS = [11, 83, 2048]  # Arbitrary values for testing
 SEEDS = [0]
 
 
-def rotate_half(x: torch.Tensor) -> torch.Tensor:
+def rotate_neox(x: torch.Tensor) -> torch.Tensor:
     x1 = x[..., :x.shape[-1] // 2]
     x2 = x[..., x.shape[-1] // 2:]
     return torch.cat((-x2, x1), dim=-1)
 
 
-def apply_rotary_pos_emb(
+def rotate_gptj(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    x = torch.stack((-x2, x1), dim=-1)
+    return x.flatten(-2)
+
+
+def apply_rope(
     q: torch.Tensor,
     k: torch.Tensor,
     cos: torch.Tensor,
     sin: torch.Tensor,
+    is_neox_style: bool,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
+    rotate_fn = rotate_neox if is_neox_style else rotate_gptj
+    q_embed = (q * cos) + (rotate_fn(q) * sin)
+    k_embed = (k * cos) + (rotate_fn(k) * sin)
     return q_embed, k_embed
 
 
-class RefRotaryEmbeddingNeox(nn.Module):
-    """Reference implementation of the GPT-NeoX style rotary embedding."""
+class RefRotaryEmbedding(nn.Module):
+    """Reference implementation of rotary embedding."""
 
     def __init__(
         self,
         dim: int,
-        max_position_embeddings: int = 2048,
+        is_neox_style: bool,
+        max_position_embeddings: int = 8192,
         base: int = 10000,
     ) -> None:
         super().__init__()
         self.rotary_dim = dim
+        self.is_neox_style = is_neox_style
         self.max_position_embeddings = max_position_embeddings
 
         # Create cos and sin embeddings.
         inv_freq = 1.0 / (base**(torch.arange(0, dim, 2) / dim))
         t = torch.arange(max_position_embeddings).float()
         freqs = torch.einsum("i,j->ij", t, inv_freq.float())
-        emb = torch.cat((freqs, freqs), dim=-1)
+        if is_neox_style:
+            emb = torch.cat((freqs, freqs), dim=-1)
+        else:
+            emb = torch.repeat_interleave(freqs, 2, -1)
         cos = emb.cos().to(dtype=inv_freq.dtype)
         sin = emb.sin().to(dtype=inv_freq.dtype)
         self.register_buffer("cos_cached", cos, persistent=False)
@@ -61,7 +76,6 @@ class RefRotaryEmbeddingNeox(nn.Module):
         query: torch.Tensor,  # [num_tokens, num_heads, head_size]
         key: torch.Tensor,  # [num_tokens, num_heads, head_size]
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-
         query_rot = query[..., :self.rotary_dim]
         query_pass = query[..., self.rotary_dim:]
         key_rot = key[..., :self.rotary_dim]
@@ -71,7 +85,9 @@ class RefRotaryEmbeddingNeox(nn.Module):
         key_rot = key_rot.transpose(0, 1)
         cos = F.embedding(positions, self.cos_cached)
         sin = F.embedding(positions, self.sin_cached)
-        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)
+
+        query_rot, key_rot = apply_rope(query_rot, key_rot, cos, sin,
+                                        self.is_neox_style)
         query_rot = query_rot.transpose(0, 1).contiguous()
         key_rot = key_rot.transpose(0, 1).contiguous()
 
@@ -82,6 +98,7 @@ class RefRotaryEmbeddingNeox(nn.Module):
         return query, key
 
 
+@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
@@ -89,7 +106,8 @@ class RefRotaryEmbeddingNeox(nn.Module):
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
 @torch.inference_mode()
-def test_rotary_embedding_neox(
+def test_rotary_embedding(
+    is_neox_style: bool,
     num_tokens: int,
     num_heads: int,
     head_size: int,
@@ -104,15 +122,15 @@ def test_rotary_embedding_neox(
     torch.random.manual_seed(seed)
     torch.cuda.manual_seed(seed)
 
-    positions = torch.randint(0, max_position, (num_tokens, ), device='cuda')
+    positions = torch.randint(0, max_position, (num_tokens, ), device="cuda")
     query = torch.randn(num_tokens,
                         num_heads * head_size,
                         dtype=dtype,
-                        device='cuda')
+                        device="cuda")
     key = torch.randn(num_tokens,
                       num_heads * head_size,
                       dtype=dtype,
-                      device='cuda')
+                      device="cuda")
 
     # Create the rotary embedding.
     inv_freq = 1.0 / (base**(torch.arange(0, rotary_dim, 2) / rotary_dim))
@@ -126,20 +144,22 @@ def test_rotary_embedding_neox(
     # Run the kernel. The kernel is in-place, so we need to clone the inputs.
     out_query = query.clone()
     out_key = key.clone()
-    pos_encoding_ops.rotary_embedding_neox(
+    pos_encoding_ops.rotary_embedding(
         positions,
         out_query,
         out_key,
         head_size,
         cos_sin_cache,
+        is_neox_style,
     )
 
     # Run the reference implementation.
-    ref_rotary_embedding = RefRotaryEmbeddingNeox(
+    ref_rotary_embedding = RefRotaryEmbedding(
         dim=rotary_dim,
+        is_neox_style=is_neox_style,
         max_position_embeddings=max_position,
         base=base,
-    ).to(dtype=dtype, device='cuda')
+    ).to(dtype=dtype, device="cuda")
     ref_query, ref_key = ref_rotary_embedding(
         positions,
         query.view(num_tokens, num_heads, head_size),