Improve rope settings for llama3 (#380)

ch05/07_gpt_to_llama/tests/tests.py

@@ -58,10 +58,10 @@ def set_seed():
     torch.manual_seed(123)
 
 
-def test_rope(notebook):
+def test_rope_llama2(notebook):
     # Settings
     batch_size = 1
-    context_len = 5
+    context_len = 4096
     num_heads = 4
     head_dim = 16
 
@@ -76,19 +76,51 @@ def test_rope(notebook):
     queries_rot = notebook.compute_rope(queries, cos, sin)
     keys_rot = notebook.compute_rope(keys, cos, sin)
 
-    class RoPEConfig:
-        rope_type = "default"
-        rope_scaling = None
-        factor = 1.0
-        dim: int = head_dim
-        rope_theta = 10000
-        max_position_embeddings: int = 4096
-        hidden_size = head_dim * num_heads
-        num_attention_heads = num_heads
+    rot_emb = LlamaRotaryEmbedding(
+        dim=head_dim,
+        max_position_embeddings=context_len,
+        base=10_000
+    )
 
-    config = RoPEConfig()
+    position_ids = torch.arange(context_len, dtype=torch.long).unsqueeze(0)
+    ref_cos, ref_sin = rot_emb(queries, position_ids)
+    ref_queries_rot, ref_keys_rot = apply_rotary_pos_emb(queries, keys, ref_cos, ref_sin)
+
+    torch.testing.assert_close(sin, ref_sin.squeeze(0))
+    torch.testing.assert_close(cos, ref_cos.squeeze(0))
+    torch.testing.assert_close(keys_rot, ref_keys_rot)
+    torch.testing.assert_close(queries_rot, ref_queries_rot)
+
+
+def test_rope_llama3(notebook):
+    # Settings
+    batch_size = 1
+    context_len = 8192
+    num_heads = 4
+    head_dim = 16
+    theta_base = 50_000
+
+    # Instantiate RoPE parameters
+    cos, sin = notebook.precompute_rope_params(
+        head_dim=head_dim,
+        context_length=context_len,
+        theta_base=theta_base
+    )
+
+    # Dummy query and key tensors
+    queries = torch.randn(batch_size, num_heads, context_len, head_dim)
+    keys = torch.randn(batch_size, num_heads, context_len, head_dim)
+
+    # Apply rotary position embeddings
+    queries_rot = notebook.compute_rope(queries, cos, sin)
+    keys_rot = notebook.compute_rope(keys, cos, sin)
+
+    rot_emb = LlamaRotaryEmbedding(
+        dim=head_dim,
+        max_position_embeddings=context_len,
+        base=theta_base
+    )
 
-    rot_emb = LlamaRotaryEmbedding(config=config)
     position_ids = torch.arange(context_len, dtype=torch.long).unsqueeze(0)
     ref_cos, ref_sin = rot_emb(queries, position_ids)
     ref_queries_rot, ref_keys_rot = apply_rotary_pos_emb(queries, keys, ref_cos, ref_sin)
@@ -108,7 +140,7 @@ def test_silu(notebook):
 @pytest.mark.skipif(torch.__version__ < "2.4", reason="Requires PyTorch 2.4 or newer")
 def test_rmsnorm(notebook):
     example_batch = torch.randn(2, 3, 4)
-    rms_norm = notebook.RMSNorm(emb_dim=example_batch.shape[-1])
-    rmsnorm_pytorch = torch.nn.RMSNorm(example_batch.shape[-1], eps=1e-6)
+    rms_norm = notebook.RMSNorm(emb_dim=example_batch.shape[-1], eps=1e-5)
+    rmsnorm_pytorch = torch.nn.RMSNorm(example_batch.shape[-1], eps=1e-5)
 
     assert torch.allclose(rms_norm(example_batch), rmsnorm_pytorch(example_batch))