[LLaMa] Fix last norm layer to use RMSNorm instead of LayerNorm

flash_attn/models/gpt.py

@@ -377,13 +377,18 @@ class GPTModel(GPTPreTrainedModel):
             else:
                 # Set prenorm=False here since we don't need the residual
                 if not self.parallel_block:
-                    hidden_states = dropout_add_layer_norm(
+                    fused_add_norm_fn = (dropout_add_rms_norm if isinstance(self.ln_f, RMSNorm)
+                                         else dropout_add_layer_norm)
+                    hidden_states = fused_add_norm_fn(
                         hidden_states, residual, self.ln_f.weight, self.ln_f.bias,
                         self.drop_f.p if self.training else 0.0, self.ln_f.eps, prenorm=False,
                         residual_in_fp32=self.residual_in_fp32
                     )
                 else:
-                    hidden_states, _ = dropout_add_layer_norm_parallel_residual(
+                    fused_add_norm_fn = (dropout_add_rms_norm_parallel_residual
+                                         if isinstance(self.ln_f, RMSNorm)
+                                         else dropout_add_layer_norm_parallel_residual)
+                    hidden_states, _ = fused_add_norm_fn(
                         hidden_states, hidden_states2, residual, self.ln_f.weight, self.ln_f.bias,
                         None, None, self.drop_f.p if self.training else 0.0, self.ln_f.eps,
                         prenorm=False, residual_in_fp32=self.residual_in_fp32

tests/models/test_llama.py

@@ -176,13 +176,13 @@ def test_llama_parallel(model_name, world_size):
     print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
     print(f'HF fp16 max diff: {(out_hf - out_ref).abs().max().item()}')
     print(f'HF fp16 mean diff: {(out_hf - out_ref).abs().mean().item()}')
-    assert (out - out_ref).abs().max().item() < 3 * (out_hf - out_ref).abs().max().item()
+    assert (out - out_ref).abs().max().item() < 2 * (out_hf - out_ref).abs().max().item()
 
     print(f'Logits max diff: {(logits - logits_ref).abs().max().item()}')
     print(f'Logits mean diff: {(logits - logits_ref).abs().mean().item()}')
     print(f'HF fp16 max diff: {(logits_hf - logits_ref).abs().max().item()}')
     print(f'HF fp16 mean diff: {(logits_hf - logits_ref).abs().mean().item()}')
-    assert (logits - logits_ref).abs().max().item() < 3 * (logits_hf - logits_ref).abs().max().item()
+    assert (logits - logits_ref).abs().max().item() < 2 * (logits_hf - logits_ref).abs().max().item()
 
 
 @pytest.mark.parametrize('model_name', ["7B"])
@@ -267,11 +267,10 @@ def test_llama_generation(model_name):
     del model
 
     hf_error = (logits_hf - logits_ref).abs().max().item()
-    # For some reason logits_parallel is off by quite a bit more than 2x
-    assert (logits_parallel - logits_ref).abs().max().item() < 8 * hf_error
+    assert (logits_parallel - logits_ref).abs().max().item() < 2 * hf_error
 
     print(f'HF fp16 logits max diff: {hf_error}')
-    print(f'Logits max diff: {(logits - logits_parallel).abs().max().item() }')
-    assert (logits - logits_parallel).abs().max().item() < 2 * hf_error
-    print(f'Logits CG max diff: {(logits_cg - logits_parallel).abs().max().item() }')
+    print(f'Logits max diff: {(logits - logits_ref).abs().max().item() }')
+    assert (logits - logits_ref).abs().max().item() < 2 * hf_error
+    print(f'Logits CG max diff: {(logits_cg - logits_ref).abs().max().item() }')
     assert torch.equal(logits_cg, logits)