def test_llama_optimized(model_name):
    """Check that our implementation of LLaMa (with all optimizations enabled) matches the
    HF implementation: the output of our forward pass in fp16 should be around the same as the HF
    forward pass in fp16, when compared to the HF forward pass in fp32.
    """
    checkpoint_path = (
        Path(os.environ.get("CHECKPOINT_DIR", current_dir.parent.parent / "checkpoints")) / "llama"
    )

    dtype = torch.float16
    device = "cuda"
    if "/" in model_name:  # Download from HF
        config = llama_config_to_gpt2_config(
            AutoConfig.from_pretrained(model_name, trust_remote_code=True)
        )
    else:
        config = config_from_checkpoint(checkpoint_path, model_name, checkpoint_format="meta")
        config = llama_config_to_gpt2_config(config)
    config.use_flash_attn = True
    config.fused_bias_fc = True
    config.fused_mlp = False  # We don't have fused GatedMLP yet
    config.fused_dropout_add_ln = True
    config.residual_in_fp32 = True

    if "/" in model_name:  # Download from HF
        pretrained_state_dict = remap_state_dict_hf_llama(
            state_dict_from_pretrained(model_name), config
        )
    else:
        pretrained_state_dict = _pretrained_state_dict_from_checkpoint(
            checkpoint_path, model_name, config, checkpoint_format="meta"
        )
    model = GPTLMHeadModel(config, device=device, dtype=dtype)
    model.load_state_dict(pretrained_state_dict)
    model.eval()

    torch.manual_seed(0)
    batch_size = 2
    max_seqlen = 256
    seqlens = torch.randint(max_seqlen // 2, max_seqlen + 1, (batch_size,), device=device)
    input_ids = torch.randint(
        0, config.vocab_size, (batch_size, max_seqlen), dtype=torch.long, device=device
    )
    with torch.no_grad():
        out = model.transformer(input_ids)
        logits = model(input_ids).logits
    del model

    # Without device_map, the model is loaded on the CPU, which is very slow
    # Need auto here since the 13B fp32 model doesn't fit in memory on a A100 40GB
    model_ref = LlamaForCausalLM.from_pretrained(
        model_name if "/" in model_name else Path(checkpoint_path) / f"{model_name}-hf",
        device_map="auto",
    )
    model_ref.eval()
    with torch.no_grad():
        out_ref = model_ref.model(input_ids).last_hidden_state.to(device=device)
        logits_ref = model_ref(input_ids).logits.to(device=device)
    del model_ref

    model_hf = LlamaForCausalLM.from_pretrained(
        model_name if "/" in model_name else Path(checkpoint_path) / f"{model_name}-hf",
        torch_dtype=dtype,
        device_map={"": device},
    )
    model_hf.eval()
    with torch.no_grad():
        out_hf = model_hf.model(input_ids).last_hidden_state
        logits_hf = model_hf(input_ids).logits
    del model_hf

    print(f"Output max diff: {(out - out_ref).abs().max().item()}")
    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()

    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()
Check that our implementation of LLaMa (with all optimizations enabled) matches the
    HF implementation: the output of our forward pass in fp16 should be around the same as the HF
    forward pass in fp16, when compared to the HF forward pass in fp32.
    
tests/models/test_llama.py