def test_gptj_generation(model_name):
    """Check that our implementation of GPT-J (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.
    """
    dtype = torch.float16
    device = "cuda"
    config = gptj_config_to_gpt2_config(GPTJConfig.from_pretrained(model_name))
    config.use_flash_attn = True  # FlashAttention-2 supports headdim 256
    config.fused_bias_fc = True
    config.fused_mlp = True
    config.fused_dropout_add_ln = True
    # Only prenorm supports residual_in_fp32
    config.residual_in_fp32 = True

    tokenizer = AutoTokenizer.from_pretrained(model_name)
    eos_token_id = tokenizer.eos_token_id

    torch.manual_seed(0)
    batch_size = 1
    seqlen = 100
    max_length = 150
    input_ids = torch.randint(
        0, config.vocab_size, (batch_size, seqlen), dtype=torch.long, device=device
    )

    model_hf = GPTJForCausalLM.from_pretrained(
        model_name, torch_dtype=dtype, device_map={"": device}
    )
    model_hf.eval()
    print("HF fp16")
    torch.cuda.synchronize()
    start = time.time()
    out_hf = model_hf.generate(
        input_ids=input_ids, max_length=max_length, return_dict_in_generate=True, output_scores=True
    )
    torch.cuda.synchronize()
    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
    del model_hf

    model_ref = GPTJForCausalLM.from_pretrained(model_name, device_map={"": device})
    model_ref.eval()
    with torch.no_grad():
        logits_ref = model_ref(out_hf.sequences).logits[:, (seqlen - 1) : -1]
    del model_ref

    model = GPTLMHeadModel.from_pretrained(model_name, config, device=device, dtype=dtype)
    model.eval()

    print("Without CUDA graph")
    torch.cuda.synchronize()
    start = time.time()
    out = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        eos_token_id=eos_token_id,
        return_dict_in_generate=True,
        output_scores=True,
        enable_timing=True,
        teacher_outputs=out_hf.sequences,
    )
    torch.cuda.synchronize()
    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")

    # Capture graph outside the timing loop
    batch_size, seqlen_og = input_ids.shape
    model._decoding_cache = update_graph_cache(model, None, batch_size, seqlen_og, max_length)
    print("With CUDA graph")
    torch.cuda.synchronize()
    start = time.time()
    out_cg = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        cg=True,
        return_dict_in_generate=True,
        output_scores=True,
        enable_timing=True,
        teacher_outputs=out_hf.sequences,
    )
    torch.cuda.synchronize()
    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")

    with torch.no_grad():
        logits_parallel = model(out_hf.sequences).logits[:, (seqlen - 1) : -1]
    logits_hf = torch.stack(out_hf.scores, dim=1)
    logits = torch.stack(out.scores, dim=1)
    logits_cg = torch.stack(out_cg.scores, dim=1)

    del model

    hf_error = (logits_hf - logits_ref).abs().max().item()
    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_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)
Check that our implementation of GPT-J (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_gptj.py