test_llama.py

# Copyright (c) 2023, Tri Dao.

# To run the huggingface implementation, we first need to convert the weights:
# https://github.com/huggingface/transformers/pull/21955
# python -m transformers.models.llama.convert_llama_weights_to_hf --input_dir $CHECKPOINT_DIR/llama --model_size 7B --output_dir $CHECKPOINT_DIR/llama/7B-hf
# and repeat for 13B, 30B, 65B

import os
import time
from pathlib import Path

current_dir = Path(__file__).parent.absolute()

import shutil

import pytest
import torch
from einops import rearrange
from flash_attn.models.gpt import GPTLMHeadModel, combine_state_dicts_tp, shard_state_dict_tp
from flash_attn.models.llama import (
    config_from_checkpoint,
    inv_remap_state_dict_hf_llama,
    llama_config_to_gpt2_config,
    remap_state_dict_hf_llama,
    remap_state_dict_meta_llama,
    state_dicts_from_checkpoint,
)
from flash_attn.utils.distributed import all_gather_raw
from flash_attn.utils.generation import update_graph_cache
from flash_attn.utils.pretrained import state_dict_from_pretrained
from transformers import LlamaConfig, LlamaTokenizer
from transformers.models.llama.modeling_llama import LlamaForCausalLM


def _pretrained_state_dict_from_checkpoint(checkpoint_path, model_name, config, checkpoint_format):
    if checkpoint_format == "meta":
        ckpt_state_dicts = state_dicts_from_checkpoint(checkpoint_path, model_name)
        pretrained_state_dicts = [remap_state_dict_meta_llama(s, config) for s in ckpt_state_dicts]
        pretrained_state_dict = combine_state_dicts_tp(pretrained_state_dicts, config)
    else:
        pretrained_state_dict = state_dict_from_pretrained(
            Path(checkpoint_path) / f"{model_name}-hf"
        )
        pretrained_state_dict = remap_state_dict_hf_llama(pretrained_state_dict, config)
    return pretrained_state_dict


@pytest.mark.parametrize("model_name", ["7B"])
def test_llama_state_dict(model_name):
    checkpoint_path = (
        Path(os.environ.get("CHECKPOINT_DIR", current_dir.parent.parent / "checkpoints")) / "llama"
    )
    config = llama_config_to_gpt2_config(config_from_checkpoint(checkpoint_path, model_name))
    ckpt_state_dicts = state_dicts_from_checkpoint(checkpoint_path, model_name)
    pretrained_state_dict = remap_state_dict_meta_llama(ckpt_state_dicts[0], config)
    model = GPTLMHeadModel(config, device="meta")  # Without device='meta' init is very slow
    state_dict = model.state_dict()
    assert state_dict.keys() == pretrained_state_dict.keys()
    for k in state_dict.keys():
        assert state_dict[k].shape == pretrained_state_dict[k].shape


@pytest.mark.parametrize("model_name", ["7B", "13B"])
@pytest.mark.parametrize("checkpoint_format", ["meta", "hf"])
def test_llama_optimized(model_name, checkpoint_format):
    """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"
    config = config_from_checkpoint(checkpoint_path, model_name, checkpoint_format)
    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

    pretrained_state_dict = _pretrained_state_dict_from_checkpoint(
        checkpoint_path, model_name, config, checkpoint_format
    )
    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(
        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(
        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()


# torchrun --no_python --nproc_per_node=2 pytest -q -s tests/models/test_llama.py -k "parallel"
@pytest.mark.parametrize("world_size", [2])
@pytest.mark.parametrize("model_name", ["13B"])
@pytest.mark.parametrize("checkpoint_format", ["meta", "hf"])
def test_llama_parallel(model_name, world_size, checkpoint_format):
    """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.
    """
    from apex.transformer import parallel_state

    checkpoint_path = (
        Path(os.environ.get("CHECKPOINT_DIR", current_dir.parent.parent / "checkpoints")) / "llama"
    )

    dtype = torch.float16
    config = config_from_checkpoint(checkpoint_path, model_name, checkpoint_format)
    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 not torch.distributed.is_initialized():
        torch.distributed.init_process_group(backend="nccl", init_method="env://")
    device = f"cuda:{torch.distributed.get_rank()}"
    assert world_size <= torch.distributed.get_world_size()
    parallel_state.initialize_model_parallel(tensor_model_parallel_size_=world_size)
    rank = parallel_state.get_tensor_model_parallel_rank()
    process_group = parallel_state.get_tensor_model_parallel_group()

    pretrained_state_dict = _pretrained_state_dict_from_checkpoint(
        checkpoint_path, model_name, config, checkpoint_format
    )
    model = GPTLMHeadModel(config, process_group=process_group, device=device, dtype=dtype)
    model.load_state_dict(shard_state_dict_tp(pretrained_state_dict, config, world_size, rank))
    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)
        out, _ = all_gather_raw(out, process_group=process_group)
        out = rearrange(out, "(b s) d -> b s d", b=batch_size)
        logits = model(input_ids).logits
        logits = rearrange(logits, "(b s) d -> b s d", b=batch_size)
        logits, _ = all_gather_raw(logits, process_group)
        logits = rearrange(logits, "(n b) ... d -> b ... (n d)", b=batch_size)
    del model

    if rank == 0:
        # Without device_map, the model is loaded on the CPU, which is very slow
        model_ref = LlamaForCausalLM.from_pretrained(
            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(
            Path(checkpoint_path) / f"{model_name}-hf", torch_dtype=dtype, device_map="auto"
        )
        model_hf.eval()
        with torch.no_grad():
            out_hf = model_hf.model(input_ids).last_hidden_state.to(device=device)
            logits_hf = model_hf(input_ids).logits.to(device=device)
        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() < 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() < 2 * (
            logits_hf - logits_ref
        ).abs().max().item()


# @pytest.mark.parametrize('model_name', ["7B", "13B"])
@pytest.mark.parametrize("model_name", ["7B"])
@pytest.mark.parametrize("checkpoint_format", ["meta", "hf"])
def test_llama_generation(model_name, checkpoint_format):
    checkpoint_path = (
        Path(os.environ.get("CHECKPOINT_DIR", current_dir.parent.parent / "checkpoints")) / "llama"
    )

    dtype = torch.float16
    device = "cuda"
    config = config_from_checkpoint(checkpoint_path, model_name, checkpoint_format)
    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

    tokenizer = LlamaTokenizer.from_pretrained(Path(checkpoint_path) / f"{model_name}-hf")
    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 = LlamaForCausalLM.from_pretrained(
        Path(checkpoint_path) / f"{model_name}-hf", 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

    # Need auto here since the 13B fp32 model doesn't fit in memory on a A100 40GB
    model_ref = LlamaForCausalLM.from_pretrained(
        Path(checkpoint_path) / f"{model_name}-hf", device_map="auto"
    )
    model_ref.eval()
    with torch.no_grad():
        logits_ref = model_ref(out_hf.sequences).logits[:, (seqlen - 1) : -1].to(device=device)
    del model_ref

    pretrained_state_dict = _pretrained_state_dict_from_checkpoint(
        checkpoint_path, model_name, config, checkpoint_format
    )
    model = GPTLMHeadModel(config, device=device, dtype=dtype)
    model.load_state_dict(pretrained_state_dict)
    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()

    print(f"HF fp16 logits max diff: {hf_error}")
    print(f"Logits max diff: {(logits - logits_ref).abs().max().item()}")
    print(f"Logits CG max diff: {(logits_cg - logits_ref).abs().max().item()}")

    assert (logits_parallel - logits_ref).abs().max().item() < 2 * hf_error
    assert (logits - logits_ref).abs().max().item() < 2 * hf_error
    assert torch.equal(logits_cg, logits)


# torchrun --no_python --nproc_per_node=2 pytest -q -s tests/models/test_llama.py -k "llama_parallel_generation"
@pytest.mark.parametrize("world_size", [2])
@pytest.mark.parametrize("model_name", ["13B"])
@pytest.mark.parametrize("checkpoint_format", ["meta", "hf"])
def test_llama_parallel_generation(model_name, world_size, checkpoint_format):
    """Check that our implementation matches the HF implementation:
    the scores in fp16 should be around the same as the HF scores in fp16, when compared to
    the HF scores in fp32.
    """
    from apex.transformer import parallel_state

    checkpoint_path = (
        Path(os.environ.get("CHECKPOINT_DIR", current_dir.parent.parent / "checkpoints")) / "llama"
    )

    dtype = torch.float16
    config = config_from_checkpoint(checkpoint_path, model_name, checkpoint_format)
    config = llama_config_to_gpt2_config(config)
    config.use_flash_attn = False
    config.fused_bias_fc = True
    config.fused_mlp = False  # We don't have fused GatedMLP yet
    config.fused_dropout_add_ln = False
    config.residual_in_fp32 = True
    config.pad_vocab_size_multiple = 8 * world_size
    config.sequence_parallel = False  # Need to set this to False for generation

    os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "0"
    if not torch.distributed.is_initialized():
        torch.distributed.init_process_group(backend="nccl", init_method="env://")
    device = f"cuda:{torch.distributed.get_rank()}"
    assert world_size <= torch.distributed.get_world_size()
    parallel_state.initialize_model_parallel(tensor_model_parallel_size_=world_size)
    rank = parallel_state.get_tensor_model_parallel_rank()
    process_group = parallel_state.get_tensor_model_parallel_group()

    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
    )

    # Need this, otherwise when we capture the graph the process for GPU 1 would run on both
    # GPU0 and GPU1 and things would hang
    torch.cuda.set_device(device)

    pretrained_state_dict = _pretrained_state_dict_from_checkpoint(
        checkpoint_path, model_name, config, checkpoint_format
    )
    model = GPTLMHeadModel(config, process_group=process_group, device=device, dtype=dtype)
    model.load_state_dict(shard_state_dict_tp(pretrained_state_dict, config, world_size, rank))
    model.eval()

    print("Without CUDA graph")
    out = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        tensor_parallel=world_size,
        vocab_size=config.vocab_size,
        # teacher_outputs=out_hf.sequences,
        return_dict_in_generate=True,
        output_scores=True,
        enable_timing=True,
    )

    # 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")
    out_cg = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        tensor_parallel=world_size,
        vocab_size=config.vocab_size,
        cg=True,
        # teacher_outputs=out_hf.sequences,
        return_dict_in_generate=True,
        output_scores=True,
        enable_timing=True,
    )
    del model
    parallel_state.destroy_model_parallel()

    if rank == 0:
        # Without device_map, the model is loaded on the CPU, which is very slow
        model_hf = LlamaForCausalLM.from_pretrained(
            Path(checkpoint_path) / f"{model_name}-hf", torch_dtype=dtype, device_map="auto"
        )
        model_hf.eval()
        print("HF fp16")
        torch.cuda.synchronize()
        start = time.time()
        with torch.inference_mode():
            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 = LlamaForCausalLM.from_pretrained(
            Path(checkpoint_path) / f"{model_name}-hf", device_map="auto"
        )
        model_ref.eval()
        with torch.inference_mode():
            logits_ref = model_ref(out_hf.sequences).logits[:, (seqlen - 1) : -1]
        del model_ref
        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)

        hf_error = (logits_hf - logits_ref).abs().max().item()
        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)


@torch.no_grad()
@pytest.mark.parametrize("world_size", [2])
def test_llama_parallel_uneven_num_heads(world_size):
    from apex.transformer import parallel_state

    checkpoint_path = (
        Path(os.environ.get("CHECKPOINT_DIR", current_dir.parent.parent / "checkpoints")) / "llama"
    )
    num_attention_heads = world_size + 1
    model_name = f"teeny-{num_attention_heads}-heads"

    if not torch.distributed.is_initialized():
        torch.distributed.init_process_group(backend="nccl", init_method="env://")
    device = f"cuda:{torch.distributed.get_rank()}"
    assert world_size <= torch.distributed.get_world_size()
    parallel_state.initialize_model_parallel(tensor_model_parallel_size_=world_size)
    rank = parallel_state.get_tensor_model_parallel_rank()
    process_group = parallel_state.get_tensor_model_parallel_group()

    dtype = torch.float16
    llama_config = LlamaConfig(
        hidden_size=256
        * num_attention_heads,  # ParallelGatedMlp hidden_features must be divisible by 256
        intermediate_size=256 * num_attention_heads * 4,
        num_hidden_layers=4,
        num_attention_heads=num_attention_heads,
        initializer_range=0.5,  # Set crazy init range so we don't have near zero weights implying a vacuous test.
    )
    config = llama_config_to_gpt2_config(llama_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

    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
    )

    # Create a shared test model.
    if rank == 0:
        LlamaForCausalLM(config=llama_config).save_pretrained(checkpoint_path / f"{model_name}-hf")
    torch.distributed.barrier()

    # Run the standard forward pass test.
    pretrained_state_dict = _pretrained_state_dict_from_checkpoint(
        checkpoint_path, model_name, config, checkpoint_format="hf"
    )
    model = GPTLMHeadModel(config, process_group=process_group, device=device, dtype=dtype)
    model.load_state_dict(shard_state_dict_tp(pretrained_state_dict, config, world_size, rank))
    model.eval()

    # TODO: Avoid duplicate code. Modularize the comparison of two forward pass diffs.
    out = model.transformer(input_ids)
    out, _ = all_gather_raw(out, process_group=process_group)
    out = rearrange(out, "(b s) d -> b s d", b=batch_size)
    logits = model(input_ids).logits
    logits = rearrange(logits, "(b s) d -> b s d", b=batch_size)
    logits, _ = all_gather_raw(logits, process_group)
    logits = rearrange(logits, "(n b) ... d -> b ... (n d)", b=batch_size)

    if rank == 0:
        model_ref = LlamaForCausalLM.from_pretrained(
            Path(checkpoint_path) / f"{model_name}-hf", device_map="auto"
        )
        model_ref.eval()
        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(
            Path(checkpoint_path) / f"{model_name}-hf", torch_dtype=dtype, device_map="auto"
        )
        model_hf.eval()
        out_hf = model_hf.model(input_ids).last_hidden_state.to(device=device)
        logits_hf = model_hf(input_ids).logits.to(device=device)
        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() < 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() < 2 * (
            logits_hf - logits_ref
        ).abs().max().item()

        if os.path.exists(checkpoint_path / f"{model_name}-hf"):
            shutil.rmtree(checkpoint_path / f"{model_name}-hf")


@torch.no_grad()
def test_inv_remap_state_dict_hf_llama():
    checkpoint_path = (
        Path(os.environ.get("CHECKPOINT_DIR", current_dir.parent.parent / "checkpoints")) / "llama"
    )
    model_name = f"teeny"

    llama_config = LlamaConfig(
        num_attention_heads=2,
        hidden_size=256 * 2,
        intermediate_size=256 * 2 * 4,
        num_hidden_layers=4,
    )
    config = llama_config_to_gpt2_config(llama_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

    # Set up.
    LlamaForCausalLM(config=llama_config).save_pretrained(checkpoint_path / f"{model_name}-hf")

    # inv_remap_state_dict_hf_llama should be the inverse of remap_state_dict_hf_llama
    state_dict = state_dict_from_pretrained(checkpoint_path / f"{model_name}-hf")
    state_dict = {key: val for key, val in state_dict.items() if "rotary_emb.inv_freq" not in key}
    pretrained_state_dict = remap_state_dict_hf_llama(state_dict, config)
    state_dict_recover = inv_remap_state_dict_hf_llama(pretrained_state_dict, config)

    assert set(state_dict_recover.keys()) == set(state_dict.keys())

    for key in state_dict_recover.keys():
        torch.testing.assert_close(state_dict_recover[key], state_dict[key])

    # Tear down.
    if os.path.exists(checkpoint_path / f"{model_name}-hf"):
        shutil.rmtree(checkpoint_path / f"{model_name}-hf")