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import torch
import pytest
import sys
def copy_mlp(llama_mlp, orig_llama_mlp) -> None:
orig_llama_mlp.w1.weight.copy_(llama_mlp.c_fc1.weight)
orig_llama_mlp.w3.weight.copy_(llama_mlp.c_fc2.weight)
orig_llama_mlp.w2.weight.copy_(llama_mlp.c_proj.weight)
def copy_attention(llama_attn, orig_llama_attn) -> None:
n_embd = llama_attn.c_attn.weight.shape[1]
orig_llama_attn.wq.weight.copy_(llama_attn.c_attn.weight[:n_embd])
orig_llama_attn.wk.weight.copy_(llama_attn.c_attn.weight[n_embd:-n_embd])
orig_llama_attn.wv.weight.copy_(llama_attn.c_attn.weight[-n_embd:])
orig_llama_attn.wo.weight.copy_(llama_attn.c_proj.weight)
def copy_block(llama_block, orig_llama_block) -> None:
orig_llama_block.attention_norm.weight.copy_(llama_block.rms_1.scale)
copy_attention(llama_block.attn, orig_llama_block.attention)
orig_llama_block.ffn_norm.weight.copy_(llama_block.rms_2.scale)
copy_mlp(llama_block.mlp, orig_llama_block.feed_forward)
def copy_weights(llama_model, orig_llama_model) -> None:
orig_llama_model.tok_embeddings.weight.copy_(llama_model.transformer.wte.weight)
for llama_block, orig_llama_block in zip(llama_model.transformer.h, orig_llama_model.layers):
copy_block(llama_block, orig_llama_block)
orig_llama_model.norm.weight.copy_(llama_model.transformer.ln_f.scale)
orig_llama_model.output.weight.copy_(llama_model.lm_head.weight)
@torch.no_grad()
@pytest.mark.parametrize("kv_cache", (False, True))
def test_to_orig_llama(lit_llama, orig_llama, kv_cache) -> None:
block_size = 64
vocab_size = 32000
n_layer = 16
n_head = 16
n_embd = 32
batch_size = 3
llama_config = lit_llama.LLaMAConfig(
block_size=block_size, vocab_size=vocab_size, n_layer=n_layer, n_head=n_head, n_embd=n_embd
)
orig_llama_config = orig_llama.ModelArgs(
dim=n_embd,
n_layers=n_layer,
n_heads=n_head,
vocab_size=vocab_size,
norm_eps=1e-5,
max_seq_len=block_size,
max_batch_size=batch_size,
)
seq_len = orig_llama_config.max_seq_len
token_sample = torch.randint(0, orig_llama_config.vocab_size, size=(batch_size, seq_len), dtype=torch.int64)
llama_model = lit_llama.LLaMA(llama_config)
llama_model.apply(llama_model._init_weights)
orig_llama_model = orig_llama.Transformer(orig_llama_config)
copy_weights(llama_model, orig_llama_model)
orig_llama_embed = orig_llama_model.tok_embeddings(token_sample)
llama_embed = llama_model.transformer.wte(token_sample)
assert torch.allclose(orig_llama_embed, llama_embed)
llama_rope = llama_model.build_rope_cache(token_sample)
llama_mask = llama_model.build_mask_cache(token_sample)
orig_llama_mask = torch.full((1, 1, seq_len, seq_len), float("-inf"))
orig_llama_mask = torch.triu(orig_llama_mask, diagonal=1)
if kv_cache:
orig_llama_block_out = orig_llama_model.layers[0](
orig_llama_embed, 0, orig_llama_model.freqs_cis[:seq_len], orig_llama_mask
)
theirs_k_cache = orig_llama_model.layers[0].attention.cache_k
theirs_v_cache = orig_llama_model.layers[0].attention.cache_v
head_size = n_embd // n_head
kv_cache_shape = (batch_size, n_head, block_size, head_size)
ours_kv_cache = torch.zeros(kv_cache_shape), torch.zeros(kv_cache_shape)
(llama_block_out, ours_kv_cache) = llama_model.transformer.h[0](
llama_embed, llama_rope, llama_mask, seq_len, torch.arange(block_size), ours_kv_cache
)
ours_k_cache = ours_kv_cache[0].permute(0, 2, 1, 3)
ours_v_cache = ours_kv_cache[1].permute(0, 2, 1, 3)
torch.testing.assert_close(ours_k_cache, theirs_k_cache)
torch.testing.assert_close(ours_v_cache, theirs_v_cache)
else:
orig_llama_block_out = orig_llama_model.layers[0](
orig_llama_embed, 0, orig_llama_model.freqs_cis[:seq_len], orig_llama_mask
)
(llama_block_out, _) = llama_model.transformer.h[0](llama_embed, llama_rope, llama_mask, seq_len)
assert torch.allclose(orig_llama_block_out, llama_block_out)
expected = orig_llama_model(token_sample, 0)
out = llama_model(token_sample)
assert torch.allclose(out, expected)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires CUDA")
@torch.no_grad()
def test_bfloat16_llama_init(lit_llama, orig_llama) -> None:
from lit_llama.utils import EmptyInitOnDevice
block_size = 64
vocab_size = 32000
n_layer = 16
n_head = 16
n_embd = 32
llama_config = lit_llama.LLaMAConfig(
block_size=block_size, vocab_size=vocab_size, n_layer=n_layer, n_head=n_head, n_embd=n_embd
)
llama_model = lit_llama.LLaMA(llama_config)
llama_model.apply(llama_model._init_weights)
batch_size = 3
token_sample = torch.randint(0, vocab_size, size=(batch_size, block_size), dtype=torch.int64)
expected = llama_model(token_sample)
with EmptyInitOnDevice(device="cuda", dtype=torch.bfloat16):
llama_model2 = lit_llama.LLaMA(llama_config)
llama_model2.load_state_dict(llama_model.state_dict(keep_vars=True))
out = llama_model2(token_sample.cuda()).float().cpu()
torch.testing.assert_close(out, expected, atol=5e-3, rtol=1e-3)
def copy_adapter_weights(llama_model, orig_llama_model) -> None:
# copy the gating parameter
for llama_block, orig_llama_block in zip(llama_model.transformer.h, orig_llama_model.layers):
if hasattr(llama_block.attn, "gating_factor"):
llama_block.attn.gating_factor.copy_(orig_llama_block.attention.gate)
# In the original model, there is one embedding layer for all blocks combined
orig_adapter_wte = orig_llama_model.adapter_query.weight.reshape(
orig_llama_model.params.adapter_layer, orig_llama_model.params.adapter_len, orig_llama_model.params.dim
)
# In ours, the embedding layer is split across the individual attention layers
index = 0
for llama_block in llama_model.transformer.h:
if hasattr(llama_block.attn, "adapter_wte"):
llama_block.attn.adapter_wte.weight.copy_(orig_adapter_wte[index])
index += 1
def enable_gate(model):
for name, param in model.named_parameters():
if "gating_factor" in name or "gate" in name:
param.fill_(1)
@torch.no_grad()
def test_adapter_parity(orig_llama_adapter):
"""Test parity between our implementation of LLaMA-Adapter and the reference code."""
import lit_llama.adapter as lit_llama
orig_llama = orig_llama_adapter
block_size = 32
vocab_size = 100
n_layer = 2
n_head = 4
n_embd = 16
adapter_prompt_length: int = 10
adapter_start_layer: int = 0
llama_config = lit_llama.LLaMAConfig(
block_size=block_size,
vocab_size=vocab_size,
n_layer=n_layer,
n_head=n_head,
n_embd=n_embd,
adapter_prompt_length=adapter_prompt_length,
adapter_start_layer=adapter_start_layer,
)
orig_llama_config = orig_llama.ModelArgs(
dim=n_embd,
n_layers=n_layer,
n_heads=n_head,
vocab_size=vocab_size,
norm_eps=1e-5,
max_seq_len=block_size,
adapter_len=adapter_prompt_length,
adapter_layer=(n_layer - adapter_start_layer),
)
batch_size = 3
token_sample = torch.randint(
0, orig_llama_config.vocab_size, size=(batch_size, orig_llama_config.max_seq_len), dtype=torch.int64
)
llama_model = lit_llama.LLaMA(llama_config)
llama_model.apply(llama_model._init_weights)
orig_llama_model = orig_llama.Transformer(orig_llama_config)
copy_weights(llama_model, orig_llama_model)
copy_adapter_weights(llama_model, orig_llama_model)
# make the gate non-zero, otherwise the adapter is disabled and the model
# identical to regular LLaMA
enable_gate(llama_model)
enable_gate(orig_llama_model)
expected = orig_llama_model(token_sample, 0)
out = llama_model(token_sample)
assert torch.allclose(out, expected)
@pytest.mark.skipif(sys.platform in ("win32", "darwin"), reason="torch.compile not supported on this platform")
def test_model_compile(lit_llama):
llama_config = lit_llama.LLaMAConfig(block_size=8, vocab_size=8, n_layer=2, n_head=2, n_embd=4)
model = lit_llama.LLaMA(llama_config)
model.apply(model._init_weights)
model = torch.compile(model)
sample = torch.randint(model.config.vocab_size, size=(2, model.config.block_size), dtype=torch.int64)
for _ in range(3):
_ = model(sample)
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