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import pytest
import torch
import transformers
from hivemind import get_logger, use_hivemind_log_handler
from test_utils import *
from src.bloom.model import BloomForCausalLM
from src.client.remote_model import DistributedBloomForCausalLM
use_hivemind_log_handler("in_root_logger")
logger = get_logger(__file__)
@pytest.mark.forked
def test_full_model_exact_match(atol_forward=1e-3, atol_inference=1e-3):
tokenizer = transformers.BloomTokenizerFast.from_pretrained(MODEL_NAME)
model = DistributedBloomForCausalLM.from_pretrained(
MODEL_NAME, initial_peers=INITIAL_PEERS, low_cpu_mem_usage=True, torch_dtype=torch.float32
)
config = model.config
assert isinstance(model, DistributedBloomForCausalLM)
assert len(model.transformer.h) == model.config.n_layer
test_inputs = tokenizer("A cat sat on a mat", return_tensors="pt")["input_ids"]
with torch.inference_mode():
parallel_outputs = model.forward(test_inputs).logits
assert torch.all(torch.isfinite(parallel_outputs))
logger.info("Forward outputs are finite")
embs = model.transformer.word_embeddings(test_inputs)
embs = model.transformer.word_embeddings_layernorm(embs)
recurrent_outputs = []
with model.transformer.h.inference_session(max_length=embs.shape[1]) as sess:
for t in range(embs.shape[1]):
recurrent_outputs.append(sess.step(embs[:, t : t + 1, :]))
recurrent_outputs = torch.cat(recurrent_outputs, dim=1)
recurrent_outputs = model.transformer.ln_f(recurrent_outputs)
recurrent_outputs = model.lm_head(recurrent_outputs)
assert torch.allclose(recurrent_outputs, parallel_outputs, rtol=0, atol=atol_inference)
logger.info("Inference is consistent with forward")
del model, embs, recurrent_outputs
if REF_NAME:
ref_model = transformers.BloomForCausalLM.from_pretrained(
REF_NAME, low_cpu_mem_usage=True, torch_dtype=torch.float32
)
if config.vocab_size < ref_model.config.vocab_size:
ref_model.resize_token_embeddings(config.vocab_size)
logger.warning(f"Resized the reference model embeddings, new total = {ref_model.config.vocab_size}")
dummy_mask = torch.ones_like(test_inputs, dtype=torch.bool)
# note: this creates a dummy mask to make the test compatible with older transformer versions
# prior to https://github.com/huggingface/transformers/pull/17837
ref_outputs = ref_model.forward(test_inputs, attention_mask=dummy_mask).logits.float()
assert torch.allclose(ref_outputs, parallel_outputs, rtol=0, atol=atol_forward)
logger.warning(f"Distributed forward is consistent with {type(ref_model)}.forward")
del ref_model, ref_outputs, dummy_mask
else:
logger.warning("Did not test exact match with local model: REF_NAME environment variable is not set")
assert False
@pytest.mark.forked
def test_greedy_generation(max_new_tokens=4):
tokenizer = transformers.BloomTokenizerFast.from_pretrained(MODEL_NAME)
model = DistributedBloomForCausalLM.from_pretrained(
MODEL_NAME, initial_peers=INITIAL_PEERS, low_cpu_mem_usage=True, torch_dtype=torch.float32
)
inputs = tokenizer("A cat sat on a mat", return_tensors="pt")["input_ids"]
remote_outputs = model.generate(
inputs,
max_new_tokens=max_new_tokens,
)
hf_outputs = BloomForCausalLM.greedy_search(model, input_ids=inputs, max_length=inputs.size(1) + max_new_tokens)
assert torch.allclose(remote_outputs, hf_outputs), "Greedy search are not identical to HF"
inputs_batch = tokenizer(["A cat sat on a mat", "A dog sat on a mat"], return_tensors="pt", padding=True)[
"input_ids"
]
remote_outputs_batch = model.generate(
inputs_batch,
max_new_tokens=max_new_tokens,
)
hf_outputs_batch = BloomForCausalLM.greedy_search(
model, input_ids=inputs_batch, max_length=inputs_batch.size(1) + max_new_tokens
)
assert torch.allclose(
remote_outputs_batch, hf_outputs_batch
), "Greedy search are not identical to HF in multibatch mode"
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