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56 lines
2.6 KiB
Python
56 lines
2.6 KiB
Python
import pytest
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import torch
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import transformers
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from hivemind import get_logger, use_hivemind_log_handler
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from test_utils import *
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from src.client.remote_model import DistributedBloomForCausalLM
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use_hivemind_log_handler("in_root_logger")
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logger = get_logger(__file__)
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@pytest.mark.forked
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def test_full_model_exact_match(atol_forward=1e-3, atol_inference=1e-3):
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tokenizer = transformers.BloomTokenizerFast.from_pretrained(MODEL_NAME)
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model = DistributedBloomForCausalLM.from_pretrained(MODEL_NAME, initial_peers=INITIAL_PEERS)
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assert isinstance(model, DistributedBloomForCausalLM)
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assert len(model.transformer.h) == model.config.n_layer
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test_inputs = tokenizer("A cat sat on a mat", return_tensors="pt")["input_ids"]
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with torch.no_grad():
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parallel_outputs = model.forward(test_inputs).logits
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assert torch.all(torch.isfinite(parallel_outputs))
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logger.info("Forward outputs are finite")
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embs = model.transformer.word_embeddings(test_inputs)
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embs = model.transformer.word_embeddings_layernorm(embs)
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recurrent_outputs = []
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with model.transformer.h.inference_session() as sess:
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for t in range(embs.shape[1]):
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recurrent_outputs.append(sess.step(embs[:, t : t + 1, :]))
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recurrent_outputs = torch.cat(recurrent_outputs, dim=1)
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recurrent_outputs = model.transformer.ln_f(recurrent_outputs)
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dictionary = model.transformer.word_embeddings.weight.t()
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recurrent_outputs = recurrent_outputs.to(dictionary.dtype)
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recurrent_outputs = (recurrent_outputs @ dictionary).float()
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assert torch.allclose(recurrent_outputs, parallel_outputs, rtol=0, atol=atol_inference)
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logger.info("Inference is consistent with forward")
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del model, recurrent_outputs
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if REF_NAME:
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ref_model = transformers.AutoModelForCausalLM.from_pretrained(REF_NAME)
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dummy_mask = torch.ones_like(test_inputs, dtype=torch.bool)
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# note: this creates a dummy mask to make the test compatible with older transformer versions
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# prior to https://github.com/huggingface/transformers/pull/17837
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ref_outputs = ref_model.forward(test_inputs, attention_mask=dummy_mask).logits
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assert torch.allclose(ref_outputs, parallel_outputs, rtol=0, atol=atol_forward)
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logger.warning(f"Distributed forward is consistent with {type(ref_model)}.forward")
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del ref_model, ref_outputs, dummy_mask
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else:
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logger.warning("Did not test exact match with local model: REF_NAME environment variable is not set")
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assert False
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