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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import pytest
import torch
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.models.gpt.gpt_embedding import GPTEmbedding
@pytest.fixture
def gpt_embedding(transformer_config):
embedding = GPTEmbedding(config=transformer_config, vocab_size=100, max_sequence_length=4)
return embedding
class TestGPTEmbedding:
def test_constructor(self, gpt_embedding: GPTEmbedding):
assert isinstance(gpt_embedding, GPTEmbedding)
num_weights = sum([p.numel() for p in gpt_embedding.parameters()])
assert num_weights == 1248
def test_zero_parameters(self, gpt_embedding: GPTEmbedding):
sum_weights = sum([p.sum() for p in gpt_embedding.parameters()])
assert sum_weights != 0
gpt_embedding.zero_parameters()
sum_weights = sum([p.sum() for p in gpt_embedding.parameters()])
assert sum_weights == 0
def test_cpu_forward(self, gpt_embedding: GPTEmbedding):
input_ids = torch.tensor([0, 1, 2, 3], dtype=torch.int64).repeat((2, 1))
position_ids = torch.tensor([0, 1, 2, 3], dtype=torch.int64).repeat((2, 1))
embeddings = gpt_embedding(input_ids, position_ids)
assert embeddings.device.type == 'cpu'
assert embeddings.shape[0] == gpt_embedding.max_sequence_length
assert embeddings.shape[1] == input_ids.shape[0]
assert embeddings.shape[2] == gpt_embedding.config.hidden_size
def test_gpu_forward(self, gpt_embedding: GPTEmbedding):
gpt_embedding.cuda()
input_ids = torch.tensor([0, 1, 2, 3], dtype=torch.int64).repeat((2, 1)).cuda()
position_ids = torch.tensor([0, 1, 2, 3], dtype=torch.int64).repeat((2, 1)).cuda()
embeddings = gpt_embedding(input_ids, position_ids)
assert embeddings.device.type == 'cuda'
assert embeddings.shape[0] == gpt_embedding.max_sequence_length
assert embeddings.shape[1] == input_ids.shape[0]
assert embeddings.shape[2] == gpt_embedding.config.hidden_size
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