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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import pytest
import torch
from megatron.core.transformer.parallel_attention import ParallelAttention
@pytest.fixture
def parallel_attention(transformer_config):
return ParallelAttention(transformer_config)
@pytest.fixture
def checkpointed_parallel_attention(transformer_config):
transformer_config.recompute_granularity = 'selective'
return ParallelAttention(transformer_config)
class TestParallelAttention:
def test_constructor(self, parallel_attention):
assert isinstance(parallel_attention, ParallelAttention)
assert parallel_attention.layer_number == 1
num_weights = sum([p.numel() for p in parallel_attention.parameters()])
assert num_weights == 624
def test_cpu_forward(self, parallel_attention):
# we can't currently do this because the global memory buffer is on GPU
pass
def test_gpu_forward(self, parallel_attention):
config = parallel_attention.config
sequence_length = 32
micro_batch_size = 2
parallel_attention.cuda()
# [sequence length, batch size, hidden size]
hidden_states = torch.ones((sequence_length, micro_batch_size, parallel_attention.config.hidden_size))
hidden_states = hidden_states.cuda()
attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
output, bias = parallel_attention(hidden_states, attention_mask)
assert config.recompute_granularity is None
assert output.shape[0] == sequence_length
assert output.shape[1] == micro_batch_size
assert output.shape[2] == config.hidden_size
assert bias.shape[0] == config.hidden_size
def test_checkpointed_gpu_forward(self, checkpointed_parallel_attention):
config = checkpointed_parallel_attention.config
sequence_length = 32
micro_batch_size = 2
checkpointed_parallel_attention.cuda()
# [sequence length, batch size, hidden size]
hidden_states = torch.ones(
(sequence_length, micro_batch_size, checkpointed_parallel_attention.config.hidden_size)
)
hidden_states = hidden_states.cuda()
attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
output, bias = checkpointed_parallel_attention(hidden_states, attention_mask)
assert config.recompute_granularity == 'selective'
assert output.shape[0] == sequence_length
assert output.shape[1] == micro_batch_size
assert output.shape[2] == config.hidden_size
assert bias.shape[0] == config.hidden_size
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