test_attention.py

# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.

import pytest
import torch

from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.core.transformer.attention import SelfAttention
from megatron.core.transformer.transformer_config import TransformerConfig
from tests.unit_tests.test_utilities import Utils


class TestParallelAttention:

    def setup_method(self, method):
        Utils.initialize_model_parallel(1, 1)
        model_parallel_cuda_manual_seed(123)
        self.transformer_config = TransformerConfig(
            num_layers=2, hidden_size=12, num_attention_heads=4, use_cpu_initialization=True
        )
        self.parallel_attention = SelfAttention(
            self.transformer_config,
            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
            layer_number=1,
        )

    def teardown_method(self, method):
        Utils.destroy_model_parallel()

    def test_constructor(self):
        assert isinstance(self.parallel_attention, SelfAttention)
        assert self.parallel_attention.layer_number == 1

        num_weights = sum([p.numel() for p in self.parallel_attention.parameters()])
        assert num_weights == 648

    def test_cpu_forward(self):
        # we can't currently do this because the global memory buffer is on GPU
        pass

    def test_gpu_forward(self):

        config = self.parallel_attention.config
        sequence_length = 32
        micro_batch_size = 2

        self.parallel_attention.cuda()

        # [sequence length, batch size, hidden size]
        hidden_states = torch.ones(
            (sequence_length, micro_batch_size, self.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 = self.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_fused_rope_gpu_forward(self):
        self.parallel_attention.config.apply_rope_fusion = True
        config = self.parallel_attention.config
        sequence_length = 32
        micro_batch_size = 2

        self.parallel_attention.cuda()

        # [sequence length, batch size, hidden size]
        hidden_states = torch.ones(
            (sequence_length, micro_batch_size, self.parallel_attention.config.hidden_size)
        )
        hidden_states = hidden_states.cuda()

        attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
        rotary_pos_emb = torch.ones(
            sequence_length, 1, 1, self.parallel_attention.config.kv_channels
        ).cuda()
        output, bias = self.parallel_attention(
            hidden_states, attention_mask, rotary_pos_emb=rotary_pos_emb
        )

        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
        self.parallel_attention.config.apply_rope_fusion = False

    def test_checkpointed_gpu_forward(self):
        transformer_config = self.transformer_config
        transformer_config.recompute_granularity = 'selective'
        checkpointed_parallel_attention = SelfAttention(
            transformer_config,
            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
            layer_number=1,
        )
        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