docker/bloom13b/Megatron-DeepSpeed/tests/models/test_gpt_embedding.py

# 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