107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
# Run test with:
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# torchrun --no_python --nproc_per_node=8 pytest -q -s tests/modules/test_embedding_parallel.py
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import pytest
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from apex.transformer import parallel_state
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from einops import rearrange
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from flash_attn.modules.embedding import GPT2Embeddings, ParallelGPT2Embeddings
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is_sm8x = torch.cuda.get_device_capability("cuda")[0] >= 8
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@pytest.mark.parametrize("dtype", [torch.float16] + ([torch.bfloat16] if is_sm8x else []))
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# @pytest.mark.parametrize('dtype', [torch.bfloat16])
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@pytest.mark.parametrize("world_size", [1, 2, 4, 8])
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# @pytest.mark.parametrize('world_size', [2])
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@pytest.mark.parametrize("sequence_parallel", [True, False])
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# @pytest.mark.parametrize('sequence_parallel', [False])
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@pytest.mark.parametrize("has_pos_emb", [True, False])
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# @pytest.mark.parametrize('has_pos_emb', [True])
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@pytest.mark.parametrize("dim", [1024])
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def test_embedding_parallel(dim, has_pos_emb, sequence_parallel, world_size, dtype):
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vocab_size = 50264
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seqlen = 2048
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assert vocab_size % world_size == 0
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assert dim % world_size == 0
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rtol, atol = (3e-3, 5e-2) if dtype == torch.bfloat16 else (3e-3, 3e-3)
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if not torch.distributed.is_initialized():
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torch.distributed.init_process_group(backend="nccl", init_method="env://")
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device = f"cuda:{torch.distributed.get_rank()}"
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assert world_size <= torch.distributed.get_world_size()
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parallel_state.initialize_model_parallel(tensor_model_parallel_size_=world_size)
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rank = parallel_state.get_tensor_model_parallel_rank()
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# set seed
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torch.random.manual_seed(0)
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batch_size = 8
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seqlen = 1024
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assert (batch_size * seqlen) % world_size == 0
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input_ids_pt = torch.randint(0, vocab_size, (batch_size, seqlen), device=device)
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input_ids = input_ids_pt.detach().clone()
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model_pt = GPT2Embeddings(
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dim, vocab_size, seqlen if has_pos_emb else 0, device=device, dtype=dtype
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)
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model = ParallelGPT2Embeddings(
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dim,
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vocab_size,
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seqlen if has_pos_emb else 0,
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parallel_state.get_tensor_model_parallel_group(),
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sequence_parallel=sequence_parallel,
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device=device,
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dtype=dtype,
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)
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partition_vocab_size = vocab_size // world_size
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partition_dim = dim // world_size
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with torch.no_grad():
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model.word_embeddings.weight.copy_(
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model_pt.word_embeddings.weight[
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rank * partition_vocab_size : (rank + 1) * partition_vocab_size
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]
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)
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if has_pos_emb:
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model.position_embeddings.weight.copy_(
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model_pt.position_embeddings.weight[
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:, rank * partition_dim : (rank + 1) * partition_dim
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]
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)
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out = model(input_ids, combine_batch_seqlen_dim=True)
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out_pt = rearrange(model_pt(input_ids), "b s d -> (b s) d")
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partition_batch_dim = batch_size * seqlen // world_size
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assert torch.allclose(
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out,
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out_pt[rank * partition_batch_dim : (rank + 1) * partition_batch_dim]
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if sequence_parallel
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else out_pt,
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rtol=rtol,
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atol=atol,
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)
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g = torch.randn_like(out_pt)
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out_pt.backward(g)
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out.backward(
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g[rank * partition_batch_dim : (rank + 1) * partition_batch_dim] if sequence_parallel else g
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)
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parallel_state.destroy_model_parallel()
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assert torch.allclose(
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model.word_embeddings.weight.grad,
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model_pt.word_embeddings.weight.grad[
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rank * partition_vocab_size : (rank + 1) * partition_vocab_size
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],
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rtol=rtol,
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atol=atol,
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)
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if has_pos_emb:
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assert torch.allclose(
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model.position_embeddings.weight.grad,
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model_pt.position_embeddings.weight.grad[
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:, rank * partition_dim : (rank + 1) * partition_dim
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],
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rtol=rtol,
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atol=atol,
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)
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