190 lines
8.8 KiB
Python
190 lines
8.8 KiB
Python
# Run test with:
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# torchrun --no_python --nproc_per_node=8 pytest -q -s tests/models/test_gpt_parallel.py
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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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import pytest
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from einops import rearrange
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from transformers import GPT2Config
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from apex.transformer import parallel_state
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from flash_attn.models.gpt import GPTLMHeadModel, shard_state_dict_tp
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from flash_attn.losses.cross_entropy import CrossEntropyLoss
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from flash_attn.utils.distributed import allreduce_sequence_parallel_grad
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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_gpt_parallel(dim, has_pos_emb, sequence_parallel, world_size, dtype):
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head_dim = 64
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assert dim % head_dim == 0
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num_heads = dim // head_dim
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assert num_heads % world_size == 0
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vocab_size = 50264
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assert vocab_size % world_size == 0
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num_layers = 2
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rtol, atol = (3e-3, 1e-1) if dtype == torch.bfloat16 else (3e-3, 1e-2)
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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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process_group = parallel_state.get_tensor_model_parallel_group()
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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 = torch.randint(0, vocab_size, (batch_size, seqlen + 1), device=device)
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# We need to generate g here so that all processes get the same gradient,
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# as rank 0 will have an extra bias that changes the RNG.
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g = torch.randn(batch_size * seqlen, device=device)
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config = GPT2Config(n_embd=dim, n_head=num_heads, n_layer=num_layers,
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n_positions=seqlen if has_pos_emb else 0,
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vocab_size=50257, resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0,
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scale_attn_by_inverse_layer_idx=True, use_flash_attn=True,
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fused_dense_gelu_dense=True, fused_bias_fc=True, fused_dropout_add_ln=True,
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rotary_emb_fraction=0.0 if has_pos_emb else 0.5,
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pad_vocab_size_multiple=8 * world_size,
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sequence_parallel=sequence_parallel)
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model_pt = GPTLMHeadModel(config, device=device)
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def init_layer_norm(module):
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if isinstance(module, nn.LayerNorm):
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nn.init.normal_(module.weight)
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nn.init.normal_(module.bias)
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model_pt.apply(init_layer_norm)
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model = GPTLMHeadModel(config, process_group=process_group, device=device)
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total_nparams = sum(p.numel() for p in model_pt.parameters())
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sharded_nparams = sum(p.numel() for p in model.parameters())
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sharded_nparams_all = torch.empty(world_size, dtype=torch.long, device=device)
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torch.distributed.all_gather_into_tensor(
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sharded_nparams_all, torch.tensor([sharded_nparams], device=device), group=process_group
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)
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shared_nparams = sum(p.numel() for p in model.parameters()
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if getattr(p, '_shared_params', False))
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shared_nparams_all = torch.empty(world_size, dtype=torch.long, device=device)
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torch.distributed.all_gather_into_tensor(
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shared_nparams_all, torch.tensor([shared_nparams], device=device), group=process_group
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)
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assert torch.all(shared_nparams_all == shared_nparams)
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assert total_nparams == ((sharded_nparams_all - shared_nparams_all).sum().item()
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+ shared_nparams)
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# vocab_size has been rounded up here
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partition_vocab_size = config.vocab_size // world_size
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partition_dim = dim // world_size
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partition_hidden_dim = 4 * dim // world_size
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with torch.no_grad():
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model.load_state_dict(shard_state_dict_tp(model_pt.state_dict(), config, world_size, rank))
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model.tie_weights()
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with torch.autocast(device_type='cuda', dtype=dtype):
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out = model(input_ids[:, :-1]).logits
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if not sequence_parallel:
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out = rearrange(out, 'b s d -> (b s) d')
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out_pt = rearrange(model_pt(input_ids[:, :-1]).logits, '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, out_pt[:, rank * partition_vocab_size:(rank + 1) * partition_vocab_size],
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rtol=rtol, atol=atol
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)
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loss_fn = CrossEntropyLoss(inplace_backward=True, reduction='none', process_group=process_group)
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loss_fn_pt = CrossEntropyLoss(inplace_backward=True, reduction='none')
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loss = loss_fn(out, input_ids[:, 1:].flatten())
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loss_pt = loss_fn_pt(out_pt, input_ids[:, 1:].flatten())
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assert torch.allclose(loss, loss_pt, rtol=rtol, atol=atol)
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loss_pt.backward(g)
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loss.backward(g)
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allreduce_sequence_parallel_grad(model, process_group)
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parallel_state.destroy_model_parallel()
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grad_dict = shard_state_dict_tp({k: v.grad for k, v in model_pt.named_parameters()},
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config, world_size, rank)
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assert torch.allclose(
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model.transformer.embeddings.word_embeddings.weight.grad,
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grad_dict['transformer.embeddings.word_embeddings.weight'],
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rtol=rtol, atol=atol * 5
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)
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if has_pos_emb:
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assert torch.allclose(
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model.transformer.embeddings.position_embeddings.weight.grad,
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grad_dict['transformer.embeddings.position_embeddings.weight'],
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rtol=rtol, atol=atol
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)
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assert torch.allclose(model.transformer.ln_0.weight.grad, grad_dict['transformer.ln_0.weight'],
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rtol=rtol, atol=atol)
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assert torch.allclose(model.transformer.ln_0.bias.grad, grad_dict['transformer.ln_0.bias'],
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rtol=rtol, atol=atol)
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for i in range(num_layers):
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assert torch.allclose(
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model.transformer.layers[i].mixer.Wqkv.weight.grad,
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grad_dict[f'transformer.layers.{i}.mixer.Wqkv.weight'],
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rtol=rtol, atol=atol * 10
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)
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assert torch.allclose(
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model.transformer.layers[i].mixer.Wqkv.bias.grad,
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grad_dict[f'transformer.layers.{i}.mixer.Wqkv.bias'],
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rtol=rtol, atol=atol * 10
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)
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assert torch.allclose(
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model.transformer.layers[i].mixer.out_proj.weight.grad,
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grad_dict[f'transformer.layers.{i}.mixer.out_proj.weight'],
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rtol=rtol, atol=atol * 10
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)
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if rank == 0:
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assert torch.allclose(model.transformer.layers[i].mixer.out_proj.bias.grad,
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grad_dict[f'transformer.layers.{i}.mixer.out_proj.bias'],
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rtol=rtol, atol=atol * 5)
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assert torch.allclose(
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model.transformer.layers[i].mlp.fc1.weight.grad,
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grad_dict[f'transformer.layers.{i}.mlp.fc1.weight'],
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rtol=rtol, atol=atol * 10
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)
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assert torch.allclose(
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model.transformer.layers[i].mlp.fc1.bias.grad,
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grad_dict[f'transformer.layers.{i}.mlp.fc1.bias'],
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rtol=rtol, atol=atol * 10
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)
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assert torch.allclose(
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model.transformer.layers[i].mlp.fc2.weight.grad,
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grad_dict[f'transformer.layers.{i}.mlp.fc2.weight'],
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rtol=rtol, atol=atol * 10
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)
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if rank == 0:
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assert torch.allclose(model.transformer.layers[i].mlp.fc2.bias.grad,
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grad_dict[f'transformer.layers.{i}.mlp.fc2.bias'],
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rtol=rtol, atol=atol * 5)
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assert torch.allclose(model.transformer.layers[i].norm1.weight.grad,
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grad_dict[f'transformer.layers.{i}.norm1.weight'],
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rtol=rtol, atol=atol)
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assert torch.allclose(model.transformer.layers[i].norm1.bias.grad,
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grad_dict[f'transformer.layers.{i}.norm1.bias'],
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rtol=rtol, atol=atol)
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assert torch.allclose(model.transformer.layers[i].norm2.weight.grad,
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grad_dict[f'transformer.layers.{i}.norm2.weight'],
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rtol=rtol, atol=atol)
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assert torch.allclose(model.transformer.layers[i].norm2.bias.grad,
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grad_dict[f'transformer.layers.{i}.norm2.bias'],
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rtol=rtol, atol=atol)
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