# Run test with: # torchrun --no_python --nproc_per_node=8 pytest -q -s tests/models/test_gpt_parallel.py import math import pytest import torch import torch.nn as nn import torch.nn.functional as F from apex.transformer import parallel_state from einops import rearrange from flash_attn.losses.cross_entropy import CrossEntropyLoss from flash_attn.models.gpt import GPTLMHeadModel, shard_state_dict_tp from flash_attn.utils.distributed import allreduce_sequence_parallel_grad from transformers import GPT2Config is_sm8x = torch.cuda.get_device_capability("cuda")[0] >= 8 @pytest.mark.parametrize("dtype", [torch.float16] + ([torch.bfloat16] if is_sm8x else [])) # @pytest.mark.parametrize('dtype', [torch.bfloat16]) @pytest.mark.parametrize("world_size", [1, 2, 4, 8]) # @pytest.mark.parametrize('world_size', [2]) @pytest.mark.parametrize("sequence_parallel", [True, False]) # @pytest.mark.parametrize('sequence_parallel', [False]) @pytest.mark.parametrize("has_pos_emb", [True, False]) # @pytest.mark.parametrize('has_pos_emb', [True]) @pytest.mark.parametrize("dim", [1024]) def test_gpt_parallel(dim, has_pos_emb, sequence_parallel, world_size, dtype): head_dim = 64 assert dim % head_dim == 0 num_heads = dim // head_dim assert num_heads % world_size == 0 vocab_size = 50264 assert vocab_size % world_size == 0 num_layers = 2 rtol, atol = (3e-3, 1e-1) if dtype == torch.bfloat16 else (3e-3, 1e-2) if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl", init_method="env://") device = f"cuda:{torch.distributed.get_rank()}" assert world_size <= torch.distributed.get_world_size() parallel_state.initialize_model_parallel(tensor_model_parallel_size_=world_size) rank = parallel_state.get_tensor_model_parallel_rank() process_group = parallel_state.get_tensor_model_parallel_group() # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 1024 assert (batch_size * seqlen) % world_size == 0 input_ids = torch.randint(0, vocab_size, (batch_size, seqlen + 1), device=device) # We need to generate g here so that all processes get the same gradient, # as rank 0 will have an extra bias that changes the RNG. g = torch.randn(batch_size * seqlen, device=device) config = GPT2Config( n_embd=dim, n_head=num_heads, n_layer=num_layers, n_positions=seqlen if has_pos_emb else 0, vocab_size=50257, resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, scale_attn_by_inverse_layer_idx=True, use_flash_attn=True, fused_mlp=True, fused_bias_fc=True, fused_dropout_add_ln=True, residual_in_fp32=True, rotary_emb_fraction=0.0 if has_pos_emb else 0.5, pad_vocab_size_multiple=8 * world_size, sequence_parallel=sequence_parallel, ) config.vocab_size = math.ceil(config.vocab_size / (8 * world_size)) * (8 * world_size) model_pt = GPTLMHeadModel(config, device=device) def init_layer_norm(module): if isinstance(module, nn.LayerNorm): nn.init.normal_(module.weight) nn.init.normal_(module.bias) model_pt.apply(init_layer_norm) model = GPTLMHeadModel(config, process_group=process_group, device=device) total_nparams = sum(p.numel() for p in model_pt.parameters()) sharded_nparams = sum(p.numel() for p in model.parameters()) sharded_nparams_all = torch.empty(world_size, dtype=torch.long, device=device) torch.distributed.all_gather_into_tensor( sharded_nparams_all, torch.tensor([sharded_nparams], device=device), group=process_group ) shared_nparams = sum( p.numel() for p in model.parameters() if getattr(p, "_shared_params", False) ) shared_nparams_all = torch.empty(world_size, dtype=torch.long, device=device) torch.distributed.all_gather_into_tensor( shared_nparams_all, torch.tensor([shared_nparams], device=device), group=process_group ) assert torch.all(shared_nparams_all == shared_nparams) assert total_nparams == ( (sharded_nparams_all - shared_nparams_all).sum().item() + shared_nparams ) # vocab_size has been rounded up here partition_vocab_size = config.vocab_size // world_size partition_dim = dim // world_size partition_hidden_dim = 4 * dim // world_size with torch.no_grad(): model.load_state_dict(shard_state_dict_tp(model_pt.state_dict(), config, world_size, rank)) model.tie_weights() with torch.autocast(device_type="cuda", dtype=dtype): out = model(input_ids[:, :-1]).logits if not sequence_parallel: out = rearrange(out, "b s d -> (b s) d") out_pt = rearrange(model_pt(input_ids[:, :-1]).logits, "b s d -> (b s) d") partition_batch_dim = batch_size * seqlen // world_size assert torch.allclose( out, out_pt[:, rank * partition_vocab_size : (rank + 1) * partition_vocab_size], rtol=rtol, atol=atol, ) loss_fn = CrossEntropyLoss(inplace_backward=True, reduction="none", process_group=process_group) loss_fn_pt = CrossEntropyLoss(inplace_backward=True, reduction="none") loss = loss_fn(out, input_ids[:, 1:].flatten()) loss_pt = loss_fn_pt(out_pt, input_ids[:, 1:].flatten()) assert torch.allclose(loss, loss_pt, rtol=rtol, atol=atol) loss_pt.backward(g) loss.backward(g) allreduce_sequence_parallel_grad(model, process_group) parallel_state.destroy_model_parallel() grad_dict = shard_state_dict_tp( {k: v.grad for k, v in model_pt.named_parameters()}, config, world_size, rank ) assert torch.allclose( model.transformer.embeddings.word_embeddings.weight.grad, grad_dict["transformer.embeddings.word_embeddings.weight"], rtol=rtol, atol=atol * 5, ) if has_pos_emb: assert torch.allclose( model.transformer.embeddings.position_embeddings.weight.grad, grad_dict["transformer.embeddings.position_embeddings.weight"], rtol=rtol, atol=atol, ) assert torch.allclose( model.transformer.ln_f.weight.grad, grad_dict["transformer.ln_f.weight"], rtol=rtol, atol=atol, ) assert torch.allclose( model.transformer.ln_f.bias.grad, grad_dict["transformer.ln_f.bias"], rtol=rtol, atol=atol ) for i in range(num_layers): assert torch.allclose( model.transformer.layers[i].mixer.Wqkv.weight.grad, grad_dict[f"transformer.layers.{i}.mixer.Wqkv.weight"], rtol=rtol, atol=atol * 10, ) assert torch.allclose( model.transformer.layers[i].mixer.Wqkv.bias.grad, grad_dict[f"transformer.layers.{i}.mixer.Wqkv.bias"], rtol=rtol, atol=atol * 10, ) assert torch.allclose( model.transformer.layers[i].mixer.out_proj.weight.grad, grad_dict[f"transformer.layers.{i}.mixer.out_proj.weight"], rtol=rtol, atol=atol * 10, ) if rank == 0: assert torch.allclose( model.transformer.layers[i].mixer.out_proj.bias.grad, grad_dict[f"transformer.layers.{i}.mixer.out_proj.bias"], rtol=rtol, atol=atol * 5, ) assert torch.allclose( model.transformer.layers[i].mlp.fc1.weight.grad, grad_dict[f"transformer.layers.{i}.mlp.fc1.weight"], rtol=rtol, atol=atol * 10, ) assert torch.allclose( model.transformer.layers[i].mlp.fc1.bias.grad, grad_dict[f"transformer.layers.{i}.mlp.fc1.bias"], rtol=rtol, atol=atol * 10, ) assert torch.allclose( model.transformer.layers[i].mlp.fc2.weight.grad, grad_dict[f"transformer.layers.{i}.mlp.fc2.weight"], rtol=rtol, atol=atol * 10, ) if rank == 0: assert torch.allclose( model.transformer.layers[i].mlp.fc2.bias.grad, grad_dict[f"transformer.layers.{i}.mlp.fc2.bias"], rtol=rtol, atol=atol * 5, ) assert torch.allclose( model.transformer.layers[i].norm1.weight.grad, grad_dict[f"transformer.layers.{i}.norm1.weight"], rtol=rtol, atol=atol, ) assert torch.allclose( model.transformer.layers[i].norm1.bias.grad, grad_dict[f"transformer.layers.{i}.norm1.bias"], rtol=rtol, atol=atol, ) assert torch.allclose( model.transformer.layers[i].norm2.weight.grad, grad_dict[f"transformer.layers.{i}.norm2.weight"], rtol=rtol, atol=atol, ) assert torch.allclose( model.transformer.layers[i].norm2.bias.grad, grad_dict[f"transformer.layers.{i}.norm2.bias"], rtol=rtol, atol=atol, )