# Run test with: # torchrun --no_python --nproc_per_node=8 pytest -q -s tests/ops/test_fused_dense_parallel.py import math import torch import torch.nn.functional as F import pytest from apex.transformer import parallel_state from apex.transformer import tensor_parallel from flash_attn.ops.fused_dense import FusedDense, FusedDenseGeluDense from flash_attn.ops.fused_dense import ColumnParallelLinear, ParallelFusedDenseGeluDense 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_bias', [True, False]) # @pytest.mark.parametrize('has_bias', [False]) @pytest.mark.parametrize('out_features', [1024]) @pytest.mark.parametrize('in_features', [4096]) def test_fused_linear_bias(in_features, out_features, has_bias, sequence_parallel, world_size, dtype): assert out_features % world_size == 0 rtol, atol = (3e-3, 3e-2) if dtype == torch.bfloat16 else (3e-3, 3e-3) 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() # set seed torch.random.manual_seed(0) batch_size = 2 seqlen = 512 assert batch_size * seqlen % world_size == 0 x_pt = torch.randn(batch_size * seqlen, in_features, device=device, dtype=dtype, requires_grad=True) if sequence_parallel: x = tensor_parallel.scatter_to_sequence_parallel_region(x_pt).detach().clone().requires_grad_() else: x = x_pt.detach().clone().requires_grad_() model_pt = torch.nn.Linear(in_features, out_features, bias=has_bias, device=device, dtype=dtype) partition_out_features = out_features // world_size model = ColumnParallelLinear(in_features, out_features, parallel_state.get_tensor_model_parallel_group(), bias=has_bias, sequence_parallel=sequence_parallel, device=device, dtype=dtype) with torch.no_grad(): model.weight.copy_( model_pt.weight[rank * partition_out_features:(rank + 1) * partition_out_features] ) if has_bias: model.bias.copy_( model_pt.bias[rank * partition_out_features:(rank + 1) * partition_out_features] ) out = model(x) out_pt = model_pt(x_pt) assert torch.allclose( out, out_pt[:, rank * partition_out_features:(rank + 1) * partition_out_features], rtol=rtol, atol=atol ) # If we don't divide by batch_size, the gradient gets a bit too large. g = torch.randn_like(out_pt) / 32 out_pt.backward(g) out.backward(g[:, rank * partition_out_features:(rank + 1) * partition_out_features]) parallel_state.destroy_model_parallel() partition_batch_dim = batch_size * seqlen // world_size assert torch.allclose( x.grad, x_pt.grad[rank * partition_batch_dim:(rank + 1) * partition_batch_dim] if sequence_parallel else x_pt.grad, rtol=rtol, atol=atol ) # The error for d_weight and d_bias is quite a bit higher assert torch.allclose( model.weight.grad, model_pt.weight.grad[rank * partition_out_features:(rank + 1) * partition_out_features], rtol=rtol, atol=atol * 10 ) if has_bias: assert torch.allclose( model.bias.grad, model_pt.bias.grad[rank * partition_out_features:(rank + 1) * partition_out_features], rtol=rtol, atol=atol * 5 ) @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_bias2', [True, False]) # @pytest.mark.parametrize('has_bias2', [True]) @pytest.mark.parametrize('out_features', [4096]) @pytest.mark.parametrize('in_features', [1024]) def test_fused_dense_gelu_dense(in_features, out_features, has_bias2, sequence_parallel, world_size, dtype): assert out_features % world_size == 0 rtol, atol = (3e-3, 3e-2) if dtype == torch.bfloat16 else (3e-3, 3e-3) 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() # set seed torch.random.manual_seed(0) batch_size = 2 seqlen = 512 assert batch_size * seqlen % world_size == 0 x_pt = torch.randn(batch_size * seqlen, in_features, device=device, dtype=dtype, requires_grad=True) # 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. # If we don't divide by batch_size, the gradient gets a bit too large. g = torch.randn_like(x_pt) / 32 if sequence_parallel: x = tensor_parallel.scatter_to_sequence_parallel_region(x_pt).detach().clone().requires_grad_() else: x = x_pt.detach().clone().requires_grad_() model_pt_fc1 = torch.nn.Linear(in_features, out_features, device=device, dtype=dtype) model_pt_fc2 = torch.nn.Linear(out_features, in_features, bias=has_bias2, device=device, dtype=dtype) partition_out_features = out_features // world_size partition_in_features = in_features // world_size model = ParallelFusedDenseGeluDense(in_features, out_features, in_features, process_group=parallel_state.get_tensor_model_parallel_group(), bias2=has_bias2 and rank == 0, sequence_parallel=sequence_parallel, device=device, dtype=dtype) with torch.no_grad(): model.fc1.weight.copy_( model_pt_fc1.weight[rank * partition_out_features:(rank + 1) * partition_out_features] ) model.fc1.bias.copy_( model_pt_fc1.bias[rank * partition_out_features:(rank + 1) * partition_out_features] ) model.fc2.weight.copy_( model_pt_fc2.weight[:, rank * partition_out_features:(rank + 1) * partition_out_features] ) if has_bias2 and rank == 0: model.fc2.bias.copy_(model_pt_fc2.bias) out = model(x) out_pt = model_pt_fc2(F.gelu(model_pt_fc1(x_pt), approximate='tanh')) partition_batch_dim = batch_size * seqlen // world_size assert torch.allclose( out, out_pt[rank * partition_batch_dim:(rank + 1) * partition_batch_dim] if sequence_parallel else out_pt, rtol=rtol, atol=atol ) out_pt.backward(g) out.backward(g[rank * partition_batch_dim:(rank + 1) * partition_batch_dim] if sequence_parallel else g) parallel_state.destroy_model_parallel() assert torch.allclose( x.grad, x_pt.grad[rank * partition_batch_dim:(rank + 1) * partition_batch_dim] if sequence_parallel else x_pt.grad, rtol=rtol, atol=atol ) # The error for d_weight and d_bias is quite a bit higher assert torch.allclose( model.fc1.weight.grad, model_pt_fc1.weight.grad[rank * partition_out_features:(rank + 1) * partition_out_features], rtol=rtol, atol=atol * 10 ) assert torch.allclose( model.fc1.bias.grad, model_pt_fc1.bias.grad[rank * partition_out_features:(rank + 1) * partition_out_features], rtol=rtol, atol=atol * 5 ) assert torch.allclose( model.fc2.weight.grad, model_pt_fc2.weight.grad[:, rank * partition_out_features:(rank + 1) * partition_out_features], rtol=rtol, atol=atol * 10 ) if has_bias2 and rank == 0: assert torch.allclose(model.fc2.bias.grad, model_pt_fc2.bias.grad, rtol=rtol, atol=atol * 5)