Implement Tensor Parallel for transformer Block
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Tri Dao
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1e712ea8b0
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@ -1,5 +1,6 @@
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#include <torch/extension.h>
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#include "ATen/cuda/CUDAContext.h"
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#include <c10/cuda/CUDAGuard.h>
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#include "ln.h"
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@ -166,6 +167,10 @@ std::vector<at::Tensor> dropout_add_ln_fwd(const at::Tensor &x0, // Input:
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TORCH_CHECK(epsilon >= 0.f);
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// Otherwise the kernel will be launched from cuda:0 device
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// Cast to char to avoid compiler warning about narrowing
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at::cuda::CUDAGuard device_guard{(char)x0.get_device()};
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auto opts = x0.options();
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bool save_x = x1_.has_value() || (dropout_p > 0.f) || rowscale_.has_value() || colscale_.has_value() || x0_subset_.has_value() || (itype != rtype);
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@ -364,6 +369,10 @@ std::vector<at::Tensor> dropout_add_ln_bwd(const at::Tensor &dz, // BxSxhidd
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TORCH_CHECK(gamma.numel() == cols);
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// Otherwise the kernel will be launched from cuda:0 device
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// Cast to char to avoid compiler warning about narrowing
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at::cuda::CUDAGuard device_guard{(char)dz.get_device()};
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auto opts = x.options();
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auto dx0 = torch::empty(x0_sizes, opts.dtype(itype));
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@ -23,7 +23,7 @@ class Block(nn.Module):
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def __init__(self, dim, mixer_cls=None, mlp_cls=None, norm_cls=nn.LayerNorm,
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dropout_cls=nn.Dropout, prenorm=True, resid_dropout=0., drop_path=0.,
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fused_dropout_add_ln=False, return_residual=False):
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fused_dropout_add_ln=False, return_residual=False, sequence_parallel=False):
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"""
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return_residual: whether each of the sub-layers (mixer and mlp) will return the residual.
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This is for performance reason: for post-norm architecture, returning the input allows us
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@ -51,6 +51,14 @@ class Block(nn.Module):
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assert dropout_add_layer_norm is not None, 'dropout_add_ln is not installed'
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assert isinstance(self.norm1, nn.LayerNorm) and isinstance(self.dropout1, nn.Dropout)
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# Mark the norm parameters as "sequence_parallel" so that we run all-reduce on their grads.
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if sequence_parallel:
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for p in self.norm1.parameters():
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p._sequence_parallel = True
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if hasattr(self, 'norm2'):
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for p in self.norm2.parameters():
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p._sequence_parallel = True
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def forward(self, hidden_states: Tensor, residual: Optional[Tensor] = None,
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mixer_kwargs=None):
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r"""Pass the input through the encoder layer.
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@ -27,15 +27,15 @@ class FusedDenseFunc(torch.autograd.Function):
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If process_group is not None, we're doing Tensor Parallel with sequence parallelism:
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we do an all_gather_raw of x before doing the matmul.
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"""
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ctx.compute_weight_gradient = weight.requires_grad
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ctx.return_residual = return_residual
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ctx.process_group = process_group
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if torch.is_autocast_enabled():
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dtype = torch.get_autocast_gpu_dtype()
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x, weight = [a.to(dtype=dtype) for a in [x, weight]]
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bias = bias.to(dtype=dtype) if bias is not None else None
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ctx.return_residual = return_residual
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ctx.process_group = process_group
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ctx.compute_weight_gradient = weight.requires_grad
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x = x.contiguous()
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weight = weight.contiguous()
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if ctx.compute_weight_gradient:
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186
tests/modules/test_block_parallel.py
Normal file
186
tests/modules/test_block_parallel.py
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@ -0,0 +1,186 @@
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# Run test with:
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# torchrun --no_python --nproc_per_node=8 pytest -q -s tests/modules/test_block_parallel.py
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import math
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from functools import partial
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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 apex.transformer import parallel_state
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from apex.transformer import tensor_parallel
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from flash_attn.modules.mha import MHA, ParallelMHA
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from flash_attn.modules.mlp import FusedDenseGeluDense, ParallelFusedDenseGeluDense
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from flash_attn.modules.block import Block
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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('dim', [1024])
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def test_block_parallel(dim, 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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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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x_pt = torch.randn(batch_size * seqlen, dim, device=device, dtype=dtype,
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requires_grad=True)
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residual_pt = torch.randn(batch_size * seqlen, dim, device=device, requires_grad=True)
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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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# If we don't divide by batch_size, the gradient gets a bit too large.
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g = torch.randn_like(x_pt) / 32
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x = tensor_parallel.scatter_to_sequence_parallel_region(x_pt).detach().clone().requires_grad_()
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residual = tensor_parallel.scatter_to_sequence_parallel_region(residual_pt).detach().clone().requires_grad_()
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mixer_cls_pt = partial(MHA, num_heads=num_heads, rotary_emb_dim=int(head_dim // 2),
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use_flash_attn=True, device=device, dtype=dtype)
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mlp_cls_pt = partial(FusedDenseGeluDense, hidden_features=4 * dim,
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device=device, dtype=dtype)
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norm_cls = partial(nn.LayerNorm, device=device, dtype=dtype)
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model_pt = Block(dim, mixer_cls_pt, mlp_cls_pt, norm_cls, fused_dropout_add_ln=True)
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with torch.no_grad():
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nn.init.normal_(model_pt.norm1.weight)
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nn.init.normal_(model_pt.norm1.bias)
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nn.init.normal_(model_pt.norm2.weight)
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nn.init.normal_(model_pt.norm2.bias)
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mixer_cls = partial(ParallelMHA, num_heads=num_heads,
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process_group=parallel_state.get_tensor_model_parallel_group(),
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rotary_emb_dim=int(head_dim // 2), use_flash_attn=True,
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device=device, dtype=dtype)
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mlp_cls = partial(ParallelFusedDenseGeluDense, hidden_features=4 * dim,
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process_group=parallel_state.get_tensor_model_parallel_group(),
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device=device, dtype=dtype)
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model = Block(dim, mixer_cls, mlp_cls, norm_cls, fused_dropout_add_ln=True,
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sequence_parallel=True)
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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.mixer.Wqkv.weight.copy_(
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rearrange(rearrange(model_pt.mixer.Wqkv.weight, '(three o) i -> three o i', three=3)[:, rank * partition_dim:(rank + 1) * partition_dim],
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'three o i -> (three o) i')
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)
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model.mixer.Wqkv.bias.copy_(
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rearrange(rearrange(model_pt.mixer.Wqkv.bias, '(three o) -> three o', three=3)[:, rank * partition_dim:(rank + 1) * partition_dim],
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'three o -> (three o)')
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)
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model.mixer.out_proj.weight.copy_(
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model_pt.mixer.out_proj.weight[:, rank * partition_dim:(rank + 1) * partition_dim]
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)
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if rank == 0:
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model.mixer.out_proj.bias.copy_(model_pt.mixer.out_proj.bias)
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model.mlp.fc1.weight.copy_(
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model_pt.mlp.fc1.weight[rank * partition_hidden_dim:(rank + 1) * partition_hidden_dim]
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)
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model.mlp.fc1.bias.copy_(
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model_pt.mlp.fc1.bias[rank * partition_hidden_dim:(rank + 1) * partition_hidden_dim]
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)
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model.mlp.fc2.weight.copy_(
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model_pt.mlp.fc2.weight[:, rank * partition_hidden_dim:(rank + 1) * partition_hidden_dim]
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)
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if rank == 0:
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model.mlp.fc2.bias.copy_(model_pt.mlp.fc2.bias)
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model.norm1.weight.copy_(model_pt.norm1.weight)
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model.norm1.bias.copy_(model_pt.norm1.bias)
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model.norm2.weight.copy_(model_pt.norm2.weight)
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model.norm2.bias.copy_(model_pt.norm2.bias)
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mixer_kwargs = {'seqlen': seqlen}
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out, out_residual = model(x, residual, mixer_kwargs=mixer_kwargs)
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out_pt, out_residual_pt = model_pt(rearrange(x_pt, '(b s) d -> b s d', s=seqlen),
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rearrange(residual_pt, '(b s) d -> b s d', s=seqlen))
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out_pt, out_residual_pt = [rearrange(x, 'b s d -> (b s) d') for x in [out_pt, out_residual_pt]]
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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_batch_dim:(rank + 1) * partition_batch_dim],
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rtol=rtol, atol=atol
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)
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assert torch.allclose(
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out_residual, out_residual_pt[rank * partition_batch_dim:(rank + 1) * partition_batch_dim],
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rtol=rtol, atol=atol
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)
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out_pt.backward(g)
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out.backward(g[rank * partition_batch_dim:(rank + 1) * partition_batch_dim])
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# We want to iterate over parameters with _sequence_parallel=True in the same order,
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# as different ranks might have different number of parameters (e.g., only rank 0 has bias).
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params_seqparallel = {name: p for name, p in model.named_parameters()
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if getattr(p, '_sequence_parallel', False)}
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for _, p in sorted(params_seqparallel.items()):
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if getattr(p, '_sequence_parallel', False):
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torch.distributed.all_reduce(p.grad, group=parallel_state.get_tensor_model_parallel_group())
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parallel_state.destroy_model_parallel()
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assert torch.allclose(
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x.grad, x_pt.grad[rank * partition_batch_dim:(rank + 1) * partition_batch_dim],
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rtol=rtol, atol=atol
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)
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assert torch.allclose(
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residual.grad, residual_pt.grad[rank * partition_batch_dim:(rank + 1) * partition_batch_dim],
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rtol=rtol, atol=atol
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)
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# The error for d_weight and d_bias is quite a bit higher
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assert torch.allclose(
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model.mixer.Wqkv.weight.grad,
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rearrange(rearrange(model_pt.mixer.Wqkv.weight.grad, '(three o) i -> three o i', three=3)[:, rank * partition_dim:(rank + 1) * partition_dim],
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'three o i -> (three o) i'),
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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.mixer.Wqkv.bias.grad,
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rearrange(rearrange(model_pt.mixer.Wqkv.bias.grad, '(three o) -> three o', three=3)[:, rank * partition_dim:(rank + 1) * partition_dim],
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'three o -> (three o)'),
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rtol=rtol, atol=atol * 5
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)
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assert torch.allclose(
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model.mixer.out_proj.weight.grad,
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model_pt.mixer.out_proj.weight.grad[:, rank * partition_dim:(rank + 1) * partition_dim],
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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.mixer.out_proj.bias.grad, model_pt.mixer.out_proj.bias.grad, rtol=rtol, atol=atol * 5)
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assert torch.allclose(
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model.mlp.fc1.weight.grad,
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model_pt.mlp.fc1.weight.grad[rank * partition_hidden_dim:(rank + 1) * partition_hidden_dim],
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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.mlp.fc1.bias.grad,
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model_pt.mlp.fc1.bias.grad[rank * partition_hidden_dim:(rank + 1) * partition_hidden_dim],
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rtol=rtol, atol=atol * 5
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)
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assert torch.allclose(
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model.mlp.fc2.weight.grad,
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model_pt.mlp.fc2.weight.grad[:, rank * partition_hidden_dim:(rank + 1) * partition_hidden_dim],
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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.mlp.fc2.bias.grad, model_pt.mlp.fc2.bias.grad,
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rtol=rtol, atol=atol * 5)
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assert torch.allclose(model.norm1.weight.grad, model_pt.norm1.weight.grad, rtol=rtol, atol=atol * 5)
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assert torch.allclose(model.norm1.bias.grad, model_pt.norm1.bias.grad, rtol=rtol, atol=atol * 5)
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assert torch.allclose(model.norm2.weight.grad, model_pt.norm2.weight.grad, rtol=rtol, atol=atol * 5)
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assert torch.allclose(model.norm2.bias.grad, model_pt.norm2.bias.grad, rtol=rtol, atol=atol * 5)
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