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support evaluation with fstp

pull/407/head
yingtongxiong 2023-10-09 18:01:06 +08:00
parent 189a313da6
commit 21c1a7fa47
4 changed files with 124 additions and 18 deletions
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4
configs/7B_sft.py
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@ -5,7 +5,7 @@ SEQ_LEN = 2048
HIDDEN_SIZE = 4096
NUM_ATTENTION_HEAD = 32
MLP_RATIO = 8 / 3
NUM_LAYER = 4
NUM_LAYER = 32
VOCAB_SIZE = 103168
MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx"
@ -155,7 +155,7 @@ tensor parallel: tensor parallel size, usually the number of GPUs per node.
"""
parallel = dict(
zero1=-1,
tensor=dict(size=2, mode='fstp'), # the mode should be 'origin_tp' or 'fstp'
tensor=dict(size=2, mode='origin_tp'), # the mode should be 'origin_tp' or 'fstp'
pipeline=dict(size=1, interleaved_overlap=True),
sequence_parallel=True,
)

7
internlm/model/linear.py
View File

@ -54,7 +54,7 @@ class ScaleColumnParallelLinear(nn.Linear):
self.process_group = process_group
self.weight_scale = weight_scale
def forward(self, input): # pylint: disable=W0622
def forward(self, input, gather_dim=0): # pylint: disable=W0622
# If self.sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
# we do an all_gather of x before doing the matmul.
# If not, then the input is already gathered.
@ -68,6 +68,7 @@ class ScaleColumnParallelLinear(nn.Linear):
self.bias,
process_group=self.process_group,
sequence_parallel=gpc.config.parallel.sequence_parallel,
gather_dim=gather_dim,
)
@ -121,13 +122,13 @@ class RewardModelLinear(ScaleColumnParallelLinear):
class ColumnParallelLinearTorch(ColumnParallelLinear):
def forward(self, x):
def forward(self, x, gather_dim=0):
# If self.sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
# we do an all_gather of x before doing the matmul.
# If not, then the input is already gathered.
return fused_dense_func_torch(
x, self.weight, self.bias, process_group=self.process_group, sequence_parallel=self.sequence_parallel
x, self.weight, self.bias, process_group=self.process_group, sequence_parallel=self.sequence_parallel, gather_dim=gather_dim,
)

10
internlm/model/modeling_internlm.py
View File

@ -405,12 +405,10 @@ class PackedFlashInternLm1D(nn.Module):
if hasattr(self, "norm"):
hidden_states = self.norm(hidden_states.float())
if hasattr(self, "head"):
# if hidden_states.ndim == 3:
# import pdb; pdb.set_trace()
# hidden_states = self.head(hidden_states, dim=1)
# else:
# hidden_states = self.head(hidden_states)
hidden_states = self.head(hidden_states)
if hidden_states.ndim == 3:
hidden_states = self.head(hidden_states, gather_dim=1)
else:
hidden_states = self.head(hidden_states)
if not self.parallel_output:
hidden_states = gather_forward_split_backward(hidden_states, ParallelMode.TENSOR, dim=-1)

121
internlm/model/utils.py
View File

@ -5,16 +5,18 @@ from typing import Optional
import torch
import torch.nn.functional as F
from flash_attn.ops.fused_dense import FusedDenseFunc
# from flash_attn.ops.fused_dense import FusedDenseFunc
from flash_attn.utils.distributed import (
all_gather_raw,
# all_gather_raw,
all_reduce_raw,
reduce_scatter_raw,
)
from torch import Tensor
from torch.cuda.amp import custom_bwd
from torch.cuda.amp import custom_bwd, custom_fwd
from torch.distributed import ProcessGroup
import fused_dense_lib as fused_dense_cuda
from internlm.core.context import global_context as gpc
from internlm.utils.logger import get_logger
@ -94,6 +96,109 @@ def linear_bias_wgrad_torch(my_input, grad_output, has_d_bias):
grad_bias = grad_output.sum(dim=0) if has_d_bias else None
return grad_weight, grad_bias
def all_gather_raw(input_: Tensor, process_group: ProcessGroup, async_op: bool = False, gather_dim: int = 0):
world_size = torch.distributed.get_world_size(process_group)
shape = list(input_.shape)
shape[gather_dim] = shape[gather_dim] * world_size
# output = torch.empty(world_size * input_.shape[0], *input_.shape[1:],
# dtype=input_.dtype, device=input_.device)
output = torch.empty(shape, dtype=input_.dtype, device=input_.device)
handle = torch.distributed.all_gather_into_tensor(output, input_.contiguous(),
group=process_group, async_op=async_op)
return output, handle
class FusedDenseFunc(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(ctx, x, weight, bias, return_residual=False, process_group=None,
sequence_parallel=True, gather_dim=0):
"""
If process_group is not None and sequence_parallel=True, we're doing Tensor Parallel
with sequence parallelism: we do an all_gather_raw of x before doing the matmul.
"""
ctx.compute_weight_gradient = weight.requires_grad
ctx.return_residual = return_residual
ctx.process_group = process_group
ctx.sequence_parallel = sequence_parallel
ctx.gather_dim = gather_dim
if torch.is_autocast_enabled():
x = x.to(dtype=torch.get_autocast_gpu_dtype())
x = x.contiguous()
if process_group is not None and sequence_parallel:
# We want to kick off the all_gather early, before weight dtype conversion
total_x, handle_x = all_gather_raw(x, process_group, async_op=True, gather_dim=gather_dim)
else:
total_x = x
if torch.is_autocast_enabled():
weight = weight.to(dtype=torch.get_autocast_gpu_dtype())
bias = bias.to(dtype=torch.get_autocast_gpu_dtype()) if bias is not None else None
weight = weight.contiguous()
if process_group is not None and sequence_parallel:
handle_x.wait()
batch_shape, n = total_x.shape[:-1], total_x.shape[-1]
batch_dim = batch_shape.numel()
# https://github.com/pytorch/pytorch/blob/5b51849b48a7dbccd297286cc0110def4706f9e7/aten/src/ATen/native/cuda/Blas.cpp#L174
if min(batch_dim, n, *weight.shape) > 65535 * 32:
raise RuntimeError('fused_dense only supports matrix dims <= 2M')
output = F.linear(total_x, weight, bias)
if ctx.compute_weight_gradient:
ctx.save_for_backward(x, weight)
else:
ctx.save_for_backward(weight)
return output if not return_residual else (output, x)
@staticmethod
@custom_bwd
def backward(ctx, grad_output, *args):
grad_output = grad_output.contiguous()
if ctx.return_residual:
grad_input, = args
grad_input = grad_input.contiguous()
process_group = ctx.process_group
sequence_parallel = ctx.sequence_parallel
gather_dim = ctx.gather_dim
if ctx.compute_weight_gradient:
x, weight = ctx.saved_tensors
if process_group is not None and sequence_parallel:
total_x, handle_x = all_gather_raw(x, process_group, async_op=True, gather_dim=gather_dim)
else:
total_x = x
else:
weight, = ctx.saved_tensors
total_x = None
batch_shape = grad_output.shape[:-1]
batch_dim = batch_shape.numel()
grad_output = grad_output.reshape(batch_dim, grad_output.shape[-1])
if ctx.needs_input_grad[0]:
if not ctx.return_residual:
grad_input = F.linear(grad_output, weight.t())
else:
grad_input = torch.addmm(grad_input.reshape(batch_dim, grad_input.shape[-1]),
grad_output, weight)
grad_input = grad_input.reshape(*batch_shape, grad_input.shape[-1])
if process_group is not None:
reduce_fn = reduce_scatter_raw if sequence_parallel else all_reduce_raw
grad_input, handle_grad_input = reduce_fn(grad_input, process_group, async_op=True)
else:
grad_input = None
if ctx.needs_input_grad[1]:
assert ctx.compute_weight_gradient
if process_group is not None and sequence_parallel:
handle_x.wait()
grad_weight, grad_bias = fused_dense_cuda.linear_bias_wgrad(
total_x.reshape(batch_dim, total_x.shape[-1]), grad_output, ctx.needs_input_grad[2]
)
else:
grad_weight = None
grad_bias = grad_output if ctx.needs_input_grad[2] else None
if process_group is not None and ctx.needs_input_grad[0]:
handle_grad_input.wait()
return grad_input, grad_weight, grad_bias, None, None, None, None
# adpated from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/ops/fused_dense.py
class FusedDenseFuncTorch(FusedDenseFunc):
@ -108,10 +213,11 @@ class FusedDenseFuncTorch(FusedDenseFunc):
grad_input = grad_input.contiguous()
process_group = ctx.process_group
sequence_parallel = ctx.sequence_parallel
gather_dim = ctx.gather_dim
if ctx.compute_weight_gradient:
x, weight = ctx.saved_tensors
if process_group is not None and sequence_parallel:
total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
total_x, handle_x = all_gather_raw(x, process_group, async_op=True, gather_dim=gather_dim)
else:
total_x = x
else:
@ -144,7 +250,7 @@ class FusedDenseFuncTorch(FusedDenseFunc):
grad_bias = grad_output if ctx.needs_input_grad[2] else None
if process_group is not None and ctx.needs_input_grad[0]:
handle_grad_input.wait()
return grad_input, grad_weight, grad_bias, None, None, None
return grad_input, grad_weight, grad_bias, None, None, None, None
def fused_dense_func_torch(
@ -154,14 +260,15 @@ def fused_dense_func_torch(
return_residual: bool = False,
process_group: Optional[ProcessGroup] = None,
sequence_parallel: bool = True,
gather_dim: int = 0,
):
dtype_eligible = x.dtype in [torch.float16, torch.bfloat16] or (
x.dtype == torch.float32 and torch.is_autocast_enabled()
)
if x.is_cuda and weight.is_cuda and (bias is None or bias.is_cuda) and dtype_eligible:
return FusedDenseFunc.apply(x, weight, bias, return_residual, process_group, sequence_parallel)
return FusedDenseFunc.apply(x, weight, bias, return_residual, process_group, sequence_parallel, gather_dim)
else:
return FusedDenseFuncTorch.apply(x, weight, bias, return_residual, process_group, sequence_parallel)
return FusedDenseFuncTorch.apply(x, weight, bias, return_residual, process_group, sequence_parallel, gather_dim)
class _SplitForwardGatherBackward(torch.autograd.Function):