mirror of https://github.com/InternLM/InternLM
252 lines
9.4 KiB
Python
252 lines
9.4 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from flash_attn.ops.fused_dense import FusedDenseFunc
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from torch import Tensor
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from torch.cuda.amp import custom_bwd, custom_fwd
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from torch.distributed import ProcessGroup
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from internlm.core.context import global_context as gpc
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def _split(input_, parallel_mode, dim=-1):
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# skip if only one rank involved
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world_size = gpc.get_world_size(parallel_mode)
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if world_size == 1:
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return input_
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# Split along last dimension.
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dim_size = input_.size(dim)
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assert dim_size % world_size == 0, (
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f"The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), "
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f"cannot split tensor evenly"
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)
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tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
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rank = gpc.get_local_rank(parallel_mode)
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output = tensor_list[rank].contiguous()
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return output
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def _gather(input_, parallel_mode, dim=-1):
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# skip if only one rank involved
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world_size = gpc.get_world_size(parallel_mode)
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if world_size == 1:
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return input_
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# all gather
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rank = gpc.get_local_rank(parallel_mode)
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tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
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tensor_list[rank] = input_
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group = gpc.get_cpu_group(parallel_mode) if input_.device.type == "cpu" else gpc.get_group(parallel_mode)
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torch.distributed.all_gather(tensor_list, input_, group=group)
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# concat
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output = torch.cat(tensor_list, dim=dim).contiguous()
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return output
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class _GatherForwardSplitBackward(torch.autograd.Function):
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"""Gather the input from model parallel region and concatenate.
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Args:
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input_: input matrix.
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parallel_mode: parallel mode.
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dim: dimension
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"""
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@staticmethod
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def symbolic(input_):
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return _gather(input_, parallel_mode=None)
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@staticmethod
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def forward(ctx, input_, parallel_mode, dim):
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ctx.mode = parallel_mode
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ctx.dim = dim
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return _gather(input_, parallel_mode, dim)
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@staticmethod
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def backward(ctx, grad_output):
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return _split(grad_output, ctx.mode, ctx.dim), None, None
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def gather_forward_split_backward(input_, parallel_mode, dim):
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return _GatherForwardSplitBackward.apply(input_, parallel_mode, dim)
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def all_gather_raw(input_: Tensor, process_group: ProcessGroup, async_op: bool = False):
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world_size = torch.distributed.get_world_size(process_group)
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output = torch.empty(world_size * input_.shape[0], *input_.shape[1:], dtype=input_.dtype, device=input_.device)
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handle = torch.distributed.all_gather_into_tensor(
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output, input_.contiguous(), group=process_group, async_op=async_op
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)
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return output, handle
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def reduce_scatter_raw(input_: Tensor, process_group: ProcessGroup, async_op: bool = False):
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world_size = torch.distributed.get_world_size(process_group)
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assert input_.shape[0] % world_size == 0
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output = torch.empty(input_.shape[0] // world_size, *input_.shape[1:], dtype=input_.dtype, device=input_.device)
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handle = torch.distributed.reduce_scatter_tensor(
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output, input_.contiguous(), group=process_group, async_op=async_op
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)
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return output, handle
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def all_reduce_raw(input_: Tensor, process_group: ProcessGroup, async_op: bool = False):
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input_ = input_.contiguous()
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handle = torch.distributed.all_reduce(input_, group=process_group, async_op=async_op)
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return input_, handle
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class ReduceScatterFunc(torch.autograd.Function):
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"""Reduce scatter the input from the sequence parallel region and concatenate."""
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@staticmethod
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def forward(ctx, input_: Tensor, process_group: ProcessGroup) -> Tensor:
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ctx.process_group = process_group
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output, _ = reduce_scatter_raw(input_, process_group)
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return output
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@staticmethod
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def backward(ctx, grad_output: Tensor):
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grad_input, _ = all_gather_raw(grad_output, ctx.process_group)
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return grad_input, None
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class AllReduceFunc(torch.autograd.Function):
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"""Gather the input from sequence parallel region and concatenate."""
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@staticmethod
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def forward(ctx, input_: Tensor, process_group: ProcessGroup) -> Tensor:
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ctx.process_group = process_group
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output, _ = all_reduce_raw(input_, process_group)
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return output
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@staticmethod
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def backward(ctx, grad_output: Tensor):
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return grad_output, None
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# Supports autograd, but does not support async
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reduce_scatter = ReduceScatterFunc.apply
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# Supports autograd, but does not support async
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all_reduce = AllReduceFunc.apply
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def linear_bias_wgrad_torch(input, grad_output, has_d_bias):
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assert input.dtype == grad_output.dtype
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grad_weight = torch.matmul(grad_output.t(), input)
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grad_bias = grad_output.sum(dim=0) if has_d_bias else None
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return grad_weight, grad_bias
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class FusedDenseFuncTorch(torch.autograd.Function):
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@staticmethod
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@custom_fwd
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def forward(ctx, x, weight, bias, return_residual=False, process_group=None, sequence_parallel=True):
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"""
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If process_group is not None and sequence_parallel=True, we're doing Tensor Parallel
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with sequence parallelism: 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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ctx.sequence_parallel = sequence_parallel
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if torch.is_autocast_enabled():
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x = x.to(dtype=torch.get_autocast_gpu_dtype())
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x = x.contiguous()
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if process_group is not None and sequence_parallel:
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# We want to kick off the all_gather early, before weight dtype conversion
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total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
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else:
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total_x = x
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if torch.is_autocast_enabled():
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weight = weight.to(dtype=torch.get_autocast_gpu_dtype())
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bias = bias.to(dtype=torch.get_autocast_gpu_dtype()) if bias is not None else None
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weight = weight.contiguous()
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if process_group is not None and sequence_parallel:
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handle_x.wait()
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batch_shape, n = total_x.shape[:-1], total_x.shape[-1]
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batch_dim = batch_shape.numel()
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# https://github.com/pytorch/pytorch/blob/5b51849b48a7dbccd297286cc0110def4706f9e7/aten/src/ATen/native/cuda/Blas.cpp#L174
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if min(batch_dim, n, *weight.shape) > 65535 * 32:
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raise RuntimeError("fused_dense only supports matrix dims <= 2M")
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output = F.linear(total_x, weight, bias)
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if ctx.compute_weight_gradient:
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ctx.save_for_backward(x, weight)
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else:
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ctx.save_for_backward(weight)
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return output if not return_residual else (output, x)
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_output, *args):
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grad_output = grad_output.contiguous()
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if ctx.return_residual:
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(grad_input,) = args
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grad_input = grad_input.contiguous()
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process_group = ctx.process_group
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sequence_parallel = ctx.sequence_parallel
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if ctx.compute_weight_gradient:
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x, weight = ctx.saved_tensors
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if process_group is not None and sequence_parallel:
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total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
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else:
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total_x = x
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else:
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(weight,) = ctx.saved_tensors
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total_x = None
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batch_shape = grad_output.shape[:-1]
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batch_dim = batch_shape.numel()
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grad_output = grad_output.reshape(batch_dim, grad_output.shape[-1])
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if ctx.needs_input_grad[0]:
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if not ctx.return_residual:
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grad_input = F.linear(grad_output, weight.t())
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else:
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grad_input = torch.addmm(grad_input.reshape(batch_dim, grad_input.shape[-1]), grad_output, weight)
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grad_input = grad_input.reshape(*batch_shape, grad_input.shape[-1])
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if process_group is not None:
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reduce_fn = reduce_scatter_raw if sequence_parallel else all_reduce_raw
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grad_input, handle_grad_input = reduce_fn(grad_input, process_group, async_op=True)
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else:
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grad_input = None
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if ctx.needs_input_grad[1]:
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assert ctx.compute_weight_gradient
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if process_group is not None and sequence_parallel:
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handle_x.wait()
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grad_weight, grad_bias = linear_bias_wgrad_torch(
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total_x.reshape(batch_dim, total_x.shape[-1]), grad_output, ctx.needs_input_grad[2]
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)
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else:
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grad_weight = None
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grad_bias = grad_output if ctx.needs_input_grad[2] else None
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if process_group is not None and ctx.needs_input_grad[0]:
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handle_grad_input.wait()
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return grad_input, grad_weight, grad_bias, None, None, None
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def fused_dense_func_torch(
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x: Tensor,
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weight: Tensor,
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bias: Optional[Tensor] = None,
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return_residual: bool = False,
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process_group: Optional[ProcessGroup] = None,
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sequence_parallel: bool = True,
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):
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dtype_eligible = x.dtype in [torch.float16, torch.bfloat16] or (
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x.dtype == torch.float32 and torch.is_autocast_enabled()
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)
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if x.is_cuda and weight.is_cuda and (bias is None or bias.is_cuda) and dtype_eligible:
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return FusedDenseFunc.apply(x, weight, bias, return_residual, process_group, sequence_parallel)
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else:
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return FusedDenseFuncTorch.apply(x, weight, bias, return_residual, process_group, sequence_parallel)
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