mirror of https://github.com/hpcaitech/ColossalAI
477 lines
19 KiB
Python
477 lines
19 KiB
Python
from typing import Any, Optional
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import numpy as np
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import torch
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import torch.distributed as dist
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import torch.nn.functional as F
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from torch.distributed import ReduceOp
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def cast_to_fp8(inp: torch.Tensor, fp8_format="e4m3", per_channel_scale=False) -> (torch.Tensor, torch.Tensor):
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r"""
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casting torch Tensor into specified fp8 tensor with per-channel scaling or per-tensor scaling.
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Args:
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inp: input torch Tensor, should be in torch.FloatTensor, torch.HalfTensor, torch.BFloat16Tensor.
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scale: scaling factor for fp8 casting. If it is None, then it is computed automatically. Per-channel scaling
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is applied if input tensor is 2 dimension, otherwise, per-tensor scaling is applied.
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fp8_format: e4m3 or e5m2
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Returns:
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Tuples: A tuple (fp8_tensor, scale)
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"""
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if inp.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
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raise TypeError("Only float16, bfloat16, and float32 are allowed.")
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fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
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fp8_max = torch.finfo(fp8_type).max
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if per_channel_scale:
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per_channel_max = inp.abs().max(dim=-1).values.float()
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per_channel_max = torch.where(per_channel_max > 0, per_channel_max, 1.0)
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scale = fp8_max / per_channel_max[:, None]
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else:
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per_tensor_max = inp.abs().max().float()
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per_tensor_max = torch.where(per_tensor_max > 0, per_tensor_max, 1.0)
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scale = fp8_max / per_tensor_max
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scale_inv = 1.0 / scale
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ret = (scale * inp.float()).to(fp8_type)
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return ret, scale_inv
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def cast_from_fp8(
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inp: torch.Tensor, scale_inv: torch.Tensor, ret_type: torch.dtype, per_channel_scale=False
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) -> torch.Tensor:
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r"""
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Args:
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inp: should be a fp8 torch tensor in one of the types: [torch.float8_e4m3fn, torch.float8_e5m2].
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scale: scaling factor returned by cast_to_fp8 function.
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ret_type: the datatype of the returned tensor.
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Returns:
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torch.Tensor
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"""
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if inp.dtype not in [torch.float8_e4m3fn, torch.float8_e5m2]:
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raise TypeError("Only float8_e4m3fn and float8_e5m2 are allowed.")
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if per_channel_scale:
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ret = scale_inv[:, None] * inp.float()
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else:
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ret = scale_inv * inp.float()
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return ret.to(ret_type)
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def all_reduce_fp8(tensor: torch.Tensor, fp8_format="e4m3", op=ReduceOp.SUM, group=None) -> None:
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r"""
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This is an in-place operation for compressed all_reduce using fp8.
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It works like dist.all_reduce but during communication the data is cast to fp8 format.
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Args:
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tensor: torch.Tensor in fp32, fp16, bf16 datatype.
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fp8_format: e4m3 or e5m2
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op: ReduceOp.SUM or ReduceOp.AVG
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Returns:
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None
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"""
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world_size = dist.get_world_size(group=group)
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input_type = tensor.dtype
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input_shape = tensor.shape
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input_device = tensor.device
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input_size = tensor.numel()
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flat_padded_x = tensor.flatten()
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assert op in [ReduceOp.SUM, ReduceOp.AVG], "op can only be ReduceOp.SUM or ReduceOp.AVG"
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if flat_padded_x.size(0) % world_size != 0:
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pad_size = world_size - flat_padded_x.size(0) % world_size
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flat_padded_x = F.pad(flat_padded_x, (0, pad_size))
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fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
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ret, scale = cast_to_fp8(flat_padded_x, fp8_format=fp8_format)
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inp = ret.view(torch.uint8)
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input_chunks = list(torch.chunk(inp, world_size, dim=0))
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output_chunks = list(torch.chunk(torch.empty_like(inp), world_size, dim=0))
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dist.all_to_all(output_chunks, input_chunks, group=group)
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scale_list = [torch.ones(1, dtype=scale.dtype, device=input_device) for _ in range(world_size)]
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dist.all_gather(scale_list, scale, group=group)
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summed_out = torch.zeros_like(output_chunks[0]).to(input_type)
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for scale, out in zip(scale_list, output_chunks):
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out = out.view(fp8_type)
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summed_out += cast_from_fp8(out, scale, input_type)
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if op == ReduceOp.AVG:
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summed_out.div_(world_size)
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summed_out_fp8, scale = cast_to_fp8(summed_out, fp8_format=fp8_format)
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dist.all_gather(scale_list, scale, group=group)
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tensor_list = [torch.empty_like(summed_out_fp8.view(torch.uint8)) for _ in range(world_size)]
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dist.all_gather(tensor_list, summed_out_fp8.view(torch.uint8), group=group)
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for i in range(world_size):
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tensor_list[i] = tensor_list[i].view(fp8_type).to(input_type) * scale_list[i]
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out = torch.cat(tensor_list, dim=0)
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tensor.copy_(out[:input_size].view(input_shape).to(input_type))
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def all_to_all_single_fp8(
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output, input, output_split_sizes=None, input_split_sizes=None, fp8_format="e5m2", group=None, async_op=False
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) -> None:
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r"""
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This is an in-place operation for compressed all_reduce using fp8.
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It works like dist.all_to_all_single but during communication the data is cast to fp8 format.
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Args:
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tensor: torch.Tensor in fp32, fp16, bf16 datatype.
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fp8_format: e4m3 or e5m2
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Returns:
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None
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"""
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world_size = dist.get_world_size(group=group)
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input_type = input.dtype
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input_shape = input.shape
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input_device = input.device
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input = input.flatten()
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fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
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ret, scale = cast_to_fp8(input, fp8_format=fp8_format)
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inp = ret.view(torch.uint8)
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if input_split_sizes is not None:
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input_split_sizes = [input_split_sizes[i] * np.prod(input_shape[1:]) for i in range(world_size)]
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input_chunks = list(torch.split(inp, input_split_sizes))
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else:
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input_chunks = list(torch.chunk(inp, world_size, dim=0))
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if output_split_sizes is not None:
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output_chunks = [
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torch.empty((output_split_sizes[i] * np.prod(input_shape[1:]),), device=input_device, dtype=inp.dtype)
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for i in range(world_size)
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]
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else:
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if dist.get_rank() == world_size - 1:
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output_chunks = [torch.empty_like(input_chunks[-1]) for _ in range(world_size)]
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else:
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output_chunks = [torch.empty_like(input_chunks[0]) for _ in range(world_size)]
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dist.all_to_all(output_chunks, input_chunks, group=group)
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scale_list = [torch.ones(1, dtype=scale.dtype, device=input_device) for _ in range(world_size)]
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dist.all_gather(scale_list, scale, group=group)
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cast_output_chunk = [
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cast_from_fp8(out.view(fp8_type), scale, input_type) for scale, out in zip(scale_list, output_chunks)
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]
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tensor_out = torch.cat(cast_output_chunk, dim=0)
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outputs_shape = list(input_shape)
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if output_split_sizes is not None:
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outputs_shape[0] = sum(output_split_sizes)
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else:
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outputs_shape = input_shape
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output.data = tensor_out.view(outputs_shape).to(input_type)
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def cast_to_fp8_pipeline(inp: Any) -> None:
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"""
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Cast the hidden_states tensor of inp object to fp8 format before p2p communication in pipeline.
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The activations tensor is indexed by 'hidden_states' in the inp dict.
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After FP8 casting, the resulting tensor is saved as float16 or bfloat16 format but the size becomes halved.
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Metadata such as fp8_scale is saved into inp dict for communication.
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"""
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if inp is None:
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return
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# In pipeline parallelism, when inp is torch.Tensor, it only contains one element, thus can be omitted.
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if type(inp) == torch.Tensor:
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return
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assert "hidden_states" in inp, "required by pipeline parallelism."
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inp_tensor = inp["hidden_states"]
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min_val, max_val = inp_tensor.aminmax()
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amax = torch.maximum(min_val.abs(), max_val.abs())
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finfo = torch.finfo(torch.float8_e4m3fn)
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if amax > finfo.max:
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fp8_type = torch.float8_e5m2
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fp8_view_type = torch.float16
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else:
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fp8_type = torch.float8_e4m3fn
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fp8_view_type = torch.bfloat16
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finfo = torch.finfo(fp8_type)
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scale = torch.tensor(1.0).to(inp_tensor.device) if amax == 0.0 else finfo.max / amax.float()
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q_tensor = inp_tensor.data.float() * scale
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# Todo: Currently we use fp8_view_type <float16, bfloat16> to indicate which fp8 format is used. This is a temporary workaround due to 'Only support tensor for fast send'.
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# inp_tensor needs to be a float datatype to avoid error during gradient placement.
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inp_tensor.data = q_tensor.to(fp8_type).view(fp8_view_type)
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inp["fp8_scale"] = scale.float().reciprocal()
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def cast_from_fp8_pipeline(inp: Any, del_metadata=True) -> None:
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"""
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Cast the FP8 encoded hidden_states tensor back to original dtype after p2p communication in pipeline.
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del_metadata = False is useful when this function is called before p2p communication.
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"""
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if inp is None:
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return
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if type(inp) == torch.Tensor:
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return
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assert "hidden_states" in inp, "required by pipeline parallelism."
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inp_tensor = inp["hidden_states"]
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scale = inp["fp8_scale"]
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fp8_view_type = inp_tensor.dtype
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if fp8_view_type == torch.float16:
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fp8_type = torch.float8_e5m2
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elif fp8_view_type == torch.bfloat16:
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fp8_type = torch.float8_e4m3fn
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else:
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raise TypeError("Only float16, bfloat16 are implemented.")
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inp_tensor.data = inp_tensor.data.view(fp8_type).to(torch.float16) * scale
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if del_metadata:
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del inp["fp8_scale"]
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def reduce_scatter_fp8(output: torch.Tensor, input_list, group, fp8_format="e5m2") -> None:
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r"""
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This is an in-place operation for compressed reduce_scatter using fp8.
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It works like dist.reduce_scatter but during communication the data is cast to fp8 format.
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Args:
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tensor: torch.Tensor in fp32, fp16, bf16 datatype.
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fp8_format: e4m3 or e5m2
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Returns:
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None
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"""
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input_type = output.dtype
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fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
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scale_list = []
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cast_input_list = []
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output_chunks = []
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output_scale_list = []
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for input in input_list:
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ret, scale = cast_to_fp8(input, fp8_format=fp8_format)
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scale_list.append(scale)
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ret = ret.view(torch.uint8)
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cast_input_list.append(ret)
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output_chunks.append(torch.empty_like(ret))
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output_scale_list.append(torch.empty_like(scale))
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dist.all_to_all(output_chunks, cast_input_list, group=group)
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dist.all_to_all(output_scale_list, scale_list, group=group)
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summed_out = torch.zeros_like(output_chunks[0]).to(input_type)
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for scale, out in zip(output_scale_list, output_chunks):
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out = out.view(fp8_type)
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summed_out += cast_from_fp8(out, scale, input_type)
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output.data = summed_out
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def split_chunk_by_channel(
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chunk: torch.Tensor, channel_size: int, num_channels: int, rank: int = 0, world_size: int = 1
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):
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offset = chunk.numel() * rank
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end = offset + chunk.numel()
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break_points = [x for x in range(0, channel_size * num_channels + 1, channel_size) if offset <= x <= end]
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if len(break_points) == 0 or break_points[0] > offset:
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break_points.insert(0, offset)
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if break_points[-1] < end:
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break_points.append(end)
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sizes = [b - a for a, b in zip(break_points[:-1], break_points[1:])]
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return chunk.split(sizes)
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def all_gather_into_tensor_flat_fp8(
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output_tensor: torch.Tensor,
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input_tensor: torch.Tensor,
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output_shape: torch.Size,
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group: dist.ProcessGroup,
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fp8_format: str = "e4m3",
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):
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"""all gather into tensor in fp8 format
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Args:
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output_tensor (torch.Tensor): output tensor, which is flattened
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input_tensor (torch.Tensor): input tensor, which is flattened
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group (dist.ProcessGroup): process group
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fp8_format (str, optional): fp8 format, e4m3 or e5m2. Defaults to "e4m3".
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"""
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assert input_tensor.dim() == 1 and output_tensor.dim() == 1, "input/output tensor should be flattened"
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world_size = dist.get_world_size(group)
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assert (
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output_tensor.numel() == input_tensor.numel() * world_size
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), "output tensor size should be world_size times of input tensor size"
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input_type = output_tensor.dtype
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fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
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fp8_max = torch.finfo(fp8_type).max
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if len(output_shape) == 2:
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per_channel_max = torch.zeros(output_shape[0], device=output_tensor.device, dtype=torch.float)
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num_channels, channel_size = output_shape
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rank = dist.get_rank(group)
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channel_start_idx = (input_tensor.numel() * rank) // channel_size
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per_channel_splits = split_chunk_by_channel(input_tensor, channel_size, num_channels, rank, world_size)
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for i, per_channel_split in enumerate(per_channel_splits):
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idx = i + channel_start_idx
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if idx < num_channels:
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per_channel_max[idx] = per_channel_split.abs().max().float()
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dist.all_reduce(per_channel_max, op=dist.ReduceOp.MAX, group=group)
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per_channel_max = torch.where(per_channel_max > 0, per_channel_max, 1.0)
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scale = fp8_max / per_channel_max
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fp8_input = input_tensor.float()
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fp8_per_channel_splits = split_chunk_by_channel(fp8_input, channel_size, num_channels, rank, world_size)
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for i, per_channel_split in enumerate(fp8_per_channel_splits):
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idx = i + channel_start_idx
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if idx < num_channels:
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per_channel_split.mul_(scale[idx])
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fp8_input = fp8_input.to(fp8_type)
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else:
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per_tensor_max = input_tensor.abs().max().float()
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dist.all_reduce(per_tensor_max, op=dist.ReduceOp.MAX, group=group)
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per_tensor_max = torch.where(per_tensor_max > 0, per_tensor_max, 1.0)
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scale = fp8_max / per_tensor_max
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fp8_input = (scale * input_tensor.float()).to(fp8_type)
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scale_inv = 1.0 / scale
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buffer = torch.empty_like(output_tensor, dtype=fp8_type)
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dist.all_gather_into_tensor(buffer.view(torch.uint8), fp8_input.view(torch.uint8), group=group)
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numel = np.prod(output_shape)
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valid_buffer = buffer[:numel].reshape(output_shape)
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valid_buffer = cast_from_fp8(valid_buffer, scale_inv, input_type, per_channel_scale=(len(output_shape) == 2))
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output_tensor[:numel].copy_(valid_buffer.view(-1))
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def all_to_all_fp8(output_list, input_list, group=None, fp8_format="e5m2"):
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world_size = dist.get_world_size(group)
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input_type = input_list[0].dtype
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fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
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scale_list = []
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tensor_list = []
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for i in range(world_size):
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input_tensor = input_list[i]
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ret, scale = cast_to_fp8(input_tensor, fp8_format=fp8_format)
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scale_list.append(scale)
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ret = ret.view(torch.uint8)
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tensor_list.append(ret)
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output_scale_list = [torch.empty_like(x) for x in scale_list]
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output_tensor_list = [torch.empty_like(x) for x in tensor_list]
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dist.all_to_all(output_tensor_list, tensor_list, group=group)
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dist.all_to_all(output_scale_list, scale_list, group=group)
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for i in range(world_size):
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scale = output_scale_list[i]
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tensor = output_tensor_list[i]
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tensor = tensor.view(fp8_type)
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output_list[i].copy_(cast_from_fp8(tensor, scale, input_type))
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def all_to_all_single_fp8(output_tensor, input_tensor, group=None, fp8_format="e5m2"):
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world_size = dist.get_world_size(group)
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per_slice_len = input_tensor.size(0) // world_size
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input_type = input_tensor.dtype
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ret, scale = cast_to_fp8(input_tensor, fp8_format=fp8_format)
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fp8_type = ret.dtype
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input_tensor = ret.view(torch.uint8)
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tensor = torch.empty_like(input_tensor)
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scale_list = [torch.empty_like(scale) for _ in range(world_size)]
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dist.all_to_all_single(tensor, input_tensor, group=group)
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dist.all_gather(scale_list, scale, group=group)
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cast_tensor_list = []
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for i in range(world_size):
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output_part = tensor[per_slice_len * i : per_slice_len * (i + 1)].view(fp8_type)
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output_part = cast_from_fp8(output_part, scale_list[i], input_type)
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cast_tensor_list.append(output_part)
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output_tensor.copy_(torch.concatenate(cast_tensor_list, dim=0))
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def gather_fp8(output_list, input_, group=None, fp8_format="e5m2"):
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world_size = dist.get_world_size(group)
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input_type = input_.dtype
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ret, scale = cast_to_fp8(input_, fp8_format=fp8_format)
|
|
fp8_type = ret.dtype
|
|
input_ = ret.view(torch.uint8)
|
|
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
|
|
scale_list = [torch.ones(1, dtype=scale.dtype, device=input_.device) for _ in range(world_size)]
|
|
dist.all_gather(tensor_list, input_, group=group)
|
|
dist.all_gather(scale_list, scale, group=group)
|
|
|
|
for i in range(world_size):
|
|
output = tensor_list[i].view(fp8_type)
|
|
scale = scale_list[i]
|
|
output_list[i].copy_(cast_from_fp8(output, scale, input_type))
|
|
|
|
|
|
class _LinearFp8(torch.autograd.Function):
|
|
@staticmethod
|
|
def forward(
|
|
ctx: Any,
|
|
x: torch.Tensor,
|
|
w: torch.Tensor,
|
|
bias: Optional[torch.Tensor],
|
|
) -> Any:
|
|
assert (
|
|
x.dtype in (torch.bfloat16, torch.float16) and x.dtype == w.dtype
|
|
), "Only float16 and bfloat16 are allowed."
|
|
if bias is not None:
|
|
assert bias.dtype == x.dtype, "Bias should have the same dtype as input."
|
|
# ensure x and w are row-major
|
|
assert x.is_contiguous() and w.is_contiguous(), "Input and weight should be contiguous."
|
|
ctx.x_shape = x.shape
|
|
ctx.has_bias = bias is not None
|
|
ctx.out_dtype = x.dtype
|
|
x = x.reshape(-1, x.shape[-1])
|
|
|
|
x_fp8, inv_scale_x = cast_to_fp8(x, fp8_format="e4m3")
|
|
w_fp8, inv_scale_w = cast_to_fp8(w, fp8_format="e4m3")
|
|
ctx.x_fp8 = x_fp8
|
|
ctx.w_fp8_t = w_fp8.t()
|
|
ctx.inv_scale_x = inv_scale_x
|
|
ctx.inv_scale_w = inv_scale_w
|
|
out = torch._scaled_mm(
|
|
x_fp8, ctx.w_fp8_t, bias=bias, out_dtype=ctx.out_dtype, scale_a=inv_scale_x, scale_b=inv_scale_w
|
|
)[0]
|
|
return out.reshape(*ctx.x_shape[:-1], w.shape[0])
|
|
|
|
@staticmethod
|
|
def backward(ctx: Any, out_grad) -> Any:
|
|
out_grad = out_grad.reshape(-1, out_grad.shape[-1])
|
|
out_grad_fp8, out_grad_scale = cast_to_fp8(out_grad, fp8_format="e5m2")
|
|
x_grad = torch._scaled_mm(
|
|
out_grad_fp8,
|
|
ctx.w_fp8_t.contiguous().t(),
|
|
out_dtype=ctx.out_dtype,
|
|
scale_a=out_grad_scale,
|
|
scale_b=ctx.inv_scale_w,
|
|
)[0]
|
|
w_grad = torch._scaled_mm(
|
|
out_grad_fp8.t().contiguous(),
|
|
ctx.x_fp8.t().contiguous().t(),
|
|
out_dtype=ctx.out_dtype,
|
|
scale_a=out_grad_scale,
|
|
scale_b=ctx.inv_scale_x,
|
|
)[0]
|
|
bias_grad = None
|
|
if ctx.has_bias:
|
|
bias_grad = out_grad.sum(0)
|
|
return x_grad.reshape(ctx.x_shape), w_grad, bias_grad
|
|
|
|
|
|
def linear_fp8(x: torch.Tensor, w: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
return _LinearFp8.apply(x, w, bias)
|