424 lines
17 KiB
Python
424 lines
17 KiB
Python
import os
|
|
import torch
|
|
from torch import nn
|
|
|
|
from text_generation_server.utils.import_utils import SYSTEM
|
|
|
|
if SYSTEM == "cuda":
|
|
from flash_attn.layers.rotary import RotaryEmbedding
|
|
import rotary_emb
|
|
elif SYSTEM == "rocm":
|
|
from vllm._C import ops
|
|
elif SYSTEM == "xpu":
|
|
import intel_extension_for_pytorch as ipex
|
|
|
|
|
|
def _create_inv_freq(dim, base, device):
|
|
inv_freq = 1.0 / (
|
|
base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
|
|
)
|
|
return inv_freq
|
|
|
|
|
|
def _get_rope_config(config):
|
|
if os.getenv("ROPE_SCALING", None) is not None:
|
|
rope_scaling = {
|
|
"type": os.environ["ROPE_SCALING"],
|
|
"factor": float(os.environ["ROPE_FACTOR"]),
|
|
}
|
|
return rope_scaling
|
|
return getattr(config, "rope_scaling", None)
|
|
|
|
|
|
class PositionRotaryEmbedding(nn.Module):
|
|
def __init__(self, inv_freq, scaling_factor):
|
|
super().__init__()
|
|
self.inv_freq = inv_freq
|
|
self._seq_len_cached = 0
|
|
self._cos_cached = None
|
|
self._sin_cached = None
|
|
self._cos_k_cached = None
|
|
self._sin_k_cached = None
|
|
self.scaling_factor = scaling_factor
|
|
self.dynamic_args = None
|
|
|
|
def forward(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
):
|
|
# Such controlflows may add some overhead.
|
|
if SYSTEM == "cuda":
|
|
rotary_dim = cos.shape[-1]
|
|
q1 = query[..., :rotary_dim]
|
|
q2 = query[..., rotary_dim : 2 * rotary_dim]
|
|
|
|
rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
|
|
|
|
k1 = key[..., :rotary_dim]
|
|
k2 = key[..., rotary_dim : 2 * rotary_dim]
|
|
|
|
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
|
|
elif SYSTEM == "rocm":
|
|
# NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems.
|
|
# Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773
|
|
|
|
head_size = query.shape[-1]
|
|
|
|
# Inplace operation, updating query and key.
|
|
ops.rotary_embedding(query, key, head_size, cos, sin, True)
|
|
elif SYSTEM == "xpu":
|
|
ipex.llm.functional.rotary_embedding(
|
|
query, key, sin, cos, query.size(-1), True
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
|
|
)
|
|
|
|
@classmethod
|
|
def static(cls, config, dim, base, device):
|
|
inv_freq = _create_inv_freq(dim, base, device)
|
|
scaling_factor = None
|
|
rope_scaling = _get_rope_config(config)
|
|
if rope_scaling is not None:
|
|
if rope_scaling["type"] == "linear":
|
|
pass
|
|
elif rope_scaling["type"] == "dynamic":
|
|
scaling_factor = rope_scaling["factor"]
|
|
return DynamicPositionRotaryEmbedding(
|
|
dim=dim,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=base,
|
|
device=inv_freq.device,
|
|
scaling_factor=scaling_factor,
|
|
)
|
|
elif rope_scaling["type"] == "yarn":
|
|
scaling_factor = rope_scaling["factor"]
|
|
return YarnPositionRotaryEmbedding(
|
|
dim=2 * inv_freq.shape[0],
|
|
max_position_embeddings=rope_scaling[
|
|
"original_max_position_embeddings"
|
|
],
|
|
base=10000.0,
|
|
device=inv_freq.device,
|
|
scaling_factor=scaling_factor,
|
|
extrapolation_factor=1,
|
|
attn_factor=1,
|
|
beta_fast=32,
|
|
beta_slow=1,
|
|
)
|
|
elif rope_scaling["type"] == "su":
|
|
short_factor = torch.tensor(
|
|
rope_scaling["short_factor"], dtype=torch.float32, device=device
|
|
)
|
|
short_inv_freq = 1.0 / (
|
|
short_factor
|
|
* base
|
|
** (
|
|
torch.arange(0, dim, 2, device=device, dtype=torch.float32)
|
|
/ dim
|
|
)
|
|
)
|
|
long_factor = torch.tensor(
|
|
rope_scaling["long_factor"], dtype=torch.float32, device=device
|
|
)
|
|
long_inv_freq = 1.0 / (
|
|
long_factor
|
|
* base
|
|
** (
|
|
torch.arange(0, dim, 2, device=device, dtype=torch.float32)
|
|
/ dim
|
|
)
|
|
)
|
|
|
|
original_max_position_embeddings = (
|
|
config.original_max_position_embeddings
|
|
)
|
|
max_position_embeddings = config.max_position_embeddings
|
|
if max_position_embeddings <= original_max_position_embeddings:
|
|
scaling_factor = 1.0
|
|
else:
|
|
scale = max_position_embeddings / original_max_position_embeddings
|
|
scaling_factor = math.sqrt(
|
|
1 + math.log(scale) / math.log(original_max_position_embeddings)
|
|
)
|
|
|
|
return SuRotaryEmbedding(
|
|
short_inv_freq=short_inv_freq,
|
|
long_inv_freq=long_inv_freq,
|
|
scaling_factor=scaling_factor,
|
|
original_max_position_embeddings=original_max_position_embeddings,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
|
|
)
|
|
return cls(inv_freq, scaling_factor)
|
|
|
|
@classmethod
|
|
def load(cls, config, prefix, weights):
|
|
# XXX: Always load this in float32 !
|
|
dtype = weights.dtype
|
|
weights.dtype = torch.float32
|
|
inv_freq = weights.get_tensor(f"{prefix}.inv_freq")
|
|
weights.dtype = dtype
|
|
|
|
scaling_factor = None
|
|
rope_scaling = _get_rope_config(config)
|
|
if rope_scaling is not None:
|
|
scaling_factor = rope_scaling["factor"]
|
|
if rope_scaling["type"] == "linear":
|
|
pass
|
|
elif rope_scaling["type"] == "dynamic":
|
|
return DynamicPositionRotaryEmbedding(
|
|
dim=2 * inv_freq.shape[0],
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=10000.0,
|
|
device=inv_freq.device,
|
|
scaling_factor=scaling_factor,
|
|
)
|
|
elif rope_scaling["type"] == "yarn":
|
|
return YarnPositionRotaryEmbedding(
|
|
dim=2 * inv_freq.shape[0],
|
|
max_position_embeddings=rope_scaling[
|
|
"original_max_position_embeddings"
|
|
],
|
|
base=10000.0,
|
|
device=inv_freq.device,
|
|
scaling_factor=scaling_factor,
|
|
extrapolation_factor=1,
|
|
attn_factor=1,
|
|
beta_fast=32,
|
|
beta_slow=1,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
|
|
)
|
|
return cls(inv_freq, scaling_factor)
|
|
|
|
def _update_cos_sin_cache(self, dtype, device, seqlen):
|
|
# Reset the tables if the sequence length has changed,
|
|
# or if we're on a new device (possibly due to tracing for instance)
|
|
if (
|
|
seqlen > self._seq_len_cached
|
|
or self._cos_cached.device != device
|
|
or self._cos_cached.dtype != dtype
|
|
):
|
|
self._seq_len_cached = seqlen
|
|
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
|
if self.scaling_factor is not None:
|
|
t /= self.scaling_factor
|
|
# Don't do einsum, it converts fp32 to fp16
|
|
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
|
|
|
freqs = torch.outer(t, self.inv_freq.to(device=t.device))
|
|
self._cos_cached = torch.cos(freqs).to(dtype)
|
|
self._sin_cached = torch.sin(freqs).to(dtype)
|
|
|
|
def get_cos_sin(self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype):
|
|
"""
|
|
Return cos and sin for the asked position ids
|
|
"""
|
|
if SYSTEM == "rocm":
|
|
# For RoCm, we always use float cos/sin to avoid a cast.
|
|
# For NVIDIA, for some reason, the flash-attn rotary kernel requires cos/sin and query/key to be of same dtype: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary.cpp#L26
|
|
# But later on goes and cast cos/sin to float anyway: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary_cuda.cu#L29, which looks suboptimal.
|
|
dtype = torch.float32
|
|
|
|
self._update_cos_sin_cache(dtype, position_ids.device, max_s)
|
|
|
|
cos = torch.index_select(self._cos_cached, 0, position_ids)
|
|
sin = torch.index_select(self._sin_cached, 0, position_ids)
|
|
|
|
# Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow.
|
|
return cos.unsqueeze(1), sin.unsqueeze(1)
|
|
|
|
|
|
class SuRotaryEmbedding(PositionRotaryEmbedding):
|
|
def __init__(
|
|
self,
|
|
short_inv_freq,
|
|
long_inv_freq,
|
|
scaling_factor,
|
|
original_max_position_embeddings,
|
|
):
|
|
super(PositionRotaryEmbedding, self).__init__()
|
|
self.short_inv_freq = short_inv_freq
|
|
self.long_inv_freq = long_inv_freq
|
|
self.scaling_factor = scaling_factor
|
|
self.original_max_position_embeddings = original_max_position_embeddings
|
|
self._seq_len_cached = 0
|
|
self._cos_cached = None
|
|
self._sin_cached = None
|
|
self._cos_k_cached = None
|
|
self._sin_k_cached = None
|
|
self.dynamic_args = None
|
|
|
|
def _update_cos_sin_cache(self, dtype, device, seqlen):
|
|
# Reset the tables if the sequence length has changed,
|
|
# or if we're on a new device (possibly due to tracing for instance)
|
|
if (
|
|
seqlen > self._seq_len_cached
|
|
or self._cos_cached.device != device
|
|
or self._cos_cached.dtype != dtype
|
|
):
|
|
self._seq_len_cached = seqlen
|
|
|
|
t = torch.arange(seqlen, device=device, dtype=self.short_inv_freq.dtype)
|
|
short_freqs = torch.outer(
|
|
t[: self.original_max_position_embeddings],
|
|
self.short_inv_freq.to(device=t.device),
|
|
)
|
|
long_freqs = torch.outer(
|
|
t[self.original_max_position_embeddings :],
|
|
self.long_inv_freq.to(device=t.device),
|
|
)
|
|
|
|
freqs = torch.cat([short_freqs, long_freqs])
|
|
|
|
self._cos_cached = (torch.cos(freqs) * self.scaling_factor).to(dtype)
|
|
self._sin_cached = (torch.sin(freqs) * self.scaling_factor).to(dtype)
|
|
|
|
|
|
class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding):
|
|
def __init__(self, dim, max_position_embeddings, base, device, scaling_factor):
|
|
inv_freq = _create_inv_freq(dim, base, device)
|
|
super().__init__(inv_freq, scaling_factor)
|
|
self.dim = dim
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.base = base
|
|
|
|
def _update_cos_sin_cache(self, dtype, device, seqlen):
|
|
# Reset the tables if the sequence length has changed,
|
|
# or if we're on a new device (possibly due to tracing for instance)
|
|
if (
|
|
seqlen > self._seq_len_cached
|
|
or self._cos_cached.device != device
|
|
or self._cos_cached.dtype != dtype
|
|
):
|
|
if seqlen > self.max_position_embeddings:
|
|
newbase = self.base * (
|
|
(self.scaling_factor * seqlen / self.max_position_embeddings)
|
|
- (self.scaling_factor - 1)
|
|
) ** (self.dim / (self.dim - 2))
|
|
self.inv_freq = _create_inv_freq(
|
|
self.dim, newbase, self.inv_freq.device
|
|
)
|
|
self._seq_len_cached = seqlen
|
|
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
|
# Don't do einsum, it converts fp32 to fp16
|
|
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
|
|
|
freqs = torch.outer(t, self.inv_freq.to(device=t.device))
|
|
self._cos_cached = torch.cos(freqs).to(dtype)
|
|
self._sin_cached = torch.sin(freqs).to(dtype)
|
|
|
|
|
|
# Inverse dim formula to find dim based on number of rotations
|
|
import math
|
|
|
|
|
|
def find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
|
|
return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
|
|
2 * math.log(base)
|
|
)
|
|
|
|
|
|
# Find dim range bounds based on rotations
|
|
def find_correction_range(
|
|
low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
|
|
):
|
|
low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
|
|
high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
|
|
return max(low, 0), min(high, dim - 1) # Clamp values just in case
|
|
|
|
|
|
def linear_ramp_mask(min, max, dim):
|
|
if min == max:
|
|
max += 0.001 # Prevent singularity
|
|
|
|
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
|
|
ramp_func = torch.clamp(linear_func, 0, 1)
|
|
return ramp_func
|
|
|
|
|
|
def get_mscale(scale=1):
|
|
if scale <= 1:
|
|
return 1.0
|
|
return 0.1 * math.log(scale) + 1.0
|
|
|
|
|
|
class YarnPositionRotaryEmbedding(PositionRotaryEmbedding):
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
max_position_embeddings,
|
|
base,
|
|
device,
|
|
scaling_factor,
|
|
*,
|
|
extrapolation_factor,
|
|
attn_factor,
|
|
beta_fast,
|
|
beta_slow,
|
|
):
|
|
inv_freq = _create_inv_freq(dim, base, device)
|
|
super().__init__(inv_freq, scaling_factor)
|
|
self.dim = dim
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.base = base
|
|
self.extrapolation_factor = extrapolation_factor
|
|
self.attn_factor = attn_factor
|
|
self.beta_fast = beta_fast
|
|
self.beta_slow = beta_slow
|
|
self.mscale = float(
|
|
get_mscale(self.scaling_factor) * self.attn_factor
|
|
) # Get n-d magnitude scaling corrected for interpolation
|
|
|
|
def _update_cos_sin_cache(self, dtype, device, seqlen):
|
|
# Reset the tables if the sequence length has changed,
|
|
# or if we're on a new device (possibly due to tracing for instance)
|
|
if (
|
|
seqlen > self._seq_len_cached
|
|
or self._cos_cached.device != device
|
|
or self._cos_cached.dtype != dtype
|
|
):
|
|
if seqlen > self.max_position_embeddings:
|
|
inv_freq_extrapolation = _create_inv_freq(
|
|
self.dim, self.base, self.inv_freq.device
|
|
)
|
|
freqs = 1.0 / inv_freq_extrapolation
|
|
inv_freq_interpolation = 1.0 / (self.scaling_factor * freqs)
|
|
low, high = find_correction_range(
|
|
self.beta_fast,
|
|
self.beta_slow,
|
|
self.dim,
|
|
self.base,
|
|
self.max_position_embeddings,
|
|
)
|
|
inv_freq_mask = (
|
|
1 - linear_ramp_mask(low, high, self.dim // 2).float().to(device)
|
|
) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
|
|
inv_freq = (
|
|
inv_freq_interpolation * (1 - inv_freq_mask)
|
|
+ inv_freq_extrapolation * inv_freq_mask
|
|
)
|
|
|
|
self.inv_freq = inv_freq
|
|
self.mscale = float(
|
|
get_mscale(self.scaling_factor) * self.attn_factor
|
|
) # Get n-d magnitude scaling corrected for interpolation
|
|
|
|
self._seq_len_cached = seqlen
|
|
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
|
# Don't do einsum, it converts fp32 to fp16
|
|
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
|
|
|
freqs = torch.outer(t, self.inv_freq.to(device=t.device))
|
|
self._cos_cached = (torch.cos(freqs) * self.mscale).to(dtype)
|
|
self._sin_cached = (torch.sin(freqs) * self.mscale).to(dtype)
|