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server/text_generation_server/layers/mlp.py 0 → 100644
View file @ efd602c8
import torch
import math
from torch import nn
from torch.nn import functional as F
from typing import Optional, Tuple
from text_generation_server.layers import TensorParallelEmbedding, FastLinear
from text_generation_server.layers.tensor_parallel import TensorParallelHead
from text_generation_server.utils.speculate import get_speculate
class MLPSpeculatorLayerNorm(nn.Module):
"""
A L2 normalization implementation
...
Args
----
normalized_shape : int
Dimensionality of input data (size of final tensor axis)
elementwise_scale_weight : torch.Tensor
learned scaling term after normalization?
elementwise_shift_bias : torch.Tensor
learned bias term after normalization?
eps : float
Safety term to prevent division by zero. Make sure the chosen value fits in the range of your encoding scheme (i.e. fp16 requires eps >= 6e-8).
"""
def __init__(
self,
prefix,
config,
weights,
eps=1e-06,
):
super(MLPSpeculatorLayerNorm, self).__init__()
self.weight = weights.get_tensor(f"{prefix}.weight")
self.bias = weights.get_tensor(f"{prefix}.bias")
self.eps = eps
def forward(self, x):
xf = x
xf = xf * torch.rsqrt(xf.pow(2).mean(-1, keepdim=True) + self.eps)
x = xf.type_as(x)
x = self.weight * x
x = x + self.bias
return x
class MLPSpeculatorModel(torch.nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.config = config
self.n_predict = get_speculate()
self.hidden_size = config.hidden_size
self.emb = nn.ModuleList(
[
TensorParallelEmbedding(f"{prefix}.emb.{i}", weights)
for i in range(self.n_predict)
]
)
self.proj = [
FastLinear.load(
config,
prefix=f"{prefix}.proj.{i}",
weights=weights,
bias=False,
)
for i in range(self.n_predict)
]
self.head = nn.ModuleList(
[
FastLinear.load(config, f"{prefix}.head.{i}", weights, bias=False)
for i in range(self.n_predict)
]
)
self.ln = nn.ModuleList(
[
MLPSpeculatorLayerNorm(
prefix=f"{prefix}.ln.{i}",
config=config,
weights=weights,
)
for i in range(self.n_predict)
]
)
# Weights ensure that state_0 accounts for 50% of state magnitude by final head in expectation
self.state_weight = 0.5 ** (0.5 / self.n_predict)
self.emb_weight = math.sqrt(1 - self.state_weight**2)
self.activation = nn.GELU()
# TODO
self.vsize = config.vocab_size
self.inner_dim = config.speculator_config["inner_dim"]
self.top_k_tokens_per_head = [1] * self.n_predict
def forward(
self,
hidden_states: torch.Tensor,
input_ids: torch.Tensor,
):
top_k_tokens_per_head = self.top_k_tokens_per_head
# k indicates # of candidates
# h indicates # of generated tokens
state = hidden_states
b = state.size(0)
ind = input_ids.unsqueeze(0)
all_probs = torch.empty(
b, self.n_predict, self.vsize, device=state.device
) # b k h v
assert (
len(top_k_tokens_per_head) == self.n_predict
), f"You must provide a topk number for each head ({self.n_predict} heads, {len(top_k_tokens_per_head)} provided)"
for i in range(self.n_predict):
# Project and predict
z = self.emb[i](ind)
z = z.mul(self.emb_weight * math.sqrt(self.inner_dim / 2)) # b k d
state = self.proj[i](state) * self.state_weight + z
state = self.activation(self.ln[i](state)) # b k d
probs = F.log_softmax(self.head[i](state), dim=-1) # b k v
_probs, preds = probs.topk(top_k_tokens_per_head[i], dim=-1) # b k k'
# Update candidate set with new predictions
# Update distribution set with new logits
all_probs[:, i] = probs.exp()
# Update state, log_probs and ind for new predictions
state = state.unsqueeze(2).expand(
-1, -1, top_k_tokens_per_head[i], -1
) # b k k' d
state = state.reshape(-1, b, state.size(3)) # b kk' d
ind = preds.view(-1, b) # b kk'
speculative_logits = all_probs
return speculative_logits
class MLPSpeculatorHead(nn.Module):
def __init__(self, lm_head, mlp_speculator):
super().__init__()
self.lm_head = lm_head
self.mlp_speculator = mlp_speculator
def forward(
self, input: torch.Tensor
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
logits = self.lm_head(input)
# If we have too many tokens, we skip speculative logits
if input.shape[0] > 128:
return logits, None
input_ids = logits.argmax(dim=-1)
speculative_logits = self.mlp_speculator(input, input_ids)
return logits, speculative_logits
@staticmethod
def load(config, prefix: str, weights):
from pathlib import Path
from safetensors import safe_open
speculator_path = config.speculator["path"]
for fname in config.speculator["model_paths"]:
filename = str(Path(speculator_path) / fname)
routing = weights.routing
with safe_open(filename, framework="pytorch") as f:
for k in f.keys():
if k in routing and routing[k] != filename:
raise RuntimeError(
f"Key {k} was found in multiple files: {filename} and {routing[k]}"
)
routing[k] = filename
mlp_speculator = MLPSpeculatorModel(config, "speculator", weights)
lm_head = TensorParallelHead.load(config, prefix, weights)
return MLPSpeculatorHead(lm_head, mlp_speculator)
server/text_generation_server/layers/rotary.py 0 → 100644
View file @ efd602c8
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 import _custom_ops
elif SYSTEM == "ipex":
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.
torch.ops._C.rotary_embedding_tgi(query, key, head_size, cos, sin, True)
elif SYSTEM == "ipex":
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)
server/text_generation_server/layers/speculative.py 0 → 100644
View file @ efd602c8
import torch
import json
from typing import Tuple, Optional
from text_generation_server.layers.tensor_parallel import TensorParallelHead
from text_generation_server.layers.medusa import MedusaHeadV1, MedusaHeadV2
from text_generation_server.layers.mlp import MLPSpeculatorHead
class SpeculativeHead(torch.nn.Module):
def __init__(self, lm_head, speculator):
super().__init__()
self.head = lm_head
self.speculator = speculator
@staticmethod
def load(config, prefix: str, weights):
speculator = config.speculator
if speculator:
speculator_path = config.speculator["path"]
speculator_config = str(speculator_path / "config.json")
with open(speculator_config, "r") as f:
speculator_config = json.load(f)
config.speculator_config = speculator_config
try:
architecture = speculator_config["architectures"][0]
if architecture == "MLPSpeculatorPreTrainedModel":
speculator = MLPSpeculatorHead.load(config, prefix, weights)
else:
speculator = None
except KeyError:
try:
speculator = MedusaHeadV1.load(config, prefix, weights)
except:
speculator = MedusaHeadV2(config, prefix, weights)
lm_head = None
else:
lm_head = TensorParallelHead.load(config, prefix, weights)
speculator = None
return SpeculativeHead(lm_head, speculator)
def forward(
self, input: torch.Tensor
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
if self.speculator is not None:
return self.speculator(input)
assert self.head is not None
logits = self.head(input)
return logits, None
server/text_generation_server/layers/tensor_parallel.py 0 → 100644
View file @ efd602c8
import torch
from torch.nn import functional as F
from typing import Iterable, List
from text_generation_server.layers.linear import get_linear, FastLinear
from text_generation_server.layers.exl2 import Exl2Weight
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM == "ipex":
import intel_extension_for_pytorch as ipex
class LayerConcat(torch.nn.Module):
"""
Apply multiple layers to the input and concatenate their
outputs.
"""
def __init__(self, layers: Iterable[torch.nn.Module], dim: int = -1):
"""
`dim` is the dimension along which layer outputs are concatenated.
"""
super().__init__()
self.layers = layers
self.dim = dim
def forward(self, x: torch.Tensor):
outputs = [layer(x) for layer in self.layers]
return torch.cat(outputs, self.dim)
class SuperLayer(torch.nn.Module):
def __init__(self, linear):
super().__init__()
self.linear = linear
def forward(self, x):
return self.linear.forward(x)
class TensorParallelHead(SuperLayer):
def __init__(self, linear, process_group, should_gather: bool):
super().__init__(linear)
self.process_group = process_group
self.should_gather = should_gather
@staticmethod
def load(config, prefix: str, weights):
if config.quantize == "exl2":
try:
# If the piece and LM head embeddings are shared, we have
# non-quantized weights...
weight = weights.get_tensor(f"{prefix}.weight")
except:
# ...otherwise they are quantized.
weight = weights.get_weights_col(prefix, config.quantize)
should_gather = weights.process_group.size() > 1
elif weights.process_group.size() > 1:
try:
weight = weights.get_sharded(f"{prefix}.weight", dim=0)
should_gather = True
except AssertionError:
# If the vocab size is not divisible by number of shards
# just load the entire thing.
weight = weights.get_tensor(f"{prefix}.weight")
should_gather = False
else:
weight = weights.get_tensor(f"{prefix}.weight")
should_gather = False
# GPTQ,AWQ,EETQ don't quantize heads (nor embeddings)
if config.quantize in ["gptq", "awq", "eetq", "marlin"]:
quantize = None
# See above, exl2 LM head can be quantized or not.
elif config.quantize == "exl2" and not isinstance(weight, Exl2Weight):
quantize = None
else:
quantize = config.quantize
return TensorParallelHead(
get_linear(weight, bias=None, quantize=quantize),
process_group=weights.process_group,
should_gather=should_gather,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
if not self.should_gather:
return super().forward(input)
world_size = self.process_group.size()
if len(input.shape) == 2 and isinstance(self.linear, FastLinear):
out_dim = self.linear.weight.shape[0]
if input.shape[0] == 1:
world_out = input.new_empty(1, out_dim * world_size)
local_out = input.new_empty(1, out_dim)
gather_input = local_out
else:
world_out = input.new_empty(out_dim * world_size, input.shape[0])
gather_input = input.new_empty(out_dim, input.shape[0])
local_out = gather_input.T
torch.mm(input, self.linear.weight.T, out=local_out)
if SYSTEM == "ipex":
ipex.distributed.all_gather_into_tensor(
world_out, gather_input, group=self.process_group
)
else:
torch.distributed.all_gather_into_tensor(
world_out, gather_input, group=self.process_group
)
if input.shape[0] == 1:
return world_out
return world_out.T
output = super().forward(input)
world_output = [
torch.empty_like(output) for _ in range(self.process_group.size())
]
if SYSTEM == "ipex":
ipex.distributed.all_gather(world_output, output, group=self.process_group)
else:
torch.distributed.all_gather(world_output, output, group=self.process_group)
world_output = torch.cat(world_output, dim=-1)
return world_output
class TensorParallelColumnLinear(SuperLayer):
@classmethod
def load_gate_up(cls, config, prefix: str, weights, bias: bool):
"""Specific method when the QKV was joined after the fact"""
weight = weights.get_weights_col_packed_gate_up(
prefix, quantize=config.quantize
)
if bias:
raise NotImplementedError("packed_gate_up only implemented without bias")
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
return cls(linear)
@classmethod
def load_qkv(
cls,
config,
prefix: str,
weights,
bias: bool,
num_heads: int,
num_key_value_heads: int,
):
"""Specific method when the QKV was joined after the fact"""
weight = weights.get_weights_col_packed_qkv(
prefix,
quantize=config.quantize,
num_heads=num_heads,
num_key_value_heads=num_key_value_heads,
)
if bias:
raise NotImplementedError("packed_qkv only implemented for baichuan")
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
return cls(linear)
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_weights_col(prefix, config.quantize)
if bias:
bias = weights.get_sharded(f"{prefix}.bias", dim=0)
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
return cls(linear)
@classmethod
def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
if config.quantize == "exl2":
linears = []
for prefix in prefixes:
weight = weights.get_weights_col(prefix, config.quantize)
b = weights.get_tensor(f"{prefix}.bias") if bias else None
linears.append(get_linear(weight, b, config.quantize))
linear = LayerConcat(linears)
else:
weight = weights.get_multi_weights_col(
prefixes, quantize=config.quantize, dim=dim
)
if bias:
b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
bias = torch.cat(b, dim=dim)
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
return cls(linear)
class TensorParallelRowLinear(SuperLayer):
def __init__(self, linear, process_group):
super().__init__(linear)
self.process_group = process_group
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return cls(
get_linear(weight, bias, config.quantize),
process_group=weights.process_group,
)
def forward(self, input: torch.Tensor, reduce: bool = True) -> torch.Tensor:
out = super().forward(input)
if self.process_group.size() > 1 and reduce:
if SYSTEM == "ipex":
ipex.distributed.all_reduce(out, group=self.process_group)
else:
torch.distributed.all_reduce(out, group=self.process_group)
return out
class TensorParallelEmbedding(torch.nn.Module):
def __init__(self, prefix: str, weights, reduce=True):
super().__init__()
weight = weights.get_partial_sharded(f"{prefix}.weight", dim=0)
num_embeddings = weights.get_shape(f"{prefix}.weight")[0]
process_group = weights.process_group
world_size = process_group.size()
rank = process_group.rank()
block_size = (num_embeddings + world_size - 1) // world_size
self.min_id = rank * block_size
self.max_id = min(num_embeddings, (rank + 1) * block_size)
self.null_idx = weight.shape[
0
] # Usually block_size, might be less in non even vocab_size.
self.process_group = weights.process_group
self.reduce = reduce
"""Additional 0 entry used for masking"""
self.weight = torch.nn.Parameter(F.pad(weight, (0, 0, 0, 1)))
def forward(self, input: torch.Tensor) -> torch.Tensor:
# default all out of bounds values to `self.null_idx` that will then be mapped to 0
# translate for [0, self.max_id - self.min_id[
input = torch.where(
(self.min_id > input) | (input >= self.max_id),
self.null_idx,
input - self.min_id,
)
out = torch.nn.functional.embedding(input, self.weight)
if self.reduce and self.process_group.size() > 1:
if SYSTEM == "ipex":
ipex.distributed.all_reduce(out, group=self.process_group)
else:
torch.distributed.all_reduce(out, group=self.process_group)
return out
server/text_generation_server/models/__init__.py
View file @ efd602c8
import torch
import enum
import os
from loguru import logger
from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto import modeling_auto
from huggingface_hub import hf_hub_download
from typing import Optional
from huggingface_hub import hf_hub_download, HfApi
from typing import Optional, List
from pathlib import Path
from text_generation_server.utils.speculate import get_speculate, set_speculate
from text_generation_server.models.model import Model
from text_generation_server.models.causal_lm import CausalLM
from text_generation_server.models.flash_causal_lm import FlashCausalLM
from text_generation_server.models.bloom import BLOOMSharded
from text_generation_server.models.mpt import MPTSharded
from text_generation_server.models.seq2seq_lm import Seq2SeqLM
......@@ -22,6 +23,8 @@ from text_generation_server.models.t5 import T5Sharded
from text_generation_server.models.gpt_neox import GPTNeoxSharded
from text_generation_server.models.phi import Phi
from text_generation_server.utils.import_utils import SYSTEM
# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
# in PyTorch 1.12 and later.
torch.backends.cuda.matmul.allow_tf32 = True
......@@ -49,7 +52,9 @@ FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
FLASH_ATTENTION = True
try:
from text_generation_server.models.flash_causal_lm import FlashCausalLM
from text_generation_server.models.flash_rw import FlashRWSharded
from text_generation_server.models.flash_gpt2 import FlashGPT2
from text_generation_server.models.flash_neox import FlashNeoXSharded
from text_generation_server.models.flash_llama import (
FlashLlama,
......@@ -63,6 +68,12 @@ try:
from text_generation_server.models.flash_gemma import (
FlashGemma,
)
from text_generation_server.models.flash_gemma2 import (
FlashGemma2,
)
from text_generation_server.models.pali_gemma import (
PaliGemma,
)
from text_generation_server.models.flash_santacoder import (
FlashSantacoderSharded,
)
......@@ -70,30 +81,32 @@ try:
from text_generation_server.models.llava_next import LlavaNext
from text_generation_server.models.idefics2 import Idefics2
from text_generation_server.models.flash_mistral import FlashMistral
# from text_generation_server.models.flash_mixtral import FlashMixtral
from text_generation_server.models.flash_mixtral import FlashMixtral
from text_generation_server.models.flash_phi import FlashPhi
from text_generation_server.models.flash_starcoder2 import FlashStarcoder2
# from text_generation_server.models.flash_dbrx import FlashDbrx
from text_generation_server.utils.flash_attn import HAS_FLASH_ATTN_V2_CUDA
from text_generation_server.models.flash_dbrx import FlashDbrx
from text_generation_server.layers.attention import SUPPORTS_WINDOWING
except ImportError as e:
logger.warning(f"Could not import Flash Attention enabled models: {e}")
SUPPORTS_WINDOWING = False
FLASH_ATTENTION = False
HAS_FLASH_ATTN_V2_CUDA = False
if FLASH_ATTENTION:
__all__.append(FlashCausalLM)
__all__.append(FlashGPT2)
__all__.append(FlashNeoXSharded)
__all__.append(FlashRWSharded)
__all__.append(FlashSantacoderSharded)
__all__.append(FlashLlama)
__all__.append(IDEFICSSharded)
__all__.append(FlashMistral)
# __all__.append(FlashMixtral)
# __all__.append(FlashDbrx)
__all__.append(FlashMixtral)
__all__.append(FlashDbrx)
__all__.append(FlashPhi)
__all__.append(FlashQwen2)
__all__.append(FlashStarcoder2)
__all__.append(FlashGemma)
__all__.append(FlashGemma2)
__all__.append(FlashCohere)
MAMBA_AVAILABLE = True
......@@ -107,19 +120,167 @@ if MAMBA_AVAILABLE:
__all__.append(Mamba)
class ModelType(enum.Enum):
IDEFICS2 = {
"type": "idefics2",
"name": "Idefics 2",
"url": "https://huggingface.co/HuggingFaceM4/idefics2-8b",
"multimodal": True,
}
LLAVA_NEXT = {
"type": "llava_next",
"name": "Llava Next (1.6)",
"url": "https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf",
"multimodal": True,
}
LLAMA = {
"type": "llama",
"name": "Llama",
"url": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct",
}
PHI3 = {
"type": "phi3",
"name": "Phi 3",
"url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct",
}
GEMMA = {
"type": "gemma",
"name": "Gemma",
"url": "https://huggingface.co/google/gemma-7b",
}
GEMMA2 = {
"type": "gemma2",
"name": "Gemma2",
"url": "https://huggingface.co/google/gemma2-9b",
}
COHERE = {
"type": "cohere",
"name": "Cohere",
"url": "https://huggingface.co/CohereForAI/c4ai-command-r-plus",
}
DBRX = {
"type": "dbrx",
"name": "Dbrx",
"url": "https://huggingface.co/databricks/dbrx-instruct",
}
MAMBA = {
"type": "ssm",
"name": "Mamba",
"url": "https://huggingface.co/state-spaces/mamba-2.8b-slimpj",
}
MISTRAL = {
"type": "mistral",
"name": "Mistral",
"url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2",
}
MIXTRAL = {
"type": "mixtral",
"name": "Mixtral",
"url": "https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1",
}
GPT_BIGCODE = {
"type": "gpt_bigcode",
"name": "Gpt Bigcode",
"url": "https://huggingface.co/bigcode/gpt_bigcode-santacoder",
}
PHI = {
"type": "phi",
"name": "Phi",
"url": "https://huggingface.co/microsoft/phi-1_5",
}
BAICHUAN = {
"type": "baichuan",
"name": "Baichuan",
"url": "https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat",
}
FALCON = {
"type": "falcon",
"name": "Falcon",
"url": "https://huggingface.co/tiiuae/falcon-7b-instruct",
}
STARCODER2 = {
"type": "starcoder2",
"name": "StarCoder 2",
"url": "https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1",
}
QWEN2 = {
"type": "qwen2",
"name": "Qwen 2",
"url": "https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f",
}
OPT = {
"type": "opt",
"name": "Opt",
"url": "https://huggingface.co/facebook/opt-6.7b",
}
T5 = {
"type": "t5",
"name": "T5",
"url": "https://huggingface.co/google/flan-t5-xxl",
}
GALACTICA = {
"type": "galactica",
"name": "Galactica",
"url": "https://huggingface.co/facebook/galactica-120b",
}
SANTACODER = {
"type": "santacoder",
"name": "SantaCoder",
"url": "https://huggingface.co/bigcode/santacoder",
}
BLOOM = {
"type": "bloom",
"name": "Bloom",
"url": "https://huggingface.co/bigscience/bloom-560m",
}
MPT = {
"type": "mpt",
"name": "Mpt",
"url": "https://huggingface.co/mosaicml/mpt-7b-instruct",
}
GPT2 = {
"type": "gpt2",
"name": "Gpt2",
"url": "https://huggingface.co/openai-community/gpt2",
}
GPT_NEOX = {
"type": "gpt_neox",
"name": "Gpt Neox",
"url": "https://huggingface.co/EleutherAI/gpt-neox-20b",
}
IDEFICS = {
"type": "idefics",
"name": "Idefics",
"url": "https://huggingface.co/HuggingFaceM4/idefics-9b",
"multimodal": True,
}
__GLOBALS = locals()
for data in ModelType:
__GLOBALS[data.name] = data.value["type"]
def get_model(
model_id: str,
lora_adapter_ids: Optional[List[str]],
revision: Optional[str],
sharded: bool,
quantize: Optional[str],
speculate: Optional[int],
dtype: Optional[str],
trust_remote_code: bool,
max_input_tokens: int,
) -> Model:
global FLASH_ATTENTION
if dtype is None:
# Keep it as default for now and let
# every model resolve their own default dtype.
dtype = None
if quantize in ["awq", "exl2", "gptq", "marlin"]:
# These quantizers only work with float16 params.
dtype = torch.float16
else:
# Keep it as default for now and let
# every model resolve their own default dtype.
dtype = None
elif dtype == "float16":
dtype = torch.float16
elif dtype == "bfloat16":
......@@ -135,8 +296,9 @@ def get_model(
config_dict, _ = PretrainedConfig.get_config_dict(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
model_type = config_dict.get("model_type", None)
use_medusa = None
speculator = None
if "medusa_num_heads" in config_dict:
medusa_model_id = model_id
medusa_revision = revision
......@@ -156,6 +318,8 @@ def get_model(
config_dict, _ = PretrainedConfig.get_config_dict(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
# Reload model type from parent.
model_type = config_dict.get("model_type", None)
is_local = Path(medusa_model_id).exists()
if not is_local:
medusa_config = hf_hub_download(
......@@ -166,11 +330,70 @@ def get_model(
revision=medusa_revision,
filename="medusa_lm_head.safetensors",
)
use_medusa = Path(medusa_config).parent
speculator = {
"path": Path(medusa_config).parent,
"model_paths": ["medusa_lm_head.safetensors"],
}
else:
use_medusa = Path(medusa_model_id)
speculator = {
"path": Path(medusa_model_id),
"model_paths": ["medusa_lm_head.safetensors"],
}
method = "medusa"
elif model_type == "mlp_speculator":
mlp_model_id = model_id
mlp_revision = revision
model_id = config_dict["base_model_name_or_path"]
revision = "main"
speculate_mlp = config_dict["n_predict"]
if speculate is not None:
if speculate > speculate_mlp:
raise RuntimeError(
f"Speculate is set to `{speculate}` but this mlp_speculator models only has `{speculate_mlp}` heads, please make them match"
)
else:
set_speculate(speculate)
else:
set_speculate(speculate_mlp)
config_dict, _ = PretrainedConfig.get_config_dict(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
# Reload model type from parent.
model_type = config_dict.get("model_type", None)
is_local = Path(mlp_model_id).exists()
extension = ".safetensors"
if not is_local:
mlp_speculator_config = hf_hub_download(
mlp_model_id, revision=mlp_revision, filename="config.json"
)
api = HfApi()
info = api.model_info(mlp_model_id, revision=mlp_revision)
filenames = [
s.rfilename
for s in info.siblings
if s.rfilename.endswith(extension)
and len(s.rfilename.split("/")) == 1
and "arguments" not in s.rfilename
and "args" not in s.rfilename
and "training" not in s.rfilename
]
for filename in filenames:
hf_hub_download(
mlp_model_id,
revision=mlp_revision,
filename=filename,
)
speculator = {
"path": Path(mlp_speculator_config).parent,
"model_paths": filenames,
}
else:
speculator = Path(mlp_model_id)
filenames = [p for p in os.listdir(speculator) if p.endswith(extension)]
speculator = {"path": speculator, "model_paths": filenames}
method = "mlp_speculator"
else:
method = "n-gram"
......@@ -178,7 +401,6 @@ def get_model(
if speculate > 0:
logger.info(f"Using speculation {method} with {speculate} input ids.")
model_type = config_dict.get("model_type", None)
if model_type is None:
# TODO: fix how we determine model type for Mamba
if "ssm_cfg" in config_dict:
......@@ -191,18 +413,33 @@ def get_model(
quantization_config = config_dict.get("quantization_config", None)
if quantization_config is not None and quantize is None:
method = quantization_config.get("quant_method", None)
if method in {"gptq", "awq"}:
if method in {"gptq", "awq", "exl2"}:
logger.info(f"Auto selecting quantization method {method}")
quantize = method
else:
logger.info(f"Unknown quantization method {method}")
if model_type == "ssm":
if quantize == "exl2" and sharded:
raise RuntimeError(
"Sharding is currently not supported with `exl2` quantization"
)
sliding_window = config_dict.get("sliding_window", -1)
if (
(sliding_window is not None and sliding_window != -1)
and not SUPPORTS_WINDOWING
and max_input_tokens > sliding_window
):
raise ValueError(
f"The backend {SYSTEM} does not support sliding window attention that is used by the model type {model_type}. To use this model nonetheless with the {SYSTEM} backend, please launch TGI with the argument `--max-input-tokens` smaller than sliding_window={sliding_window} (got here max_input_tokens={max_input_tokens})."
)
if model_type == MAMBA:
return Mamba(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -212,14 +449,14 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if (
model_type == "gpt_bigcode"
or model_type == "gpt2"
model_type == GPT_BIGCODE
or model_type == GPT2
and model_id.startswith("bigcode/")
):
if FLASH_ATTENTION:
......@@ -227,7 +464,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -240,37 +477,69 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "bloom":
if model_type == BLOOM:
return BLOOMSharded(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "mpt":
elif model_type == MPT:
return MPTSharded(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "gpt_neox":
elif model_type == GPT2:
if FLASH_ATTENTION:
try:
return FlashGPT2(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
except RuntimeError as e:
# Lots of legacy models with various weight names.
logger.warning(f"Couldn't load flash gpt2 variant: {e}")
return CausalLM(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded GPT-2"))
else:
return CausalLM(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == GPT_NEOX:
if FLASH_ATTENTION:
return FlashNeoXSharded(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -279,7 +548,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -288,18 +557,18 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "phi":
elif model_type == PHI:
if FLASH_ATTENTION:
return FlashPhi(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -308,7 +577,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -323,20 +592,21 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "llama" or model_type == "baichuan" or model_type == "phi3":
elif model_type == LLAMA or model_type == BAICHUAN or model_type == PHI3:
if FLASH_ATTENTION:
return FlashLlama(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
lora_adapter_ids=lora_adapter_ids,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
......@@ -345,17 +615,17 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "gemma":
if model_type == GEMMA:
if FLASH_ATTENTION:
return FlashGemma(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -366,18 +636,39 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == GEMMA2:
if FLASH_ATTENTION:
return FlashGemma2(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma2"))
else:
return CausalLM(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "cohere":
if model_type == COHERE:
if FLASH_ATTENTION:
return FlashCohere(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -388,18 +679,18 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "dbrx":
if model_type == DBRX:
if FLASH_ATTENTION:
return FlashDbrx(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -410,12 +701,12 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]:
if model_type in ["RefinedWeb", "RefinedWebModel", FALCON]:
if sharded:
if FLASH_ATTENTION:
if config_dict.get("alibi", False):
......@@ -424,7 +715,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -435,7 +726,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -444,21 +735,18 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "mistral":
sliding_window = config_dict.get("sliding_window", -1)
if (
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
) or HAS_FLASH_ATTN_V2_CUDA:
if model_type == MISTRAL:
if FLASH_ATTENTION:
return FlashMistral(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -469,21 +757,18 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "mixtral":
sliding_window = config_dict.get("sliding_window", -1)
if (
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
) or HAS_FLASH_ATTN_V2_CUDA:
if model_type == MIXTRAL:
if FLASH_ATTENTION:
return FlashMixtral(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -494,16 +779,13 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "starcoder2":
sliding_window = config_dict.get("sliding_window", -1)
if (
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
) or HAS_FLASH_ATTN_V2_CUDA:
if model_type == STARCODER2:
if FLASH_ATTENTION:
return FlashStarcoder2(
model_id,
revision,
......@@ -520,18 +802,13 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "qwen2":
sliding_window = config_dict.get("sliding_window", -1)
use_sliding_window = config_dict.get("use_sliding_window", False)
sliding_window = sliding_window if use_sliding_window else None
if (
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
) or HAS_FLASH_ATTN_V2_CUDA:
if model_type == QWEN2:
if FLASH_ATTENTION:
return FlashQwen2(
model_id,
revision,
......@@ -546,62 +823,74 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "opt":
if model_type == OPT:
return OPTSharded(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "t5":
if model_type == T5:
return T5Sharded(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "idefics":
if model_type == IDEFICS:
if FLASH_ATTENTION:
return IDEFICSSharded(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == "idefics2":
if model_type == IDEFICS2:
if FLASH_ATTENTION:
return Idefics2(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == "paligemma":
if FLASH_ATTENTION:
return PaliGemma(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == "llava_next":
if model_type == LLAVA_NEXT:
if FLASH_ATTENTION:
return LlavaNext(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -620,12 +909,14 @@ def get_model(
raise NotImplementedError("4bit quantization is not supported for AutoModel")
elif quantize == "eetq":
raise NotImplementedError("Eetq quantization is not supported for AutoModel")
elif quantize == "exl2":
raise NotImplementedError("exl2 quantization is not supported for AutoModel")
if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
return CausalLM(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -634,7 +925,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -646,7 +937,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......@@ -655,7 +946,7 @@ def get_model(
model_id,
revision,
quantize=quantize,
use_medusa=use_medusa,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
......
server/text_generation_server/models/bloom.py
View file @ efd602c8
......@@ -42,10 +42,11 @@ class BLOOMSharded(CausalLM):
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
use_medusa: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
......@@ -71,7 +72,7 @@ class BLOOMSharded(CausalLM):
)
config.pad_token_id = 3
config.quantize = quantize
config.use_medusa = use_medusa
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
......@@ -82,13 +83,14 @@ class BLOOMSharded(CausalLM):
process_group=self.process_group,
prefix="transformer",
)
if config.quantize == "gptq":
if config.quantize in ["gptq", "marlin"]:
weights._set_gptq_params(model_id, revision)
model = BloomForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
......
server/text_generation_server/models/causal_lm.py
View file @ efd602c8
......@@ -7,6 +7,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenize
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.utils.chunks import concat_text_chunks
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.models.types import (
Batch,
......@@ -86,7 +87,8 @@ class CausalLMBatch(Batch):
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
inputs.append(r.inputs)
inputs.append(concat_text_chunks(r.input_chunks.chunks))
next_token_choosers.append(
NextTokenChooser.from_pb(r.parameters, device, tokenizer)
)
......@@ -482,12 +484,12 @@ class CausalLM(Model):
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
use_medusa: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if use_medusa:
raise RuntimeError("Medusa decoding is not enabled for AutoModel")
if speculator:
raise RuntimeError("Speculator decoding is not enabled for AutoModel")
if torch.cuda.is_available():
device = torch.device("cuda")
......@@ -536,6 +538,7 @@ class CausalLM(Model):
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
......
server/text_generation_server/models/custom_modeling/bloom_modeling.py
View file @ efd602c8
......@@ -32,7 +32,7 @@ from transformers.modeling_outputs import (
)
from transformers import BloomConfig, PreTrainedModel
from text_generation_server.utils.layers import (
from text_generation_server.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
......
server/text_generation_server/models/custom_modeling/clip.py
View file @ efd602c8
......@@ -15,7 +15,7 @@ from transformers.modeling_outputs import (
)
from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
from text_generation_server.utils.layers import (
from text_generation_server.layers import (
TensorParallelEmbedding,
TensorParallelColumnLinear,
TensorParallelRowLinear,
......
server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
View file @ efd602c8
......@@ -25,19 +25,28 @@ from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.import_utils import IS_ROCM_SYSTEM, IS_CUDA_SYSTEM
from text_generation_server.utils.layers import (
from text_generation_server.layers.attention import (
paged_attention,
attention,
reshape_and_cache,
)
from text_generation_server.models.globals import FLASH_DECODING
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
PositionRotaryEmbedding,
SpeculativeHead,
get_linear,
)
from text_generation_server.layers.layernorm import (
FastLayerNorm,
)
from text_generation_server.layers.rotary import (
PositionRotaryEmbedding,
)
if IS_CUDA_SYSTEM:
if SYSTEM == "cuda":
import dropout_layer_norm
else:
dropout_layer_norm = None
......@@ -52,7 +61,7 @@ class CohereRotary(PositionRotaryEmbedding):
sin: torch.Tensor,
):
# Such controlflows may add some overhead.
if IS_CUDA_SYSTEM:
if SYSTEM == "cuda":
import rotary_emb
q1 = query[..., ::2]
......@@ -64,8 +73,8 @@ class CohereRotary(PositionRotaryEmbedding):
k2 = key[..., 1::2]
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
elif IS_ROCM_SYSTEM:
from vllm import pos_encoding_ops
elif SYSTEM == "rocm":
from vllm._C import ops
# 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
......@@ -73,7 +82,7 @@ class CohereRotary(PositionRotaryEmbedding):
head_size = query.shape[-1]
# Inplace operation, updating query and key.
pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, False)
ops.rotary_embedding(query, key, head_size, cos, sin, False)
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."
......@@ -90,7 +99,7 @@ class CohereLayerNorm(nn.Module):
self.eps = eps
def forward(self, hidden_states):
if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM:
if hidden_states.shape[-1] > 8192 or SYSTEM == "rocm":
hidden_states = hidden_states.reshape(
-1, self.weight.shape[0], self.weight.shape[1]
)
......@@ -158,7 +167,7 @@ def _load_gqa(config, prefix: str, weights):
dim=0,
)
if config.quantize not in ["gptq", "awq"]:
if config.quantize not in ["gptq", "awq", "marlin"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.hidden_size // config.num_attention_heads
......@@ -251,8 +260,8 @@ class FlashCohereAttention(torch.nn.Module):
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
slots,
max_s,
):
qkv = self.query_key_value(hidden_states)
......@@ -277,7 +286,7 @@ class FlashCohereAttention(torch.nn.Module):
self.rotary_emb(query, key, cos, sin)
paged_attention.reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
# output tensor
attn_output = torch.empty_like(query)
......@@ -285,7 +294,7 @@ class FlashCohereAttention(torch.nn.Module):
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
attention(
query,
key,
value,
......@@ -296,7 +305,7 @@ class FlashCohereAttention(torch.nn.Module):
)
# Decode
else:
paged_attention.attention(
attn_output = paged_attention(
attn_output,
query,
kv_cache[0],
......@@ -456,6 +465,7 @@ class FlashCohereModel(torch.nn.Module):
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
......@@ -504,7 +514,9 @@ class FlashCohereForCausalLM(torch.nn.Module):
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
hidden_states = self.model(
input_ids,
......
server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
View file @ efd602c8
......@@ -20,22 +20,30 @@ from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple, Any
from loguru import logger
from text_generation_server.utils.import_utils import IS_XPU_SYSTEM
from text_generation_server.utils.import_utils import SYSTEM
if not IS_XPU_SYSTEM:
if SYSTEM != "ipex":
from vllm.model_executor.layers.fused_moe import fused_moe
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
from text_generation_server.layers.attention import (
paged_attention,
attention,
reshape_and_cache,
)
from text_generation_server.layers import (
FastLinear,
FastLayerNorm,
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
PositionRotaryEmbedding,
SpeculativeHead,
get_linear,
)
from text_generation_server.layers.rotary import (
PositionRotaryEmbedding,
)
from text_generation_server.layers.layernorm import (
FastLayerNorm,
)
from text_generation_server.utils.log import log_once
......@@ -155,114 +163,13 @@ def promote_scalar(x: torch.Tensor) -> torch.Tensor:
def load_attention(config, prefix, weights):
if config.n_heads != config.attn_config.kv_n_heads:
return _load_gqa(config, prefix, weights)
else:
return TensorParallelColumnLinear.load_qkv(
config,
prefix=f"{prefix}.Wqkv",
weights=weights,
bias=False,
)
def _load_gqa(config, prefix: str, weights):
assert config.d_model % config.n_heads == 0
assert config.n_heads % weights.process_group.size() == 0
head_dim = config.d_model // config.n_heads
world_size = weights.process_group.size()
rank = weights.process_group.rank()
q_block_size = config.d_model // world_size
q_start = rank * q_block_size
q_stop = (rank + 1) * q_block_size
kv_block_size = (config.attn_config.kv_n_heads * head_dim) // world_size
k_offset = config.d_model
k_start = k_offset + rank * kv_block_size
k_stop = k_offset + (rank + 1) * kv_block_size
v_offset = config.d_model + config.attn_config.kv_n_heads * head_dim
v_start = v_offset + rank * kv_block_size
v_stop = v_offset + (rank + 1) * kv_block_size
if config.quantize in ["gptq", "awq"]:
try:
qweight_slice = weights._get_slice(f"{prefix}.qweight")
q_qweight = qweight_slice[:, q_start:q_stop]
k_qweight = qweight_slice[:, k_start:k_stop]
v_qweight = qweight_slice[:, v_start:v_stop]
qweight = torch.cat([q_qweight, k_qweight, v_qweight], dim=1)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{config.quantize}` weight, make sure the model is already quantized"
)
qzeros_slice = weights._get_slice(f"{prefix}.qzeros")
q_qzeros = qzeros_slice[:, q_start:q_stop]
k_qzeros = qzeros_slice[:, k_start:k_stop]
v_qzeros = qzeros_slice[:, v_start:v_stop]
qzeros = torch.cat([q_qzeros, k_qzeros, v_qzeros], dim=1)
scales_slice = weights._get_slice(f"{prefix}.scales")
q_scales = scales_slice[:, q_start:q_stop]
k_scales = scales_slice[:, k_start:k_stop]
v_scales = scales_slice[:, v_start:v_stop]
scales = torch.cat([q_scales, k_scales, v_scales], dim=1)
bits, groupsize, desc_act, quant_method = weights._get_gptq_params()
from text_generation_server.utils.layers import HAS_EXLLAMA
use_exllama = (
bits == 4 and HAS_EXLLAMA and config.quantize == "gptq" and not desc_act
)
if config.quantize == "gptq" and quant_method == "gptq":
g_idx_slice = weights._get_slice(f"{prefix}.g_idx")
q_g_idx = g_idx_slice[:, q_start:q_stop]
k_g_idx = g_idx_slice[:, k_start:k_stop]
v_g_idx = g_idx_slice[:, v_start:v_stop]
w = [q_g_idx, k_g_idx, v_g_idx]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
elif config.quantize == "gptq" and quant_method == "awq":
log_once(
logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
)
from text_generation_server.utils.awq.conversion_utils import (
fast_awq_to_gptq,
)
qweight, qzeros = fast_awq_to_gptq(qweight, qzeros)
if use_exllama:
g_idx = None
else:
g_idx = (
torch.arange(qweight.shape[0] * (32 // bits), device=qweight.device)
// groupsize
).to(dtype=torch.int32)
else:
g_idx = None
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
else:
qkv_slice = weights._get_slice(f"{prefix}.Wqkv.weight")
q = qkv_slice[q_start:q_stop]
k = qkv_slice[k_start:k_stop]
v = qkv_slice[v_start:v_stop]
weight = torch.cat([q, k, v], dim=0)
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
return TensorParallelColumnLinear(
get_linear(weight, bias=None, quantize=config.quantize)
return TensorParallelColumnLinear.load_qkv(
config,
prefix=f"{prefix}.Wqkv",
weights=weights,
bias=False,
num_heads=config.n_heads,
num_key_value_heads=config.attn_config.kv_n_heads,
)
......@@ -410,9 +317,7 @@ class DbrxAttention(torch.nn.Module):
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
paged_attention.reshape_and_cache(
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
)
reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
# output tensor
attn_output = torch.empty_like(query)
......@@ -420,7 +325,7 @@ class DbrxAttention(torch.nn.Module):
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
......@@ -431,7 +336,7 @@ class DbrxAttention(torch.nn.Module):
)
# Decode
else:
paged_attention.attention(
attn_output = paged_attention(
attn_output,
query,
kv_cache[0],
......@@ -817,7 +722,9 @@ class FlashDbrxForCausalLM(torch.nn.Module):
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
hidden_states = self.model(
input_ids,
......
server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py 0 → 100644
View file @ efd602c8
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.layers.attention import (
paged_attention,
attention,
reshape_and_cache,
)
from text_generation_server.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
SpeculativeHead,
get_linear,
)
from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.layers.layernorm import (
FastRMSNorm,
)
class Gemma2Config(PretrainedConfig):
def __init__(
self,
vocab_size=256128,
hidden_size=3072,
intermediate_size=24576,
num_hidden_layers=28,
num_attention_heads=16,
num_key_value_heads=16,
head_dim=256,
hidden_act="gelu_pytorch_tanh",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=True,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.head_dim = head_dim
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class Gemma2FastRMSNorm(FastRMSNorm):
@classmethod
def load(cls, prefix, weights, eps=1e-6):
dtype = weights.dtype
weights.dtype = torch.float32
weight = weights.get_tensor(f"{prefix}.weight") + 1
weights.dtype = dtype
new = cls(weight, eps)
new.dtype = dtype
return new
# perform the multiplication in full precision and downcast after
def forward(self, hidden_states, residual=None):
if residual is not None:
hidden_states += residual
residual = hidden_states
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * self.weight
return hidden_states.to(self.dtype), residual
def load_attention(config, prefix, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
return TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
def _load_gqa(config, prefix: str, weights):
assert config.num_attention_heads % weights.process_group.size() == 0
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
if config.quantize not in ["gptq", "awq", "marlin"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.head_dim
num_heads = config.num_attention_heads // weights.process_group.size()
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
assert list(weight.shape) == [
(num_heads + 2 * num_key_value_heads) * head_size,
config.hidden_size,
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
return TensorParallelColumnLinear(
get_linear(weight, bias=None, quantize=config.quantize)
)
class FlashGemma2Attention(torch.nn.Module):
def __init__(self, prefix: str, config, weights, causal: bool, is_sliding: bool):
super().__init__()
self.num_heads = config.num_attention_heads
self.head_size = config.head_dim
self.causal = causal
if is_sliding:
self.window_size = config.sliding_window
else:
self.window_size = -1
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
# self.softmax_scale = self.head_size**-0.5
self.softmax_scale = config.query_pre_attn_scalar**-0.5
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.num_key_value_heads = (
config.num_key_value_heads // weights.process_group.size()
)
self.query_key_value = load_attention(config, prefix, weights)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
self.num_groups = self.num_heads // self.num_key_value_heads
self.kv_head_mapping = torch.arange(
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
).repeat_interleave(self.num_groups)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.query_key_value(hidden_states)
query, kv = qkv.split(
[
self.head_size * self.num_heads,
2 * self.head_size * self.num_key_value_heads,
],
dim=1,
)
query = query.view(-1, self.num_heads, self.head_size)
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
# output tensor
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
causal=self.causal,
window_size_left=self.window_size,
)
# Decode
else:
attn_output = paged_attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
class Gemma2MLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.hidden_act
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate=(
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
),
)
)
# Fuse gate and up proj
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.intermediate_size = (
config.intermediate_size // weights.process_group.size()
)
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
class FlashGemma2Layer(nn.Module):
def __init__(self, prefix, config, weights, causal: bool, is_sliding: bool):
super().__init__()
self.self_attn = FlashGemma2Attention(
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
causal=causal,
is_sliding=is_sliding,
)
self.mlp = Gemma2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.pre_feedforward_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.pre_feedforward_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.post_feedforward_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.post_feedforward_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
# Self Attention
attn_output = self.self_attn(
normed_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
# faster post attention rms norm
normed_attn_res_output, _ = self.post_attention_layernorm(attn_output)
normed_attn_res_output = normed_attn_res_output + res
res = normed_attn_res_output
pre_normed, _ = self.pre_feedforward_layernorm(normed_attn_res_output)
mlp_output = self.mlp(pre_normed)
post_hidden_states, _ = self.post_feedforward_layernorm(mlp_output)
return post_hidden_states, normed_attn_res_output
class FlashGemma2Model(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.layers = nn.ModuleList(
[
FlashGemma2Layer(
prefix=f"{prefix}.layers.{layer_id}",
config=config,
weights=weights,
causal=causal,
is_sliding=layer_id % 2 == 0,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
)
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
def forward(
self,
inputs_embeds: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = inputs_embeds
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
position_ids, max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class FlashGemma2ForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
embed_norm = config.hidden_size**0.5
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.embed_tokens.weight *= embed_norm
self.model = FlashGemma2Model(
prefix=prefix, config=config, weights=weights, causal=causal
)
self.lm_head = SpeculativeHead.load(
prefix=(
f"{prefix}.embed_tokens"
if config.tie_word_embeddings
else f"{prefix}.lm_head"
),
config=config,
weights=weights,
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
input_embeds = self.embed_tokens(input_ids)
hidden_states = self.model(
input_embeds,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits, speculative_logits = self.lm_head(hidden_states)
return logits, speculative_logits
server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View file @ efd602c8
......@@ -26,14 +26,20 @@ from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
from text_generation_server.layers.attention import (
paged_attention,
attention,
reshape_and_cache,
)
from text_generation_server.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
PositionRotaryEmbedding,
SpeculativeHead,
get_linear,
)
from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.layers.layernorm import (
FastRMSNorm,
)
......@@ -97,8 +103,13 @@ class GemmaConfig(PretrainedConfig):
class GemmaFastRMSNorm(FastRMSNorm):
@classmethod
def load(cls, prefix, weights, eps=1e-6):
dtype = weights.dtype
weights.dtype = torch.float32
weight = weights.get_tensor(f"{prefix}.weight") + 1
return cls(weight, eps)
weights.dtype = dtype
new = cls(weight, eps)
new.dtype = dtype
return new
# perform the multiplication in full precision and downcast after
def forward(self, hidden_states, residual=None):
......@@ -109,7 +120,7 @@ class GemmaFastRMSNorm(FastRMSNorm):
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * self.weight
return hidden_states.to(self.weight.dtype), residual
return hidden_states.to(self.dtype), residual
def load_attention(config, prefix, weights):
......@@ -134,7 +145,7 @@ def _load_gqa(config, prefix: str, weights):
dim=0,
)
if config.quantize not in ["gptq", "awq"]:
if config.quantize not in ["gptq", "awq", "marlin"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.head_dim
......@@ -151,15 +162,11 @@ def _load_gqa(config, prefix: str, weights):
class FlashGemmaAttention(torch.nn.Module):
def __init__(
self,
prefix: str,
config,
weights,
):
def __init__(self, prefix: str, config, weights, causal: bool):
super().__init__()
self.num_heads = config.num_attention_heads
self.head_size = config.head_dim
self.causal = causal
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
......@@ -218,9 +225,7 @@ class FlashGemmaAttention(torch.nn.Module):
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
paged_attention.reshape_and_cache(
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
)
reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
# output tensor
attn_output = torch.empty_like(query)
......@@ -228,7 +233,7 @@ class FlashGemmaAttention(torch.nn.Module):
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
......@@ -236,10 +241,11 @@ class FlashGemmaAttention(torch.nn.Module):
cu_seqlen_prefill,
max_s,
self.softmax_scale,
causal=self.causal,
)
# Decode
else:
paged_attention.attention(
attn_output = paged_attention(
attn_output,
query,
kv_cache[0],
......@@ -293,11 +299,10 @@ class GemmaMLP(nn.Module):
class FlashGemmaLayer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
prefix = f"model.layers.{layer_id}"
self.self_attn = FlashGemmaAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
prefix=f"{prefix}.self_attn", config=config, weights=weights, causal=causal
)
self.mlp = GemmaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
......@@ -349,41 +354,34 @@ class FlashGemmaLayer(nn.Module):
class FlashGemmaModel(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
embed_norm = config.hidden_size**0.5
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
)
self.embed_tokens.weight *= embed_norm
self.layers = nn.ModuleList(
[
FlashGemmaLayer(
layer_id,
config,
weights,
prefix=f"{prefix}.layers.{layer_id}",
config=config,
weights=weights,
causal=causal,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = GemmaFastRMSNorm.load(
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
)
self.gradient_checkpointing = False
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
def forward(
self,
input_ids: torch.Tensor,
inputs_embeds: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
......@@ -392,7 +390,7 @@ class FlashGemmaModel(torch.nn.Module):
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
# Get rotary cos and sin for this forward
# Avoid to index in each layer
......@@ -421,13 +419,30 @@ class FlashGemmaModel(torch.nn.Module):
class FlashGemmaForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
self.model = FlashGemmaModel(config, weights)
embed_norm = config.hidden_size**0.5
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.embed_tokens.weight *= embed_norm
self.model = FlashGemmaModel(
prefix=prefix, config=config, weights=weights, causal=causal
)
self.lm_head = SpeculativeHead.load(
config,
prefix="model.embed_tokens" if config.tie_word_embeddings else "lm_head",
prefix=(
f"{prefix}.embed_tokens"
if config.tie_word_embeddings
else f"{prefix}.lm_head"
),
config=config,
weights=weights,
)
......@@ -441,10 +456,13 @@ class FlashGemmaForCausalLM(torch.nn.Module):
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
input_embeds = self.embed_tokens(input_ids)
hidden_states = self.model(
input_ids,
input_embeds,
position_ids,
cu_seqlen_prefill,
kv_cache,
......
server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py 0 → 100644
View file @ efd602c8
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple
from text_generation_server.layers.attention import (
paged_attention,
attention,
reshape_and_cache,
)
from text_generation_server.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
SpeculativeHead,
get_linear,
)
def load_qkv(config, prefix: str, weights, head_size, num_heads):
if config.quantize == "gptq":
return _load_qkv_gptq(
config,
prefix,
weights,
)
elif config.quantize == "marlin":
raise RuntimeError(
"GPT-2 models with marlin quantization are not yet supported"
)
else:
return _load_qkv(config, prefix, weights, head_size, num_heads)
def _load_qkv_gptq(config, prefix: str, weights):
world_size = weights.process_group.size()
rank = weights.process_group.rank()
# Weights
weight = weights.get_weights_col_packed_qkv(
f"{prefix}.c_attn",
config.quantize,
config.num_attention_heads,
config.num_attention_heads,
)
# Bias
slice_ = weights._get_slice(f"{prefix}.c_attn.bias")
shape = slice_.get_shape()
total_size = shape[0]
assert total_size % 3 == 0, f"Prepacked is not divisible by {3}"
single_size = total_size // 3
assert single_size % world_size == 0
block_size = single_size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
tensors = []
for i in range(3):
tensor = slice_[start + i * single_size : stop + i * single_size]
tensors.append(tensor)
bias = torch.cat(tensors, dim=0)
bias = bias.to(device=weights.device)
return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
def _load_qkv(config, prefix: str, weights, head_size, num_heads):
"""Load QKV from a single, transposed matrix."""
slice_ = weights._get_slice(f"{prefix}.c_attn.weight")
shape = slice_.get_shape()
total_size = shape[1]
assert total_size % 3 == 0, f"Prepacked is not divisible by {3}"
world_size = weights.process_group.size()
single_size = total_size // 3
assert single_size % world_size == 0
rank = weights.process_group.rank()
# Weights
block_size = single_size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
tensors = []
for i in range(3):
tensor = slice_[:, start + i * single_size : stop + i * single_size]
tensors.append(tensor)
weight = torch.cat(tensors, dim=1).T
weight = weight.to(dtype=weights.dtype)
weight = weight.to(device=weights.device)
# Bias
slice_ = weights._get_slice(f"{prefix}.c_attn.bias")
shape = slice_.get_shape()
total_size = shape[0]
single_size = total_size // 3
block_size = single_size // world_size
assert single_size % world_size == 0
start = rank * block_size
stop = (rank + 1) * block_size
b = []
for i in range(3):
tensor = slice_[start + i * single_size : stop + i * single_size]
b.append(tensor)
bias = torch.cat(b, dim=0)
bias = bias.to(dtype=weights.dtype)
bias = bias.to(device=weights.device)
assert list(bias.shape) == [
3 * num_heads * head_size
], f"{weight.shape} != {[3 * num_heads * head_size]}"
return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
def load_row(config, prefix: str, weights, bias: bool):
"""load_row, but with transposed weight matrices."""
if config.quantize == "gptq":
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
else:
weight = weights.get_sharded(f"{prefix}.weight", dim=0).T
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return TensorParallelRowLinear(
get_linear(weight, bias, config.quantize), process_group=weights.process_group
)
def load_col(config, prefix: str, weights, bias: bool):
"""load_col, but with transposed weight matrices."""
if config.quantize == "gptq":
weight = weights.get_multi_weights_col(
[prefix], quantize=config.quantize, dim=1
)
else:
weight = weights.get_sharded(f"{prefix}.weight", dim=1).T
if bias:
bias = weights.get_sharded(f"{prefix}.bias", dim=0)
else:
bias = None
return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
class FlashGPT2Attention(torch.nn.Module):
def __init__(
self,
prefix: str,
config,
weights,
):
super().__init__()
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.softmax_scale = self.head_size**-0.5
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.query_key_value = load_qkv(
config,
prefix=prefix,
weights=weights,
head_size=self.head_size,
num_heads=self.num_heads,
)
self.o_proj = load_row(
config,
prefix=f"{prefix}.c_proj",
weights=weights,
bias=True,
)
self.kv_head_mapping = torch.arange(
0, self.num_heads, dtype=torch.int32, device=weights.device
)
def forward(
self,
hidden_states,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
query, key, value = self.query_key_value(hidden_states).split(
self.head_size * self.num_heads, dim=1
)
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_heads, self.head_size)
value = value.view(-1, self.num_heads, self.head_size)
reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
# output tensor
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
attention(
query,
key,
value,
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
)
# Decode
else:
attn_output = paged_attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
class GPT2MLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.activation_function
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate=(
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
),
)
)
self.c_fc = load_col(
config, prefix=f"{prefix}.c_fc", weights=weights, bias=True
)
self.c_proj = load_row(
config,
prefix=f"{prefix}.c_proj",
weights=weights,
bias=True,
)
intermediate_size = (
config.n_inner if config.n_inner is not None else 4 * config.hidden_size
)
self.intermediate_size = intermediate_size // weights.process_group.size()
def forward(self, hidden_states):
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
return self.c_proj(hidden_states)
class FlashGPT2Layer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.self_attn = FlashGPT2Attention(
prefix=f"{prefix}.attn", config=config, weights=weights
)
self.mlp = GPT2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.ln_1", weights=weights, eps=config.layer_norm_epsilon
)
self.post_attention_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.ln_2",
weights=weights,
eps=config.layer_norm_epsilon,
)
def forward(
self,
hidden_states,
residual,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
attn_output = self.self_attn(
hidden_states,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states = attn_output + residual
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
mlp_output = self.mlp(hidden_states)
return residual + mlp_output, residual
class FlashGPT2Model(torch.nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.layers = nn.ModuleList(
[
FlashGPT2Layer(
prefix=(
f"h.{layer_id}" if not prefix else f"{prefix}.h.{layer_id}"
),
config=config,
weights=weights,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = nn.LayerNorm.load(
prefix="ln_f" if not prefix else f"{prefix}.ln_f",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.gradient_checkpointing = False
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
def forward(
self,
inputs_embeds: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
true_max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
) -> torch.Tensor:
hidden_states = inputs_embeds
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states = self.norm(hidden_states)
return hidden_states
class FlashGPT2ForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.embed_tokens = TensorParallelEmbedding(
prefix=("wte" if not prefix else f"{prefix}.wte"),
weights=weights,
)
self.embed_positions = TensorParallelEmbedding(
prefix=("wpe" if not prefix else f"{prefix}.wpe"),
weights=weights,
)
self.model = FlashGPT2Model(prefix, config, weights)
self.lm_head = SpeculativeHead.load(
config,
prefix="wte" if not prefix else f"{prefix}.wte",
weights=weights,
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor] = None,
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
token_embeds = self.embed_tokens(input_ids)
position_embeds = self.embed_positions(position_ids)
inputs_embeds = token_embeds + position_embeds
hidden_states = self.model(
inputs_embeds,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
true_max_s=max_s,
prefill_cache_indices=prefill_cache_indices,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits, speculative_logits = self.lm_head(hidden_states)
return logits, speculative_logits
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