tocmo0nlord/axolotl
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Phi2 multipack (#1173)

Browse Source 
* phi2 multipack

* update validation and examples for phi

* more updates to phi examples

* make sure to use the correct collator for phi multipack

* phi needs attention mask now for multipack

* if the special token already exists in the tokenizer, don't require in lora modules to save

* fix qlora yml for phi, fix phi test validation

* test qlora too

* make sure flash attention is enabled for the test

* don't use remote code for phi anymore

* reduce sequence len for sample packing phi
This commit is contained in:
Wing Lian
2024-01-23 12:54:36 -05:00
committed by GitHub
parent b715cd549a
commit 814aee6603
18 changed files with 201 additions and 2269 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
examples/phi/phi-ft.yml
View File

@@ -1,8 +1,6 @@
base_model: microsoft/phi-1_5
model_type: PhiForCausalLM
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: false
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
@@ -18,7 +16,7 @@ output_dir: ./phi-sft-out
sequence_len: 2048
sample_packing: true
pad_to_sequence_len:
pad_to_sequence_len: true
adapter:
lora_model_dir:
@@ -35,7 +33,7 @@ wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_torch
adam_beta2: 0.95
@@ -45,18 +43,20 @@ lr_scheduler: cosine
learning_rate: 0.000003
train_on_inputs: false
group_by_length: true
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing:
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 4
@@ -68,7 +68,4 @@ fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
bos_token: "<|endoftext|>"
eos_token: "<|endoftext|>"
unk_token: "<|endoftext|>"
pad_token: "<|endoftext|>"

21
examples/phi/phi-qlora.yml
View File

@@ -1,8 +1,6 @@
base_model: microsoft/phi-1_5
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: false
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
@@ -16,9 +14,9 @@ dataset_prepared_path:
val_set_size: 0.05
output_dir: ./phi-sft-out
sequence_len: 1024
sample_packing: false # not CURRENTLY compatible with LoRAs
pad_to_sequence_len:
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
adapter: qlora
lora_model_dir:
@@ -35,7 +33,7 @@ wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_torch
adam_beta2: 0.95
@@ -45,18 +43,20 @@ lr_scheduler: cosine
learning_rate: 0.000003
train_on_inputs: false
group_by_length: true
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing:
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 4
@@ -68,7 +68,4 @@ fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
bos_token: "<|endoftext|>"
eos_token: "<|endoftext|>"
unk_token: "<|endoftext|>"
pad_token: "<|endoftext|>"

25
examples/phi/phi2-ft.yml
View File

@@ -1,8 +1,6 @@
base_model: microsoft/phi-2
model_revision: 834565c # pin model repo to the previous architecture
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
@@ -17,19 +15,16 @@ val_set_size: 0.05
output_dir: ./phi-sft-out
sequence_len: 2048
sample_packing: false # currently unsupported
pad_to_sequence_len:
sample_packing: true
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
lora_target_linear: true
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
lora_modules_to_save:
- embd
- lm_head
wandb_project:
wandb_entity:
@@ -38,14 +33,14 @@ wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_8bit
optimizer: adamw_torch
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 1e-5
learning_rate: 0.000003
train_on_inputs: false
group_by_length: false
@@ -54,6 +49,8 @@ fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:

2
src/axolotl/core/trainer_builder.py
View File

@@ -930,7 +930,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
]
]
if use_batch_sampler_collator:
if self.cfg.model_config_type in ["mixtral", "qwen2"]:
if self.cfg.model_config_type in ["mixtral", "qwen2", "falcon", "phi"]:
collator = V2BatchSamplerDataCollatorForSeq2Seq
else:
collator = BatchSamplerDataCollatorForSeq2Seq

8
src/axolotl/models/phi/__init__.py
View File

@@ -1,8 +0,0 @@
"""
MixFormers model architecture used for phi models
"""
from .configuration_mixformer_sequential import MixFormerSequentialConfig # noqa
from .configuration_phi import PhiConfig # noqa
from .modeling_mixformer_sequential import MixFormerSequentialForCausalLM # noqa
from .modeling_phi import PhiForCausalLM # noqa

63
src/axolotl/models/phi/configuration_mixformer_sequential.py
View File

@@ -1,63 +0,0 @@
# pylint: skip-file
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import math
from typing import Any, Dict, List, Optional, Union
from transformers import PretrainedConfig
class MixFormerSequentialConfig(PretrainedConfig):
"""MixFormer (sequential for DeepSpeed) configuration."""
model_type = "mixformer-sequential"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
"input_emb_layer": "embd_layer", # `input_emb_layer` key is for backward compatibility
"blocks": "architecture", # `blocks` key is for backward compatibility
}
def __init__(
self,
vocab_size: Optional[int] = 50304,
n_positions: Optional[int] = 2048,
n_embd: Optional[int] = 1024,
n_layer: Optional[int] = 20,
n_inner: Optional[int] = None,
n_head: Optional[int] = 16,
rotary_dim: Optional[int] = 32,
activation_function: Optional[str] = "gelu_new",
embd_layer: Optional[str] = "default",
architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
embd_pdrop: Optional[float] = 0.0,
resid_pdrop: Optional[float] = 0.0,
layer_norm_epsilon: Optional[float] = 1e-5,
initializer_range: Optional[float] = 0.02,
tie_word_embeddings: Optional[bool] = False,
pad_vocab_size_multiple: Optional[int] = 64,
**kwargs
) -> None:
self.vocab_size = int(
math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
)
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_inner = n_inner
self.n_head = n_head
self.rotary_dim = min(rotary_dim, n_embd // n_head)
self.activation_function = activation_function
self.embd_layer = embd_layer
self.architecture = architecture
self.embd_pdrop = embd_pdrop
self.resid_pdrop = resid_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

65
src/axolotl/models/phi/configuration_phi.py
View File

@@ -1,65 +0,0 @@
# pylint: skip-file
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import math
from typing import Optional
from transformers import PretrainedConfig
class PhiConfig(PretrainedConfig):
"""Phi configuration."""
model_type = "phi"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size: int = 50304,
n_positions: int = 2048,
n_embd: int = 1024,
n_layer: int = 20,
n_inner: Optional[int] = None,
n_head: int = 16,
n_head_kv: Optional[int] = None,
rotary_dim: Optional[int] = 32,
activation_function: Optional[str] = "gelu_new",
flash_attn: bool = False,
flash_rotary: bool = False,
fused_dense: bool = False,
attn_pdrop: float = 0.0,
embd_pdrop: float = 0.0,
resid_pdrop: float = 0.0,
layer_norm_epsilon: float = 1e-5,
initializer_range: float = 0.02,
tie_word_embeddings: bool = False,
pad_vocab_size_multiple: int = 64,
**kwargs
) -> None:
self.vocab_size = int(
math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
)
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_inner = n_inner
self.n_head = n_head
self.n_head_kv = n_head_kv
self.rotary_dim = min(rotary_dim, n_embd // n_head)
self.activation_function = activation_function
self.flash_attn = flash_attn
self.flash_rotary = flash_rotary
self.fused_dense = fused_dense
self.attn_pdrop = attn_pdrop
self.embd_pdrop = embd_pdrop
self.resid_pdrop = resid_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

930
src/axolotl/models/phi/modeling_mixformer_sequential.py
View File

@@ -1,930 +0,0 @@
# pylint: skip-file
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# BSD 3-Clause License
#
# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import annotations
import copy
import inspect
from dataclasses import dataclass, field
from typing import Any, Dict, Optional, Tuple
import torch
import torch.nn as nn
from einops import rearrange
from flash_attn.flash_attn_interface import (
flash_attn_kvpacked_func,
flash_attn_qkvpacked_func,
flash_attn_varlen_qkvpacked_func,
)
from transformers import PretrainedConfig, PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import CausalLMOutputWithPast
from ...monkeypatch.utils import get_cu_seqlens_from_pos_ids
from .configuration_mixformer_sequential import MixFormerSequentialConfig
@dataclass
class InferenceParams:
"""Inference parameters that are passed to the main model in order
to efficienly calculate and store the context during inference.
Adapted from https://github.com/Dao-AILab/flash-attention."""
max_sequence_len: int
max_batch_size: int
sequence_len_offset: int = 0
batch_size_offset: int = 0
key_value_memory_dict: dict = field(default_factory=dict)
fused_ft_kernel: bool = False
lengths_per_sample: Optional[torch.Tensor] = None
class Embedding(nn.Module):
"""Token embedding with dropout."""
def __init__(self, config: PretrainedConfig) -> None:
super().__init__()
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.wte(input_ids)
hidden_states = self.drop(hidden_states)
return hidden_states
class RotaryEmbedding(nn.Module):
"""PyTorch implementation of `flash-attn` RotaryEmbedding layer.
Adapted from https://github.com/Dao-AILab/flash-attention."""
def __init__(
self,
dim: int,
base: Optional[int] = 10000,
scale_base: Optional[float] = None,
device: Optional[str] = None,
**kwargs,
) -> None:
super().__init__()
if scale_base is not None:
raise NotImplementedError
# Generate and save the inverse frequency buffer (non-trainable)
self.dim = dim
self.base = base
self.scale_base = scale_base
self.device = device
inv_freq = 1.0 / (
base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
)
self.register_buffer("inv_freq", inv_freq)
scale = (
(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
/ (1.4 * dim)
if scale_base is not None
else None
)
self.register_buffer("scale", scale)
self._seq_len_cached = 0
self._cos_cached = None
self._sin_cached = None
self._cos_k_cached = None
self._sin_k_cached = None
def _update_cos_sin_cache(
self, x: torch.FloatTensor, seqlen_offset: Optional[int] = 0
) -> None:
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
seqlen = x.shape[1] + seqlen_offset
# Re-generate the inverse frequency buffer if it's not fp32
# (for instance if model.half() was called)
if self.inv_freq.dtype != "torch.float32":
self.inv_freq = 1.0 / (
self.base
** (
torch.arange(
0, self.dim, 2, device=self.device, dtype=torch.float32
)
/ self.dim
)
)
if (
seqlen > self._seq_len_cached
or self._cos_cached.device != x.device
or self._cos_cached.dtype != x.dtype
):
self._seq_len_cached = seqlen
t = torch.arange(seqlen, device=x.device, dtype=torch.float32)
# 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, dtype=torch.float32)
)
if self.scale is None:
self._cos_cached = torch.cos(freqs).to(x.dtype)
self._sin_cached = torch.sin(freqs).to(x.dtype)
else:
power = (
torch.arange(
seqlen, dtype=self.scale.dtype, device=self.scale.device
)
- seqlen // 2
) / self.scale_base
scale = self.scale.to(device=power.device) ** rearrange(
power, "s -> s 1"
)
# We want the multiplication by scale to happen in fp32
self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
def apply_rotary_emb_qkv(
self,
qkv: torch.FloatTensor,
sin: torch.FloatTensor,
cos: torch.FloatTensor,
sin_k: Optional[torch.FloatTensor] = None,
cos_k: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
_, seqlen, three, _, headdim = qkv.shape
assert three == 3
rotary_seqlen, rotary_dim = cos.shape
rotary_dim *= 2
assert rotary_dim <= headdim
assert seqlen <= rotary_seqlen
cos_k = cos if cos_k is None else cos_k
sin_k = sin if sin_k is None else sin_k
assert (
sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
)
q_rot = qkv[:, :, 0, :, :rotary_dim]
q_pass = qkv[:, :, 0, :, rotary_dim:]
k_rot = qkv[:, :, 1, :, :rotary_dim]
k_pass = qkv[:, :, 1, :, rotary_dim:]
# Splits the queries and keys in half
q1, q2 = q_rot.chunk(2, dim=-1)
k1, k2 = k_rot.chunk(2, dim=-1)
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(
sin[:seqlen], "s d -> s 1 d"
)
# Casts to fp32 are necessary to prevent fp16 overflow issues
q1, q2, k1, k2, c, s = [
t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]
]
# Computes the new keys and queries, recasting to original dtype
q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
return torch.cat(
[
torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
qkv[:, :, 2:3, :, :],
],
axis=2,
)
def forward(
self, qkv: torch.Tensor, seqlen_offset: int = 0
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Perform the forward pass.
Args:
qkv: Query, key and value tensors of shape (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim).
seqlen_offset: Used in generation where the passed `qkv` is only the last token in the batch.
Returns:
New `qkv` and the cached sinusoids.
"""
self._update_cos_sin_cache(qkv, seqlen_offset)
return self.apply_rotary_emb_qkv(
qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:]
)
def _update_kv_cache(kv, inference_params, layer_idx):
"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
Adapted from https://github.com/Dao-AILab/flash-attention."""
# Pre-allocate memory for key-values for inference.
num_heads, head_dim = kv.shape[-2:]
if layer_idx not in inference_params.key_value_memory_dict:
kv_cache = torch.empty(
inference_params.max_batch_size,
inference_params.max_sequence_len,
2,
num_heads,
head_dim,
dtype=kv.dtype,
device=kv.device,
)
inference_params.key_value_memory_dict[layer_idx] = kv_cache
else:
kv_cache = inference_params.key_value_memory_dict[layer_idx]
# Adjust key and value for inference
batch_start = inference_params.batch_size_offset
batch_end = batch_start + kv.shape[0]
sequence_start = inference_params.sequence_len_offset
sequence_end = sequence_start + kv.shape[1]
assert batch_end <= (
kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0] # noqa
)
assert sequence_end <= (
kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2] # noqa
)
assert kv_cache is not None
kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
return kv
class MLP(nn.Module):
"""Multi-Layer Perceptron.
Reference:
Attention Is All You Need.
https://arxiv.org/pdf/1706.03762.pdf.
"""
def __init__(
self,
config: PretrainedConfig,
n_inner: Optional[int] = None,
act_fn: Optional[str] = None,
) -> None:
super().__init__()
act_fn = config.activation_function if act_fn is None else act_fn
assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
self.fc1 = nn.Linear(config.n_embd, n_inner)
self.fc2 = nn.Linear(n_inner, config.n_embd)
self.act = ACT2FN[act_fn]
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
old_keys = [
prefix + "fc_in.weight",
prefix + "fc_out.weight",
prefix + "fc_in.bias",
prefix + "fc_out.bias",
]
new_keys = [
prefix + "fc1.weight",
prefix + "fc2.weight",
prefix + "fc1.bias",
prefix + "fc2.bias",
]
if all(k in state_dict for k in old_keys) and not all(
k in state_dict for k in new_keys
):
# Older version of `MLP` saved with different key names.
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
return super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
)
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class FusedMLP(nn.Module):
"""Fused Multi-Layer Perceptron from `flash-attn`.
Reference:
https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/ops/fused_dense.py.
"""
def __init__(
self,
config: PretrainedConfig,
n_inner: Optional[int] = None,
act_fn: Optional[str] = None,
raise_on_missing: bool = False,
) -> None:
super().__init__()
act_fn = config.activation_function if act_fn is None else act_fn
assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
gelu_activations = ["gelu_new", "gelu_fast", "gelu_approx"] # noqa
activation = "gelu_approx" if act_fn in gelu_activations else "relu" # noqa
self.mlp = MLP(config, n_inner=n_inner, act_fn=act_fn)
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
return self.mlp(hidden_states)
class SelfAttention(nn.Module):
"""Implement the scaled dot product attention with softmax.
Adapted from https://github.com/Dao-AILab/flash-attention.
Arguments
---------
softmax_scale: The temperature to use for the softmax attention.
(default: 1/sqrt(d_keys) where d_keys is computed at
runtime)
attention_dropout: The dropout rate to apply to the attention
(default: 0.0)
"""
def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
super().__init__()
self.causal = causal
self.softmax_scale = softmax_scale
self.drop = nn.Dropout(attention_dropout)
def forward(
self, qkv, causal=None, key_padding_mask=None, cu_seqlens=None, max_seqlen=None
):
"""Implements the multihead softmax attention.
Arguments
---------
qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
causal: if passed, will override self.causal
key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
False means to mask out. (B, S)
"""
causal = self.causal if causal is None else causal
if cu_seqlens is not None:
return flash_attn_varlen_qkvpacked_func(
qkv.squeeze(0),
cu_seqlens,
max_seqlen,
dropout_p=self.drop.p,
softmax_scale=self.softmax_scale,
causal=causal,
)
else:
return flash_attn_qkvpacked_func(
qkv,
dropout_p=self.drop.p,
softmax_scale=self.softmax_scale,
causal=causal,
)
class CrossAttention(nn.Module):
"""Implement the scaled dot product attention with softmax.
Adapted from https://github.com/Dao-AILab/flash-attention.
Arguments
---------
softmax_scale: The temperature to use for the softmax attention.
(default: 1/sqrt(d_keys) where d_keys is computed at
runtime)
attention_dropout: The dropout rate to apply to the attention
(default: 0.0)
"""
def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
super().__init__()
self.causal = causal
self.softmax_scale = softmax_scale
self.drop = nn.Dropout(attention_dropout)
def forward(self, q, kv, causal=None, key_padding_mask=None):
"""Implements the multihead softmax attention.
Arguments
---------
q: The tensor containing the query. (B, Sq, H, D)
kv: The tensor containing the key and value. (B, Sk, 2, H, D)
causal: if passed, will override self.causal
key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
False means to mask out. (B, Sk)
"""
causal = self.causal if causal is None else causal
return flash_attn_kvpacked_func(
q,
kv,
dropout_p=self.drop.p,
softmax_scale=self.softmax_scale,
causal=causal,
)
def find_mha_dims(
config: PretrainedConfig,
n_head: Optional[int] = None,
head_dim: Optional[int] = None,
) -> Tuple[int, int]:
"""Validate and return the number of heads and head dimension for multi-head attention.
Args:
config: Model configuration.
n_head: Number of heads.
head_dim: Head dimension.
Returns:
Number of heads and head dimension.
"""
assert all(
hasattr(config, attr) for attr in ["n_embd", "n_head"]
), "`config` must have `n_embd` and `n_head` attributes."
if head_dim is None:
assert (
config.n_embd % config.n_head == 0
), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."
if n_head is None and head_dim is None:
head_dim = config.n_embd // config.n_head
n_head = config.n_head
elif n_head is None or head_dim is None:
raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
return n_head, head_dim
class MHA(nn.Module):
"""Multi-head attention layer.
Adapted from https://github.com/Dao-AILab/flash-attention."""
def __init__(
self,
config: PretrainedConfig,
rotary_dim: Optional[int] = None,
n_head: Optional[int] = None,
head_dim: Optional[int] = None,
bias: Optional[bool] = True,
dropout: Optional[float] = 0.0,
softmax_scale: Optional[float] = None,
causal: Optional[bool] = True,
layer_idx: Optional[int] = None,
rotary_emb_scale_base: Optional[float] = None,
return_residual: Optional[bool] = False,
checkpointing: Optional[bool] = False,
device: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
fused_dense: Optional[bool] = True,
flash_attn: Optional[bool] = True,
cutlass_attn: Optional[bool] = False,
flash_rotary: Optional[bool] = True,
raise_on_missing: Optional[bool] = False,
) -> None:
super().__init__()
factory_kwargs = {"device": device, "dtype": dtype}
n_head, head_dim = find_mha_dims(config, n_head, head_dim)
self.hidden_size = config.n_embd
self.n_head = n_head
self.head_dim = head_dim
self.op_size = n_head * head_dim
self.causal = causal
self.layer_idx = layer_idx
self.rotary_emb_dim = (
rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
)
self.fused_dense = fused_dense
self.flash_attn = flash_attn
self.cutlass_attn = cutlass_attn
self.flash_rotary = flash_rotary
self.return_residual = return_residual
self.checkpointing = checkpointing
if self.rotary_emb_dim > 0:
rotary_kwargs = {"device": device}
if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
rotary_kwargs["scale_base"] = rotary_emb_scale_base
self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
else:
pass
self.Wqkv = nn.Linear(
self.hidden_size, 3 * self.op_size, bias=bias, **factory_kwargs
)
self.out_proj = nn.Linear(
self.op_size, self.hidden_size, bias=bias, **factory_kwargs
)
self.inner_attn = SelfAttention(
causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
)
self.inner_cross_attn = CrossAttention(
causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
)
def _update_kv_cache(
self, kv: torch.FloatTensor, inference_params: InferenceParams
) -> None:
"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
Adapted from https://github.com/Dao-AILab/flash-attention."""
assert (
self.layer_idx is not None
), "Generation requires layer_idx in the constructor"
return _update_kv_cache(kv, inference_params, self.layer_idx)
def forward(
self,
x: torch.FloatTensor,
x_kv: Optional[torch.FloatTensor] = None,
key_padding_mask: Optional[torch.BoolTensor] = None,
cu_seqlens: Optional[torch.LongTensor] = None,
max_seqlen: Optional[int] = None,
mixer_subset: Optional[torch.LongTensor] = None,
past_cache: Optional[InferenceParams] = None,
**kwargs,
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
"""Perform the forward pass.
Args:
x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
is the is the sum of the sequence lengths in the batch.
x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
key_padding_mask: boolean mask, True means to keep, False means to mask out.
(batch, seqlen). Only applicable when not using FlashAttention.
cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into x. Only applicable when using
FlashAttention.
max_seqlen: int. Maximum sequence length in the batch.
mixer_subset: for cross-attention only. If not None, will take a subset of x
before applying the query projection. Useful for e.g., ViT where we only care
about the CLS token in the last layer.
past_cache: For generation only.
Returns:
(batch, seqlen, hidden_dim) if cu_seqlens is None and max_seqlen is None,
else (total, hidden_dim) where total is the is the sum of the sequence lengths
in the batch.
"""
if cu_seqlens is not None:
assert max_seqlen is not None
assert key_padding_mask is None
assert self.flash_attn
# assert self.rotary_emb_dim == 0
if key_padding_mask is not None:
assert cu_seqlens is None
assert max_seqlen is None
assert not self.flash_attn
if past_cache is not None:
assert key_padding_mask is None
assert cu_seqlens is None and max_seqlen is None
attn_kwargs = {"key_padding_mask": key_padding_mask}
assert x_kv is None and mixer_subset is None
qkv = self.Wqkv(x)
qkv = rearrange(
qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim
)
if past_cache is None:
if self.rotary_emb_dim > 0:
qkv = self.rotary_emb(qkv)
context = self.inner_attn(
qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, **attn_kwargs
)
else:
if self.rotary_emb_dim > 0:
qkv = self.rotary_emb(qkv, seqlen_offset=past_cache.sequence_len_offset)
q = qkv[:, :, 0]
kv = self._update_kv_cache(qkv[:, :, 1:], past_cache)
# If we're processing the prompt, causal=None (use self.causal).
# If we're decoding, then causal=False.
causal = None if past_cache.sequence_len_offset == 0 else False
context = self.inner_cross_attn(q, kv, causal=causal)
out = rearrange(context, "... h d -> ... (h d)")
out = self.out_proj(out)
return out if not self.return_residual else (out, x)
class ParallelBlock(nn.Module):
"""Parallel block.
This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
"""
def __init__(
self,
config: PretrainedConfig,
mixer: Optional[Dict[str, Any]] = None,
mlp: Optional[Dict[str, Any]] = None,
block_idx: Optional[int] = None,
) -> None:
super().__init__()
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.block_idx = block_idx
self.mixer = MHA(config, layer_idx=block_idx)
self.mlp = MLP(config)
def forward(
self,
hidden_states: torch.FloatTensor,
past_cache: Optional[torch.FloatTensor] = None,
cu_seqlens: Optional[torch.LongTensor] = None,
max_seqlen: Optional[int] = None,
) -> torch.FloatTensor:
residual = hidden_states
hidden_states = self.ln(hidden_states)
attn_outputs = self.mixer(
hidden_states,
past_cache=past_cache,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
)
if isinstance(attn_outputs, tuple):
attn_outputs = attn_outputs[0]
attn_outputs = self.resid_dropout(attn_outputs)
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
hidden_states = attn_outputs + feed_forward_hidden_states + residual
return hidden_states
class CausalLMHead(nn.Module):
"""Causal Language Modeling head.
Reference:
Improving Language Understanding by Generative Pre-Training.
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
"""
def __init__(self, config: PretrainedConfig) -> None:
super().__init__()
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.linear = nn.Linear(config.n_embd, config.vocab_size)
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = self.ln(hidden_states)
logits = self.linear(hidden_states).to(torch.float32)
return logits
class CausalLMLoss(nn.Module):
"""Causal Language Modeling loss.
Reference:
Improving Language Understanding by Generative Pre-Training.
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
"""
def __init__(self, shift_labels: Optional[bool] = True) -> None:
super().__init__()
self.shift_labels = shift_labels
self.loss_fct = nn.CrossEntropyLoss()
def forward(
self, logits: torch.FloatTensor, labels: torch.LongTensor
) -> torch.FloatTensor:
if self.shift_labels:
logits = logits[..., :-1, :].contiguous()
labels = labels[..., 1:].contiguous()
loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
return loss
class MixFormerSequentialPreTrainedModel(PreTrainedModel):
"""MixFormer (sequential for DeepSpeed) pre-trained model."""
config_class = MixFormerSequentialConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
def __init__(self, *inputs, **kwargs) -> None:
super().__init__(*inputs, **kwargs)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, **kwargs
) -> Dict[str, Any]:
if "use_cache" in kwargs and not kwargs["use_cache"]:
return {"input_ids": input_ids}
if past_key_values is None or not (
isinstance(past_key_values, InferenceParams)
):
past_key_values = InferenceParams(
max_batch_size=input_ids.shape[0],
max_sequence_len=self.config.n_positions,
sequence_len_offset=0,
batch_size_offset=0,
fused_ft_kernel=False,
key_value_memory_dict={},
)
else:
# assume past_key_values has cached all but last token in input_ids
past_key_values.sequence_len_offset = len(input_ids[0]) - 1
input_ids = input_ids[:, -1].unsqueeze(-1)
return {"input_ids": input_ids, "past_key_values": past_key_values, **kwargs}
class PackedSequential(nn.Sequential):
def forward(
self,
input,
cu_seqlens: Optional[torch.LongTensor] = None,
max_seqlen: Optional[int] = None,
):
for module in self:
sig = inspect.signature(module.forward)
if "cu_seqlens" in sig.parameters:
input = module(input, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
else:
input = module(input)
return input
class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
"""MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""
_keys_to_ignore_on_load_missing = [""]
_keys_to_ignore_on_load_unexpected = [
r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"
]
_no_split_modules = ["ParallelBlock"]
def __init__(self, config: MixFormerSequentialConfig) -> None:
super().__init__(config)
modules = [Embedding(config)]
block_config = config.architecture
if not isinstance(block_config, list):
block_config = [block_config for _ in range(config.n_layer)]
if config.n_layer != len(block_config):
config.n_layer = len(block_config)
for block_idx, block in enumerate(block_config):
# `block_cls` with `legacy` value is for backward compatibility
# `path` key is for backward compatibility
block = copy.deepcopy(block) or {"block_cls": "parallel"}
block.pop("path", None) or block.pop("block_cls", None)
block["block_idx"] = block_idx
modules.append(ParallelBlock(config, **block))
modules.append(CausalLMHead(config))
self.layers = PackedSequential(*modules)
self.loss = CausalLMLoss()
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.layers[0].wte
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
self.layers[0].wte = new_embeddings
def get_output_embeddings(self) -> nn.Linear:
return self.layers[-1].linear
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
self.layers[-1].linear = new_embeddings
def forward(
self,
input_ids: torch.LongTensor,
labels: Optional[torch.LongTensor] = None,
past_key_values: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
**kwargs,
) -> CausalLMOutputWithPast:
cu_seqlens: Optional[torch.LongTensor] = None
max_seqlen: Optional[int] = None
if position_ids is not None:
batch_size, seq_length = input_ids.shape
position_ids = position_ids.view(-1, seq_length).long()
cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
cu_seqlens = cu_seqlens.squeeze()
if not past_key_values:
lm_logits = self.layers(
input_ids, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
)
else:
hidden_layer = self.layers[0](input_ids)
for module in self.layers[1:-1]:
hidden_layer = module(
hidden_layer,
past_cache=past_key_values,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
)
lm_logits = self.layers[-1](hidden_layer)
loss = None
if labels is not None:
loss = self.loss(lm_logits, labels)
return CausalLMOutputWithPast(
loss=loss, logits=lm_logits, past_key_values=past_key_values
)

1092
src/axolotl/models/phi/modeling_phi.py
View File

File diff suppressed because it is too large Load Diff

12
src/axolotl/monkeypatch/phi/__init__.py Normal file
View File

@@ -0,0 +1,12 @@
"""
Patches to support multipack for phi2
"""
import transformers
from axolotl.monkeypatch.utils import get_unpad_data
def replace_phi_attn_with_multipack_flash_attn():
transformers.models.phi.modeling_phi._get_unpad_data = ( # pylint: disable=protected-access
get_unpad_data
)

14
src/axolotl/utils/config.py
View File

@@ -364,20 +364,6 @@ def validate_config(cfg):
"`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
)
if cfg.model_type == "MixFormerSequentialForCausalLM" and cfg.adapter is not None:
LOG.warning("Use AutoModelForCausalLM for phi/MixFormer models with qLoRA")
if cfg.model_config_type == "mixformer-sequential":
if cfg.sample_packing:
if cfg.adapter is not None:
LOG.warning(
"phi/MixFormer models are not currently compatible with LoRA and sample_packing"
)
if cfg.model_type == "AutoModelForCausalLM":
raise ValueError(
"`model_type: MixFormerSequentialForCausalLM` required for sample_packing"
)
if cfg.datasets:
for idx, ds_cfg in enumerate(cfg.datasets):
if not ds_cfg.type:

2
src/axolotl/utils/data.py
View File

@@ -397,7 +397,7 @@ def load_tokenized_prepared_datasets(
LOG.info("shuffle merged datasets")
dataset = dataset.shuffle(seed=seed)
dataset, _ = process_datasets_for_packing(cfg, dataset, None, tokenizer)
dataset, _ = process_datasets_for_packing(cfg, dataset, None)
if cfg.local_rank == 0:
LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")

2
src/axolotl/utils/lora_embeddings.py
View File

@@ -7,8 +7,6 @@ def get_linear_embedding_layers(model_type):
"""
returns the linear embedding layers needed for loras, dependent on the model arch
"""
if model_type == "phi-msft":
return ["embd.wte", "lm_head.linear"]
if model_type == "gpt_neox":
return ["embed_in", "embed_out"]
if model_type == "falcon":

23
src/axolotl/utils/models.py
View File

@@ -169,6 +169,7 @@ def load_tokenizer(cfg):
# pylint: disable=too-many-boolean-expressions
if (
(getattr(tokenizer, k) is None or getattr(tokenizer, k) != val)
and (len(tokenizer.encode(val)) > 1)
and cfg.adapter
and (
not cfg.lora_modules_to_save
@@ -342,6 +343,12 @@ def load_model(
LOG.info("patching falcon with flash attention")
replace_falcon_attn_with_multipack_flash_attn()
if cfg.model_config_type == "phi" and cfg.flash_attention and cfg.sample_packing:
from axolotl.monkeypatch.phi import replace_phi_attn_with_multipack_flash_attn
LOG.info("patching phi with flash attention")
replace_phi_attn_with_multipack_flash_attn()
if cfg.model_config_type == "qwen2" and cfg.flash_attention and cfg.sample_packing:
from axolotl.monkeypatch.qwen2 import (
replace_qwen2_attn_with_multipack_flash_attn,
@@ -448,7 +455,7 @@ def load_model(
"flash_attention_2"
)
else:
if model_config.model_type in ["mixtral", "qwen2", "falcon"]:
if model_config.model_type in ["mixtral", "qwen2", "falcon", "phi"]:
model_kwargs["attn_implementation"] = "flash_attention_2"
model_config._attn_implementation = ( # pylint: disable=protected-access
"flash_attention_2"
@@ -458,10 +465,6 @@ def load_model(
model_config._attn_implementation = ( # pylint: disable=protected-access
"eager"
)
if model_config.model_type == "phi-msft":
model_config.flash_attn = True
model_config.flash_rotary = True
model_config.fused_dense = True
try:
if (
@@ -518,16 +521,6 @@ def load_model(
# device=cfg.device,
# )
# model.train() # sets to train instead of eval mode
elif model_type == "PhiForCausalLM" or model_config.model_type == "phi-msft":
from axolotl.models.phi import PhiForCausalLM
model = PhiForCausalLM.from_pretrained(
base_model,
config=model_config,
load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
**model_kwargs,
)
elif model_type == "MambaLMHeadModel":
# FIXME this is janky at best and hacked together to make it work
MambaLMHeadModel = fix_mamba_attn_for_loss() # pylint: disable=invalid-name

9
src/axolotl/utils/trainer.py
View File

@@ -106,19 +106,16 @@ def drop_long_seq(sample, sequence_len=2048):
return len(sample["input_ids"]) <= sequence_len and len(sample["input_ids"]) > 0
def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
with zero_first(is_main_process()):
if cfg.is_preprocess:
max_input_len = np.max(get_dataset_lengths(train_dataset))
LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True)
# Phi doesn't want the attention_mask feature when training
if (
"CodeGenTokenizer" in tokenizer.__class__.__name__
or (cfg.is_mistral_derived_model and cfg.flash_attention)
or cfg.model_config_type == "mamba"
):
cfg.is_mistral_derived_model and cfg.flash_attention
) or cfg.model_config_type == "mamba":
LOG.info("dropping attention_mask column")
train_dataset = train_dataset.remove_columns("attention_mask")
if eval_dataset:

123
tests/e2e/patched/test_phi_multipack.py Normal file
View File

@@ -0,0 +1,123 @@
"""
E2E tests for lora llama
"""
import logging
import os
import unittest
from pathlib import Path
from axolotl.cli import load_datasets
from axolotl.common.cli import TrainerCliArgs
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.dict import DictDefault
from ..utils import with_temp_dir
LOG = logging.getLogger("axolotl.tests.e2e")
os.environ["WANDB_DISABLED"] = "true"
class TestPhiMultipack(unittest.TestCase):
"""
Test case for Phi2 models
"""
@with_temp_dir
def test_ft_packed(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "microsoft/phi-1_5",
"model_type": "PhiForCausalLM",
"tokenizer_type": "AutoTokenizer",
"sequence_len": 1024,
"sample_packing": True,
"flash_attention": True,
"pad_to_sequence_len": True,
"load_in_8bit": False,
"adapter": None,
"val_set_size": 0.1,
"special_tokens": {
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"dataset_shard_num": 10,
"dataset_shard_idx": 0,
"num_epochs": 1,
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"eval_steps": 10,
"save_steps": 10,
"bf16": "auto",
}
)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "pytorch_model.bin").exists()
@with_temp_dir
def test_qlora_packed(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "microsoft/phi-1_5",
"model_type": "PhiForCausalLM",
"tokenizer_type": "AutoTokenizer",
"sequence_len": 1024,
"sample_packing": True,
"flash_attention": True,
"pad_to_sequence_len": True,
"load_in_8bit": False,
"adapter": "qlora",
"lora_r": 64,
"lora_alpha": 32,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.1,
"special_tokens": {
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"dataset_shard_num": 10,
"dataset_shard_idx": 0,
"num_epochs": 1,
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"eval_steps": 10,
"save_steps": 10,
"bf16": "auto",
}
)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "adapter_model.bin").exists()

52
tests/e2e/test_phi.py
View File

@@ -7,9 +7,6 @@ import os
import unittest
from pathlib import Path
import pytest
from transformers.utils import is_torch_bf16_gpu_available
from axolotl.cli import load_datasets
from axolotl.common.cli import TrainerCliArgs
from axolotl.train import train
@@ -27,17 +24,15 @@ class TestPhi(unittest.TestCase):
Test case for Phi2 models
"""
@pytest.mark.skip(reason="fixme later")
@with_temp_dir
def test_phi2_ft(self, temp_dir):
def test_phi_ft(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "microsoft/phi-2",
"trust_remote_code": True,
"base_model": "microsoft/phi-1_5",
"model_type": "AutoModelForCausalLM",
"tokenizer_type": "AutoTokenizer",
"sequence_len": 512,
"sequence_len": 2048,
"sample_packing": False,
"load_in_8bit": False,
"adapter": None,
@@ -64,13 +59,9 @@ class TestPhi(unittest.TestCase):
"max_steps": 10,
"save_steps": 10,
"eval_steps": 10,
"save_safetensors": True,
"bf16": "auto",
}
)
if is_torch_bf16_gpu_available():
cfg.bf16 = True
else:
cfg.fp16 = True
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -78,25 +69,24 @@ class TestPhi(unittest.TestCase):
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "pytorch_model.bin").exists()
@pytest.mark.skip(reason="multipack no longer supported atm")
@with_temp_dir
def test_ft_packed(self, temp_dir):
def test_phi_qlora(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "microsoft/phi-2",
"trust_remote_code": True,
"model_type": "PhiForCausalLM",
"base_model": "microsoft/phi-1_5",
"model_type": "AutoModelForCausalLM",
"tokenizer_type": "AutoTokenizer",
"sequence_len": 512,
"sample_packing": True,
"sequence_len": 2048,
"sample_packing": False,
"load_in_8bit": False,
"adapter": None,
"adapter": "qlora",
"lora_r": 64,
"lora_alpha": 32,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.1,
"special_tokens": {
"unk_token": "<|endoftext|>",
"bos_token": "<|endoftext|>",
"eos_token": "<|endoftext|>",
"pad_token": "<|endoftext|>",
},
"datasets": [
@@ -112,18 +102,18 @@ class TestPhi(unittest.TestCase):
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"optimizer": "paged_adamw_8bit",
"lr_scheduler": "cosine",
"flash_attention": True,
"max_steps": 10,
"save_steps": 10,
"eval_steps": 10,
"bf16": "auto",
}
)
if is_torch_bf16_gpu_available():
cfg.bf16 = True
else:
cfg.fp16 = True
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "pytorch_model.bin").exists()
assert (Path(temp_dir) / "adapter_model.bin").exists()

8
tests/test_validation.py
View File

@@ -742,11 +742,11 @@ class ValidationCheckModelConfig(BaseValidation):
check_model_config(cfg, model_config)
def test_phi2_add_tokens_adapter(self):
def test_phi_add_tokens_adapter(self):
cfg = DictDefault(
{"adapter": "qlora", "load_in_4bit": True, "tokens": ["<|imstart|>"]}
)
model_config = DictDefault({"model_type": "phi-msft"})
model_config = DictDefault({"model_type": "phi"})
with pytest.raises(
ValueError,
@@ -759,7 +759,7 @@ class ValidationCheckModelConfig(BaseValidation):
"adapter": "qlora",
"load_in_4bit": True,
"tokens": ["<|imstart|>"],
"lora_modules_to_save": ["embed_tokens", "lm_head"],
"lora_modules_to_save": ["embd.wte", "lm_head.linear"],
}
)
@@ -774,7 +774,7 @@ class ValidationCheckModelConfig(BaseValidation):
"adapter": "qlora",
"load_in_4bit": True,
"tokens": ["<|imstart|>"],
"lora_modules_to_save": ["embd.wte", "lm_head.linear"],
"lora_modules_to_save": ["embed_tokens", "lm_head"],
}
)