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"""module for patching with unsloth optimizations"""
import inspect
import logging
import re
import types
from typing import Tuple
from peft import PeftModelForCausalLM
from transformers.models.llama.modeling_llama import (
LlamaFlashAttention2,
LlamaForCausalLM,
)
LOG = logging.getLogger("axolotl.monkeypatch.unsloth")
ORIGINAL_CEL_CODE = """ if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
"""
PATCHED_CEL_CODE = """ if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = fast_cross_entropy_loss(
logits = shift_logits,
labels = shift_labels,
)
"""
ORIGINAL_QKV_CODE = """
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
""".lstrip(
"\n"
)
PATCHED_QKV_CODE = """
query_states, key_states, value_states = self.apply_qkv(self, hidden_states)
""".lstrip(
"\n"
)
ORIGINAL_O_CODE = """
attn_output = self.o_proj(attn_output)
""".lstrip(
"\n"
)
PATCHED_O_CODE = """
attn_output = self.apply_o(self, attn_output)
""".lstrip(
"\n"
)
def original_apply_qkv(self, hidden_states):
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
return query_states, key_states, value_states
def original_apply_o(self, hidden_states):
attn_output = self.o_proj(hidden_states)
return attn_output
def get_forward_code() -> str:
forward = inspect.getsource(LlamaForCausalLM.forward)
return forward
def test_cel_is_patchable() -> bool:
forward = get_forward_code()
return ORIGINAL_CEL_CODE in forward
def get_self_attn_code() -> str:
forward = inspect.getsource(LlamaFlashAttention2.forward)
return forward
def test_self_attn_is_patchable() -> bool:
qkv = get_self_attn_code()
return ORIGINAL_QKV_CODE in qkv and ORIGINAL_QKV_CODE in qkv
def integrate_cross_entropy_loss_patch():
forward = get_forward_code()
LlamaForCausalLM._original_forward = forward # pylint: disable=protected-access
forward, _ = detab_code(forward)
assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
forward = forward.replace(
"@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
)
forward = forward.replace(
"@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
"",
)
forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
forward = forward.replace(
"def forward(",
"def fast_cross_entropy_loss_forward(",
1,
)
# load imports necessary
import transformers.models.llama.modeling_llama
items_to_import = []
for item in dir(transformers.models.llama.modeling_llama):
if item in forward:
items_to_import.append(item)
exec( # pylint: disable=exec-used # nosec B102
"from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
globals(),
)
exec( # pylint: disable=exec-used # nosec B102
"from transformers.models.llama.modeling_llama import ("
+ ", ".join(x for x in items_to_import)
+ ")",
globals(),
)
exec(forward, globals()) # pylint: disable=exec-used # nosec B102
print("patching unsloth fast_cross_entropy_loss")
LlamaForCausalLM.forward = fast_cross_entropy_loss_forward # pylint: disable=undefined-variable # noqa: F821
def detab_code(code: str) -> Tuple[str, str]:
spaces = re.match(r"([\s\t]{1,})", code).group(0)
code = re.sub(r"^" + spaces, "", code, flags=re.MULTILINE)
return code, spaces
def patch_self_attn_lora():
self_attn_forward = get_self_attn_code()
LlamaFlashAttention2._original_forward = ( # pylint: disable=protected-access
self_attn_forward
)
self_attn_forward, _ = detab_code(self_attn_forward)
assert ORIGINAL_QKV_CODE in self_attn_forward, "Original qkv code not found"
assert ORIGINAL_O_CODE in self_attn_forward, "Original o code not found"
self_attn_forward = self_attn_forward.replace(ORIGINAL_QKV_CODE, PATCHED_QKV_CODE)
self_attn_forward = self_attn_forward.replace(ORIGINAL_O_CODE, PATCHED_O_CODE)
self_attn_forward = self_attn_forward.replace(
"def forward(",
"def unsloth_attn_forward(",
1,
)
# load imports necessary
import transformers.models.llama.modeling_llama
items_to_import = []
for item in dir(transformers.models.llama.modeling_llama):
if item in self_attn_forward:
items_to_import.append(item)
exec( # pylint: disable=exec-used # nosec B102
"from transformers.models.llama.modeling_llama import ("
+ ", ".join(x for x in items_to_import)
+ ")",
globals(),
)
exec(self_attn_forward, globals()) # pylint: disable=exec-used # nosec B102
print("patching unsloth attn lora")
LlamaFlashAttention2.forward = (
unsloth_attn_forward # pylint: disable=undefined-variable # noqa: F821
)
def integrate_lora_mlp_patch(peft_model: PeftModelForCausalLM):
if peft_model.base_model.config.model_type in ["llama", "mistral"]:
from unsloth.kernels import apply_lora_mlp_swiglu
apply_lora_mlp = apply_lora_mlp_swiglu
elif peft_model.base_model.config.model_type == "gemma":
from unsloth.kernels import apply_lora_mlp_geglu_approx
apply_lora_mlp = apply_lora_mlp_geglu_approx
else:
raise NotImplementedError(
f"Model type {peft_model.base_model.config.model_type} not supported"
)
for idx, layer in enumerate(peft_model.model.model.layers):
layer_modules = [
getattr(layer.mlp, linear_proj)
for linear_proj in ["gate_proj", "up_proj", "down_proj"]
]
is_mlp_lora = all(hasattr(module, "lora_A") for module in layer_modules)
mlp_no_bias = all(
getattr(module, "base_layer", module).bias is None
for module in layer_modules
)
mlp_not_dora = all(
getattr(module, "lora_magnitude_vector", None) is None
for module in layer_modules
)
if is_mlp_lora and mlp_no_bias and mlp_not_dora:
layer.mlp.forward = types.MethodType(apply_lora_mlp, layer.mlp)
else:
logging.warning("unable to apply unsloth lora mlp patch to layer %d", idx)
def integrate_lora_patch(peft_model: PeftModelForCausalLM, cfg):
from unsloth.kernels import apply_lora_o, apply_lora_qkv
for idx, layer in enumerate(peft_model.model.model.layers):
if cfg.unsloth_lora_qkv:
layer_modules = [
getattr(layer.self_attn, linear_proj)
for linear_proj in ["q_proj", "k_proj", "v_proj"]
]
is_qkv_lora = all(hasattr(module, "lora_A") for module in layer_modules)
qkv_no_bias = all(
getattr(module, "base_layer", module).bias is None
for module in layer_modules
)
qkv_not_dora = all(
getattr(module, "lora_magnitude_vector", None) is None
for module in layer_modules
)
if is_qkv_lora and qkv_no_bias and qkv_not_dora:
layer.self_attn.apply_qkv = apply_lora_qkv
else:
layer.self_attn.apply_qkv = original_apply_qkv
logging.warning(
"unable to apply unsloth lora qkv patch to layer %d", idx
)
if cfg.unsloth_lora_o:
layer_modules = [
getattr(layer.self_attn, linear_proj) for linear_proj in ["o_proj"]
]
is_o_lora = all(hasattr(module, "lora_A") for module in layer_modules)
o_no_bias = all(
getattr(module, "base_layer", module).bias is None
for module in layer_modules
)
o_not_dora = all(
getattr(module, "lora_magnitude_vector", None) is None
for module in layer_modules
)
if is_o_lora and o_no_bias and o_not_dora:
layer.self_attn.apply_o = apply_lora_o
else:
layer.self_attn.apply_o = original_apply_o
logging.warning(
"unable to apply unsloth lora o_proj patch to layer %d", idx
)
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