lmdeploy.pytorch is designed to ease new model deployment and prototype verification. If you are willing to use our engine, here is the tutorial.
Let's begin with Llama.
Before delving into the details, it's essential to acquaint ourselves with the input specifications of the model. In order to accommodate new features within our engine, there are some deviations from the typical transformer inputs.
-
To circumvent the need for batch padding, continuous batching is employed. Consequently, the
input_idsnow represents the concatenation of all input sequences in the batch, followed by aunsqueeze(0)operation to align with the originalinput_idsdimension. -
In an effort to optimize memory usage for the key/value cache, we implement paged attention. This transforms the
past_key_valueinto a substantial tensor with dimensions[num_blocks, block_size, num_heads, head_dim]. Here,num_blocksdenotes the number of page blocks, andblock_sizeindicates the size of each block. -
Accompanying these changes, additional inputs are imperative to support the modified inputs described above. These include the block table and history length. It's important to note that these supplementary inputs are not explicitly listed as arguments in the original forward method. Instead, a context object is utilized to furnish this essential information.
Due to the alterations in the input structure mentioned earlier, the forward methods for both LlamaModel and LlamaAttention modules need to be adjusted. Below are the modified implementations:
For LlamaModel:
# lmdeploy/pytorch/models/llama.py
class LlamaModel(nn.Module):
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
"""Rewrite implementation of LlamaModel.forward."""
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
# decoder layers
for idx, decoder_layer in enumerate(self.layers):
past_key_value = past_key_values[idx]
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
hidden_states = self.norm(hidden_states)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=None,
attentions=None,
)For LlamaAttention:
# lmdeploy/pytorch/models/llama.py
from lmdeploy.pytorch.kernels import apply_rotary_pos_emb, fill_kv_cache, paged_attention_fwd
class LlamaAttention(nn.Module):
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
Optional[Tuple[torch.Tensor]]]:
"""Rewrite of LlamaAttention.forward."""
context = self.context.context
history_lengths = context.history_lengths
position_ids_1d = context.position_ids_1d
block_offsets = context.block_offsets
# qkv proj
query_states = q_proj(hidden_states)
key_states = k_proj(hidden_states)
value_states = v_proj(hidden_states)
query_states = query_states.view(-1, num_heads, head_dim)
key_states = key_states.view(-1, num_kv_heads, head_dim)
value_states = value_states.view(-1, num_kv_heads, head_dim)
# rotary embedding
max_seq_len = position_ids.size(-1)
kv_seq_len = max_seq_len + max(history_lengths)
if kv_seq_len >= self.rotary_emb.max_seq_len_cached:
cos, sin = self.rotary_emb(value_states,
seq_len=kv_seq_len + 128)
query_states, key_states = apply_rotary_pos_emb(
query_states,
key_states,
self.rotary_emb.cos_cached,
self.rotary_emb.sin_cached,
position_ids,
position_ids_1d,
q_embed=query_states,
k_embed=key_states)
# fill kv cache
kv_seq_length = context.kv_seq_length
q_seq_length = context.q_seq_length
q_start_loc = context.q_start_loc
fill_kv_cache(key_states,
value_states,
past_key_value[0],
past_key_value[1],
q_start_loc,
q_seq_length,
block_offsets=block_offsets,
history_lengths=history_lengths,
context=context)
# attention
attn_output = query_states
block_size = past_key_value[0].size(1)
paged_attention_fwd(
query_states,
past_key_value[0],
past_key_value[1],
attn_output,
block_offsets,
q_start_loc=q_start_loc,
q_seqlens=q_seq_length,
kv_seqlens=kv_seq_length,
max_seqlen=max_seq_len,
)
hidden_size = num_heads * head_dim
attn_output = attn_output.reshape(*hidden_states.shape[:-1], hidden_size)
# o proj
attn_output = o_proj(attn_output)
return attn_output, None, past_key_valueNote: The additional arguments like history_lengths and block_offsets are accessed from the context object, which acts as a container for the necessary inputs required by continuous batching and paged attention. Refer to the context info for more detail about context object.
We have replaced certain operations with our custom Triton kernel for two reasons:
- The custom Triton kernel allows us to incorporate new features, such as
paged_attention_fwd. - Fused kernels offer superior performance compared to the pure PyTorch implementation.
Now that we have the updated implementations for the two modules, let's register them in lmdeploy/pytorch/models/module_map.py.
# lmdeploy/pytorch/models/module_map.py
MODEL_MAP.update({
'transformers.models.llama.modeling_llama.LlamaAttention':
'lmdeploy.pytorch.models.llama.LlamaAttention',
'transformers.models.llama.modeling_llama.LlamaModel':
'lmdeploy.pytorch.models.llama.LlamaModel'
})In this mapping, the revised modules are associated with their original counterparts. When creating an Engine, the ModelAgent will automatically patch the model. Subsequently, we can conduct inference using these updated implementations.
If we aim to enable tensor parallelism (TP), it is necessary to partition the weights in the model. Let's build upon the previously mentioned modifications to accommodate TP in the Llama model:
In Llama (as well as in most Language Model models), the weight partition primarily affects the Linear layers. Specifically, for the following components:
- In
LlamaAttention:q_proj,k_proj,v_projrequire column-wise partitioning, whileo_projnecessitates row-wise partitioning. - In
LlamaMLP:gate_projandup_projrequire column-wise partitioning, whiledown_projrequires row-wise partitioning.
We can implement the _distribution_partition_fn in each of the rewritten modules:
# lmdeploy/pytorch/models/llama.py
from ..dist_utils import (colwise_parallelize_linear_fn,
rowwise_parallelize_linear_fn)
class LlamaAttention(nn.Module):
@classmethod
def _distribute_partition_fn(cls, mod_name: str, mod: nn.Module,
device_mesh: DeviceMesh):
"""Distribution partition callback."""
if mod_name in ['q_proj', 'k_proj', 'v_proj']:
colwise_parallelize_linear_fn(mod,
device_mesh=device_mesh,
to_local=True)
elif mod_name in ['o_proj']:
rowwise_parallelize_linear_fn(mod,
device_mesh=device_mesh,
to_local=True)
class LlamaMLP(nn.Module):
@classmethod
def _distribute_partition_fn(cls, mod_name: str, mod: nn.Module,
device_mesh: DeviceMesh):
"""Distribution partition callback."""
if mod_name in ['gate_proj', 'up_proj']:
colwise_parallelize_linear_fn(mod,
device_mesh=device_mesh,
to_local=True)
elif mod_name in ['down_proj']:
rowwise_parallelize_linear_fn(mod,
device_mesh=device_mesh,
to_local=True)In the process of loading model weights, the _distribute_partition_fn is called to distribute the weights of specific modules across different devices. Following the weight partitioning, it becomes necessary to perform all_reduce on the output tensors of o_proj and down_proj. While one option is to include all_reduce directly in the forward method, an alternative approach is to introduce the _distribute_output_fn call:
# lmdeploy/pytorch/models/llama.py
import torch.distributed as dist
class LlamaAttention(nn.Module):
@classmethod
def _distribute_output_fn(cls, outputs, device_mesh: DeviceMesh):
"""Distribution output hook."""
dist.all_reduce(outputs[0])
return outputs
class LlamaMLP(nn.Module):
@classmethod
def _distribute_output_fn(cls, outputs, device_mesh: DeviceMesh):
"""Distribution output hook."""
dist.all_reduce(outputs)
return outputsIt is essential to remember to add LlamaMLP to the module_map:
# lmdeploy/pytorch/models/module_map.py
MODEL_MAP.update({
'transformers.models.llama.modeling_llama.LlamaMLP':
'lmdeploy.pytorch.models.llama.LlamaMLP'
})With these adjustments, the model is now capable of utilizing multiple GPUs for deploying Large Language Models (LLM). This enables efficient distribution of computations across different devices in a parallelized manner.
When the output of the model does not meet expectations, we would like to debug a specific module to determine if the added rewrite is correct. lmdeploy.pytorch provides some tools to assist with accuracy alignment. Let’s take LlamaAttention module as an example.
First, create an instance of the module that we want to debug:
import torch
from transformers import AutoModelForCausalLM
# get module
model_path = 'meta-llama/Llama-2-7b-chat-hf'
dtype = torch.float16
model = AutoModelForCausalLM.from_pretrained(model_path).to(torch.float16).cuda()
self_attn = model.model.layers[0].self_attnExtract the inputs/outputs with ModuleIOExtractor.
from lmdeploy.pytorch.tools.make_inputs import ModuleIOExtractor
# extract module input/output
input_ids = torch.tensor([[1, 2, 3, 4, 5]]).cuda()
extractor = ModuleIOExtractor(model, self_attn)
attn_args, attn_kwargs, attn_output = extractor.extract(input_ids)The inputs of rewrite module are different from the inputs of origin module:
- Module requires some special inputs, which are passed through
StepContext. We can create one withmake_step_context. input_ids,hidden_statesshould be continuous. We can usecontinuous_tensorto do the process.past_key_valueshould be paged to meet the demand of paged attention.
Based on the reason above, the input should be updated:
from lmdeploy.pytorch.tools.make_inputs import make_step_context
from lmdeploy.pytorch.tools.layout_convert import continuous_tensor
# create patched input/output
context = make_step_context(input_ids,
kv_cache_dtype=dtype,
num_key_value_heads=32)
seq_length = context.q_seq_length
attn_kwargs['hidden_states'] = continuous_tensor(
attn_kwargs['hidden_states'],
seq_length)
attn_kwargs['past_key_value'] = context.kv_caches[0]Then you can start the rewrite and compare the correctness of the results.
from lmdeploy.pytorch.models import patch
# patch and test
patched_self_attn = patch(self_attn, extra_args=['context'])
with torch.inference_mode():
patched_output = patched_self_attn.patched_forward(*attn_args,
**attn_kwargs,
context=context)
torch.testing.assert_close(patched_output[0],
continuous_tensor(attn_output[0], seq_length))Adjust the rewrite module until the output can be aligned.
@dataclass
class StepContext:
"""context of Model.
"""
inputs: ModelInputs
block_offsets: torch.LongTensor
position_ids: torch.LongTensor
position_ids_1d: torch.LongTensor
q_start_loc: torch.LongTensor
history_lengths: torch.LongTensor
seq_length: torch.LongTensor
max_seq_length: int
kv_seq_length: torch.LongTensor
kv_caches: List
is_decoding: bool
world_size: int = 1
json_config: Dict = None
local_adapter_ids: torch.LongTensor = None
global_adapter_ids: torch.LongTensor = None
adapter_offsets: torch.LongTensor = None
max_rank: int = 0- How to invoke the original forward method?
A common approach is to add hooks to a method rather than performing a complete rewrite. To access the unpatched module, you can utilize self.origin_mod within the rewritten method.
- How to register modules in remote code?
For modules located in remote code, pinpointing them via qualname might be challenging. lmdeploy.pytorch facilitates registration using abbreviations for such modules:n:
MODULE_MAP.update({
'modeling_internlm.InternLMAttention':
'lmdeploy.pytorch.models.internlm.PatchedInternLMAttention',
})Note
Although abbreviations are supported, they tend to have lower priority. It is advisable to register modules using their complete qualname for more robust and accurate mapping.
- How to support different modules with the same name?
You can accommodate multiple modules with the same name within a single rewrite module by providing distinct implementations based on their attributes. For instance, consider baichuan2 7b/13b:
class BaichuanModel(nn.Module):
def forward(self, ...):
if self.config.num_hidden_layers == 32:
return forward_7b(...)
else:
return forward_default(...)- How to perform post-initialization for a rewrite module?
To execute tasks after model weight loading, introduce a _update_model_fn method in your rewrite module. This method will be automatically called post-initialization:
class LlamaAttention:
def _update_model_fn(self):
# ADD YOUR CODE HEREHere, you can include any additional post-initialization steps or configurations needed for your specific use case.