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import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from einops import rearrange
from timm.layers import DropPath
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (BaseModelOutput,
BaseModelOutputWithPooling)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from awq.quantize import W8A8OF16LinearDynamicInputScale
import awq_inference_engine
from tinychat.models.internvl.internvit import (FlashAttention,
InternRMSNorm,
InternVisionEmbeddings,
InternAttention,
InternMLP,
InternVisionEncoderLayer,
InternVisionEncoder)
from tinychat.models.internvl.configuration_internvl import InternVisionConfig
try:
from flash_attn.bert_padding import pad_input, unpad_input
from flash_attn.flash_attn_interface import \
flash_attn_varlen_qkvpacked_func
has_flash_attn = True
except:
print('FlashAttention2 is not installed.')
has_flash_attn = False
logger = logging.get_logger(__name__)
class QuantInternVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`InternEncoderLayer`].
Args:
config (`InternConfig`):
The corresponding vision configuration for the `InternEncoder`.
"""
def __init__(self, module: InternVisionEncoder, bsz=64, seqlen=1024):
super().__init__()
self.config = module.config
# stochastic depth decay rule
self.layers = nn.ModuleList([QuantInternVisionEncoderLayer(layer, self.config) for layer in module.layers])
self.gradient_checkpointing = True
self.bsz = bsz
self.seqlen = seqlen
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Embedded representation of the inputs. Should be float, not int tokens.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = torch.utils.checkpoint.checkpoint(
encoder_layer,
hidden_states)
else:
layer_outputs = encoder_layer(
hidden_states,
)
hidden_states = layer_outputs
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states
)
class QuantInternRMSNorm(nn.Module):
def __init__(self, module: nn.Module, use_per_token_quant=True):
super().__init__()
self.weight = nn.Parameter(module.weight.data, requires_grad=False)
self.bias = nn.Parameter(module.bias.data, requires_grad=False)
self.variance_epsilon = module.eps
self.use_per_token_quant = use_per_token_quant
def forward(self, hidden_states):
bsz, seqlen, hidden_size = hidden_states.shape
output = torch.empty((bsz * seqlen), hidden_size, device=hidden_states.device, dtype=torch.int8)
scale = torch.empty((bsz * seqlen), device=hidden_states.device, dtype=hidden_states.dtype)
awq_inference_engine.rms_norm_general(
output,
hidden_states,
self.weight,
self.bias,
scale,
self.variance_epsilon,
self.use_per_token_quant,
)
return output, scale
class QuantInternAttention(nn.Module):
def __init__(self, module: InternAttention, config: InternVisionConfig, init_only=False):
super().__init__()
self.config = config
self.embed_dim = module.embed_dim
self.num_heads = module.num_heads
self.head_dim = self.embed_dim // self.num_heads
self.scale = module.scale
self.use_flash_attn = config.use_flash_attn
self.qkv = W8A8OF16LinearDynamicInputScale.from_linear(module.qkv, init_only=init_only)
self.proj = W8A8OF16LinearDynamicInputScale.from_linear(module.proj, init_only=init_only)
self.qk_normalization = module.qk_normalization
if self.qk_normalization:
self.q_norm = QuantInternRMSNorm(module.q_norm)
self.k_norm = QuantInternRMSNorm(module.k_norm)
if self.use_flash_attn:
from tinychat.models.internvl.internvit import FlashAttention
self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
def forward(self, hidden_states: torch.Tensor, scale_in: torch.Tensor):
bsz, seqlen, hidden_size = hidden_states.shape
qkv_out = torch.empty(bsz * seqlen, 3 * hidden_size, dtype=torch.float16, device=hidden_states.device)
self.qkv(hidden_states.reshape(-1, hidden_size), scale_in, qkv_out)
qkv = rearrange(qkv_out.view(bsz, seqlen, -1), 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
if self.qk_normalization:
q, k, v = qkv.unbind(2)
q, _ = self.q_norm(q.flatten(-2, -1)); q = q.view_as(q)
k, _ = self.k_norm(k.flatten(-2, -1)); k = k.view_as(k)
qkv = torch.stack([q, k, v], dim=2)
attn_out, _ = self.inner_attn(qkv, need_weights=False, causal=False)
attn_out = rearrange(attn_out, 'b s h d -> (b s) (h d)')
quant_out = torch.empty_like(attn_out, dtype=torch.int8)
scale_proj_in = torch.empty(bsz * seqlen, device=hidden_states.device, dtype=torch.float16)
awq_inference_engine.invoke_quant(quant_out, attn_out, scale_proj_in)
proj_out = torch.empty_like(attn_out)
self.proj(quant_out, scale_proj_in, proj_out)
return proj_out
class QuantInternMLP(nn.Module):
def __init__(self, module: InternMLP, config: InternVisionConfig):
super().__init__()
self.config = config
self.act = module.act
self.fc1 = W8A8OF16LinearDynamicInputScale.from_linear(module.fc1)
self.fc2 = W8A8OF16LinearDynamicInputScale.from_linear(module.fc2)
def forward(self, hidden_states: torch.Tensor, scale_in: torch.Tensor):
bsz, seqlen, hidden_size = hidden_states.shape
device = hidden_states.device
fc1_out = torch.empty((bsz * seqlen), self.config.intermediate_size, dtype=torch.float16, device=device)
self.fc1(hidden_states.reshape(-1, hidden_size), scale_in, fc1_out)
tmp = torch.empty(
((bsz * seqlen) * self.config.intermediate_size),
device=device,
dtype=torch.float16,
)
act_out = torch.empty_like(fc1_out, dtype=torch.int8)
scale_act = torch.empty(bsz * seqlen, device=device, dtype=torch.float16)
awq_inference_engine.gelu_and_quant(act_out, fc1_out, scale_act, tmp)
fc2_out = torch.empty((bsz * seqlen), hidden_size, dtype=torch.float16, device=device)
self.fc2(act_out, scale_act, fc2_out)
return fc2_out
class QuantInternVisionEncoderLayer(nn.Module):
def __init__(self, module: InternVisionEncoderLayer, config: InternVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.intermediate_size = config.intermediate_size
self.attn = QuantInternAttention(module.attn, config)
self.mlp = QuantInternMLP(module.mlp, config)
self.norm1 = QuantInternRMSNorm(module.norm1)
self.norm2 = QuantInternRMSNorm(module.norm2)
self.ls1 = module.ls1
self.ls2 = module.ls2
def forward(self, hidden_states: torch.Tensor):
bsz, seqlen, hidden_size = hidden_states.shape
residual = hidden_states
norm1_out, scale1 = self.norm1(hidden_states)
attn_out = self.attn(norm1_out.view(bsz, seqlen, hidden_size), scale1)
hidden_states = residual + attn_out.view(bsz, seqlen, hidden_size) * self.ls1
residual = hidden_states
norm2_out, scale2 = self.norm2(hidden_states)
mlp_out = self.mlp(norm2_out.view(bsz, seqlen, hidden_size), scale2)
hidden_states = residual + mlp_out.view(bsz, seqlen, hidden_size) * self.ls2
return hidden_states
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