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Zero
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from __future__ import annotations
import math
from typing import Optional
import torch
import torch.nn.functional as F
import numpy as np
from tensorrt_llm._common import default_net
from ..._utils import trt_dtype_to_np, str_dtype_to_trt
from ...functional import (
Tensor,
chunk,
concat,
constant,
expand,
shape,
silu,
slice,
permute,
expand_mask,
expand_dims_like,
unsqueeze,
matmul,
softmax,
squeeze,
cast,
gelu,
)
from ...functional import expand_dims, view, bert_attention
from ...layers import LayerNorm, Linear, Conv1d, Mish, RowLinear, ColumnLinear
from ...module import Module
class FeedForward(Module):
def __init__(self, dim, dim_out=None, mult=4, dropout=0.0):
super().__init__()
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim
self.project_in = Linear(dim, inner_dim)
self.ff = Linear(inner_dim, dim_out)
def forward(self, x):
return self.ff(gelu(self.project_in(x)))
class AdaLayerNormZero(Module):
def __init__(self, dim):
super().__init__()
self.linear = Linear(dim, dim * 6)
self.norm = LayerNorm(dim, elementwise_affine=False, eps=1e-6)
def forward(self, x, emb=None):
emb = self.linear(silu(emb))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = chunk(emb, 6, dim=1)
x = self.norm(x)
ones = constant(np.ones(1, dtype=np.float32)).cast(x.dtype)
if default_net().plugin_config.remove_input_padding:
x = x * (ones + scale_msa) + shift_msa
else:
x = x * (ones + unsqueeze(scale_msa, 1)) + unsqueeze(shift_msa, 1)
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class AdaLayerNormZero_Final(Module):
def __init__(self, dim):
super().__init__()
self.linear = Linear(dim, dim * 2)
self.norm = LayerNorm(dim, elementwise_affine=False, eps=1e-6)
def forward(self, x, emb):
emb = self.linear(silu(emb))
scale, shift = chunk(emb, 2, dim=1)
ones = constant(np.ones(1, dtype=np.float32)).cast(x.dtype)
if default_net().plugin_config.remove_input_padding:
x = self.norm(x) * (ones + scale) + shift
else:
x = self.norm(x) * unsqueeze((ones + scale), 1)
x = x + unsqueeze(shift, 1)
return x
class ConvPositionEmbedding(Module):
def __init__(self, dim, kernel_size=31, groups=16):
super().__init__()
assert kernel_size % 2 != 0
self.conv1d1 = Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2)
self.conv1d2 = Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2)
self.mish = Mish()
def forward(self, x, mask=None): # noqa: F722
if default_net().plugin_config.remove_input_padding:
x = unsqueeze(x, 0)
x = permute(x, [0, 2, 1])
x = self.mish(self.conv1d2(self.mish(self.conv1d1(x))))
out = permute(x, [0, 2, 1])
if default_net().plugin_config.remove_input_padding:
out = squeeze(out, 0)
return out
class Attention(Module):
def __init__(
self,
processor: AttnProcessor,
dim: int,
heads: int = 16,
dim_head: int = 64,
dropout: float = 0.0,
context_dim: Optional[int] = None, # if not None -> joint attention
context_pre_only=None,
):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
self.processor = processor
self.dim = dim # hidden_size
self.heads = heads
self.inner_dim = dim_head * heads
self.dropout = dropout
self.attention_head_size = dim_head
self.context_dim = context_dim
self.context_pre_only = context_pre_only
self.tp_size = 1
self.num_attention_heads = heads // self.tp_size
self.num_attention_kv_heads = heads // self.tp_size # 8
self.dtype = str_dtype_to_trt("float32")
self.attention_hidden_size = self.attention_head_size * self.num_attention_heads
self.to_q = ColumnLinear(
dim,
self.tp_size * self.num_attention_heads * self.attention_head_size,
bias=True,
dtype=self.dtype,
tp_group=None,
tp_size=self.tp_size,
)
self.to_k = ColumnLinear(
dim,
self.tp_size * self.num_attention_heads * self.attention_head_size,
bias=True,
dtype=self.dtype,
tp_group=None,
tp_size=self.tp_size,
)
self.to_v = ColumnLinear(
dim,
self.tp_size * self.num_attention_heads * self.attention_head_size,
bias=True,
dtype=self.dtype,
tp_group=None,
tp_size=self.tp_size,
)
if self.context_dim is not None:
self.to_k_c = Linear(context_dim, self.inner_dim)
self.to_v_c = Linear(context_dim, self.inner_dim)
if self.context_pre_only is not None:
self.to_q_c = Linear(context_dim, self.inner_dim)
self.to_out = RowLinear(
self.tp_size * self.num_attention_heads * self.attention_head_size,
dim,
bias=True,
dtype=self.dtype,
tp_group=None,
tp_size=self.tp_size,
)
if self.context_pre_only is not None and not self.context_pre_only:
self.to_out_c = Linear(self.inner_dim, dim)
def forward(
self,
x, # noised input x
rope_cos,
rope_sin,
input_lengths,
c=None, # context c
scale=1.0,
rope=None,
c_rope=None, # rotary position embedding for c
) -> torch.Tensor:
if c is not None:
return self.processor(self, x, c=c, input_lengths=input_lengths, scale=scale, rope=rope, c_rope=c_rope)
else:
return self.processor(
self, x, rope_cos=rope_cos, rope_sin=rope_sin, input_lengths=input_lengths, scale=scale
)
def rotate_every_two_3dim(tensor: Tensor) -> Tensor:
shape_tensor = concat(
[shape(tensor, i) / 2 if i == (tensor.ndim() - 1) else shape(tensor, i) for i in range(tensor.ndim())]
)
if default_net().plugin_config.remove_input_padding:
assert tensor.ndim() == 2
x1 = slice(tensor, [0, 0], shape_tensor, [1, 2])
x2 = slice(tensor, [0, 1], shape_tensor, [1, 2])
x1 = expand_dims(x1, 2)
x2 = expand_dims(x2, 2)
zero = constant(np.ascontiguousarray(np.zeros([1], dtype=trt_dtype_to_np(tensor.dtype))))
x2 = zero - x2
x = concat([x2, x1], 2)
out = view(x, concat([shape(x, 0), shape(x, 1) * 2]))
else:
assert tensor.ndim() == 3
x1 = slice(tensor, [0, 0, 0], shape_tensor, [1, 1, 2])
x2 = slice(tensor, [0, 0, 1], shape_tensor, [1, 1, 2])
x1 = expand_dims(x1, 3)
x2 = expand_dims(x2, 3)
zero = constant(np.ascontiguousarray(np.zeros([1], dtype=trt_dtype_to_np(tensor.dtype))))
x2 = zero - x2
x = concat([x2, x1], 3)
out = view(x, concat([shape(x, 0), shape(x, 1), shape(x, 2) * 2]))
return out
def apply_rotary_pos_emb_3dim(x, rope_cos, rope_sin):
if default_net().plugin_config.remove_input_padding:
rot_dim = shape(rope_cos, -1) # 64
new_t_shape = concat([shape(x, 0), rot_dim]) # (-1, 64)
x_ = slice(x, [0, 0], new_t_shape, [1, 1])
end_dim = shape(x, -1) - shape(rope_cos, -1)
new_t_unrotated_shape = concat([shape(x, 0), end_dim]) # (2, -1, 960)
x_unrotated = slice(x, concat([0, rot_dim]), new_t_unrotated_shape, [1, 1])
out = concat([x_ * rope_cos + rotate_every_two_3dim(x_) * rope_sin, x_unrotated], dim=-1)
else:
rot_dim = shape(rope_cos, 2) # 64
new_t_shape = concat([shape(x, 0), shape(x, 1), rot_dim]) # (2, -1, 64)
x_ = slice(x, [0, 0, 0], new_t_shape, [1, 1, 1])
end_dim = shape(x, 2) - shape(rope_cos, 2)
new_t_unrotated_shape = concat([shape(x, 0), shape(x, 1), end_dim]) # (2, -1, 960)
x_unrotated = slice(x, concat([0, 0, rot_dim]), new_t_unrotated_shape, [1, 1, 1])
out = concat([x_ * rope_cos + rotate_every_two_3dim(x_) * rope_sin, x_unrotated], dim=-1)
return out
class AttnProcessor:
def __init__(self):
pass
def __call__(
self,
attn,
x, # noised input x
rope_cos,
rope_sin,
input_lengths,
scale=1.0,
rope=None,
) -> torch.FloatTensor:
query = attn.to_q(x)
key = attn.to_k(x)
value = attn.to_v(x)
# k,v,q all (2,1226,1024)
query = apply_rotary_pos_emb_3dim(query, rope_cos, rope_sin)
key = apply_rotary_pos_emb_3dim(key, rope_cos, rope_sin)
# attention
inner_dim = key.shape[-1]
norm_factor = math.sqrt(attn.attention_head_size)
q_scaling = 1.0 / norm_factor
mask = None
if not default_net().plugin_config.remove_input_padding:
N = shape(x, 1)
B = shape(x, 0)
seq_len_2d = concat([1, N])
max_position_embeddings = 4096
# create position ids
position_ids_buffer = constant(np.expand_dims(np.arange(max_position_embeddings).astype(np.int32), 0))
tmp_position_ids = slice(position_ids_buffer, starts=[0, 0], sizes=seq_len_2d)
tmp_position_ids = expand(tmp_position_ids, concat([B, N])) # BxL
tmp_input_lengths = unsqueeze(input_lengths, 1) # Bx1
tmp_input_lengths = expand(tmp_input_lengths, concat([B, N])) # BxL
mask = tmp_position_ids < tmp_input_lengths # BxL
mask = mask.cast("int32")
if default_net().plugin_config.bert_attention_plugin:
qkv = concat([query, key, value], dim=-1)
# TRT plugin mode
assert input_lengths is not None
if default_net().plugin_config.remove_input_padding:
qkv = qkv.view(concat([-1, 3 * inner_dim]))
max_input_length = constant(
np.zeros(
[
2048,
],
dtype=np.int32,
)
)
else:
max_input_length = None
context = bert_attention(
qkv,
input_lengths,
attn.num_attention_heads,
attn.attention_head_size,
q_scaling=q_scaling,
max_input_length=max_input_length,
)
else:
assert not default_net().plugin_config.remove_input_padding
def transpose_for_scores(x):
new_x_shape = concat([shape(x, 0), shape(x, 1), attn.num_attention_heads, attn.attention_head_size])
y = x.view(new_x_shape)
y = y.transpose(1, 2)
return y
def transpose_for_scores_k(x):
new_x_shape = concat([shape(x, 0), shape(x, 1), attn.num_attention_heads, attn.attention_head_size])
y = x.view(new_x_shape)
y = y.permute([0, 2, 3, 1])
return y
query = transpose_for_scores(query)
key = transpose_for_scores_k(key)
value = transpose_for_scores(value)
attention_scores = matmul(query, key, use_fp32_acc=False)
if mask is not None:
attention_mask = expand_mask(mask, shape(query, 2))
attention_mask = cast(attention_mask, attention_scores.dtype)
attention_scores = attention_scores + attention_mask
attention_probs = softmax(attention_scores, dim=-1)
context = matmul(attention_probs, value, use_fp32_acc=False).transpose(1, 2)
context = context.view(concat([shape(context, 0), shape(context, 1), attn.attention_hidden_size]))
context = attn.to_out(context)
if mask is not None:
mask = mask.view(concat([shape(mask, 0), shape(mask, 1), 1]))
mask = expand_dims_like(mask, context)
mask = cast(mask, context.dtype)
context = context * mask
return context
# DiT Block
class DiTBlock(Module):
def __init__(self, dim, heads, dim_head, ff_mult=2, dropout=0.1):
super().__init__()
self.attn_norm = AdaLayerNormZero(dim)
self.attn = Attention(
processor=AttnProcessor(),
dim=dim,
heads=heads,
dim_head=dim_head,
dropout=dropout,
)
self.ff_norm = LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout)
def forward(
self, x, t, rope_cos, rope_sin, input_lengths, scale=1.0, rope=ModuleNotFoundError
): # x: noised input, t: time embedding
# pre-norm & modulation for attention input
norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
# attention
# norm ----> (2,1226,1024)
attn_output = self.attn(x=norm, rope_cos=rope_cos, rope_sin=rope_sin, input_lengths=input_lengths, scale=scale)
# process attention output for input x
if default_net().plugin_config.remove_input_padding:
x = x + gate_msa * attn_output
else:
x = x + unsqueeze(gate_msa, 1) * attn_output
ones = constant(np.ones(1, dtype=np.float32)).cast(x.dtype)
if default_net().plugin_config.remove_input_padding:
norm = self.ff_norm(x) * (ones + scale_mlp) + shift_mlp
else:
norm = self.ff_norm(x) * (ones + unsqueeze(scale_mlp, 1)) + unsqueeze(shift_mlp, 1)
# norm = self.ff_norm(x) * (ones + scale_mlp) + shift_mlp
ff_output = self.ff(norm)
if default_net().plugin_config.remove_input_padding:
x = x + gate_mlp * ff_output
else:
x = x + unsqueeze(gate_mlp, 1) * ff_output
return x
class TimestepEmbedding(Module):
def __init__(self, dim, freq_embed_dim=256, dtype=None):
super().__init__()
# self.time_embed = SinusPositionEmbedding(freq_embed_dim)
self.mlp1 = Linear(freq_embed_dim, dim, bias=True, dtype=dtype)
self.mlp2 = Linear(dim, dim, bias=True, dtype=dtype)
def forward(self, timestep):
t_freq = self.mlp1(timestep)
t_freq = silu(t_freq)
t_emb = self.mlp2(t_freq)
return t_emb
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