# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import List, Optional
import numpy as np
import tensorrt as trt
import torch
from .._common import default_net, precision
from .._utils import (fp32_array, int32_array, is_same_dtype, trt_dtype_to_np,
trt_dtype_to_str)
from ..functional import (ACT2FN, AllReduceFusionParams, AttentionMaskType,
Conditional, LayerNormType, PositionEmbeddingType,
RopeEmbeddingUtils, RotaryScalingType, Tensor,
allgather, arange, bert_attention, cast, clip, concat,
constant, embedding, expand, expand_dims, expand_mask,
generate_alibi_biases, generate_alibi_slopes,
gpt_attention, matmul)
from ..functional import max as fmax
from ..functional import (minimum, repeat_interleave, shape, slice, softmax,
split, unsqueeze, where)
from ..module import Module
from ..parameter import Parameter
from ..quantization import QuantMode
from ..quantization.functional import dequantize, quantize
from .linear import ColumnLinear, RowLinear
from .lora import LoraRuntimeParams
from .normalization import GroupNorm, LayerNorm, RmsNorm
from ..functional import maximum # isort:skip
layernorm_map = {
LayerNormType.LayerNorm: LayerNorm,
LayerNormType.RmsNorm: RmsNorm,
LayerNormType.GroupNorm: GroupNorm,
}
[docs]
def make_causal_mask(bsz, tgt_len, past_key_values_length, dtype):
_range = arange(start=constant(int32_array(0)),
end=tgt_len,
dtype=trt_dtype_to_str(dtype))
mask = repeat_interleave(_range, tgt_len, 0).view(concat([tgt_len,
tgt_len]))
mask = where(mask < mask.transpose(-1, -2), 1.0, 0.0)
zero = constant(fp32_array(0))
zero = expand_dims(zero, [0, 1])
zero = expand(zero, concat([tgt_len, past_key_values_length]))
mask = concat([zero, mask], dim=1)
mask *= np.finfo(trt_dtype_to_np(dtype)).min.item()
mask = mask.view(concat([1, 1, tgt_len, tgt_len + past_key_values_length]))
mask = expand(mask,
concat([bsz, 1, tgt_len, tgt_len + past_key_values_length]))
return mask
[docs]
def compute_relative_bias(query_length,
key_length,
num_buckets,
max_distance,
bidirectional,
rel_attn_table,
tp_size=1,
tp_group=None,
tp_rank=None):
def make_relative_position_bucket(relative_position, bidirectional,
num_buckets, max_distance):
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += where(relative_position > 0, num_buckets, 0)
relative_position = relative_position.abs()
else:
relative_position = 0 - minimum(relative_position, 0)
max_exact = num_buckets // 2
is_small = relative_position < max_exact
max_exact_fp = constant(fp32_array(max_exact))
tmp = cast(relative_position, "float32") / max_exact_fp
tmp = tmp.log()
const1 = math.log(max_distance / max_exact)
const2 = constant(fp32_array(num_buckets - max_exact))
relative_position_if_large = tmp / const1 * const2
relative_position_if_large = cast(relative_position_if_large, "int32")
relative_position_if_large = max_exact + relative_position_if_large
relative_position_if_large = minimum(relative_position_if_large,
num_buckets - 1)
relative_buckets += where(is_small, relative_position,
relative_position_if_large)
return relative_buckets
context_position = arange(start=constant(int32_array(0)),
end=query_length,
dtype=trt_dtype_to_str(trt.int32))
context_position = unsqueeze(context_position, -1)
memory_position = arange(start=constant(int32_array(0)),
end=key_length,
dtype=trt_dtype_to_str(trt.int32))
memory_position = unsqueeze(memory_position, 0)
relative_position = memory_position - context_position
relative_position_bucket = make_relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional,
num_buckets,
max_distance,
)
# shape (query_length, key_length, num_heads)
values = embedding(relative_position_bucket,
rel_attn_table,
tp_size=tp_size,
tp_group=tp_group,
tp_rank=tp_rank)
# shape (1, num_heads, query_length, key_length)
values = unsqueeze(values.permute([2, 0, 1]), 0)
return values
[docs]
class AttentionParams(object):
def __init__(self,
sequence_length: Tensor = None,
context_lengths: Tensor = None,
host_context_lengths: Tensor = None,
max_context_length: int = None,
host_request_types: Tensor = None,
encoder_input_lengths: Tensor = None,
encoder_max_input_length: Tensor = None,
host_runtime_perf_knobs: Tensor = None):
self.sequence_length = sequence_length
self.context_lengths = context_lengths
self.host_context_lengths = host_context_lengths
# max allowed context length. Required to
# compute scratch memory size.
self.max_context_length = max_context_length
self.host_request_types = host_request_types
self.encoder_input_lengths = encoder_input_lengths
self.encoder_max_input_length = encoder_max_input_length
self.host_runtime_perf_knobs = host_runtime_perf_knobs
# const parameters that will be reused by all layers.
self.embed_positions = None
self.rotary_inv_freq = None
self.embed_positions_for_gpt_attention = None
self.embed_positions_short_factors = None
self.embed_positions_long_factors = None
self.embed_positions_short_factors_for_attention_plugin = None
self.embed_positions_long_factors_for_attention_plugin = None
self.short_mscale = 1.0
self.long_mscale = 1.0
self.short_inv_freq = None
self.long_inv_freq = None
[docs]
def fill_attention_const_params_for_rope(
self,
embed_positions: Tensor = None,
rotary_inv_freq: Tensor = None,
embed_positions_for_gpt_attention: Tensor = None):
self.embed_positions = embed_positions
self.rotary_inv_freq = rotary_inv_freq
self.embed_positions_for_gpt_attention = embed_positions_for_gpt_attention
return self
[docs]
def fill_attention_const_params_for_long_rope(
self, embed_positions_short_factors, embed_positions_long_factors,
embed_positions_short_factors_for_attention_plugin,
embed_positions_long_factors_for_attention_plugin, short_mscale,
long_mscale, short_inv_freq, long_inv_freq):
self.embed_positions_short_factors = embed_positions_short_factors
self.embed_positions_long_factors = embed_positions_long_factors
self.embed_positions_short_factors_for_attention_plugin = embed_positions_short_factors_for_attention_plugin
self.embed_positions_long_factors_for_attention_plugin = embed_positions_long_factors_for_attention_plugin
self.short_mscale = short_mscale
self.long_mscale = long_mscale
self.short_inv_freq = short_inv_freq
self.long_inv_freq = long_inv_freq
return self
[docs]
def is_valid_cross_attn(self, do_cross_attention):
if do_cross_attention:
if self.encoder_input_lengths is None:
return False
if self.encoder_max_input_length is None:
return False
return True
[docs]
def is_valid(self, gpt_attention_plugin, remove_input_padding,
use_kv_cache):
if gpt_attention_plugin:
if use_kv_cache and self.sequence_length is None:
return False
if self.context_lengths is None:
return False
if self.host_request_types is None:
return False
if self.max_context_length is None:
return False
if self.host_runtime_perf_knobs is None:
return False
if remove_input_padding:
if self.host_context_lengths is None:
return False
if not gpt_attention_plugin:
return False
return True
[docs]
class SpecDecodingParams:
def __init__(self,
spec_decoding_is_generation_length_variable: bool = False,
spec_decoding_max_generation_length: int = 1,
spec_decoding_generation_lengths: Tensor = None,
spec_decoding_position_offsets: Tensor = None,
spec_decoding_packed_mask: Tensor = None):
self.spec_decoding_is_generation_length_variable = spec_decoding_is_generation_length_variable
self.spec_decoding_max_generation_length = spec_decoding_max_generation_length
self.spec_decoding_generation_lengths = spec_decoding_generation_lengths
self.spec_decoding_position_offsets = spec_decoding_position_offsets
self.spec_decoding_packed_mask = spec_decoding_packed_mask
[docs]
class KeyValueCacheParams:
def __init__(self,
past_key_value: List[Tensor] = None,
host_past_key_value_lengths: Tensor = None,
host_max_attention_window_sizes: Tensor = None,
host_sink_token_length: Tensor = None,
kv_cache_block_offsets: Tensor = None,
host_kv_cache_block_offsets: Tensor = None,
host_kv_cache_pool_pointers: Tensor = None,
host_kv_cache_pool_mapping: Tensor = None,
cache_indirection: Tensor = None,
past_key_value_length: Tensor = None,
cross_kv_cache_block_offsets: Tensor = None,
host_cross_kv_cache_block_offsets: Tensor = None,
host_cross_kv_cache_pool_pointers: Tensor = None,
host_cross_kv_cache_pool_mapping: Tensor = None):
self.past_key_value = past_key_value
self.host_past_key_value_lengths = host_past_key_value_lengths
self.host_max_attention_window_sizes = host_max_attention_window_sizes
self.host_sink_token_length = host_sink_token_length
self.kv_cache_block_offsets = kv_cache_block_offsets
self.host_kv_cache_block_offsets = host_kv_cache_block_offsets
self.host_kv_cache_pool_pointers = host_kv_cache_pool_pointers
self.host_kv_cache_pool_mapping = host_kv_cache_pool_mapping
self.cross_kv_cache_block_offsets = cross_kv_cache_block_offsets
self.host_cross_kv_cache_block_offsets = host_cross_kv_cache_block_offsets
self.host_cross_kv_cache_pool_pointers = host_cross_kv_cache_pool_pointers
self.host_cross_kv_cache_pool_mapping = host_cross_kv_cache_pool_mapping
self.cache_indirection = cache_indirection
# self.past_key_value_length = past_key_value_length
[docs]
def get_first_past_key_value(self):
if self.past_key_value is None:
return None
return self.past_key_value[0]
[docs]
def fill_none_tensor_list(self, list_size):
if self.past_key_value is None:
self.past_key_value = tuple([None] * list_size)
[docs]
def is_valid(self, gpt_attention_plugin):
if gpt_attention_plugin:
if self.host_past_key_value_lengths is None:
return False
if self.host_max_attention_window_sizes is None:
return False
if self.host_sink_token_length is None:
return False
if self.cache_indirection is None:
return False
return True
[docs]
class BlockSparseAttnParams:
def __init__(self,
block_size: int = 64,
homo_head_pattern: bool = False,
num_local_blocks: int = 16,
vertical_stride: int = 8):
self.block_size = block_size
self.homo_head_pattern = homo_head_pattern
self.num_local_blocks = num_local_blocks
self.vertical_stride = vertical_stride
[docs]
class Attention(Module):
def __init__(self,
*,
local_layer_idx,
hidden_size,
num_attention_heads,
num_kv_heads=None,
max_position_embeddings=1024,
num_layers=1,
apply_query_key_layer_scaling=False,
attention_head_size=None,
qk_layernorm=False,
layernorm_type=LayerNormType.LayerNorm,
inner_layernorm=False,
eps=1e-05,
attention_mask_type=AttentionMaskType.padding,
bias=True,
dtype=None,
position_embedding_type=PositionEmbeddingType.learned_absolute,
rotary_embedding_base=10000.0,
rotary_embedding_scaling=None,
rotary_embedding_percentage=1.0,
rope_scaling_short_factors=None,
rope_scaling_long_factors=None,
rope_scaling_short_mscale=None,
rope_scaling_long_mscale=None,
original_max_position_embeddings=1024,
tp_group=None,
tp_size=1,
tp_rank=0,
quant_mode: QuantMode = QuantMode(0),
q_scaling=1.0,
cross_attention=False,
relative_attention=False,
max_distance=0,
num_buckets=0,
dense_bias=None,
clip_qkv=None,
alibi_bias_max=8,
skip_cross_qkv=False,
max_attn_value=0.0,
block_sparse_params=None,
use_implicit_relative_attention=False,
reorder=False,
layer_idx_in_cache_pool=None):
super().__init__()
self.local_layer_idx = local_layer_idx
self.cross_attention = cross_attention
self.attention_mask_type = attention_mask_type
self.attention_head_size = hidden_size // num_attention_heads if attention_head_size is None else attention_head_size
self.num_kv_heads = num_kv_heads
self.layer_idx_in_cache_pool = layer_idx_in_cache_pool if layer_idx_in_cache_pool is not None else local_layer_idx
assert num_attention_heads % tp_size == 0, \
"num_attention_heads must be divisible by tp_size"
self.num_attention_heads = num_attention_heads // tp_size
self.num_attention_kv_heads = (
num_kv_heads + tp_size - 1
) // tp_size if num_kv_heads is not None else self.num_attention_heads
self.hidden_size = hidden_size
self.attention_hidden_size = self.attention_head_size * self.num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.original_max_position_embeddings = original_max_position_embeddings
self.bias = bias
self.tp_group = tp_group
self.tp_size = tp_size
self.tp_rank = tp_rank
self.dtype = dtype
self.dense_bias = dense_bias
if dense_bias is None:
self.dense_bias = bias
self.num_layers = num_layers
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
self.norm_factor = math.sqrt(self.attention_head_size)
self.q_scaling = q_scaling
if self.apply_query_key_layer_scaling:
self.norm_factor *= self.num_layers
self.q_scaling *= self.num_layers
# Whether to scale ALiBi bias. Mathematically, it's equivalent to
# normalizing QK after adding bias.
# - False, inv_sqrt_Dh * Q*K^T + alibi_bias
# - True, inv_sqrt_Dh * Q*K^T + inv_sqrt_Dh * alibi_bias
self.scale_alibi_bias = position_embedding_type == PositionEmbeddingType.alibi_with_scale
self.alibi_bias_max = alibi_bias_max
self.position_embedding_type = position_embedding_type
self.relative_attention = relative_attention
self.max_distance = max_distance
self.num_buckets = num_buckets
self.rotary_embedding_base = rotary_embedding_base
self.rotary_embedding_scaling = rotary_embedding_scaling
self.rotary_embedding_scale_type = RotaryScalingType.none
self.rotary_embedding_scale = 1.0
self.short_mscale = 1.0
self.long_mscale = 1.0
self.rotary_embedding_percentage = rotary_embedding_percentage
self.use_implicit_relative_attention = self.relative_attention and use_implicit_relative_attention
if rotary_embedding_scaling is not None:
rotary_scaling_type = rotary_embedding_scaling.get(
"type", rotary_embedding_scaling.get("rope_type"))
self.rotary_embedding_scale_type = RotaryScalingType.from_string(
rotary_scaling_type)
self.rotary_embedding_scale = rotary_embedding_scaling.get(
"factor", 1.0)
self.rotary_embedding_dim = 0
if self.position_embedding_type.is_rope():
self.rotary_embedding_dim = int(self.attention_head_size *
rotary_embedding_percentage)
elif self.position_embedding_type.is_alibi():
alibi_scale = 1. / self.norm_factor if self.scale_alibi_bias else 1.
alibi_slopes = generate_alibi_slopes(
self.num_attention_heads * self.tp_size,
tp_size=self.tp_size,
tp_rank=self.tp_rank,
alibi_scale=alibi_scale,
alibi_bias_max=self.alibi_bias_max)
self.register_parameter(
'alibi_slopes',
Parameter(alibi_slopes, dtype='float32', is_buffer=True))
self.quant_mode = quant_mode
self.max_attn_value = max_attn_value
self.register_parameter('kv_cache_scaling_factor', None)
self.register_parameter('attention_output_orig_quant_scale', None)
self.block_sparse_params = block_sparse_params if block_sparse_params is not None else BlockSparseAttnParams(
)
# The output feature size is therefore (h/tp + 2*kvh/tp) * d, where h is num_heads,
# d is head_size, kvh is the num_kv_heads and tp is tensor_parallel_size.
# In ColumnLinear op, the output dim is calculated by (h + 2*kvh) * d / tp,
# which matches the desired output size (h/tp + 2*kvh/tp) * d after splitting
# out dim is not necessarily hidden_size + kv specific size (in MQA/GQA), but num_heads * heads_size
# example: d_model != num_heads * head_size in Flan-T5/ByT5/Gemma
self.qkv = ColumnLinear(
hidden_size,
tp_size * self.num_attention_heads * self.attention_head_size +
(2 * tp_size * self.num_attention_kv_heads *
self.attention_head_size),
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False,
is_qkv=True)
self.dense = RowLinear(tp_size * self.num_attention_heads *
self.attention_head_size,
hidden_size,
bias=self.dense_bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size)
# see optimize_model's add_lora for LoRA initialization
self.qkv_lora = None
# per-layer relative attention table
if self.use_implicit_relative_attention:
self.rel_attn_table = Parameter(shape=(num_attention_heads //
tp_size, num_buckets),
dtype=dtype)
self.qk_layernorm = qk_layernorm
self.layernorm_type = layernorm_type
ln_type = layernorm_map[layernorm_type]
if self.qk_layernorm:
self.q_layernorm = ln_type(self.attention_head_size,
eps=eps,
dtype=dtype)
self.k_layernorm = ln_type(self.attention_head_size,
eps=eps,
dtype=dtype)
self.inner_layernorm = ln_type(self.hidden_size, dtype=dtype,
eps=eps) if inner_layernorm else None
if clip_qkv is not None:
self.clip_qkv = fp32_array([clip_qkv])
else:
self.clip_qkv = None
self.skip_cross_qkv = skip_cross_qkv
[docs]
@staticmethod
def create_attention_const_params(model_cls, config):
# get rotary parameters.
hidden_size = config.hidden_size
num_attention_heads = config.num_attention_heads
attention_head_size = config.head_size
max_position_embeddings = config.max_position_embeddings
position_embedding_type = config.position_embedding_type
rotary_embedding_base = getattr(config, 'rotary_base', 10000.0)
rotary_embedding_scaling = getattr(config, 'rotary_scaling', None)
rotary_embedding_percentage = getattr(config, 'rotary_pct', 1.0)
# only rope need the const parameters.
if not position_embedding_type.is_rope():
return
# attention head size
attention_head_size = hidden_size // num_attention_heads if attention_head_size is None else attention_head_size
# rotary embedding dim.
rotary_embedding_dim = getattr(
config, 'rotary_dim',
int(attention_head_size * rotary_embedding_percentage))
# rotary scaling.
rotary_embedding_scale_type = RotaryScalingType.none
rotary_embedding_scale = 1.0
if rotary_embedding_scaling is not None:
rotary_scaling_type = rotary_embedding_scaling.get(
"type", rotary_embedding_scaling.get("rope_type"))
rotary_embedding_scale_type = RotaryScalingType.from_string(
rotary_scaling_type)
rotary_embedding_scale = rotary_embedding_scaling.get("factor", 1.0)
if position_embedding_type == PositionEmbeddingType.long_rope:
rope_scaling_short_factors, rope_scaling_long_factors = None, None
rope_scaling_short_mscale, rope_scaling_long_mscale = None, None
original_max_position_embeddings = max_position_embeddings
if hasattr(config, "longrope_scaling_short_factors"):
rope_scaling_short_factors = np.asarray(
config.longrope_scaling_short_factors).astype(np.float32)
rope_scaling_long_factors = np.asarray(
config.longrope_scaling_long_factors).astype(np.float32)
original_max_position_embeddings = config.original_max_position_embeddings
if config.architecture == "Phi3SmallForCausalLM" or config.architecture == "PhiMoEForCausalLM":
rope_scaling_short_mscale = config.longrope_short_mscale
rope_scaling_long_mscale = config.longrope_long_mscale
embed_positions_short_factors, embed_positions_long_factors, \
(short_inv_freq, embed_positions_short_factors_for_attention_plugin), \
(long_inv_freq, embed_positions_long_factors_for_attention_plugin), mscale \
= RopeEmbeddingUtils.create_sinusoidal_positions_long_rope(
max_position_embeddings,
original_max_position_embeddings, rotary_embedding_dim,
rotary_embedding_base, rope_scaling_short_factors,
rope_scaling_long_factors, rope_scaling_short_mscale, rope_scaling_long_mscale)
if rope_scaling_short_mscale is not None:
assert rope_scaling_long_mscale is not None
short_mscale = rope_scaling_short_mscale
long_mscale = rope_scaling_long_mscale
else:
short_mscale = long_mscale = mscale
short_inv_freq = short_inv_freq.reshape(1, -1)
long_inv_freq = long_inv_freq.reshape(1, -1)
model_cls.register_parameter(
'embed_positions_short_factors',
Parameter(embed_positions_short_factors,
dtype='float32',
is_buffer=True))
model_cls.register_parameter(
'embed_positions_long_factors',
Parameter(embed_positions_long_factors,
dtype='float32',
is_buffer=True))
model_cls.register_parameter(
'embed_positions_short_factors_for_attention_plugin',
Parameter(
embed_positions_short_factors_for_attention_plugin,
dtype='float32',
is_buffer=True))
model_cls.register_parameter(
'embed_positions_long_factors_for_attention_plugin',
Parameter(embed_positions_long_factors_for_attention_plugin,
dtype='float32',
is_buffer=True))
model_cls.short_mscale = short_mscale
model_cls.long_mscale = long_mscale
model_cls.register_parameter(
'short_inv_freq',
Parameter(short_inv_freq, dtype='float32', is_buffer=True))
model_cls.register_parameter(
'long_inv_freq',
Parameter(long_inv_freq, dtype='float32', is_buffer=True))
else:
# Rotary const weights.
embed_positions = RopeEmbeddingUtils.create_sinusoidal_positions(
max_position_embeddings,
rotary_embedding_dim,
)
rotary_inv_freq, embed_positions_for_gpt_attention = RopeEmbeddingUtils.create_sinusoidal_positions_for_attention_plugin(
max_position_embeddings, rotary_embedding_dim,
rotary_embedding_base, rotary_embedding_scale,
rotary_embedding_scale_type, rotary_embedding_scaling)
model_cls.register_parameter(
'embed_positions',
Parameter(embed_positions, dtype='float32', is_buffer=True))
model_cls.register_parameter(
'rotary_inv_freq',
Parameter(rotary_inv_freq, dtype='float32', is_buffer=True))
model_cls.register_parameter(
'embed_positions_for_gpt_attention',
Parameter(embed_positions_for_gpt_attention,
dtype='float32',
is_buffer=True))
[docs]
@staticmethod
def fill_attention_params(model_cls, attention_params):
if model_cls.position_embedding_type.is_rope():
if attention_params is None:
attention_params = AttentionParams()
if model_cls.position_embedding_type == PositionEmbeddingType.long_rope:
if hasattr(model_cls, "embed_positions_short_factors"):
return attention_params.fill_attention_const_params_for_long_rope(
model_cls.embed_positions_short_factors.value,
model_cls.embed_positions_long_factors.value, model_cls.
embed_positions_short_factors_for_attention_plugin.
value, model_cls.
embed_positions_long_factors_for_attention_plugin.value,
model_cls.short_mscale, model_cls.long_mscale,
model_cls.short_inv_freq.value,
model_cls.long_inv_freq.value)
else:
return attention_params.fill_attention_const_params_for_rope(
model_cls.embed_positions.value,
model_cls.rotary_inv_freq.value,
model_cls.embed_positions_for_gpt_attention.value)
# Fill nothing.
return attention_params
[docs]
def forward(self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
spec_decoding_params=None,
kv_cache_params=None,
attention_params=None,
encoder_output: Optional[Tensor] = None,
position_embedding=None,
norm_before_bmm1=False,
lora_layer_params=None,
cross_kv_cache_gen: Optional[Tensor] = None,
cross_qkv_reuse: Optional[Tensor] = None,
reduce_fusion_params: Optional[AllReduceFusionParams] = None):
assert isinstance(hidden_states, Tensor)
spec_decoding_params = SpecDecodingParams(
) if spec_decoding_params is None else spec_decoding_params
alibi_slopes = None
if self.position_embedding_type.is_alibi():
alibi_slopes = self.alibi_slopes.value
if default_net().plugin_config.gpt_attention_plugin:
alibi_slopes = cast(alibi_slopes, hidden_states.dtype)
qkv_lora_params = None
if lora_layer_params is not None:
if not self.cross_attention:
qkv_lora_params = lora_layer_params.get_runtime_params(
0, "attn_qkv")
else:
qkv_lora_params = lora_layer_params.get_runtime_params(
0, "cross_attn_qkv")
unfuse_qkv_gemm = self.qkv is None
if unfuse_qkv_gemm:
qkv_gemm = [self.q, self.k, self.v]
qkv = [gemm(hidden_states) for gemm in qkv_gemm]
if default_net(
).plugin_config.lora_plugin and qkv_lora_params is not None:
lora = self.qkv.lora(hidden_states, qkv_lora_params)
kv_size = self.attention_head_size * self.num_attention_kv_heads
qkv_lora = split(lora,
[self.attention_hidden_size, kv_size, kv_size],
dim=1)
qkv = [tensor + lora for tensor, lora in zip(qkv, qkv_lora)]
else:
qkv = self.qkv(hidden_states, qkv_lora_params)
if self.clip_qkv is not None:
qkv = clip(qkv, -self.clip_qkv, self.clip_qkv)
if default_net().plugin_config.remove_input_padding:
if unfuse_qkv_gemm:
for tensor in qkv:
assert tensor.ndim() == 2
else:
assert qkv.ndim() == 2
if default_net(
).plugin_config.lora_plugin and qkv_lora_params is None and lora_layer_params is not None:
if not self.cross_attention:
q_lora_params = lora_layer_params.get_runtime_params(
0, "attn_q")
k_lora_params = lora_layer_params.get_runtime_params(
0, "attn_k")
v_lora_params = lora_layer_params.get_runtime_params(
0, "attn_v")
else:
q_lora_params = lora_layer_params.get_runtime_params(
0, "cross_attn_q")
k_lora_params = lora_layer_params.get_runtime_params(
0, "cross_attn_k")
v_lora_params = lora_layer_params.get_runtime_params(
0, "cross_attn_v")
assert (q_lora_params is not None and k_lora_params is not None and v_lora_params is not None) or \
(q_lora_params is None and k_lora_params is None and v_lora_params is None), "q_lora_params, k_lora_params and v_lora_params should be all enabled or all disabled at the same time."
if q_lora_params is not None and k_lora_params is not None and v_lora_params is not None:
qkv_lora_runtime_params = LoraRuntimeParams(
lora_ranks=[
q_lora_params.lora_ranks[0],
k_lora_params.lora_ranks[0],
v_lora_params.lora_ranks[0],
],
lora_weights_pointers=[
q_lora_params.lora_weights_pointers[0],
k_lora_params.lora_weights_pointers[0],
v_lora_params.lora_weights_pointers[0],
],
host_request_types=q_lora_params.host_request_types,
host_context_lengths=q_lora_params.host_context_lengths,
max_encoder_context_length=q_lora_params.
max_encoder_context_length,
host_encoder_input_lengths=q_lora_params.
host_encoder_input_lengths,
)
q_lora, k_lora, v_lora = self.qkv_lora(hidden_states,
qkv_lora_runtime_params)
qkv_lora = concat([q_lora, k_lora, v_lora],
dim=q_lora.rank() - 1)
qkv = qkv + qkv_lora
if self.qk_layernorm:
base_shape = shape(qkv, 0) if qkv.ndim() == 2 else concat(
[shape(qkv, 0), shape(qkv, 1)])
# here we assume that q, k and v have the same number of attention heads
# TODO: allow different number of attention heads for q, k and v.
qkv = qkv.view(
concat([
base_shape, self.num_attention_heads, 3,
self.attention_head_size
]))
query, key, value = split(qkv, 1, dim=qkv.ndim() - 2)
q_shape = concat([
base_shape, self.num_attention_heads, self.attention_head_size
])
query = query.view(q_shape)
key = key.view(q_shape)
value = value.view(q_shape)
query = self.q_layernorm(query)
key = self.k_layernorm(key)
qkv = concat([query, key, value], dim=query.ndim() - 2)
qkv = qkv.view(concat([base_shape, self.attention_hidden_size * 3]))
if self.position_embedding_type == PositionEmbeddingType.chatglm:
qkv = RopeEmbeddingUtils.apply_rotary_pos_emb_chatglm(
qkv,
position_embedding,
self.num_attention_heads,
self.attention_head_size,
self.max_position_embeddings,
self.rotary_embedding_scale,
default_net().plugin_config.remove_input_padding,
)
self.rotary_embedding_scale_type = RotaryScalingType.none
self.rotary_embedding_scale = 1.0
paged_kv_cache = default_net().plugin_config.paged_kv_cache
assert attention_params is None or attention_params.is_valid(
default_net().plugin_config.gpt_attention_plugin,
default_net().plugin_config.remove_input_padding, use_cache)
if use_cache:
assert kv_cache_params is None or kv_cache_params.is_valid(
default_net().plugin_config.gpt_attention_plugin)
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
)
# if cross attention, cross QKV only needs to be calculated once in the
# 1st decoding step --> write to cross KV cache --> remains constant
# during the entire decoding steps.
# 1st and >1st steps are distinguished by a boolean tensor `cross_kv_cache_gen` passed at runtime
# also, cross KV cache max length is set from encoder output seqlen,
# this maps to the max context length concept in decoder-only models
cross_qkv = None
if self.cross_attention and encoder_output:
assert isinstance(encoder_output, Tensor)
def compute_cross_qkv(encoder_output):
cross_qkv = self.qkv(encoder_output, qkv_lora_params)
if default_net(
).plugin_config.lora_plugin and qkv_lora_params is None and lora_layer_params is not None:
cross_q_lora, cross_k_lora, cross_v_lora = self.qkv_lora(
encoder_output,
qkv_lora_runtime_params,
is_cross_attention=True)
cross_qkv_lora = concat(
[cross_q_lora, cross_k_lora, cross_v_lora],
dim=cross_q_lora.rank() - 1)
cross_qkv = cross_qkv + cross_qkv_lora
return cross_qkv
if self.skip_cross_qkv:
conditional = Conditional(cross_kv_cache_gen)
cond_in1 = conditional.add_input(encoder_output)
cond_in2 = conditional.add_input(cross_qkv_reuse)
## True branch: context phase, compute cross qkv
cross_qkv_true = compute_cross_qkv(cond_in1)
## False branch: generation phase, no compute but need to obey shape constraints
# because TRT's IfConditional requires the output shape of two subgraphs to be identical
# our 1st attempt was to stack encoder_output [B, S, H] or [N, H] --> cross qkv [B, S, 3*H] or [N, 3*H],
# but it still introduces unnecessary concat. A better solution is to create a dummy torch tensor `cross_qkv_resue`
# with the correct shape and reuse it in every generation step
cross_qkv_false = cond_in2
cross_qkv = conditional.add_output(cross_qkv_true,
cross_qkv_false)
else:
cross_qkv = compute_cross_qkv(encoder_output)
if default_net().plugin_config.gpt_attention_plugin:
if self.cross_attention and (past_key_value is not None):
past_key_value = kv_cache_params.past_key_value[1]
assert self.attention_mask_type in [
AttentionMaskType.causal, AttentionMaskType.bidirectional,
AttentionMaskType.bidirectionalglm,
AttentionMaskType.blocksparse
], 'Plugin only support masked MHA.'
# KV cache scales.
if self.kv_cache_scaling_factor is not None:
kv_orig_quant_scale = constant(fp32_array(
[1.0])) / self.kv_cache_scaling_factor.value
kv_quant_orig_scale = self.kv_cache_scaling_factor.value
else:
kv_orig_quant_scale = None
kv_quant_orig_scale = None
# Attention output scales
assert (
not default_net().plugin_config.use_fp8_context_fmha
) or self.quant_mode.has_fp8_qdq(
), "FP8 Context FMHA must be used together with the fp8 quantization workflow."
attention_output_orig_quant_scale = self.attention_output_orig_quant_scale.value if self.attention_output_orig_quant_scale is not None else None
if self.position_embedding_type == PositionEmbeddingType.long_rope:
short = slice(
attention_params.
embed_positions_short_factors_for_attention_plugin,
concat([0, 0, 0]),
concat([
self.max_position_embeddings,
self.rotary_embedding_dim // 2, 2
]))
long = slice(
attention_params.
embed_positions_long_factors_for_attention_plugin,
concat([0, 0, 0]),
concat([
self.max_position_embeddings,
self.rotary_embedding_dim // 2, 2
]))
short = short.view((1, -1))
long = long.view((1, -1))
embed_positions = concat([short, long], dim=0)
select = where(
fmax(attention_params.sequence_length, dim=0) <=
self.original_max_position_embeddings, 0, 1)
rotary_cos_sin = slice(embed_positions,
concat([select, 0]),
sizes=concat([1, shape(long, 1)]))
short_inv_freq = attention_params.short_inv_freq
long_inv_freq = attention_params.long_inv_freq
concat_inv_freq = concat([short_inv_freq, long_inv_freq], dim=0)
rotary_inv_freq = slice(concat_inv_freq,
concat([select, 0]),
sizes=concat(
[1, shape(long_inv_freq, 1)]))
rotary_inv_freq = rotary_inv_freq.view((-1, ))
else:
# The rotary inv freq can be pre-computed.
rotary_inv_freq = getattr(attention_params, "rotary_inv_freq",
None)
# Rotary cos/sin cache.
rotary_cos_sin = getattr(attention_params,
"embed_positions_for_gpt_attention",
None)
# check if the cache is provided.
if self.position_embedding_type.is_rope():
assert (rotary_inv_freq is not None) and (
rotary_cos_sin is not None
), "rotary_inv_freq and embed_positions_for_gpt_attention must be provided."
context, past_key_value = gpt_attention(
qkv=qkv,
past_key_value=past_key_value,
sequence_length=attention_params.sequence_length,
host_past_key_value_lengths=kv_cache_params.
host_past_key_value_lengths,
host_max_attention_window_sizes=kv_cache_params.
host_max_attention_window_sizes,
host_sink_token_length=kv_cache_params.host_sink_token_length,
context_lengths=attention_params.context_lengths,
cache_indirection=kv_cache_params.cache_indirection,
host_request_types=attention_params.host_request_types,
layer_idx=self.local_layer_idx,
num_heads=self.num_attention_heads,
num_kv_heads=self.num_attention_kv_heads,
layer_idx_in_cache_pool=self.layer_idx_in_cache_pool,
hidden_size_per_head=self.attention_head_size,
q_scaling=self.q_scaling,
rotary_embedding_dim=self.rotary_embedding_dim,
rotary_embedding_base=self.rotary_embedding_base,
rotary_embedding_scale_type=self.rotary_embedding_scale_type,
rotary_embedding_short_m_scale=attention_params.short_mscale,
rotary_embedding_long_m_scale=attention_params.long_mscale,
rotary_embedding_scale=self.rotary_embedding_scale,
rotary_embedding_max_positions=self.max_position_embeddings,
rotary_embedding_original_max_positions=self.
original_max_position_embeddings,
position_embedding_type=self.position_embedding_type,
rotary_inv_freq=rotary_inv_freq,
rotary_cos_sin=rotary_cos_sin,
kv_orig_quant_scale=kv_orig_quant_scale,
kv_quant_orig_scale=kv_quant_orig_scale,
attention_output_orig_quant_scale=
attention_output_orig_quant_scale,
kv_cache_quant_mode=self.quant_mode,
max_context_length=attention_params.max_context_length,
mask_type=self.attention_mask_type,
block_sparse_block_size=self.block_sparse_params.block_size,
block_sparse_homo_head_pattern=self.block_sparse_params.
homo_head_pattern,
block_sparse_num_local_blocks=self.block_sparse_params.
num_local_blocks,
block_sparse_vertical_stride=self.block_sparse_params.
vertical_stride,
alibi_slopes=alibi_slopes,
tp_size=self.tp_size,
tp_rank=self.tp_rank,
kv_cache_block_offsets=kv_cache_params.kv_cache_block_offsets
if not self.cross_attention else
kv_cache_params.cross_kv_cache_block_offsets,
host_kv_cache_block_offsets=kv_cache_params.
host_kv_cache_block_offsets if not self.cross_attention else
kv_cache_params.host_cross_kv_cache_block_offsets,
host_kv_cache_pool_pointers=kv_cache_params.
host_kv_cache_pool_pointers if not self.cross_attention else
kv_cache_params.host_cross_kv_cache_pool_pointers,
host_kv_cache_pool_mapping=kv_cache_params.
host_kv_cache_pool_mapping if not self.cross_attention else
kv_cache_params.host_cross_kv_cache_pool_mapping,
do_cross_attention=self.cross_attention,
cross_qkv=cross_qkv,
cross_qkv_length=attention_params.encoder_max_input_length,
encoder_input_lengths=attention_params.encoder_input_lengths,
relative_attention_bias=self.rel_attn_table.value
if self.relative_attention else None,
max_distance=self.max_distance,
host_context_lengths=attention_params.host_context_lengths,
use_cache=use_cache,
spec_decoding_is_generation_length_variable=spec_decoding_params
.spec_decoding_is_generation_length_variable,
spec_decoding_max_generation_length=spec_decoding_params.
spec_decoding_max_generation_length,
spec_decoding_generation_lengths=spec_decoding_params.
spec_decoding_generation_lengths,
spec_decoding_position_offsets=spec_decoding_params.
spec_decoding_position_offsets,
spec_decoding_packed_mask=spec_decoding_params.
spec_decoding_packed_mask,
qk_tanh_scale=self.max_attn_value,
host_runtime_perf_knobs=attention_params.host_runtime_perf_knobs
)
else:
# plain TensorRT mode
assert paged_kv_cache == False
def transpose_for_scores(x,
rotary: bool = False,
is_kv: bool = False):
_num_attention_heads = self.num_attention_kv_heads if is_kv else self.num_attention_heads
new_x_shape = concat([
shape(x, 0),
shape(x, 1), _num_attention_heads, self.attention_head_size
])
if rotary:
return x.view(new_x_shape)
else:
return x.view(new_x_shape).permute([0, 2, 1, 3])
# qkv after projection is of shape
# [bs, seqlen, (num_attention_heads + 2 * num_attention_kv_heads), attention_head_size].
# The projected and split qkv after transpose_for_scores():
# Q[bs, num_attention_heads, seqlen, attention_head_size]
# K[bs, num_attention_kv_heads, seqlen, attention_head_size]
# V[bs, num_attention_kv_heads, seqlen, attention_head_size]
kv_size = self.attention_head_size * self.num_attention_kv_heads
if unfuse_qkv_gemm:
query, key, value = qkv[0], qkv[1], qkv[2]
else:
query, key, value = split(
qkv, [self.attention_hidden_size, kv_size, kv_size], dim=2)
# in cross attention mode, replace kv by encoder_output
if self.cross_attention and encoder_output is not None:
encoder_qkv = self.qkv(encoder_output)
_, key, value = split(
encoder_qkv, [self.attention_hidden_size, kv_size, kv_size],
dim=2)
query = transpose_for_scores(
query, rotary=self.position_embedding_type.is_rope())
key = transpose_for_scores(
key, is_kv=True, rotary=self.position_embedding_type.is_rope())
value = transpose_for_scores(value, is_kv=True)
if self.position_embedding_type.is_rope():
if self.position_embedding_type == PositionEmbeddingType.long_rope:
sequence_length = shape(hidden_states, 1)
short = slice(
attention_params.embed_positions_short_factors,
concat([0, 0, 0]),
concat([
1,
max(sequence_length,
self.original_max_position_embeddings),
self.rotary_embedding_dim
]))
long = slice(
attention_params.embed_positions_long_factors,
concat([0, 0, 0]),
concat([
1,
max(sequence_length,
self.original_max_position_embeddings),
self.rotary_embedding_dim
]))
embed_positions = concat([short, long], dim=0)
select = where(
sequence_length <=
self.original_max_position_embeddings, 0, 1)
embed_positions = slice(embed_positions,
concat([select, 0, 0]),
sizes=shape(short))
embed_positions = cast(embed_positions, self.dtype)
elif is_same_dtype(self.dtype, trt.bfloat16):
embed_positions = cast(attention_params.embed_positions,
trt.bfloat16)
else:
embed_positions = cast(attention_params.embed_positions,
query.dtype)
if self.rotary_embedding_dim is not None:
# When shape(hidden_states, 1) > 1(Context phase), the embedding start from 0,
# otherwise (Generation phase) move start to position
if not use_cache:
# Only context phase is involved when kv cache is disabled.
start = 0
else:
start = where(
shape(hidden_states, 1) > 1, 0,
shape(past_key_value, 3))
size = where(
shape(hidden_states, 1) > 1, shape(hidden_states, 1), 1)
sincos = slice(embed_positions, concat([0, start, 0]),
concat([1, size, self.rotary_embedding_dim]))
sin, cos = split(sincos,
self.rotary_embedding_dim // 2,
dim=-1)
key_rot_size = concat([
shape(key, 0),
shape(key, 1),
shape(key, 2), self.rotary_embedding_dim
])
query_rot_size = concat([
shape(query, 0),
shape(query, 1),
shape(query, 2), self.rotary_embedding_dim
])
remaining = shape(key, 3) - self.rotary_embedding_dim
key_pass_size = concat([
shape(key, 0),
shape(key, 1),
shape(key, 2), remaining
])
query_pass_size = concat([
shape(query, 0),
shape(query, 1),
shape(query, 2), remaining
])
k_rot = slice(key, [0, 0, 0, 0], key_rot_size)
k_pass = slice(key, [0, 0, 0, self.rotary_embedding_dim],
key_pass_size)
q_rot = slice(query, [0, 0, 0, 0], query_rot_size)
q_pass = slice(query, [0, 0, 0, self.rotary_embedding_dim],
query_pass_size)
k_rot = RopeEmbeddingUtils.apply_rotary_pos_emb(
k_rot, [cos, sin], self.position_embedding_type)
q_rot = RopeEmbeddingUtils.apply_rotary_pos_emb(
q_rot, [cos, sin], self.position_embedding_type)
key = concat([k_rot, k_pass], dim=3)
query = concat([q_rot, q_pass], dim=3)
else:
key = RopeEmbeddingUtils.apply_rotary_pos_emb(
key, [cos, sin], self.position_embedding_type)
query = RopeEmbeddingUtils.apply_rotary_pos_emb(
query, [cos, sin], self.position_embedding_type)
key = key.permute([0, 2, 1, 3])
query = query.permute([0, 2, 1, 3])
if past_key_value is not None and not self.cross_attention:
if self.kv_cache_scaling_factor is not None:
past_key_value = dequantize(
past_key_value,
self.kv_cache_scaling_factor.value,
output_type=self.dtype)
# past_key_value [bs, 2, num_heads, max_seq_len, head_dim]
past_key, past_value = split(past_key_value, 1, dim=1)
key_shape = concat([
shape(past_key, 0),
shape(past_key, 2),
shape(past_key, 3),
shape(past_key, 4)
])
past_key = past_key.view(key_shape, zero_is_placeholder=False)
past_value = past_value.view(key_shape,
zero_is_placeholder=False)
key = concat([past_key, key], dim=2)
value = concat([past_value, value], dim=2)
if use_cache:
key_inflated_shape = concat([
shape(key, 0), 1,
shape(key, 1),
shape(key, 2),
shape(key, 3)
])
inflated_key = key.view(key_inflated_shape,
zero_is_placeholder=False)
inflated_value = value.view(key_inflated_shape,
zero_is_placeholder=False)
past_key_value = concat([inflated_key, inflated_value], dim=1)
# TRT quantizes the tensor value by doing `cast(clip(fp_value / scale))` while
# the plugin quantizes it by doing `cast(clip(fp_value * scale))`.
if self.kv_cache_scaling_factor is not None:
past_key_value = quantize(
past_key_value,
self.kv_cache_scaling_factor.value,
dtype='fp8'
if self.quant_mode.has_fp8_kv_cache() else 'int8')
# MQA broadcast
if self.num_attention_heads // self.num_attention_kv_heads > 1:
key = repeat_interleave(
key,
self.num_attention_heads // self.num_attention_kv_heads, 1)
value = repeat_interleave(
value,
self.num_attention_heads // self.num_attention_kv_heads, 1)
key_length = shape(key, 2)
# The following code creates a 2D tensor with 0s in the lower triangular (including the diagonal) and
# +INF in the upper triangular parts. This bias tensor will be added to the output of the Q*K^T matrix
# multiplication (BMM1). The +INF elements will be transformed to 0s by the Softmax operator that
# follows. The elements that corresponds to 0s in the bias are unaffected by the bias tensor.
#
# Note that when we added to another bias tensor B (for example, with AliBi), the values in the lower-
# triangular part of the B tensor are not affected and the upper-triangular ones are set to +INF.
if self.attention_mask_type == AttentionMaskType.causal and not self.cross_attention:
if self.position_embedding_type.is_alibi():
query_length = shape(query, 2)
# bsz, tatget_length, past_key_value_length
buffer = make_causal_mask(shape(query, 0), query_length,
key_length - query_length,
trt.float32)
starts = concat([0, 0, 0, 0])
sizes = concat([1, 1, query_length, key_length])
generated_mask = slice(buffer, starts, sizes)
else:
query_length = shape(query, 2)
starts = concat([0, 0, key_length - query_length, 0])
sizes = concat([1, 1, query_length, key_length])
if self.position_embedding_type == PositionEmbeddingType.long_rope:
buf_shape = (self.original_max_position_embeddings,
self.original_max_position_embeddings)
else:
buf_shape = (self.max_position_embeddings,
self.max_position_embeddings)
select_buf = np.expand_dims(
np.tril(np.ones(buf_shape)).astype(bool), (0, 1))
select_buf = np.logical_not(select_buf)
mask_buf = np.zeros_like(select_buf, np.float32)
mask_buf[select_buf] = float('-inf')
buffer = constant(mask_buf)
generated_mask = slice(buffer, starts, sizes)
elif self.attention_mask_type == AttentionMaskType.bidirectional and not self.cross_attention:
query_length = shape(query, 2)
zero_buf = np.expand_dims(
np.zeros((self.max_position_embeddings,
self.max_position_embeddings),
dtype=np.float32), (0, 1))
zero_buf[:, :, :-1, -1] = 1
zero_buf *= -10000
mask = constant(zero_buf)
# context phase, query_length
mask_size = where(query_length > 1, query_length, 1)
mask_start = where(query_length > 1,
self.max_position_embeddings - mask_size, 1)
start = concat([0, 0, mask_start, mask_start])
size = concat([1, 1, mask_size, mask_size])
generated_mask = slice(mask, start, size)
if attention_mask is not None:
if self.cross_attention:
batch_size = shape(attention_mask, 0)
query_len = shape(attention_mask, 1)
encoder_input_len = shape(attention_mask, 2)
attention_mask = attention_mask.view(
concat([batch_size, 1, query_len, encoder_input_len]))
attention_mask = where(attention_mask == 0, float('-inf'),
0.0)
else:
attention_mask = expand_mask(attention_mask,
shape(query, 2))
bias = attention_mask
if self.position_embedding_type.is_alibi():
alibi_biases = generate_alibi_biases(alibi_slopes, key_length)
bias = alibi_biases if bias is None else bias + alibi_biases
if self.relative_attention:
query_length = shape(query, 2)
if self.use_implicit_relative_attention:
relative_bias = compute_relative_bias(
query_length + key_length - 1,
key_length,
self.num_buckets,
self.max_distance,
False, # bidirectional
self.rel_attn_table.value.transpose(1, 0),
tp_size=self.tp_size,
tp_group=self.tp_group,
tp_rank=self.tp_rank)
else:
relative_bias = unsqueeze(self.rel_attn_table.value, 0)
start = concat([0, 0, query_length + key_length - 2, 0])
size = concat([
shape(relative_bias, 0),
shape(relative_bias, 1), 1, key_length
])
relative_bias = slice(relative_bias, start, size)
key = key.permute([0, 1, 3, 2])
with precision('float32'):
if norm_before_bmm1:
# Apply norm on query earlier to prevent matmul fp16 overflow.
query /= (self.q_scaling * self.norm_factor)
attention_scores = matmul(query, key)
if not norm_before_bmm1:
attention_scores = attention_scores / (self.q_scaling *
self.norm_factor)
if self.max_attn_value > 0:
attention_scores = self.max_attn_value * ACT2FN['tanh'](
attention_scores / self.max_attn_value)
if self.attention_mask_type in [
AttentionMaskType.causal,
AttentionMaskType.bidirectional
] and not self.cross_attention:
bias = generated_mask if bias is None else bias + generated_mask
if bias is not None:
bias = cast(bias, attention_scores.dtype)
attention_scores = attention_scores + bias
if self.relative_attention:
attention_scores = attention_scores + relative_bias
attention_probs = softmax(attention_scores, dim=-1)
# A dummy reshape WAR for mha fusion
attention_probs = attention_probs.view(
concat([
shape(attention_probs, 0),
shape(attention_probs, 1),
shape(attention_probs, 2),
shape(value, 2)
]))
context = matmul(attention_probs, value,
use_fp32_acc=False).permute([0, 2, 1, 3])
context = context.view(
concat([
shape(context, 0),
shape(context, 1), self.attention_hidden_size
]))
dense_lora_params = None
if lora_layer_params is not None:
dense_lora_params = lora_layer_params.get_runtime_params(
0, "attn_dense")
if self.inner_layernorm is not None:
context = self.inner_layernorm(context)
context = self.dense(context,
lora_runtime_params=dense_lora_params,
reduce_fusion_params=reduce_fusion_params)
if use_cache:
return (context, past_key_value)
else:
return context
[docs]
def set_rel_attn_table(self, max_seq_len, precomputed_relative_attention):
self.rel_attn_table = Parameter(shape=(self.num_attention_heads,
max_seq_len + 1,
max_seq_len + 1),
dtype=self.dtype)
self.rel_attn_table.value = precomputed_relative_attention
[docs]
def postprocess(self, tllm_key, weights, **kwargs):
if tllm_key.endswith("kv_cache_scaling_factor") and weights is None:
return {tllm_key: torch.ones(1, )}
else:
return {tllm_key: weights}
[docs]
class BertAttention(Module):
def __init__(self,
hidden_size,
num_attention_heads,
max_position_embeddings=1024,
num_layers=1,
attention_head_size=None,
num_kv_heads=None,
q_scaling=1.0,
apply_query_key_layer_scaling=False,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
tp_rank=0,
cp_group=None,
cp_size=1,
relative_attention=False,
max_distance=0,
num_buckets=0,
quant_mode=QuantMode(0)):
super().__init__()
self.attention_head_size = hidden_size // num_attention_heads if attention_head_size is None else attention_head_size
self.num_attention_heads = num_attention_heads // tp_size
self.num_attention_kv_heads = (
num_kv_heads + tp_size - 1
) // tp_size if num_kv_heads is not None else self.num_attention_heads
self.hidden_size = hidden_size
self.attention_hidden_size = self.attention_head_size * self.num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.norm_factor = math.sqrt(self.attention_head_size)
self.tp_group = tp_group
self.tp_size = tp_size
self.tp_rank = tp_rank
self.cp_group = cp_group
self.cp_size = cp_size
self.num_layers = num_layers
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
self.norm_factor = math.sqrt(self.attention_head_size)
self.q_scaling = q_scaling
if self.apply_query_key_layer_scaling:
self.norm_factor *= self.num_layers
self.q_scaling *= self.num_layers
self.dtype = dtype
# add quant mode to control quantization
self.quant_mode = quant_mode
self.relative_attention = relative_attention
self.max_distance = max_distance
self.num_buckets = num_buckets
# out dim is not necessarily hidden_size + kv specific size (in MQA/GQA), but num_heads * heads_size
# example: d_model != num_heads * head_size in Flan-T5
self.qkv = ColumnLinear(hidden_size,
tp_size * self.attention_hidden_size +
(2 * tp_size * self.num_attention_kv_heads *
self.attention_head_size),
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False,
is_qkv=True)
self.dense = RowLinear(tp_size * self.num_attention_heads *
self.attention_head_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size)
# see optimize_model's add_lora for LoRA initialization
self.qkv_lora = None
# per-layer relative attention table
if relative_attention:
self.rel_attn_table = Parameter(shape=(num_attention_heads //
tp_size, num_buckets),
dtype=dtype)
[docs]
def forward(self,
hidden_states: Tensor,
attention_mask=None,
input_lengths=None,
max_input_length=None,
lora_layer_params=None):
assert isinstance(hidden_states, Tensor)
qkv_lora_params = None
if lora_layer_params is not None:
qkv_lora_params = lora_layer_params.get_runtime_params(
0, "attn_qkv")
qkv = self.qkv(hidden_states, qkv_lora_params)
if default_net().plugin_config.remove_input_padding:
assert qkv.ndim() == 2
if default_net(
).plugin_config.lora_plugin and qkv_lora_params is None and lora_layer_params is not None:
q_lora_params = lora_layer_params.get_runtime_params(0, "attn_q")
k_lora_params = lora_layer_params.get_runtime_params(0, "attn_k")
v_lora_params = lora_layer_params.get_runtime_params(0, "attn_v")
assert (q_lora_params is not None and k_lora_params is not None and v_lora_params is not None) or \
(q_lora_params is None and k_lora_params is None and v_lora_params is None), "q_lora_params, k_lora_params and v_lora_params should be all enabled or all disabled at the same time."
if q_lora_params is not None and k_lora_params is not None and v_lora_params is not None:
qkv_lora_params = LoraRuntimeParams(
lora_ranks=[
q_lora_params.lora_ranks[0],
k_lora_params.lora_ranks[0],
v_lora_params.lora_ranks[0],
],
lora_weights_pointers=[
q_lora_params.lora_weights_pointers[0],
k_lora_params.lora_weights_pointers[0],
v_lora_params.lora_weights_pointers[0],
],
host_request_types=q_lora_params.host_request_types,
host_context_lengths=q_lora_params.host_context_lengths)
q_lora, k_lora, v_lora = self.qkv_lora(hidden_states,
qkv_lora_params)
qkv_lora = concat([q_lora, k_lora, v_lora],
dim=q_lora.rank() - 1)
qkv = qkv + qkv_lora
if default_net().plugin_config.bert_attention_plugin:
# TRT plugin mode
assert input_lengths is not None
assert self.cp_size == 1
context = bert_attention(
qkv,
input_lengths,
self.num_attention_heads,
self.attention_head_size,
q_scaling=self.q_scaling,
relative_attention=self.relative_attention,
max_distance=self.max_distance,
relative_attention_bias=self.rel_attn_table.value
if self.relative_attention else None,
max_input_length=max_input_length)
else:
# plain TRT mode
def transpose_for_scores(x):
new_x_shape = concat([
shape(x, 0),
shape(x, 1), self.num_attention_heads,
self.attention_head_size
])
return x.view(new_x_shape).permute([0, 2, 1, 3])
kv_size = self.attention_head_size * self.num_attention_kv_heads
query, key, value = split(
qkv, [self.attention_hidden_size, kv_size, kv_size], dim=2)
if self.cp_size > 1 and self.cp_group is not None:
key = allgather(key, self.cp_group, gather_dim=1)
value = allgather(value, self.cp_group, gather_dim=1)
query = transpose_for_scores(query)
key = transpose_for_scores(key)
value = transpose_for_scores(value)
key = key.permute([0, 1, 3, 2])
attention_scores = matmul(query, key, use_fp32_acc=False)
attention_scores = attention_scores / (self.q_scaling *
self.norm_factor)
if self.relative_attention:
query_len = shape(attention_scores, 2)
key_len = shape(attention_scores, 3)
bias = compute_relative_bias(
query_len,
key_len,
self.num_buckets,
self.max_distance,
True, # bidirectional
self.rel_attn_table.value.transpose(1, 0),
tp_size=self.tp_size,
tp_group=self.tp_group,
tp_rank=self.tp_rank)
attention_scores = attention_scores + bias
if attention_mask is not None:
attention_mask = expand_mask(attention_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).permute([0, 2, 1, 3])
context = context.view(
concat([
shape(context, 0),
shape(context, 1), self.attention_hidden_size
]))
dense_lora_params = None
if lora_layer_params is not None:
dense_lora_params = lora_layer_params.get_runtime_params(
0, "attn_dense")
context = self.dense(context, lora_runtime_params=dense_lora_params)
return context
[docs]
class CogVLMAttention(Attention):
def __init__(
self,
*,
local_layer_idx,
hidden_size,
num_attention_heads,
num_kv_heads=None,
max_position_embeddings=1024,
attention_mask_type=AttentionMaskType.causal,
bias=True,
dtype=None,
position_embedding_type=PositionEmbeddingType.learned_absolute,
rotary_embedding_base=10000.0,
rotary_embedding_scaling=None,
tp_group=None,
tp_size=1,
tp_rank=0,
quant_mode: QuantMode = QuantMode(0),
dense_bias=None,
):
super().__init__(local_layer_idx=local_layer_idx,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
num_kv_heads=num_kv_heads,
max_position_embeddings=max_position_embeddings,
dtype=dtype,
attention_mask_type=attention_mask_type,
bias=bias,
position_embedding_type=position_embedding_type,
rotary_embedding_base=rotary_embedding_base,
rotary_embedding_scaling=rotary_embedding_scaling,
tp_group=tp_group,
tp_size=tp_size,
tp_rank=tp_rank,
quant_mode=quant_mode)
self.vis_qkv = ColumnLinear(
hidden_size,
tp_size * self.num_attention_heads * self.attention_head_size +
(2 * tp_size * self.num_attention_kv_heads *
self.attention_head_size),
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False,
is_qkv=True)
self.vis_dense = RowLinear(tp_size * self.num_attention_heads *
self.attention_head_size,
hidden_size,
bias=self.dense_bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size)
[docs]
def forward(self,
hidden_states: Tensor,
use_cache=False,
kv_cache_params=None,
attention_params=None,
vision_token_mask=None,
position_embedding=None):
assert isinstance(hidden_states, Tensor)
assert (default_net().plugin_config.gpt_attention_plugin)
vision_qkv = self.vis_qkv(hidden_states)
language_qkv = self.qkv(hidden_states)
qkv = where(vision_token_mask, vision_qkv, language_qkv)
qkv = RopeEmbeddingUtils.apply_rotary_pos_emb_cogvlm(
qkv, position_embedding, self.num_attention_heads,
self.attention_head_size, self.max_position_embeddings,
self.rotary_embedding_scale,
default_net().plugin_config.remove_input_padding)
assert attention_params is None or attention_params.is_valid(
default_net().plugin_config.gpt_attention_plugin,
default_net().plugin_config.remove_input_padding, use_cache)
assert kv_cache_params is None or kv_cache_params.is_valid(
default_net().plugin_config.gpt_attention_plugin)
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
)
if default_net().plugin_config.gpt_attention_plugin:
if self.cross_attention and (past_key_value is not None):
past_key_value = kv_cache_params.past_key_value[1]
assert self.attention_mask_type in [
AttentionMaskType.causal, AttentionMaskType.bidirectional,
AttentionMaskType.bidirectionalglm
], 'Plugin only support masked MHA.'
# KV cache scales.
kv_orig_quant_scale = constant(
fp32_array([1.0])
) / self.kv_cache_scaling_factor.value if self.quant_mode.has_kv_cache_quant(
) else None
kv_quant_orig_scale = self.kv_cache_scaling_factor.value if self.quant_mode.has_kv_cache_quant(
) else None
# Attention output scales
assert (
not default_net().plugin_config.use_fp8_context_fmha
) or self.quant_mode.has_fp8_qdq(
), "FP8 Context FMHA must be used together with the fp8 quantization workflow."
attention_output_orig_quant_scale = self.attention_output_orig_quant_scale.value if self.attention_output_orig_quant_scale is not None else None
context, past_key_value = gpt_attention(
qkv=qkv,
past_key_value=past_key_value,
sequence_length=attention_params.sequence_length,
host_past_key_value_lengths=kv_cache_params.
host_past_key_value_lengths,
host_max_attention_window_sizes=kv_cache_params.
host_max_attention_window_sizes,
host_sink_token_length=kv_cache_params.host_sink_token_length,
context_lengths=attention_params.context_lengths,
cache_indirection=kv_cache_params.cache_indirection,
host_request_types=attention_params.host_request_types,
layer_idx=self.local_layer_idx,
num_heads=self.num_attention_heads,
num_kv_heads=self.num_attention_kv_heads,
hidden_size_per_head=self.attention_head_size,
q_scaling=self.q_scaling,
position_embedding_type=self.position_embedding_type,
kv_orig_quant_scale=kv_orig_quant_scale,
kv_quant_orig_scale=kv_quant_orig_scale,
attention_output_orig_quant_scale=
attention_output_orig_quant_scale,
kv_cache_quant_mode=self.quant_mode,
max_context_length=attention_params.max_context_length,
mask_type=self.attention_mask_type,
alibi_slopes=None,
tp_size=self.tp_size,
tp_rank=self.tp_rank,
kv_cache_block_offsets=kv_cache_params.kv_cache_block_offsets,
host_kv_cache_block_offsets=kv_cache_params.
host_kv_cache_block_offsets,
host_kv_cache_pool_pointers=kv_cache_params.
host_kv_cache_pool_pointers,
host_kv_cache_pool_mapping=kv_cache_params.
host_kv_cache_pool_mapping,
do_cross_attention=self.cross_attention,
cross_qkv=None,
cross_qkv_length=attention_params.encoder_max_input_length,
encoder_input_lengths=attention_params.encoder_input_lengths,
relative_attention_bias=self.rel_attn_table.value
if self.relative_attention else None,
max_distance=self.max_distance,
host_context_lengths=attention_params.host_context_lengths,
use_cache=use_cache,
spec_decoding_position_offsets=None,
spec_decoding_packed_mask=None,
host_runtime_perf_knobs=attention_params.host_runtime_perf_knobs
)
vision_dense = self.vis_dense(context)
language_dense = self.dense(context)
context = where(vision_token_mask, vision_dense, language_dense)
if use_cache:
return (context, past_key_value)
else:
return context