File size: 5,475 Bytes
5000658 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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.
from tensorrt_llm.models.llama.model import LLaMAForCausalLM
from ..._common import default_net
from ..._utils import pad_vocab_size
from ...functional import ACT2FN, stack
from ...layers import ColumnLinear
from ...mapping import Mapping
from ...module import Module, ModuleList
from .config import MedusaConfig
class MedusaLayer(Module):
def __init__(
self,
hidden_size,
hidden_act="silu",
dtype=None,
mapping=Mapping(),
):
super().__init__()
self.linear = ColumnLinear(hidden_size,
hidden_size,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
gather_output=True)
self.hidden_act = hidden_act
def forward(self, x):
return x + ACT2FN[self.hidden_act](self.linear(x))
class MedusaHead(Module):
def __init__(
self,
num_layers,
hidden_size,
vocab_size,
hidden_act="silu",
dtype=None,
mapping=Mapping(),
):
super().__init__()
self.medusa_layers = ModuleList([
MedusaLayer(hidden_size=hidden_size,
hidden_act=hidden_act,
dtype=dtype,
mapping=mapping) for _ in range(num_layers)
])
self.lm_head = ColumnLinear(hidden_size,
vocab_size,
bias=False,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
gather_output=True)
return
def forward(self, x):
hidden_states = x
for layer in self.medusa_layers:
hidden_states = layer(hidden_states)
return self.lm_head(hidden_states)
class MedusaForCausalLm(LLaMAForCausalLM):
config_class = MedusaConfig
def __init__(self, config: MedusaConfig):
super().__init__(config)
self.num_medusa_heads = config.num_medusa_heads
self.num_medusa_layers = config.num_medusa_layers
self.hidden_size = config.hidden_size
self.vocab_size = config.vocab_size
vocab_size_padded = pad_vocab_size(self.vocab_size,
config.mapping.tp_size)
self.medusa_heads = ModuleList([
MedusaHead(num_layers=self.num_medusa_layers,
hidden_size=config.hidden_size,
vocab_size=vocab_size_padded,
hidden_act=config.hidden_act,
dtype=config.dtype,
mapping=config.mapping)
for _ in range(self.num_medusa_heads)
])
self.max_medusa_token_len = config.max_draft_len
def forward(self, *args, **kwargs):
output_original = True
hidden_states = super().forward(*args, **kwargs)
if kwargs['use_cache']:
if default_net().plugin_config.paged_kv_cache:
lm_logits, hidden_states = hidden_states
else:
lm_logits, presents, hidden_states = hidden_states
if self.mapping.is_last_pp_rank():
medusa_logits = []
for i in range(self.num_medusa_heads):
medusa_logits.append(self.medusa_heads[i](hidden_states))
# [num_medusa_heads, batch_size, num_medusa_tokens + 1, padded_vocab_size].
# Remove padding [num_medusa_heads, batch_size * num_medusa_tokens + 1, padded_vocab_size].
medusa_logits = stack(medusa_logits, dim=0)
medusa_logits.mark_output('medusa_logits', self.config.logits_dtype)
else:
hidden_states.mark_output('hidden_states_output', self.config.dtype)
if kwargs['use_cache'] and default_net(
).plugin_config.paged_kv_cache == False:
if self.mapping.is_last_pp_rank():
if output_original:
return (medusa_logits, lm_logits, presents)
return (medusa_logits, presents)
return (hidden_states, presents)
else:
if self.mapping.is_last_pp_rank():
if output_original:
return medusa_logits, lm_logits
return medusa_logits
return hidden_states
def prepare_inputs(self, *args, **kwargs):
kwargs['speculative_decoding_draft_tokens_external'] = False
kwargs['max_draft_len'] = self.max_medusa_token_len
return super().prepare_inputs(*args, **kwargs)
|