# 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 collections import OrderedDict
import tensorrt as trt
from tensorrt_llm.models.llama.model import LLaMAForCausalLM
from ..._common import default_net
from ..._utils import pad_vocab_size
from ...functional import ACT2FN, Tensor, stack
from ...layers import ColumnLinear
from ...mapping import Mapping
from ...module import Module, ModuleList
from ..generation_mixin import GenerationMixin
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)
[docs]
class MedusaForCausalLm(LLaMAForCausalLM):
def __init__(self, config):
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
[docs]
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