mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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143 lines
5.3 KiB
Python
143 lines
5.3 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from tensorrt_llm.models.llama.model import LLaMAForCausalLM
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from ..._common import default_net
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from ..._utils import pad_vocab_size
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from ...functional import ACT2FN, stack
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from ...layers import ColumnLinear
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from ...mapping import Mapping
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from ...module import Module, ModuleList
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class MedusaLayer(Module):
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def __init__(
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self,
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hidden_size,
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hidden_act="silu",
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dtype=None,
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mapping=Mapping(),
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):
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super().__init__()
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self.linear = ColumnLinear(hidden_size,
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hidden_size,
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dtype=dtype,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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gather_output=True)
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self.hidden_act = hidden_act
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def forward(self, x):
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return x + ACT2FN[self.hidden_act](self.linear(x))
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class MedusaHead(Module):
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def __init__(
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self,
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num_layers,
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hidden_size,
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vocab_size,
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hidden_act="silu",
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dtype=None,
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mapping=Mapping(),
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):
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super().__init__()
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self.medusa_layers = ModuleList([
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MedusaLayer(hidden_size=hidden_size,
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hidden_act=hidden_act,
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dtype=dtype,
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mapping=mapping) for _ in range(num_layers)
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])
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self.lm_head = ColumnLinear(hidden_size,
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vocab_size,
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bias=False,
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dtype=dtype,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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gather_output=True)
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return
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def forward(self, x):
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hidden_states = x
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for layer in self.medusa_layers:
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hidden_states = layer(hidden_states)
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return self.lm_head(hidden_states)
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class MedusaForCausalLm(LLaMAForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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self.num_medusa_heads = config.num_medusa_heads
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self.num_medusa_layers = config.num_medusa_layers
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self.hidden_size = config.hidden_size
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self.vocab_size = config.vocab_size
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vocab_size_padded = pad_vocab_size(self.vocab_size,
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config.mapping.tp_size)
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self.medusa_heads = ModuleList([
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MedusaHead(num_layers=self.num_medusa_layers,
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hidden_size=config.hidden_size,
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vocab_size=vocab_size_padded,
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hidden_act=config.hidden_act,
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dtype=config.dtype,
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mapping=config.mapping)
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for _ in range(self.num_medusa_heads)
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])
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self.max_medusa_token_len = config.max_draft_len
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def forward(self, *args, **kwargs):
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output_original = True
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hidden_states = super().forward(*args, **kwargs)
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if kwargs['use_cache']:
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if default_net().plugin_config.paged_kv_cache:
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lm_logits, hidden_states = hidden_states
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else:
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lm_logits, presents, hidden_states = hidden_states
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if self.mapping.is_last_pp_rank():
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medusa_logits = []
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for i in range(self.num_medusa_heads):
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medusa_logits.append(self.medusa_heads[i](hidden_states))
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# [num_medusa_heads, batch_size, num_medusa_tokens + 1, padded_vocab_size].
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# Remove padding [num_medusa_heads, batch_size * num_medusa_tokens + 1, padded_vocab_size].
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medusa_logits = stack(medusa_logits, dim=0)
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medusa_logits.mark_output('medusa_logits', self.config.logits_dtype)
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else:
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hidden_states.mark_output('hidden_states_output', self.config.dtype)
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if kwargs['use_cache'] and default_net(
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).plugin_config.paged_kv_cache == False:
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if self.mapping.is_last_pp_rank():
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if output_original:
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return (medusa_logits, lm_logits, presents)
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return (medusa_logits, presents)
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return (hidden_states, presents)
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else:
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if self.mapping.is_last_pp_rank():
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if output_original:
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return medusa_logits, lm_logits
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return medusa_logits
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return hidden_states
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def prepare_inputs(self, *args, **kwargs):
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kwargs['speculative_decoding_draft_tokens_external'] = False
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kwargs['max_draft_len'] = self.max_medusa_token_len
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return super().prepare_inputs(*args, **kwargs)
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