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https://github.com/NVIDIA/TensorRT-LLM.git
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300 lines
13 KiB
Python
300 lines
13 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 collections import OrderedDict
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import tensorrt as trt
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from tensorrt_llm._common import default_net
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from tensorrt_llm.bindings import KVCacheType
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from tensorrt_llm.functional import Tensor, cast, categorical_sample
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from tensorrt_llm.models import LLaMAForCausalLM
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from tensorrt_llm.models.generation_mixin import GenerationMixin
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from ..._utils import pad_vocab_size, str_dtype_to_trt
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from .drafter import Drafter
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from .redrafter_helper import (_beam_search_candidates, _beams2tree,
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_process_logits_and_hidden_states)
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class ReDrafterForCausalLM(LLaMAForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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self.dtype = str_dtype_to_trt(config.dtype)
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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.drafter = Drafter.from_config(config, vocab_size_padded)
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self.num_beams = config.redrafter_num_beams
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self.beam_candidate_length = config.redrafter_draft_len_per_beam
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self.beam_length = self.beam_candidate_length + 1 # including true token
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self.greedy_search = config.redrafter_greedy_search
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self.is_rnn = config.redrafter_is_rnn
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assert self.dtype == self.drafter.dtype, f"{self.dtype} != {self.drafter.dtype}"
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def _fwd_helper(self, hidden_states, lm_logits, embedding, drafter,
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kwargs: dict):
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'''
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Must enable remove_input_padding:
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hidden_states [total_tokens, H]
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lm_logits [total_tokens, V]
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1. process_logits: context vs gen
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a. Context: just return the last hidden states, and logits/probs
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b. Gen:
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i. verify: use lm_logits, draft_probs, draft_indices, draft_tokens
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ii. select hidden state and update probs
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3. Sample token based on probs
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4. Generate candidates using hidden_states, sampled token
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5. Using beams, generate validation buffers, mark them as output
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6. Mark all the outputs
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'''
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num_beams = self.num_beams
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beam_length = self.beam_length
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# Get the inputs needed
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rand_data_sample = kwargs['rand_data_sample']
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position_ids_base = kwargs['position_ids_base']
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# Step 1: Process logits and hidden states
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# process the base model output (verify for gen-phase)
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probs, draft_input, num_accepted_tokens, \
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accepted_beam_index = _process_logits_and_hidden_states(
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self, lm_logits, hidden_states, kwargs)
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# NOTE: num_accepted_tokens doesn't include true token so add 1 here
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num_accepted_tokens = num_accepted_tokens + 1
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# At this point:
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# probs : [bs, V]
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# hidden_states : [bs, H]
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# Step 2: Sample token
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next_token = categorical_sample(probs, rand_data_sample)
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# Step 3: beam search
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new_draft_tokens, new_draft_logits = _beam_search_candidates(
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draft_input, next_token, embedding, drafter, self.num_beams,
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self.beam_length, self.is_rnn)
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# Step 4: tree processing
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active_tokens_flattened, new_draft_token_indices, new_mask, \
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new_position_offsets, packed_position_ids, next_num_gen_tokens, max_gen_token, \
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total_gen_token = _beams2tree(new_draft_tokens, num_beams, beam_length,
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position_ids_base + num_accepted_tokens)
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# Step 5: mark all the tensors we need
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num_accepted_tokens.mark_output('num_accepted_tokens')
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accepted_beam_index.mark_output('accepted_beam_index')
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max_gen_token.mark_output('max_gen_token')
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total_gen_token.mark_output('total_gen_token')
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next_num_gen_tokens.mark_output('next_spec_decoding_generation_lengths')
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active_tokens_flattened.mark_output('next_flat_tokens')
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new_draft_tokens.mark_output('next_draft_tokens')
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new_draft_logits.mark_output('next_draft_probs')
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new_draft_token_indices.mark_output('next_draft_indices')
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new_mask.mark_output('spec_decoding_mask')
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new_position_offsets.mark_output('next_spec_decoding_position_offsets')
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packed_position_ids.mark_output('packed_position_ids')
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return next_token, probs, draft_input
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def forward(self, *args, **kwargs):
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"""
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0. run base model, get logits, hidden_states
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"""
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extra_args = [
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'draft_tokens',
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'draft_indices',
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'draft_probs',
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'device_request_types',
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'redrafter_inverted_temperature',
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'rand_data_validation',
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'rand_data_sample',
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'position_ids_base',
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]
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use_cache = True
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base_kwargs = {k: v for k, v in kwargs.items() if k not in extra_args}
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if use_cache and default_net().plugin_config.paged_kv_cache is False:
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lm_logits, presents, hidden_states = super().forward(
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*args, **base_kwargs)
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else:
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lm_logits, hidden_states, _ = super().forward(*args, **base_kwargs)
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# lm_logits could be in fp32
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lm_logits_cast = cast(lm_logits, self.dtype) # no-op if same type
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self.register_network_output("hidden_states",
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hidden_states) # debugging
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new_draft_tokens, new_draft_logits, probs = self._fwd_helper(
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hidden_states,
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lm_logits_cast,
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self.transformer.vocab_embedding,
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self.drafter,
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kwargs=kwargs)
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return new_draft_tokens, new_draft_logits, probs
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def prepare_inputs(self, *args, **kwargs):
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"""
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Inputs needed:
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Assuming, max_gen_tokens = 1 + nb*(bl - 1), counting true token
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device_request_types: [bs]
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draft_tokens: [bs, nb, bl]
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draft_indices: [bs, nb, bl]
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draft_probs: [bs, nb, bl-1, V]
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spec_decoding_generation_lengths: [bs]
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spec_decoding_position_offsets: [bs, max_gen_tokens]
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spec_decoding_packed_mask: [bs, max_gen_tokens, packed_length] **
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redrafter_inverted_temperature: [bs]
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rand_data_sample: [bs]
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rand_data_validation: [bs, nb, bl-1]
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** The mask is tricky since the boolean mask will need to be
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packed in runtime. So, the last dim will be:
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packed_length = ceil(max_gen_tokens/32)
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"""
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default_range = GenerationMixin.default_range
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remove_input_padding = default_net().plugin_config.remove_input_padding
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use_gpt_attention_plugin = default_net(
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).plugin_config.gpt_attention_plugin
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use_gemm_plugin = default_net().plugin_config.gemm_plugin
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paged_kv_cache = default_net().plugin_config.paged_kv_cache
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max_batch_size = kwargs['max_batch_size']
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assert max_batch_size is not None
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bb_range = default_range(max_batch_size)
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bb0_range = default_range(max_batch_size, min_range=0, opt_offset=1)
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num_beam_tokens = self.num_beams * self.beam_length
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max_draft_tokens = num_beam_tokens - self.num_beams # ignore the true token
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max_gen_token_len = 1 + max_draft_tokens # for the true token
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max_gen_token_len_range = default_range(max_gen_token_len)
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bb_max_gen_token_len_range = default_range(max_gen_token_len *
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max_batch_size,
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min_range=0)
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kwargs['speculative_decoding_draft_tokens_external'] = False
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kwargs['max_draft_len'] = max_draft_tokens
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kwargs['spec_decoding_is_generation_length_variable'] = True
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inputs = super().prepare_inputs(*args, **kwargs)
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assert inputs['spec_decoding_params'] is not None
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enable_two_optimization_profiles = GenerationMixin.has_ctx_gen_opt_profiles(
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use_gpt_attention_plugin=use_gpt_attention_plugin,
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use_gemm_plugin=use_gemm_plugin,
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remove_input_padding=remove_input_padding,
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kv_cache_type=KVCacheType.PAGED
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if paged_kv_cache else KVCacheType.CONTINUOUS)
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if enable_two_optimization_profiles:
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bb_range = [bb_range, bb_range]
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bb0_range = [bb0_range, bb0_range]
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max_gen_token_len_range = [
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max_gen_token_len_range, max_gen_token_len_range
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]
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bb_max_gen_token_len_range = [
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bb_max_gen_token_len_range, bb_max_gen_token_len_range
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]
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num_beams_range = [self.num_beams, self.num_beams]
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beam_length_range = [self.beam_length, self.beam_length]
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candidate_length_range = [
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self.beam_candidate_length, self.beam_candidate_length
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]
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vocab_size_range = [self.vocab_size, self.vocab_size]
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else:
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bb_range = [bb_range]
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bb0_range = [bb0_range]
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max_gen_token_len_range = [max_gen_token_len_range]
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bb_max_gen_token_len_range = [bb_max_gen_token_len_range]
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num_beams_range = [self.num_beams]
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beam_length_range = [self.beam_length]
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candidate_length_range = [self.beam_candidate_length]
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vocab_size_range = [self.vocab_size]
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device_request_types = Tensor(name='device_request_types',
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dtype=trt.int32,
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shape=[-1],
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dim_range=OrderedDict([
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('batch_size', bb_range),
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]))
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draft_tokens = Tensor(name='draft_tokens',
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dtype=trt.int32,
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shape=[-1, self.num_beams, self.beam_length],
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dim_range=OrderedDict([
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('batch_size_wt0', bb0_range),
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('num_beams', num_beams_range),
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('beam_length', beam_length_range),
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]))
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draft_indices = Tensor(name='draft_indices',
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dtype=trt.int32,
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shape=[-1, self.num_beams, self.beam_length],
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dim_range=OrderedDict([
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('batch_size_wt0', bb0_range),
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('num_beams', num_beams_range),
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('beam_length', beam_length_range),
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]))
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draft_probs = Tensor(
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name='draft_probs',
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dtype=self.dtype,
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shape=[-1, self.num_beams, self.beam_length - 1, self.vocab_size],
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dim_range=OrderedDict([
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('batch_size_wt0', bb0_range),
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('num_beams', num_beams_range),
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('candidate_length', candidate_length_range),
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('vocab_size', vocab_size_range),
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]))
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redrafter_inverted_temperature = Tensor(
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name='redrafter_inverted_temperature',
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dtype=self.dtype,
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shape=[-1],
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dim_range=OrderedDict([
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("batch_size", bb_range),
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]))
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rand_data_validation = Tensor(
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name='rand_data_validation',
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dtype=self.dtype,
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shape=[-1, self.num_beams, self.beam_length - 1],
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dim_range=OrderedDict([
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('batch_size_wt0', bb0_range),
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('num_beams', num_beams_range),
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('candidate_length', candidate_length_range),
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]))
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rand_data_sample = Tensor(name='rand_data_sample',
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dtype=self.dtype,
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shape=[-1],
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dim_range=OrderedDict([
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('batch_size', bb_range),
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]))
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position_ids_base = Tensor(
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name="position_ids_base",
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dtype=trt.int32,
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shape=[-1],
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dim_range=OrderedDict([
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("batch_size", bb_range),
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]),
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)
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inputs[
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'device_request_types'] = device_request_types # needed by process_logits
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inputs['draft_tokens'] = draft_tokens
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inputs['draft_indices'] = draft_indices
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inputs['draft_probs'] = draft_probs
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inputs[
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'redrafter_inverted_temperature'] = redrafter_inverted_temperature
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inputs['rand_data_validation'] = rand_data_validation
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inputs['rand_data_sample'] = rand_data_sample
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inputs['position_ids_base'] = position_ids_base
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return inputs
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