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from typing import List
import torch
def get_redrafter_specific_tensor_names() -> List[str]:
return [
# inputs
'device_request_types',
'draft_tokens',
'draft_indices',
'draft_probs',
'redrafter_inverted_temperature',
'rand_data_sample',
'rand_data_validation',
'position_ids_base',
# outputs
'next_spec_decoding_generation_lengths',
'next_spec_decoding_position_offsets',
'spec_decoding_mask',
'next_draft_tokens',
'next_draft_indices',
'next_draft_probs',
'next_flat_tokens',
'num_accepted_tokens',
'accepted_beam_index',
'max_gen_token',
'total_gen_token',
'packed_position_ids',
]
def get_redrafter_tensor_names() -> List[str]:
return [
# inputs
'spec_decoding_generation_lengths',
'spec_decoding_position_offsets',
'spec_decoding_packed_mask',
] + get_redrafter_specific_tensor_names()
def init_allocate_redrafter_tensors(session, batch_size):
# define the buffers for ReDrafter
session.flat_tokens = torch.zeros(
[batch_size * (session.max_draft_tokens + 1)],
dtype=torch.int32,
device=session.device)
session.next_flat_tokens = torch.zeros(
[batch_size * (session.max_draft_tokens + 1)],
dtype=torch.int32,
device=session.device)
session.position_ids_base = torch.zeros([batch_size],
dtype=torch.int32,
device=session.device)
session.packed_position_ids = torch.zeros(
[batch_size * (session.max_draft_tokens + 1)],
dtype=torch.int32,
device=session.device)
session.accept_lengths = torch.ones([batch_size],
dtype=torch.int32,
device=session.device)
session.draft_tokens = torch.zeros([
batch_size, session._model_config.redrafter_num_beams,
session._model_config.redrafter_draft_len_per_beam + 1
],
dtype=torch.int32,
device=session.device)
session.draft_indices = torch.zeros([
batch_size, session._model_config.redrafter_num_beams,
session._model_config.redrafter_draft_len_per_beam + 1
],
dtype=torch.int32,
device=session.device)
session.draft_probs = torch.zeros([
batch_size, session._model_config.redrafter_num_beams,
session._model_config.redrafter_draft_len_per_beam, session.vocab_size
],
dtype=session.dtype,
device=session.device)
session.next_draft_tokens = torch.zeros([
batch_size, session._model_config.redrafter_num_beams,
session._model_config.redrafter_draft_len_per_beam + 1
],
dtype=torch.int32,
device=session.device)
session.next_draft_indices = torch.zeros([
batch_size, session._model_config.redrafter_num_beams,
session._model_config.redrafter_draft_len_per_beam + 1
],
dtype=torch.int32,
device=session.device)
session.next_draft_probs = torch.zeros([
batch_size, session._model_config.redrafter_num_beams,
session._model_config.redrafter_draft_len_per_beam, session.vocab_size
],
dtype=session.dtype,
device=session.device)
session.next_spec_decoding_position_offsets = torch.zeros(
[batch_size, session.max_draft_tokens + 1],
dtype=torch.int32,
device=session.device)
session.next_spec_decoding_generation_lengths = torch.zeros(
[batch_size], dtype=torch.int32, device=session.device)
session.spec_decoding_generation_lengths = torch.zeros(
[batch_size], dtype=torch.int32, device=session.device)
session.spec_decoding_mask = torch.zeros([
batch_size, session.max_draft_tokens + 1, session.max_draft_tokens + 1
],
dtype=torch.bool,
device=session.device)
session.spec_decoding_packed_mask = torch.zeros([
batch_size * session.max_draft_tokens + 1,
(session.max_draft_tokens + 1 + 31) // 32
],
dtype=torch.int32,
device=session.device)
session.spec_decoding_position_offsets = torch.zeros(
[batch_size, session.max_draft_tokens + 1],
dtype=torch.int32,
device=session.device)
session.accepted_beam_index = torch.zeros([batch_size],
dtype=torch.int32,
device=session.device)
session.max_gen_token = torch.zeros(1,
dtype=torch.int32,
device=session.device)
session.total_gen_token = torch.zeros(1,
dtype=torch.int32,
device=session.device)
torch.manual_seed(0) # use seed=0 for context
session.rand_data_sample = torch.rand([batch_size],
dtype=session.dtype,
device=session.device)
# print(session.rand_data_sample)
session.rand_data_validation = torch.rand([
batch_size, session._model_config.redrafter_num_beams,
session._model_config.redrafter_draft_len_per_beam
],
dtype=session.dtype,
device=session.device)
# print(session.rand_data_validation)
session.buffer['flat_tokens'] = session.flat_tokens
session.buffer['next_flat_tokens'] = session.next_flat_tokens
session.buffer['num_accepted_tokens'] = session.accept_lengths
session.buffer['draft_tokens'] = session.draft_tokens
session.buffer['draft_indices'] = session.draft_indices
session.buffer['draft_probs'] = session.draft_probs
session.buffer['accepted_beam_index'] = session.accepted_beam_index
session.buffer[
'spec_decoding_generation_lengths'] = session.spec_decoding_generation_lengths
session.buffer['spec_decoding_mask'] = session.spec_decoding_mask
session.buffer[
'spec_decoding_position_offsets'] = session.spec_decoding_position_offsets
session.buffer[
'spec_decoding_packed_mask'] = session.spec_decoding_packed_mask
session.buffer['rand_data_sample'] = session.rand_data_sample
session.buffer['rand_data_validation'] = session.rand_data_validation
session.buffer[
'next_spec_decoding_generation_lengths'] = session.next_spec_decoding_generation_lengths
session.buffer['next_draft_tokens'] = session.next_draft_tokens
session.buffer['next_draft_indices'] = session.next_draft_indices
session.buffer['next_draft_probs'] = session.next_draft_probs
session.buffer[
'next_spec_decoding_position_offsets'] = session.next_spec_decoding_position_offsets
session.buffer['max_gen_token'] = session.max_gen_token
session.buffer['total_gen_token'] = session.total_gen_token
session.buffer['position_ids_base'] = session.position_ids_base
session.buffer['packed_position_ids'] = session.packed_position_ids
# NOTE: device_request_types is created with host_request_types
return
def set_redrafter_ctx_tensors(session, add_tensor, add_tensor_with_bs):
# Add all output tensors
add_tensor(session.buffer['next_spec_decoding_generation_lengths'],
'next_spec_decoding_generation_lengths')
add_tensor(session.buffer['next_spec_decoding_position_offsets'],
'next_spec_decoding_position_offsets')
add_tensor(session.buffer['spec_decoding_mask'], 'spec_decoding_mask')
add_tensor(session.buffer['next_flat_tokens'], 'next_flat_tokens')
add_tensor(session.buffer['next_draft_tokens'], 'next_draft_tokens')
add_tensor(session.buffer['next_draft_indices'], 'next_draft_indices')
add_tensor(session.buffer['next_draft_probs'], 'next_draft_probs')
add_tensor(session.buffer['num_accepted_tokens'], 'num_accepted_tokens')
add_tensor(session.buffer['accepted_beam_index'], 'accepted_beam_index')
add_tensor(session.buffer['packed_position_ids'], 'packed_position_ids')
# add all input tensors
add_tensor_with_bs(session.buffer['spec_decoding_generation_lengths'],
'spec_decoding_generation_lengths', 0)
add_tensor_with_bs(session.buffer['spec_decoding_position_offsets'],
'spec_decoding_position_offsets', 0)
add_tensor_with_bs(session.buffer['spec_decoding_packed_mask'],
'spec_decoding_packed_mask', 0)
add_tensor_with_bs(session.buffer['draft_tokens'], 'draft_tokens', 0)
add_tensor_with_bs(session.buffer['draft_indices'], 'draft_indices', 0)
add_tensor_with_bs(session.buffer['draft_probs'], 'draft_probs', 0)
add_tensor_with_bs(session.buffer['rand_data_validation'],
'rand_data_validation', 0)
add_tensor(session.buffer['rand_data_sample'], 'rand_data_sample')
add_tensor(session.buffer['redrafter_inverted_temperature'],
'redrafter_inverted_temperature')
add_tensor(session.buffer['max_gen_token'], 'max_gen_token')
add_tensor(session.buffer['total_gen_token'], 'total_gen_token')
add_tensor(session.buffer['position_ids_base'], 'position_ids_base')
return
def set_redrafter_gen_tensors(session, batch_size, add_tensor,
add_tensor_with_shape):
torch.cuda.nvtx.range_push("set_redrafter_gen_tensors")
# add output tensors
add_tensor(session.buffer['next_spec_decoding_generation_lengths'],
'next_spec_decoding_generation_lengths')
add_tensor(session.buffer['next_flat_tokens'], 'next_flat_tokens')
add_tensor(session.buffer['next_draft_tokens'], 'next_draft_tokens')
add_tensor(session.buffer['next_draft_indices'], 'next_draft_indices')
add_tensor(session.buffer['next_draft_probs'], 'next_draft_probs')
add_tensor(session.buffer['next_spec_decoding_position_offsets'],
'next_spec_decoding_position_offsets')
add_tensor(session.buffer['spec_decoding_mask'], 'spec_decoding_mask')
add_tensor(session.buffer['num_accepted_tokens'], 'num_accepted_tokens')
add_tensor(session.buffer['accepted_beam_index'], 'accepted_beam_index')
add_tensor(session.buffer['packed_position_ids'], 'packed_position_ids')
# add all input tensors
add_tensor(session.buffer['spec_decoding_generation_lengths'],
'spec_decoding_generation_lengths')
# position offsets vary for ReDrafter and should already be updated at this point.
# Just need to provide the updated shape for ReDrafter.
max_gen_len = session.host_max_gen_token
position_offsets = session.buffer['spec_decoding_position_offsets'].view(
-1)[:batch_size * max_gen_len]
add_tensor_with_shape(position_offsets.view(batch_size, max_gen_len),
'spec_decoding_position_offsets',
(batch_size, max_gen_len))
add_tensor(session.buffer['spec_decoding_packed_mask'],
'spec_decoding_packed_mask')
add_tensor(session.buffer['draft_tokens'], 'draft_tokens')
add_tensor(session.buffer['draft_indices'], 'draft_indices')
add_tensor(session.buffer['draft_probs'], 'draft_probs')
add_tensor(session.buffer['rand_data_validation'], 'rand_data_validation')
add_tensor(session.buffer['rand_data_sample'], 'rand_data_sample')
add_tensor(session.buffer['redrafter_inverted_temperature'],
'redrafter_inverted_temperature')
add_tensor(session.buffer['max_gen_token'], 'max_gen_token')
add_tensor(session.buffer['total_gen_token'], 'total_gen_token')
add_tensor(session.buffer['position_ids_base'], 'position_ids_base')
torch.cuda.nvtx.range_pop()
return
def redrafter_convert_spec_decoding_mask_to_packed_mask(
session, spec_decoding_generation_lengths):
torch.cuda.nvtx.range_push("mask_conversion")
torch.ops.tensorrt_llm.convert_spec_decoding_mask_to_packed_mask(
spec_decoding_generation_lengths, session.spec_decoding_mask,
session.max_draft_tokens, session.spec_decoding_packed_mask, None)
torch.cuda.nvtx.range_pop()
return
def exchange_redrafter_buffers(session):
# NOTE: shouldn't incur any copies
def swap_buffers(name: str):
next_name = "next_" + name
session.buffer[name], session.buffer[next_name] = session.buffer[
next_name], session.buffer[name]
torch.cuda.nvtx.range_push("exchange_redrafter_buffers")
session.host_max_gen_token = session.buffer['max_gen_token'].cpu().item()
session.host_total_gen_token = session.buffer['total_gen_token'].cpu().item(
)
swap_buffers('spec_decoding_generation_lengths')
swap_buffers('spec_decoding_position_offsets')
swap_buffers('draft_probs')
swap_buffers('draft_indices')
swap_buffers('draft_tokens')
swap_buffers("flat_tokens")
torch.cuda.nvtx.range_pop()
return
def process_redrafter_outputs(session, step, batch_size, last_draft_tokens,
new_draft_tokens):
torch.cuda.nvtx.range_push("process_redrafter_outputs")
best_path = session.buffer["accepted_beam_index"]
session.accept_lengths = best_path_lengths = session.buffer[
"num_accepted_tokens"]
accepted_tokens = [None] * batch_size
# print(best_path, best_path_lengths)
for b in range(batch_size):
torch.cuda.nvtx.range_push(f"accept_tokens_{b}")
# use new beam0 to get latest true token
accepted_tokens[b] = new_draft_tokens[b, 0, :1]
if step > 0:
verified_tokens = last_draft_tokens[b, best_path[b],
1:best_path_lengths[b]]
accepted_tokens[b] = torch.concat(
[verified_tokens, accepted_tokens[b]])
torch.cuda.nvtx.range_pop()
# print("Accept", accepted_tokens)
session.new_tokens = torch.nested.to_padded_tensor(
torch.nested.nested_tensor(accepted_tokens, dtype=torch.int32),
session.end_ids[0]) #FIXME end id padding.
torch.cuda.nvtx.range_pop()
return best_path, best_path_lengths
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