mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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484 lines
19 KiB
Python
484 lines
19 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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import os
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import sys
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import tempfile
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import unittest
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from itertools import product
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import numpy as np
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import pytest
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import tensorrt as trt
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import torch
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from parameterized import parameterized
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from transformers import BloomConfig, BloomForCausalLM
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import tensorrt_llm
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from tensorrt_llm import Builder
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from tensorrt_llm._utils import str_dtype_to_torch
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from tensorrt_llm.network import net_guard
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from tensorrt_llm.plugin.plugin import ContextFMHAType
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from tensorrt_llm.runtime import ModelConfig, SamplingConfig
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from tensorrt_llm.runtime.generation import _prepare_attention_mask
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sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
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from examples.bloom.weight import load_from_hf_bloom
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import getSMVersion
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class TestBloom(unittest.TestCase):
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def _gen_hf_bloom(self, hidden_act, n_layer, max_length, dtype):
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bloom_config = BloomConfig(
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hidden_act=hidden_act,
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n_layer=n_layer,
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max_length=max_length,
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torch_dtype=dtype,
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)
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hf_bloom = BloomForCausalLM(bloom_config).cuda().eval()
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return bloom_config, hf_bloom
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def _gen_tensorrt_llm_network(self, network, builder, hf_bloom,
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bloom_config, batch_size, input_len,
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output_len, fp16, gpt_attention_plugin,
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tensor_parallel,
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apply_query_key_layer_scaling):
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num_layers = bloom_config.n_layer
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num_heads = bloom_config.n_head
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hidden_size = bloom_config.hidden_size
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vocab_size = bloom_config.vocab_size
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n_positions = input_len + output_len
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with net_guard(network):
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kv_dtype = trt.float16 if fp16 else trt.float32
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# Initialize model
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tensorrt_llm_bloom = tensorrt_llm.models.BloomForCausalLM(
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num_layers=num_layers,
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num_heads=num_heads,
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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hidden_act='gelu',
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max_position_embeddings=n_positions,
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dtype=kv_dtype,
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mapping=tensorrt_llm.Mapping(
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world_size=tensor_parallel,
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tp_size=tensor_parallel), # TP only
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)
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inputs = tensorrt_llm_bloom.prepare_inputs(batch_size,
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input_len,
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output_len,
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use_cache=True,
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max_beam_width=1)
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load_from_hf_bloom(tensorrt_llm_bloom, hf_bloom, fp16=fp16)
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# Prepare
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network.set_named_parameters(tensorrt_llm_bloom.named_parameters())
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tensorrt_llm_bloom(*inputs)
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return network
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def _gen_tensorrt_llm_runtime(self,
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log_level,
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dtype,
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world_size,
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rank,
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bloom_config,
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hf_bloom,
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model,
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use_plugin,
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batch_size,
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input_len,
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output_len,
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use_refit,
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fast_building=False,
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apply_query_key_layer_scaling=False,
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context_fmha_type=ContextFMHAType.disabled,
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enable_remove_input_padding=False):
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mapping = tensorrt_llm.Mapping(world_size, rank, tp_size=world_size)
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runtime = None
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builder = Builder()
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fp16 = (dtype == 'float16')
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with tempfile.TemporaryDirectory() as tmpdirname:
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network = builder.create_network()
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if use_plugin:
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network.plugin_config.set_gpt_attention_plugin(dtype)
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if fast_building:
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network.plugin_config.set_gemm_plugin(dtype)
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network.plugin_config.set_layernorm_plugin(dtype)
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network.plugin_config.set_context_fmha(context_fmha_type)
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if enable_remove_input_padding:
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network.plugin_config.enable_remove_input_padding()
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self._gen_tensorrt_llm_network(network, builder, hf_bloom,
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bloom_config, batch_size, input_len,
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output_len, fp16, use_plugin,
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world_size,
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apply_query_key_layer_scaling)
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builder_config = builder.create_builder_config(
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name='bloom',
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precision=dtype,
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timing_cache='model.cache',
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tensor_parallel=world_size, # TP only
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use_refit=use_refit,
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)
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engine_buffer = builder.build_engine(network, builder_config)
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runtime = tensorrt_llm.runtime.generation._Runtime(
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engine_buffer, mapping)
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return runtime, engine_buffer
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def load_test_cases():
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test_cases = list(
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product([False, True], [
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ContextFMHAType.disabled, ContextFMHAType.enabled,
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ContextFMHAType.enabled_with_fp32_acc
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], [False], ['float16', 'float32']))
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return test_cases
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@parameterized.expand(load_test_cases())
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def test_bloom(self, use_gpt_attention_plugin, context_fmha_type,
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enable_remove_input_padding, dtype):
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model = 'bloom'
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log_level = 'error'
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world_size = 1
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rank = 0
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hidden_act = 'gelu'
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n_layer = 2
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max_length = 2
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batch_size = 4
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beam_width = 1
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seq_len = 128
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total_length = seq_len + max_length
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bloom_config, hf_bloom = self._gen_hf_bloom(hidden_act, n_layer,
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max_length, dtype)
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if bloom_config.hidden_size // bloom_config.n_head < 32 and use_gpt_attention_plugin:
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pytest.skip("unsupported head_size")
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runtime, _ = self._gen_tensorrt_llm_runtime(
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log_level,
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dtype,
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world_size,
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rank,
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bloom_config,
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hf_bloom,
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model,
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use_gpt_attention_plugin,
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batch_size,
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seq_len,
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max_length,
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use_refit=False,
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fast_building=True,
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context_fmha_type=context_fmha_type,
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enable_remove_input_padding=enable_remove_input_padding)
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# compare context
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pad_token_id = 3
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ctx_ids = torch.randint(100, (batch_size, seq_len)).int().cuda()
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ctx_ids[0][-1] = pad_token_id
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ctx_ids[1][-3:] = pad_token_id
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ctx_ids[2][-5:] = pad_token_id
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ctx_context_lengths = seq_len * torch.ones(
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(batch_size), dtype=torch.int32, device='cuda')
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ctx_position_ids = torch.tensor(range(seq_len),
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dtype=torch.int32).reshape([
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1, seq_len
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]).expand([batch_size, seq_len]).cuda()
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ctx_last_token_ids = ctx_context_lengths.clone()
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ctx_attention_mask = _prepare_attention_mask(ctx_ids)
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ctx_host_request_types = torch.tensor([0] * batch_size,
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dtype=torch.int32)
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ctx_sequence_length = torch.tensor([seq_len] * batch_size,
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dtype=torch.int32).cuda()
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ctx_host_past_key_value_lengths = torch.tensor([0] * batch_size,
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dtype=torch.int32)
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cache_indirections = [
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torch.full((
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batch_size,
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beam_width,
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total_length,
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),
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0,
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dtype=torch.int32,
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device='cuda'),
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torch.full((
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batch_size,
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beam_width,
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total_length,
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),
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0,
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dtype=torch.int32,
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device='cuda')
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] # ping-pong buffers
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ctx_buffer = {
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'input_ids': ctx_ids,
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'position_ids': ctx_position_ids,
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'context_lengths': ctx_context_lengths,
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'last_token_ids': ctx_last_token_ids,
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'host_request_types': ctx_host_request_types,
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'cache_indirection': cache_indirections[0],
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}
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ctx_host_context_lengths = None
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if use_gpt_attention_plugin:
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ctx_buffer['sequence_length'] = ctx_sequence_length
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ctx_buffer[
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'host_past_key_value_lengths'] = ctx_host_past_key_value_lengths
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if enable_remove_input_padding:
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ctx_host_context_lengths = ctx_context_lengths.cpu()
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ctx_buffer["host_context_lengths"] = ctx_host_context_lengths
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else:
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ctx_buffer['attention_mask'] = ctx_attention_mask
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ctx_shape = {k: v.shape for k, v in ctx_buffer.items()}
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for i in range(bloom_config.n_layer):
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shape = (batch_size, 2, bloom_config.n_head, 0,
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bloom_config.hidden_size // bloom_config.n_head)
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past_buffer = torch.zeros((1, ),
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dtype=str_dtype_to_torch(dtype),
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device='cuda')
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ctx_shape.update({
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f'past_key_value_{i}': shape,
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})
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shape = (batch_size, 2, bloom_config.n_head, seq_len,
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bloom_config.hidden_size // bloom_config.n_head)
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ctx_buffer.update({
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f'past_key_value_{i}':
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past_buffer,
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f'present_key_value_{i}':
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torch.zeros(shape,
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dtype=str_dtype_to_torch(dtype),
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device='cuda'),
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})
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context = runtime.ctx_context
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runtime._set_shape(context, ctx_shape)
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runtime._set_buffer(context, ctx_buffer)
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runtime._run(context)
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torch.cuda.synchronize()
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res = ctx_buffer['logits']
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with torch.no_grad():
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hf_outputs = hf_bloom.forward(ctx_ids,
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attention_mask=ctx_attention_mask)
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torch.cuda.synchronize()
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ref = hf_outputs.logits[:, -1, :]
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np.testing.assert_allclose(ref.cpu().numpy(),
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res.cpu().numpy(),
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atol=1e-2)
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# compare generation
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step = 1
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gen_id = torch.randint(100, (batch_size, 1)).int().cuda()
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gen_context_lengths = ctx_context_lengths.clone()
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gen_host_request_types = torch.tensor([1] * batch_size,
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dtype=torch.int32)
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gen_position_ids = torch.ones_like(gen_id).cuda() * seq_len
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gen_last_token_ids = torch.zeros_like(gen_context_lengths).cuda()
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gen_attention_mask = torch.cat([
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ctx_attention_mask,
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ctx_attention_mask.new_ones((ctx_attention_mask.shape[0], 1))
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],
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dim=-1)
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gen_sequence_length = torch.tensor([seq_len + step] * batch_size,
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dtype=torch.int32).cuda()
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gen_host_past_key_value_lengths = torch.tensor([seq_len + step - 1] *
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batch_size,
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dtype=torch.int32)
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step1_buffer = {
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'input_ids': gen_id,
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'context_lengths': gen_context_lengths.contiguous(),
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'position_ids': gen_position_ids.contiguous(),
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'last_token_ids': gen_last_token_ids.contiguous(),
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'host_request_types': gen_host_request_types.contiguous(),
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'cache_indirection': cache_indirections[1].contiguous(),
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}
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gen_host_context_lengths = None
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if use_gpt_attention_plugin:
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step1_buffer['sequence_length'] = gen_sequence_length
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step1_buffer[
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'host_past_key_value_lengths'] = gen_host_past_key_value_lengths
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gen_host_context_lengths = gen_context_lengths.cpu()
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step1_buffer['host_context_lengths'] = gen_host_context_lengths
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else:
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step1_buffer['attention_mask'] = gen_attention_mask
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step1_shape = {k: v.shape for k, v in step1_buffer.items()}
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for i in range(bloom_config.n_layer):
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shape = (batch_size, 2, bloom_config.n_head, seq_len,
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bloom_config.hidden_size // bloom_config.n_head)
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step1_shape.update({
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f'past_key_value_{i}': shape,
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})
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step1_buffer.update({
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f'past_key_value_{i}':
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ctx_buffer[f'present_key_value_{i}'],
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})
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context = runtime.context_1
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runtime._set_shape(context, step1_shape)
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runtime._set_buffer(context, step1_buffer)
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runtime._run(context)
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torch.cuda.synchronize()
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res = step1_buffer['logits']
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with torch.no_grad():
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hf_outputs = hf_bloom.forward(
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gen_id,
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attention_mask=gen_attention_mask,
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past_key_values=hf_outputs.past_key_values,
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use_cache=True)
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torch.cuda.synchronize()
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ref = hf_outputs.logits[:, -1, :]
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np.testing.assert_allclose(ref.cpu().numpy(),
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res.cpu().numpy(),
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atol=1e-1)
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@parameterized.expand(load_test_cases())
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def test_greedy_search(self, use_gpt_attention_plugin, context_fmha_type,
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enable_remove_input_padding, dtype):
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# Skip tests that are not supported in pre-ampere architecture
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if getSMVersion() < 80:
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if context_fmha_type == ContextFMHAType.enabled:
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pytest.skip(
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"ContextFMHAType is not supported in pre-ampere architecture"
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)
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elif context_fmha_type == ContextFMHAType.enabled_with_fp32_acc:
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pytest.skip(
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"ContextFMHAType with fp32 acc is not supported in pre-ampere architecture"
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)
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model = 'bloom'
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log_level = 'error'
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world_size = 1
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rank = 0
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hidden_act = 'gelu'
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n_layer = 2
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max_new_tokens = 1
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batch_size = 4
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seq_len = 128
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do_sample = False
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early_stoppping = False
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num_beams = 1
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num_beam_groups = 1
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temperature = 1
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top_k = 0
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top_p = 0.0
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length_penalty = 1
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repetition_penalty = 1
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bloom_config, hf_bloom = self._gen_hf_bloom(hidden_act, n_layer,
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max_new_tokens, dtype)
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runtime, engine_buffer = self._gen_tensorrt_llm_runtime(
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log_level,
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dtype,
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world_size,
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rank,
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bloom_config,
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hf_bloom,
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model,
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use_gpt_attention_plugin,
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batch_size,
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seq_len,
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max_new_tokens,
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use_refit=False,
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fast_building=True,
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context_fmha_type=context_fmha_type,
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enable_remove_input_padding=enable_remove_input_padding)
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model_config = ModelConfig(
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vocab_size=bloom_config.vocab_size,
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num_layers=bloom_config.n_layer,
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num_heads=bloom_config.n_head,
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num_kv_heads=bloom_config.n_head,
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hidden_size=bloom_config.hidden_size,
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gpt_attention_plugin=use_gpt_attention_plugin,
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remove_input_padding=enable_remove_input_padding,
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dtype=dtype)
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mapping = tensorrt_llm.Mapping(world_size, rank, tp_size=world_size)
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decoder = tensorrt_llm.runtime.GenerationSession(
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model_config, engine_buffer, mapping)
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pad_token_id = 3
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eos_token_id = 2
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sampling_config = SamplingConfig(end_id=eos_token_id,
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pad_id=pad_token_id,
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num_beams=num_beams,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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length_penalty=length_penalty,
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repetition_penalty=repetition_penalty)
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input_ids = torch.randint(100, (batch_size, seq_len)).int().cuda()
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input_ids[0][-1] = pad_token_id
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input_ids[1][-3:] = pad_token_id
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input_ids[2][-5:] = pad_token_id
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context_lengths = torch.ones(
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(batch_size)).type(torch.int32).cuda() * seq_len
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decoder.setup(batch_size,
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max_context_length=seq_len,
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max_new_tokens=max_new_tokens,
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beam_width=num_beams)
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output_ids = decoder.decode(input_ids, context_lengths, sampling_config)
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# TODO: change to actual ragged tensor after BLOOM plugin supports it
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output_ids_x = decoder.decode(input_ids, context_lengths,
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sampling_config)
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torch.cuda.synchronize()
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torch.testing.assert_close(output_ids, output_ids_x)
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res = output_ids.squeeze()
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res = res[:, -max_new_tokens:]
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ref_output_ids = hf_bloom.generate(
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input_ids,
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do_sample=do_sample,
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early_stopping=early_stoppping,
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num_beams=num_beams,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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num_beam_groups=num_beam_groups,
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max_new_tokens=max_new_tokens,
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length_penalty=length_penalty,
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repetition_penalty=repetition_penalty,
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id)
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torch.cuda.synchronize()
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ref = ref_output_ids[:, -max_new_tokens:]
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np.testing.assert_allclose(ref.cpu().numpy(), res.cpu().numpy())
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if __name__ == '__main__':
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unittest.main()
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