mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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d8b408e6dc
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
598 lines
26 KiB
Python
598 lines
26 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 random
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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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from pathlib import Path
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import numpy as np
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import pytest
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import torch
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from parameterized import parameterized
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from transformers import LlamaConfig, LlamaForCausalLM
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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_trt
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from tensorrt_llm.layers import PositionEmbeddingType
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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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sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
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from examples.llama.weight import load_from_hf_llama
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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 TestLLaMA(unittest.TestCase):
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EOS_TOKEN = 2
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PAD_TOKEN = 2
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def _gen_tensorrt_llm_network(self, network, hf_llama,
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llama_config: LlamaConfig, batch_size,
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beam_width, input_len, output_len, dtype,
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rank, tensor_parallel):
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list(range(tensor_parallel))
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with net_guard(network):
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kv_dtype = str_dtype_to_trt(dtype)
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# Initialize model
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tensorrt_llm_llama = tensorrt_llm.models.LLaMAForCausalLM(
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num_layers=llama_config.num_hidden_layers,
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num_heads=llama_config.num_attention_heads,
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num_kv_heads=llama_config.num_key_value_heads,
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hidden_size=llama_config.hidden_size,
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vocab_size=llama_config.vocab_size,
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hidden_act=llama_config.hidden_act,
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max_position_embeddings=llama_config.max_position_embeddings,
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dtype=kv_dtype,
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mlp_hidden_size=llama_config.intermediate_size,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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mapping=tensorrt_llm.Mapping(world_size=tensor_parallel,
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tp_size=tensor_parallel),
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)
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load_from_hf_llama(tensorrt_llm_llama,
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hf_llama,
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dtype=dtype,
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mapping=tensorrt_llm.Mapping(
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world_size=tensor_parallel,
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rank=rank,
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tp_size=tensor_parallel))
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# Prepare
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network.set_named_parameters(tensorrt_llm_llama.named_parameters())
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inputs = tensorrt_llm_llama.prepare_inputs(batch_size, input_len,
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output_len, True,
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beam_width)
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# Forward
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tensorrt_llm_llama(*inputs)
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return network
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def _gen_tensorrt_llm_engine(self,
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dtype,
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rank,
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world_size,
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llama_config,
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hf_llama,
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model_name,
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use_plugin,
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batch_size,
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beam_width,
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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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context_fmha_flag=ContextFMHAType.disabled,
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enable_remove_input_padding=False):
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builder = Builder()
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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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if enable_remove_input_padding:
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network.plugin_config.enable_remove_input_padding()
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network.plugin_config.set_context_fmha(context_fmha_flag)
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self._gen_tensorrt_llm_network(network, hf_llama, llama_config,
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batch_size, beam_width, input_len,
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output_len, dtype, rank, world_size)
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builder_config = builder.create_builder_config(
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name=model_name,
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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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return engine_buffer
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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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llama_config,
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hf_llama,
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model_name,
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use_plugin,
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batch_size,
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beam_width,
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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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context_fmha_flag=ContextFMHAType.disabled,
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enable_remove_input_padding=False):
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tensorrt_llm.logger.set_level(log_level)
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mapping = tensorrt_llm.Mapping(world_size, rank, tp_size=world_size)
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engine_buffer = self._gen_tensorrt_llm_engine(
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dtype, rank, world_size, llama_config, hf_llama, model_name,
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use_plugin, batch_size, beam_width, input_len, output_len,
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use_refit, fast_building, context_fmha_flag,
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enable_remove_input_padding)
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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], [False, True], [
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ContextFMHAType.disabled, ContextFMHAType.enabled,
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ContextFMHAType.enabled_with_fp32_acc
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], [False, True], ['float16'], [0]))
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test_cases.append(
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(False, True, ContextFMHAType.disabled, False, 'bfloat16', 0))
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test_cases.append(
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(False, True, ContextFMHAType.enabled, False, 'float16', 1)) # MQA
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test_cases.append(
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(False, True, ContextFMHAType.disabled, False, 'float32', 0))
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test_cases.append((False, True, ContextFMHAType.disabled, False,
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'bfloat16', 2)) # GQA
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test_cases.append(
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(False, True, ContextFMHAType.enabled, False, 'float16', 2)) # GQA
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test_cases.append((False, True, ContextFMHAType.enabled_with_fp32_acc,
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False, 'float16', 4)) # GQA
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return test_cases
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def custom_name_func(testcase_func, param_num, param):
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return "%s_%s" % (
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testcase_func.__name__,
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parameterized.to_safe_name("_".join(str(x) for x in param.args)),
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)
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@parameterized.expand(load_test_cases, name_func=custom_name_func)
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def test_llama(self, use_refit, fast_building, context_fmha_flag,
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enable_remove_input_padding, dtype, num_kv_heads):
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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_flag == 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_flag == 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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elif dtype == 'bfloat16':
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pytest.skip(
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"bfloat16 is not supported in pre-ampere architecture")
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PRECHECKED_GOOD_RANDOM_SEEDS = [1, 4, 5, 8]
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model = 'llama'
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log_level = 'error'
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use_plugin = True # gpt plugin
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batch_size = 4
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beam_width = 1
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input_len = 4
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output_len = 2
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max_seq_len = input_len + output_len
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world_size = 1
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head_size = 32
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rank = 0
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llama_config = LlamaConfig()
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llama_config.hidden_act = 'silu'
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llama_config.num_hidden_layers = 2
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llama_config.max_position_embeddings = 64
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llama_config.vocab_size = 128
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llama_config.num_attention_heads = 2 if num_kv_heads <= 1 else 2 * num_kv_heads
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llama_config.hidden_size = llama_config.num_attention_heads * head_size
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llama_config.intermediate_size = ((
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(llama_config.hidden_size * 4 * 2 // 3) + head_size - 1) //
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head_size) * head_size
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if hasattr(llama_config, "num_key_value_heads"):
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llama_config.num_key_value_heads = num_kv_heads if num_kv_heads != 0 else llama_config.num_attention_heads
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print(llama_config.num_key_value_heads)
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assert (llama_config.num_attention_heads %
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llama_config.num_key_value_heads) == 0
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llama_config.pad_token_id = self.PAD_TOKEN
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llama_config.eos_token_id = self.EOS_TOKEN
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seed_idx = random.randint(0, len(PRECHECKED_GOOD_RANDOM_SEEDS) - 1)
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torch.manual_seed(PRECHECKED_GOOD_RANDOM_SEEDS[seed_idx])
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hf_llama = LlamaForCausalLM(llama_config).cuda()
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runtime, _ = self._gen_tensorrt_llm_runtime(
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log_level, dtype, world_size, rank, llama_config, hf_llama, model,
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use_plugin, batch_size, beam_width, input_len, output_len,
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use_refit, fast_building, context_fmha_flag,
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enable_remove_input_padding)
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key_value_cache_buffers = []
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head_size = llama_config.hidden_size // llama_config.num_attention_heads
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for i in range(llama_config.num_hidden_layers):
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key_value_cache_buffers.append(
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torch.zeros((
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batch_size,
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2,
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llama_config.num_key_value_heads,
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max_seq_len,
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head_size,
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),
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
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device='cuda'))
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# compare context
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step = 0
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ctx_ids = torch.randint(100, (batch_size, input_len)).int().cuda()
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ctx_context_lengths = input_len * torch.ones(
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(batch_size), dtype=torch.int32, device='cuda')
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ctx_position_ids = torch.tensor(range(input_len),
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dtype=torch.int32).reshape([
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1, input_len
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]).expand([batch_size,
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input_len]).cuda()
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ctx_last_token_ids = ctx_context_lengths.clone()
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ctx_host_request_types = torch.tensor([0] * batch_size,
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dtype=torch.int32)
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# We need sequence_lengths start as context_lengths for step 0,
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# and it will be added one after each step.
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sequence_length_buffer = ctx_context_lengths.detach().clone()
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with torch.no_grad():
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hf_outputs = hf_llama.forward(ctx_ids)
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torch.cuda.synchronize()
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ref = hf_outputs.logits[:, -1, :]
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if enable_remove_input_padding:
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ctx_ids = ctx_ids.view([1, batch_size * input_len])
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ctx_position_ids = ctx_position_ids.view(
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[1, batch_size * input_len])
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ctx_last_token_ids = torch.cumsum(ctx_last_token_ids, dim=0).int()
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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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max_seq_len,
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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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max_seq_len,
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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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'context_lengths': ctx_context_lengths,
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'position_ids': ctx_position_ids,
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'last_token_ids': ctx_last_token_ids,
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'cache_indirection': cache_indirections[0],
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'host_request_types': ctx_host_request_types,
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}
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if enable_remove_input_padding:
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ctx_buffer['host_context_lengths'] = ctx_context_lengths.cpu()
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ctx_shape = {k: v.shape for k, v in ctx_buffer.items()}
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kv_shape = (batch_size, 2, llama_config.num_key_value_heads,
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max_seq_len, head_size)
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for i in range(llama_config.num_hidden_layers):
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ctx_shape[f'past_key_value_{i}'] = kv_shape
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ctx_buffer[f'past_key_value_{i}'] = key_value_cache_buffers[i]
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ctx_buffer[f'present_key_value_{i}'] = key_value_cache_buffers[i]
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ctx_buffer['sequence_length'] = sequence_length_buffer
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ctx_shape['sequence_length'] = ctx_buffer['sequence_length'].shape
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ctx_shape['host_past_key_value_lengths'] = (batch_size, )
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ctx_buffer['host_past_key_value_lengths'] = torch.tensor(
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[0] * batch_size, dtype=torch.int32)
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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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np.testing.assert_allclose(ref.to(torch.float32).cpu().numpy(),
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res.to(torch.float32).cpu().numpy(),
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atol=0.12)
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# compare generation
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step = 1
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step1_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_position_ids = torch.ones_like(step1_id).int().cuda() * input_len
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gen_last_token_ids = torch.zeros_like(gen_context_lengths).int().cuda()
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gen_host_request_types = torch.tensor([1] * batch_size,
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dtype=torch.int32)
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with torch.no_grad():
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hf_outputs = hf_llama.forward(
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step1_id,
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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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if enable_remove_input_padding:
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step1_id = step1_id.view([1, batch_size])
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gen_position_ids = gen_position_ids.view([1, batch_size])
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gen_last_token_ids = torch.ones_like(
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gen_context_lengths).int().cuda()
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gen_last_token_ids = torch.cumsum(gen_last_token_ids, dim=0).int()
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step1_buffer = {
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'input_ids': step1_id,
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'context_lengths': gen_context_lengths,
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'position_ids': gen_position_ids,
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'last_token_ids': gen_last_token_ids,
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'host_request_types': gen_host_request_types,
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'cache_indirection': cache_indirections[1],
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}
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if enable_remove_input_padding:
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step1_buffer['host_context_lengths'] = gen_context_lengths.cpu()
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step1_shape = {k: v.shape for k, v in step1_buffer.items()}
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for i in range(llama_config.num_hidden_layers):
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step1_shape[f'past_key_value_{i}'] = kv_shape
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step1_shape['sequence_length'] = (batch_size, )
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step1_shape['host_past_key_value_lengths'] = (batch_size, )
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for i in range(llama_config.num_hidden_layers):
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step1_buffer[f'past_key_value_{i}'] = key_value_cache_buffers[i]
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step1_buffer[f'present_key_value_{i}'] = key_value_cache_buffers[i]
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step1_buffer[
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'host_past_key_value_lengths'] = sequence_length_buffer.cpu()
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sequence_length_buffer = torch.add(sequence_length_buffer, step)
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step1_buffer['sequence_length'] = sequence_length_buffer
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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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np.testing.assert_allclose(ref.to(torch.float32).cpu().numpy(),
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res.to(torch.float32).cpu().numpy(),
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atol=0.12)
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def get_loader_test_cases():
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test_cases = []
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test_cases.extend(
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list(
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product([
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("llama-7b-hf", "7B"),
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], [
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(1, 0),
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(2, 0),
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(2, 1),
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], [
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-1,
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0,
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1,
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])))
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test_cases.extend(
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list(
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product([
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("llama-7b-4gqa-hf", "7B-4GQA"),
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], [(1, 0), (2, 0), (2, 1), (4, 0), (4, 1)], [-1, 0, 1])))
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test_cases.extend(
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list(
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product([
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("llama-7b-4gqa-hf", "7B-4GQA"),
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], [(8, 0), (8, 7)], [-1, 0, 1])))
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return test_cases
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def loader_name_func(testcase_func, param_num, param):
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expand_params = lambda params: '_'.join([
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expand_params(x) if isinstance(x, (list, tuple)) else str(x)
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for x in params
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])
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name = expand_params(param.args)
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return "%s_%s" % (
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testcase_func.__name__,
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parameterized.to_safe_name(name),
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)
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@parameterized.expand(get_loader_test_cases, name_func=loader_name_func)
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def test_loaders(self, paths, tp_info, emb_sharding_dim):
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model_root = os.getenv("LLM_MODELS_ROOT")
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if model_root is None:
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pytest.skip("Skipping since real weights are unavailable.")
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model_root = Path(
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model_root,
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"llama-synthetic" if paths[0].find("gqa") >= 0 else "llama-models")
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hf_path = Path(model_root, paths[0])
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meta_path = Path(model_root, paths[1])
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if not hf_path.exists():
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pytest.skip(f"Skipping since the path {hf_path} does not exist.")
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if not meta_path.exists():
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|
pytest.skip(f"Skipping since the path {meta_path} does not exist.")
|
|
|
|
def print_corner(name, t: np.ndarray):
|
|
if len(t.shape) == 1:
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tl = t[:2]
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br = t[-2:]
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|
elif len(t.shape) == 2:
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tl = t[:2, :2]
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|
br = t[-2:, -2:]
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print(name, np.concatenate([tl, br]).flatten())
|
|
|
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def print_layers(m: tensorrt_llm.models.LLaMAForCausalLM):
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print_corner("vocab", m.vocab_embedding.weight._value)
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print_corner("lm_head", m.lm_head.weight._value)
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print_corner("ln_f", m.ln_f.weight._value)
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|
print_corner("qkv", m.layers[0].attention.qkv.weight._value)
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print_corner("gate", m.layers[0].mlp.gate.weight._value)
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|
print_corner("inorm", m.layers[0].input_layernorm.weight._value)
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print(flush=True)
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|
return
|
|
|
|
import tensorrt as trt
|
|
|
|
from examples.llama.weight import load_from_meta_llama
|
|
tp_size = tp_info[0]
|
|
rank = tp_info[1]
|
|
dtype = "float16"
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|
use_parallel_embedding = (emb_sharding_dim >= 0)
|
|
embedding_sharding_dim = abs(emb_sharding_dim)
|
|
hf_llama = LlamaForCausalLM.from_pretrained(
|
|
hf_path,
|
|
device_map={
|
|
"model": "cpu",
|
|
"lm_head": "cpu"
|
|
}, # Load to CPU memory
|
|
torch_dtype="auto")
|
|
assert hf_llama.config.torch_dtype == torch.float16
|
|
kv_dtype = trt.float16 if hf_llama.config.torch_dtype == torch.float16 else trt.float32
|
|
max_context_length = 128 # for loader tests this value does not matter
|
|
tensorrt_llm_llama_wHF = tensorrt_llm.models.LLaMAForCausalLM(
|
|
num_layers=hf_llama.config.num_hidden_layers,
|
|
num_heads=hf_llama.config.num_attention_heads,
|
|
num_kv_heads=hf_llama.config.num_key_value_heads,
|
|
hidden_size=hf_llama.config.hidden_size,
|
|
vocab_size=hf_llama.config.vocab_size,
|
|
hidden_act=hf_llama.config.hidden_act,
|
|
max_position_embeddings=hf_llama.config.max_position_embeddings,
|
|
dtype=kv_dtype,
|
|
mlp_hidden_size=hf_llama.config.intermediate_size,
|
|
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
|
|
mapping=tensorrt_llm.Mapping(world_size=tp_size,
|
|
rank=rank,
|
|
tp_size=tp_size),
|
|
use_parallel_embedding=use_parallel_embedding,
|
|
embedding_sharding_dim=embedding_sharding_dim)
|
|
# print_layers(tensorrt_llm_llama_wHF)
|
|
load_from_hf_llama(tensorrt_llm_llama_wHF,
|
|
hf_llama,
|
|
mapping=tensorrt_llm.Mapping(world_size=tp_size,
|
|
rank=rank,
|
|
tp_size=tp_size),
|
|
dtype=dtype)
|
|
# print_layers(tensorrt_llm_llama_wHF)
|
|
|
|
tensorrt_llm_llama_wMETA = tensorrt_llm.models.LLaMAForCausalLM(
|
|
num_layers=hf_llama.config.num_hidden_layers,
|
|
num_heads=hf_llama.config.num_attention_heads,
|
|
num_kv_heads=hf_llama.config.num_key_value_heads,
|
|
hidden_size=hf_llama.config.hidden_size,
|
|
vocab_size=hf_llama.config.vocab_size,
|
|
hidden_act=hf_llama.config.hidden_act,
|
|
max_position_embeddings=hf_llama.config.max_position_embeddings,
|
|
dtype=kv_dtype,
|
|
mlp_hidden_size=hf_llama.config.intermediate_size,
|
|
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
|
|
mapping=tensorrt_llm.Mapping(world_size=tp_size,
|
|
rank=rank,
|
|
tp_size=tp_size),
|
|
use_parallel_embedding=use_parallel_embedding,
|
|
embedding_sharding_dim=embedding_sharding_dim)
|
|
# print_layers(tensorrt_llm_llama_wMETA)
|
|
load_from_meta_llama(tensorrt_llm_llama_wMETA,
|
|
meta_path,
|
|
mapping=tensorrt_llm.Mapping(world_size=tp_size,
|
|
rank=rank,
|
|
tp_size=tp_size),
|
|
dtype=dtype)
|
|
# print_layers(tensorrt_llm_llama_wMETA)
|
|
# token embedding
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.vocab_embedding.weight._value,
|
|
tensorrt_llm_llama_wMETA.vocab_embedding.weight._value,
|
|
atol=1e-3)
|
|
# output
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.lm_head.weight._value,
|
|
tensorrt_llm_llama_wMETA.lm_head.weight._value,
|
|
atol=1e-3)
|
|
# norm
|
|
np.testing.assert_allclose(tensorrt_llm_llama_wHF.ln_f.weight._value,
|
|
tensorrt_llm_llama_wMETA.ln_f.weight._value,
|
|
atol=1e-3)
|
|
# Checking all of the layers takes too much time, just check one random layer
|
|
l = np.random.randint(0, tensorrt_llm_llama_wHF.num_layers)
|
|
# for l in range(tensorrt_llm_llama_wHF.num_layers):
|
|
if l >= 0:
|
|
print(f"Checking Layer-{l} weights ...", flush=True)
|
|
# layer{l}.input_layernorm
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.layers[l].input_layernorm.weight._value,
|
|
tensorrt_llm_llama_wMETA.layers[l].input_layernorm.weight.
|
|
_value,
|
|
atol=1e-3)
|
|
# layer{l}.post_layernorm
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.layers[l].post_layernorm.weight._value,
|
|
tensorrt_llm_llama_wMETA.layers[l].post_layernorm.weight._value,
|
|
atol=1e-3)
|
|
# layer{l}.mlp.gate
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.layers[l].mlp.gate.weight._value,
|
|
tensorrt_llm_llama_wMETA.layers[l].mlp.gate.weight._value,
|
|
atol=1e-3)
|
|
# layer{l}.mlp.proj
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.layers[l].mlp.proj.weight._value,
|
|
tensorrt_llm_llama_wMETA.layers[l].mlp.proj.weight._value,
|
|
atol=1e-3)
|
|
# layer{l}.mlp.fc
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.layers[l].mlp.fc.weight._value,
|
|
tensorrt_llm_llama_wMETA.layers[l].mlp.fc.weight._value,
|
|
atol=1e-3)
|
|
# layer{l}.dense
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.layers[l].attention.dense.weight._value,
|
|
tensorrt_llm_llama_wMETA.layers[l].attention.dense.weight.
|
|
_value,
|
|
atol=1e-3)
|
|
# layer{l}.qkv
|
|
np.testing.assert_allclose(
|
|
tensorrt_llm_llama_wHF.layers[l].attention.qkv.weight._value,
|
|
tensorrt_llm_llama_wMETA.layers[l].attention.qkv.weight._value,
|
|
atol=1e-3)
|
|
return
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|