# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import time from pathlib import Path import tensorrt as trt import torch import torch.multiprocessing as mp from transformers import LlamaConfig, LlamaForCausalLM from weight import (get_scaling_factors, load_from_awq_llama, load_from_binary, load_from_gptq_llama, load_from_hf_llama, load_from_meta_llama) import tensorrt_llm from tensorrt_llm._utils import str_dtype_to_trt from tensorrt_llm.builder import Builder from tensorrt_llm.layers.attention import PositionEmbeddingType from tensorrt_llm.logger import logger from tensorrt_llm.mapping import Mapping from tensorrt_llm.models import (fp8_quantize, smooth_quantize, weight_only_groupwise_quantize, weight_only_quantize) from tensorrt_llm.network import net_guard from tensorrt_llm.plugin.plugin import ContextFMHAType from tensorrt_llm.quantization import QuantMode from weight import parse_ft_config # isort:skip MODEL_NAME = "llama" # 2 routines: get_engine_name, serialize_engine # are direct copy from gpt example, TODO: put in utils? import onnx import tensorrt as trt from onnx import TensorProto, helper def trt_dtype_to_onnx(dtype): if dtype == trt.float16: return TensorProto.DataType.FLOAT16 elif dtype == trt.float32: return TensorProto.DataType.FLOAT elif dtype == trt.int32: return TensorProto.DataType.INT32 else: raise TypeError("%s is not supported" % dtype) def to_onnx(network, path): inputs = [] for i in range(network.num_inputs): network_input = network.get_input(i) inputs.append( helper.make_tensor_value_info( network_input.name, trt_dtype_to_onnx(network_input.dtype), list(network_input.shape))) outputs = [] for i in range(network.num_outputs): network_output = network.get_output(i) outputs.append( helper.make_tensor_value_info( network_output.name, trt_dtype_to_onnx(network_output.dtype), list(network_output.shape))) nodes = [] for i in range(network.num_layers): layer = network.get_layer(i) layer_inputs = [] for j in range(layer.num_inputs): ipt = layer.get_input(j) if ipt is not None: layer_inputs.append(layer.get_input(j).name) layer_outputs = [ layer.get_output(j).name for j in range(layer.num_outputs) ] nodes.append( helper.make_node(str(layer.type), name=layer.name, inputs=layer_inputs, outputs=layer_outputs, domain="com.nvidia")) onnx_model = helper.make_model(helper.make_graph(nodes, 'attention', inputs, outputs, initializer=None), producer_name='NVIDIA') onnx.save(onnx_model, path) def get_engine_name(model, dtype, tp_size, pp_size, rank): if pp_size == 1: return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank) return '{}_{}_tp{}_pp{}_rank{}.engine'.format(model, dtype, tp_size, pp_size, rank) def serialize_engine(engine, path): logger.info(f'Serializing engine to {path}...') tik = time.time() with open(path, 'wb') as f: f.write(bytearray(engine)) tok = time.time() t = time.strftime('%H:%M:%S', time.gmtime(tok - tik)) logger.info(f'Engine serialized. Total time: {t}') def parse_arguments(): parser = argparse.ArgumentParser() parser.add_argument('--world_size', type=int, default=1) parser.add_argument('--tp_size', type=int, default=1) parser.add_argument('--pp_size', type=int, default=1) parser.add_argument('--model_dir', type=str, default=None) parser.add_argument('--ft_model_dir', type=str, default=None) parser.add_argument('--meta_ckpt_dir', type=str, default=None) parser.add_argument('--quant_ckpt_path', type=str, default=None) parser.add_argument('--dtype', type=str, default='float16', choices=['float32', 'bfloat16', 'float16']) parser.add_argument( '--timing_cache', type=str, default='model.cache', help= 'The path of to read timing cache from, will be ignored if the file does not exist' ) parser.add_argument('--log_level', type=str, default='info') parser.add_argument('--vocab_size', type=int, default=32000) parser.add_argument('--n_layer', type=int, default=32) parser.add_argument('--n_positions', type=int, default=2048) parser.add_argument('--n_embd', type=int, default=4096) parser.add_argument('--n_head', type=int, default=32) parser.add_argument('--n_kv_head', type=int, default=None) parser.add_argument('--multiple_of', type=int, default=256) parser.add_argument('--ffn_dim_multiplier', type=float, default=1.0) parser.add_argument('--inter_size', type=int, default=None) parser.add_argument('--hidden_act', type=str, default='silu') parser.add_argument('--rms_norm_eps', type=float, default=1e-06) parser.add_argument('--max_batch_size', type=int, default=8) parser.add_argument('--max_input_len', type=int, default=2048) parser.add_argument('--max_output_len', type=int, default=512) parser.add_argument('--max_beam_width', type=int, default=1) parser.add_argument('--rotary_base', type=float, default=10000.0) parser.add_argument('--rotary_scaling', nargs=2, type=str, default=None) parser.add_argument('--use_gpt_attention_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16', 'bfloat16', 'float32']) parser.add_argument('--use_gemm_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16', 'bfloat16', 'float32']) parser.add_argument('--use_rmsnorm_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16', 'float32', 'bfloat16']) parser.add_argument('--parallel_build', default=False, action='store_true') parser.add_argument('--enable_context_fmha', default=False, action='store_true') parser.add_argument('--enable_context_fmha_fp32_acc', default=False, action='store_true') parser.add_argument('--visualize', default=False, action='store_true') parser.add_argument('--enable_debug_output', default=False, action='store_true') parser.add_argument('--gpus_per_node', type=int, default=8) parser.add_argument('--builder_opt', type=int, default=None) parser.add_argument( '--output_dir', type=str, default='llama_outputs', help= 'The path to save the serialized engine files, timing cache file and model configs' ) parser.add_argument('--remove_input_padding', default=False, action='store_true') # Arguments related to the quantization of the model. parser.add_argument( '--use_smooth_quant', default=False, action="store_true", help= 'Use the SmoothQuant method to quantize activations and weights for the various GEMMs.' 'See --per_channel and --per_token for finer-grained quantization options.' ) parser.add_argument( '--per_channel', default=False, action="store_true", help= 'By default, we use a single static scaling factor for the GEMM\'s result. ' 'per_channel instead uses a different static scaling factor for each channel. ' 'The latter is usually more accurate, but a little slower.') parser.add_argument( '--per_token', default=False, action="store_true", help= 'By default, we use a single static scaling factor to scale activations in the int8 range. ' 'per_token chooses at run time, and for each token, a custom scaling factor. ' 'The latter is usually more accurate, but a little slower.') parser.add_argument( '--per_group', default=False, action="store_true", help= 'By default, we use a single static scaling factor to scale weights in the int4 range. ' 'per_group chooses at run time, and for each group, a custom scaling factor. ' 'The flag is built for GPTQ/AWQ quantization.') parser.add_argument('--group_size', type=int, default=128, help='Group size used in GPTQ/AWQ quantization.') parser.add_argument( '--int8_kv_cache', default=False, action="store_true", help= 'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV' ) parser.add_argument( '--use_parallel_embedding', action="store_true", default=False, help= 'By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled' ) parser.add_argument( '--embedding_sharding_dim', type=int, default=1, # Meta does TP on hidden dim choices=[0, 1], help= 'By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). ' 'To shard it along hidden dimension, set embedding_sharding_dim=1' 'Note: embedding sharing is only enabled when embedding_sharding_dim = 0' ) parser.add_argument( '--enable_fp8', default=False, action='store_true', help='Use FP8 Linear layer for Attention QKV/Dense and MLP.') parser.add_argument( '--fp8_kv_cache', default=False, action="store_true", help= 'By default, we use dtype for KV cache. fp8_kv_cache chooses int8 quantization for KV' ) parser.add_argument( '--quantized_fp8_model_path', type=str, default=None, help='Path of a quantized model checkpoint in .npz format') parser.add_argument( '--use_weight_only', default=False, action="store_true", help='Quantize weights for the various GEMMs to INT4/INT8.' 'See --weight_only_precision to set the precision') parser.add_argument( '--weight_only_precision', const='int8', type=str, nargs='?', default='int8', choices=['int8', 'int4', 'int4_awq', 'int4_gptq'], help= 'Define the precision for the weights when using weight-only quantization.' 'You must also use --use_weight_only for that argument to have an impact.' ) parser.add_argument( '--use_inflight_batching', action="store_true", default=False, help="Activates inflight batching mode of gptAttentionPlugin.") parser.add_argument( '--paged_kv_cache', action="store_true", default=False, help= 'By default we use contiguous KV cache. By setting this flag you enable paged KV cache' ) parser.add_argument('--tokens_per_block', type=int, default=64, help='Number of tokens per block in paged KV cache') parser.add_argument( '--max_num_tokens', type=int, default=None, help='Define the max number of tokens supported by the engine') parser.add_argument( '--strongly_typed', default=False, action="store_true", help= 'This option is introduced with trt 9.1.0.1+ and will reduce the building time significantly for fp8.' ) parser.add_argument( '--use_custom_all_reduce', action='store_true', help= 'Activates latency-optimized algorithm for all-reduce instead of NCCL.') args = parser.parse_args() tensorrt_llm.logger.set_level(args.log_level) assert not ( args.use_smooth_quant and args.use_weight_only ), "You cannot enable both SmoothQuant and INT8 weight-only together." if not args.remove_input_padding: if args.use_gpt_attention_plugin: logger.warning( f"It is recommended to specify --remove_input_padding when using GPT attention plugin" ) if args.use_inflight_batching: if not args.use_gpt_attention_plugin: args.use_gpt_attention_plugin = 'float16' logger.info( f"Using GPT attention plugin for inflight batching mode. Setting to default '{args.use_gpt_attention_plugin}'" ) if not args.remove_input_padding: args.remove_input_padding = True logger.info( "Using remove input padding for inflight batching mode.") if not args.paged_kv_cache: args.paged_kv_cache = True logger.info("Using paged KV cache for inflight batching mode.") if args.use_smooth_quant: args.quant_mode = QuantMode.use_smooth_quant(args.per_token, args.per_channel) elif args.use_weight_only: if args.per_group: args.quant_mode = QuantMode.from_description( quantize_weights=True, quantize_activations=False, per_token=False, per_channel=False, per_group=True, use_int4_weights=True) else: args.quant_mode = QuantMode.use_weight_only( args.weight_only_precision == 'int4') else: args.quant_mode = QuantMode(0) if args.int8_kv_cache: args.quant_mode = args.quant_mode.set_int8_kv_cache() elif args.fp8_kv_cache: args.quant_mode = args.quant_mode.set_fp8_kv_cache() if args.enable_fp8: args.quant_mode = args.quant_mode.set_fp8_qdq() if args.rotary_scaling is not None: rotary_scaling = { "type": args.rotary_scaling[0], "factor": float(args.rotary_scaling[1]) } assert rotary_scaling["type"] in ["linear", "dynamic"] assert rotary_scaling["factor"] > 1.0 args.rotary_scaling = rotary_scaling if rotary_scaling["type"] == "dynamic": assert not args.remove_input_padding, "TODO: Not supported yet" # Since gpt_attenttion_plugin is the only way to apply RoPE now, # force use the plugin for now with the correct data type. args.use_gpt_attention_plugin = args.dtype if args.model_dir is not None: hf_config = LlamaConfig.from_pretrained(args.model_dir) args.inter_size = hf_config.intermediate_size # override the inter_size for LLaMA args.n_embd = hf_config.hidden_size args.n_head = hf_config.num_attention_heads if hasattr(hf_config, "num_key_value_heads"): args.n_kv_head = hf_config.num_key_value_heads args.n_layer = hf_config.num_hidden_layers args.n_positions = hf_config.max_position_embeddings args.vocab_size = hf_config.vocab_size args.hidden_act = hf_config.hidden_act args.rms_norm_eps = hf_config.rms_norm_eps elif args.meta_ckpt_dir is not None: with open(Path(args.meta_ckpt_dir, "params.json")) as fp: meta_config: dict = json.load(fp) args.n_embd = meta_config["dim"] args.n_head = meta_config["n_heads"] args.n_layer = meta_config["n_layers"] args.n_kv_head = meta_config.get("n_kv_heads", args.n_head) args.multiple_of = meta_config["multiple_of"] args.ffn_dim_multiplier = meta_config.get("ffn_dim_multiplier", 1) n_embd = int(4 * args.n_embd * 2 / 3) args.inter_size = args.multiple_of * ( (int(n_embd * args.ffn_dim_multiplier) + args.multiple_of - 1) // args.multiple_of) args.rms_norm_eps = meta_config["norm_eps"] elif args.ft_model_dir is not None: n_embd, n_head, n_layer, n_positions, vocab_size, hidden_act, inter_size, n_kv_head = parse_ft_config( Path(args.ft_model_dir) / "config.ini") args.inter_size = inter_size # override the inter_size for LLaMA args.n_kv_head = n_kv_head args.n_embd = n_embd args.n_head = n_head args.n_layer = n_layer args.n_positions = n_positions args.vocab_size = vocab_size args.hidden_act = hidden_act args.rms_norm_eps = 1e-06 logger.warning("Set rms_norm_eps to 1e-06 directly.") assert args.use_gpt_attention_plugin, "LLaMa must use gpt attention plugin" if args.n_kv_head is None: args.n_kv_head = args.n_head elif args.n_kv_head != args.n_head: assert (args.n_head % args.n_kv_head) == 0, \ "MQA/GQA requires the number of heads to be divisible by the number of K/V heads." assert (args.n_kv_head % args.tp_size) == 0 or (args.tp_size % args.n_kv_head) == 0, \ "MQA/GQA requires either the number of K/V heads to be divisible by the tensor parallelism size OR " \ "the tensor parallelism size to be divisible by the number of K/V heads." if args.dtype == 'bfloat16': assert args.use_gemm_plugin, "Please use gemm plugin when dtype is bfloat16" assert args.pp_size * args.tp_size == args.world_size if args.max_num_tokens is not None: assert args.enable_context_fmha if args.inter_size is None: # this should not be need when loading a real model # but it is helpful when creating a dummy model without loading any real weights n_embd = int(4 * args.n_embd * 2 / 3) args.inter_size = args.multiple_of * ( (int(n_embd * args.ffn_dim_multiplier) + args.multiple_of - 1) // args.multiple_of) logger.info(f"Setting inter_size to {args.inter_size}.") return args def build_rank_engine(builder: Builder, builder_config: tensorrt_llm.builder.BuilderConfig, engine_name, rank, args): ''' @brief: Build the engine on the given rank. @param rank: The rank to build the engine. @param args: The cmd line arguments. @return: The built engine. ''' dtype = str_dtype_to_trt(args.dtype) mapping = Mapping(world_size=args.world_size, rank=rank, tp_size=args.tp_size, pp_size=args.pp_size) assert args.n_layer % args.pp_size == 0, \ f"num_layers {args.n_layer} must be a multiple of pipeline parallelism size {args.pp_size}" # Initialize Module tensorrt_llm_llama = tensorrt_llm.models.LLaMAForCausalLM( num_layers=args.n_layer, num_heads=args.n_head, num_kv_heads=args.n_kv_head, hidden_size=args.n_embd, vocab_size=args.vocab_size, hidden_act=args.hidden_act, max_position_embeddings=args.n_positions, dtype=dtype, mlp_hidden_size=args.inter_size, position_embedding_type=PositionEmbeddingType.rope_gpt_neox, mapping=mapping, rotary_base=args.rotary_base, rotary_scaling=args.rotary_scaling, use_parallel_embedding=args.use_parallel_embedding, embedding_sharding_dim=args.embedding_sharding_dim, quant_mode=args.quant_mode, rms_norm_eps=args.rms_norm_eps) if args.use_smooth_quant: tensorrt_llm_llama = smooth_quantize(tensorrt_llm_llama, args.quant_mode) elif args.use_weight_only: if args.weight_only_precision == 'int8': tensorrt_llm_llama = weight_only_quantize(tensorrt_llm_llama, args.quant_mode) elif args.weight_only_precision == 'int4': tensorrt_llm_llama = weight_only_quantize(tensorrt_llm_llama, args.quant_mode) elif args.weight_only_precision == 'int4_awq': tensorrt_llm_llama = weight_only_groupwise_quantize( model=tensorrt_llm_llama, quant_mode=args.quant_mode, group_size=args.group_size, zero=False, pre_quant_scale=True, exclude_modules=[]) elif args.weight_only_precision == 'int4_gptq': tensorrt_llm_llama = weight_only_groupwise_quantize( model=tensorrt_llm_llama, quant_mode=args.quant_mode, group_size=args.group_size, zero=True, pre_quant_scale=False) elif args.enable_fp8 or args.fp8_kv_cache: logger.info(f'Loading scaling factors from ' f'{args.quantized_fp8_model_path}') quant_scales = get_scaling_factors(args.quantized_fp8_model_path, num_layers=args.n_layer, quant_mode=args.quant_mode) tensorrt_llm_llama = fp8_quantize(tensorrt_llm_llama, quant_mode=args.quant_mode, quant_scales=quant_scales) if args.per_group: load_func = load_from_awq_llama if args.weight_only_precision == 'int4_awq' else load_from_gptq_llama load_func(tensorrt_llm_llama=tensorrt_llm_llama, quant_ckpt_path=args.quant_ckpt_path, mapping=mapping, dtype=args.dtype) elif args.meta_ckpt_dir is not None: load_from_meta_llama(tensorrt_llm_llama, args.meta_ckpt_dir, mapping, args.dtype) elif args.model_dir is not None: logger.info(f'Loading HF LLaMA ... from {args.model_dir}') tik = time.time() hf_llama = LlamaForCausalLM.from_pretrained( args.model_dir, device_map={ "model": "cpu", "lm_head": "cpu" }, # Load to CPU memory torch_dtype="auto") tok = time.time() t = time.strftime('%H:%M:%S', time.gmtime(tok - tik)) logger.info(f'HF LLaMA loaded. Total time: {t}') load_from_hf_llama(tensorrt_llm_llama, hf_llama, mapping=mapping, dtype=args.dtype) del hf_llama elif args.ft_model_dir is not None: load_from_binary(tensorrt_llm_llama, args.ft_model_dir, mapping, fp16=(args.dtype == 'float16'), multi_query_mode=(args.n_kv_head != args.n_head)) # Module -> Network network = builder.create_network() network.trt_network.name = engine_name if args.use_gpt_attention_plugin: network.plugin_config.set_gpt_attention_plugin( dtype=args.use_gpt_attention_plugin) if args.use_gemm_plugin: network.plugin_config.set_gemm_plugin(dtype=args.use_gemm_plugin) if args.use_rmsnorm_plugin: network.plugin_config.set_rmsnorm_plugin(dtype=args.use_rmsnorm_plugin) # Quantization plugins. if args.use_smooth_quant: network.plugin_config.set_smooth_quant_gemm_plugin(dtype=args.dtype) network.plugin_config.set_rmsnorm_quantization_plugin(dtype=args.dtype) network.plugin_config.set_quantize_tensor_plugin() network.plugin_config.set_quantize_per_token_plugin() assert not (args.enable_context_fmha and args.enable_context_fmha_fp32_acc) if args.enable_context_fmha: network.plugin_config.set_context_fmha(ContextFMHAType.enabled) if args.enable_context_fmha_fp32_acc: network.plugin_config.set_context_fmha( ContextFMHAType.enabled_with_fp32_acc) if args.use_weight_only: if args.per_group: network.plugin_config.set_weight_only_groupwise_quant_matmul_plugin( dtype='float16') else: network.plugin_config.set_weight_only_quant_matmul_plugin( dtype='float16') if args.world_size > 1: network.plugin_config.set_nccl_plugin(args.dtype, args.use_custom_all_reduce) if args.remove_input_padding: network.plugin_config.enable_remove_input_padding() if args.paged_kv_cache: network.plugin_config.enable_paged_kv_cache(args.tokens_per_block) with net_guard(network): # Prepare network.set_named_parameters(tensorrt_llm_llama.named_parameters()) # Forward inputs = tensorrt_llm_llama.prepare_inputs(args.max_batch_size, args.max_input_len, args.max_output_len, True, args.max_beam_width, args.max_num_tokens) tensorrt_llm_llama(*inputs) if args.enable_debug_output: # mark intermediate nodes' outputs for k, v in tensorrt_llm_llama.named_network_outputs(): v = v.trt_tensor v.name = k network.trt_network.mark_output(v) v.dtype = dtype if args.visualize: model_path = os.path.join(args.output_dir, 'test.onnx') to_onnx(network.trt_network, model_path) tensorrt_llm.graph_rewriting.optimize(network) engine = None # Network -> Engine engine = builder.build_engine(network, builder_config) if rank == 0: config_path = os.path.join(args.output_dir, 'config.json') builder.save_config(builder_config, config_path) return engine def build(rank, args): torch.cuda.set_device(rank % args.gpus_per_node) logger.set_level(args.log_level) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) # when doing serializing build, all ranks share one engine builder = Builder() cache = None for cur_rank in range(args.world_size): # skip other ranks if parallel_build is enabled if args.parallel_build and cur_rank != rank: continue # NOTE: when only int8 kv cache is used together with paged kv cache no int8 tensors are exposed to TRT int8_trt_flag = args.quant_mode.has_act_and_weight_quant() or ( not args.paged_kv_cache and args.quant_mode.has_int8_kv_cache()) builder_config = builder.create_builder_config( name=MODEL_NAME, precision=args.dtype, timing_cache=args.timing_cache if cache is None else cache, tensor_parallel=args.tp_size, pipeline_parallel=args.pp_size, parallel_build=args.parallel_build, num_layers=args.n_layer, num_heads=args.n_head, num_kv_heads=args.n_kv_head, hidden_size=args.n_embd, vocab_size=args.vocab_size, hidden_act=args.hidden_act, max_position_embeddings=args.n_positions, max_batch_size=args.max_batch_size, max_input_len=args.max_input_len, max_output_len=args.max_output_len, max_num_tokens=args.max_num_tokens, int8=int8_trt_flag, fp8=args.quant_mode.has_fp8_qdq(), quant_mode=args.quant_mode, strongly_typed=args.strongly_typed, opt_level=args.builder_opt) engine_name = get_engine_name(MODEL_NAME, args.dtype, args.tp_size, args.pp_size, cur_rank) engine = build_rank_engine(builder, builder_config, engine_name, cur_rank, args) assert engine is not None, f'Failed to build engine for rank {cur_rank}' if cur_rank == 0: # Use in-memory timing cache for multiple builder passes. if not args.parallel_build: cache = builder_config.trt_builder_config.get_timing_cache() serialize_engine(engine, os.path.join(args.output_dir, engine_name)) if rank == 0: ok = builder.save_timing_cache( builder_config, os.path.join(args.output_dir, "model.cache")) assert ok, "Failed to save timing cache." if __name__ == '__main__': args = parse_arguments() tik = time.time() if args.parallel_build and args.world_size > 1 and \ torch.cuda.device_count() >= args.world_size: logger.warning( f'Parallelly build TensorRT engines. Please make sure that all of the {args.world_size} GPUs are totally free.' ) mp.spawn(build, nprocs=args.world_size, args=(args, )) else: args.parallel_build = False logger.info('Serially build TensorRT engines.') build(0, args) tok = time.time() t = time.strftime('%H:%M:%S', time.gmtime(tok - tik)) logger.info(f'Total time of building all {args.world_size} engines: {t}')