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from __future__ import annotations
import os
import pathlib
import typing
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
str_type_map = {"fp32": torch.float32,
"fp16": torch.float16, "bf16": torch.bfloat16}
class BaseBelleWeights:
def __init__(self, head_num, size_per_head, layer_num, vocab_size, max_seq_len, tensor_para_size, pipeline_para_size,
weights_data_type: typing.Union[str, np.dtype],
inference_data_type: str,
has_adapters: bool = False,
adapter_inter_size: int = 0,
gpt_with_moe: bool = False,
has_positional_encoding: bool = True,
has_pre_decoder_layernorm: bool = False,
has_post_decoder_layernorm: bool = True,
int8_mode: int = 0,
inter_size: int = 0):
assert(head_num % tensor_para_size == 0)
if int8_mode == 1:
torch_infer_dtype = str_type_map[inference_data_type]
assert torch_infer_dtype == torch.float16 or torch_infer_dtype == torch.bfloat16, "Weight only quant only supported for infer type fp16 or bf16."
quant = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix
self.weight_transpose_calibrate_quantize = lambda x: quant(
x, torch.int8)
else:
assert int8_mode == 0, "Invalid int8 mode for BELLE. Must be 0 or 1"
self.head_num = head_num
self.size_per_head = size_per_head
self.layer_num = layer_num
self.vocab_size = vocab_size
self.max_seq_len = max_seq_len
self.tensor_para_size = tensor_para_size
self.pipeline_para_size = pipeline_para_size
self.layers_per_device = layer_num // pipeline_para_size
self.has_adapters = has_adapters
self.adapter_inter_size = adapter_inter_size
self.gpt_with_moe = gpt_with_moe
self.has_positional_encoding = has_positional_encoding
self.has_pre_decoder_layernorm = has_pre_decoder_layernorm
self.has_post_decoder_layernorm = has_post_decoder_layernorm
local_head_num = head_num // tensor_para_size
global_head_num = head_num
local_hidden_units = local_head_num * size_per_head
global_hidden_units = global_head_num * size_per_head
local_inter_size = local_hidden_units * 4
if inter_size != 0:
assert inter_size % tensor_para_size == 0, f"inter_size({inter_size}) \% tensor_para_size({tensor_para_size}) must be 0"
local_inter_size = inter_size // tensor_para_size
local_adapter_inter_size = self.adapter_inter_size // tensor_para_size
self.local_head_num = local_head_num
self.global_head_num = global_head_num
self.local_hidden_units = local_hidden_units
self.global_hidden_units = global_hidden_units
self.local_inter_size = local_inter_size
self.int8_mode = int8_mode
self.share_embed = False
if isinstance(weights_data_type, str):
try:
weights_data_type = {
"fp16": np.float16,
"fp32": np.float32,
"float16": np.float16,
"float32": np.float32,
}[weights_data_type]
except KeyError:
raise ValueError(
f"Don't know how to interpret weights_data_type: {weights_data_type}")
assert weights_data_type in [np.float32, np.float16]
self.weights_data_type = weights_data_type
self.inference_data_type = inference_data_type
self.w = []
self.int8_w = []
self.scale = []
# Transformer blocks
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # self_layernorm_gamma
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # self_layernorm_beta
self.w.extend([torch.zeros(global_hidden_units, local_hidden_units * 3,
dtype=str_type_map[self.inference_data_type])] * layer_num) # self_kernel
self.w.extend([torch.zeros(local_hidden_units * 3, dtype=str_type_map[self.inference_data_type])]
* layer_num) # self_bias
self.w.extend([torch.zeros(local_hidden_units, global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # self_output_kernel
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # self_output_bias
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # ffn_layernorm_gamma
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # ffn_layernorm_beta
self.w.extend([torch.zeros(global_hidden_units, local_inter_size,
dtype=str_type_map[self.inference_data_type])] * layer_num) # ffn_kernel1
self.w.extend([torch.zeros(local_inter_size, dtype=str_type_map[
self.inference_data_type])] * layer_num) # ffn_bias1
self.w.extend([torch.zeros(local_inter_size, global_hidden_units,
dtype=str_type_map[self.inference_data_type])] * layer_num) # ffn_kernel2
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # ffn_bias2
optional_adapter_offset = 0
# After Transformer blocks
if self.has_pre_decoder_layernorm:
self.w.append(torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])) # embedding layernorm gamma
self.w.append(torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])) # embedding layernorm beta
optional_adapter_offset += 2
if self.has_post_decoder_layernorm:
self.w.append(torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])) # final layernorm gamma
self.w.append(torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])) # final layernorm beta
optional_adapter_offset += 2
if self.has_positional_encoding:
self.w.append(torch.zeros(max_seq_len, global_hidden_units, dtype=str_type_map[
self.inference_data_type])) # position_encoding_table
optional_adapter_offset += 1
self.pre_embed_idx = len(self.w)
self.w.append(torch.zeros(vocab_size, global_hidden_units,
dtype=str_type_map[self.inference_data_type])) # embedding_table
self.post_embed_idx = len(self.w)
self.w.append(torch.zeros(vocab_size, global_hidden_units, dtype=str_type_map[
self.inference_data_type])) # post embedding_kernel
self.adapter_offset = 2 + optional_adapter_offset
self.w.extend([torch.empty(
0, dtype=str_type_map[self.inference_data_type])] * layer_num) # gating_weight
self.adapter_offset += layer_num
# adapters
if self.has_adapters:
self.w.extend([torch.zeros(global_hidden_units, local_adapter_inter_size,
dtype=str_type_map[self.inference_data_type])] * layer_num) # adaptor1_kernel1
self.w.extend([torch.zeros(local_adapter_inter_size, dtype=str_type_map[
self.inference_data_type])] * layer_num) # adaptor1_bias1
self.w.extend([torch.zeros(local_adapter_inter_size, global_hidden_units,
dtype=str_type_map[self.inference_data_type])] * layer_num) # adaptor1_kernel2
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # adaptor1_bias2
self.w.extend([torch.zeros(global_hidden_units, local_adapter_inter_size,
dtype=str_type_map[self.inference_data_type])] * layer_num) # adaptor2_kernel1
self.w.extend([torch.zeros(local_adapter_inter_size, dtype=str_type_map[
self.inference_data_type])] * layer_num) # adaptor2_bias1
self.w.extend([torch.zeros(local_adapter_inter_size, global_hidden_units,
dtype=str_type_map[self.inference_data_type])] * layer_num) # adaptor2_kernel2
self.w.extend([torch.zeros(global_hidden_units, dtype=str_type_map[
self.inference_data_type])] * layer_num) # adaptor2_bias2
# Initialization
self._map(lambda w: torch.nn.init.normal_(w, mean=0., std=1.))
if (self.int8_mode != 0):
self.int8_w.extend([torch.zeros(global_hidden_units, local_hidden_units *
3, dtype=torch.int8)] * layer_num) # self_int8_kernel
self.scale.extend([torch.zeros(
local_hidden_units * 3, dtype=torch.float)] * layer_num) # self_scale
self.int8_w.extend([torch.zeros(local_hidden_units, global_hidden_units, dtype=torch.int8)]
* layer_num) # self_output_int8_kernel
# self_output_scale
self.scale.extend(
[torch.zeros(global_hidden_units, dtype=torch.float)] * layer_num)
self.int8_w.extend([torch.zeros(global_hidden_units, local_inter_size,
dtype=torch.int8)] * layer_num) # ffn_int8_kernel1
self.scale.extend(
[torch.zeros(local_inter_size, dtype=torch.float)] * layer_num) # ffn_scale1
self.int8_w.extend([torch.zeros(local_inter_size, global_hidden_units,
dtype=torch.int8)] * layer_num) # ffn_int8_kernel2
self.scale.extend(
[torch.zeros(global_hidden_units, dtype=torch.float)] * layer_num) # ffn_scale2
if self.has_adapters:
self.int8_w.extend([torch.zeros(global_hidden_units, local_adapter_inter_size,
dtype=torch.int8)] * layer_num) # adaptor1_int8_kernel1
self.scale.extend([torch.zeros(local_adapter_inter_size, dtype=torch.float)]
* layer_num) # adaptor1_scale1
self.int8_w.extend([torch.zeros(local_adapter_inter_size, global_hidden_units,
dtype=torch.int8)] * layer_num) # adaptor1_int8_kernel2
self.scale.extend([torch.zeros(
global_hidden_units, dtype=torch.float)] * layer_num) # adaptor1_scale2
self.int8_w.extend([torch.zeros(global_hidden_units, local_adapter_inter_size,
dtype=torch.int8)] * layer_num) # adaptor2_int8_kernel1
self.scale.extend([torch.zeros(local_adapter_inter_size, dtype=torch.float)]
* layer_num) # adaptor2_scale1
self.int8_w.extend([torch.zeros(local_adapter_inter_size, global_hidden_units,
dtype=torch.int8)] * layer_num) # adaptor2_int8_kernel2
self.scale.extend([torch.zeros(
global_hidden_units, dtype=torch.float)] * layer_num) # adaptor2_scale2
def __getitem__(self, idx):
return self.w[idx]
def __setitem__(self, idx, val):
self.w[idx] = val
def __len__(self):
return len(self.w)
def _map(self, func):
assert(self.pre_embed_idx < self.post_embed_idx,
"Pre decoder embedding index should be lower than post decoder embedding index.")
for i in range(len(self.w)):
if isinstance(self.w[i], list):
for j in range(len(self.w[i])):
self.w[i][j] = func(self.w[i][j])
else:
if self.share_embed and i == self.post_embed_idx:
# If sharing the pre and post embedding, any mapping to
# the pre decoder weight will give the same output to the
# post decoder weight, so we just copy here.
self.w[self.post_embed_idx] = self.w[self.pre_embed_idx]
else:
self.w[i] = func(self.w[i])
def _map_int8(self, func):
for i in range(len(self.int8_w)):
if isinstance(self.int8_w[i], list):
for j in range(len(self.int8_w[i])):
self.int8_w[i][j] = func(self.int8_w[i][j])
else:
self.int8_w[i] = func(self.int8_w[i])
for i in range(len(self.scale)):
if isinstance(self.scale[i], list):
for j in range(len(self.scale[i])):
self.scale[i][j] = func(self.scale[i][j])
else:
self.scale[i] = func(self.scale[i])
def _map_int8_scales(self, func):
for i in range(len(self.scale)):
if isinstance(self.scale[i], list):
for j in range(len(self.scale[i])):
self.scale[i][j] = func(self.scale[i][j])
else:
self.scale[i] = func(self.scale[i])
def load(self, ckpt_path, tp_rank, pipeline_para_rank):
if not os.path.exists(ckpt_path):
raise FileNotFoundError(f"Failed to find {ckpt_path}")
w = []
type_map = {np.float32: torch.float32, np.float16: torch.float16}
# Load
def is_load(i): return i >= self.layers_per_device * \
pipeline_para_rank and i < self.layers_per_device * \
(pipeline_para_rank + 1)
def load_to_torch(npdata: str, is_load: bool):
if is_load:
return torch.from_numpy(npdata).to(str_type_map[self.inference_data_type])
#return torch.from_numpy(np.fromfile(file_path, dtype=self.weights_data_type)).to(str_type_map[self.inference_data_type])
else:
return torch.empty(0).to(str_type_map[self.inference_data_type])
def get_np_data(h5f, layername, layer_num, weight_type, tp_rank=None):
if tp_rank is None:
return [load_to_torch(h5f[f'model.layers.{i}.{layername}.{weight_type}']["weights"][:], is_load(i)) for i in range(layer_num)]
else:
return [load_to_torch(h5f[f'model.layers.{i}.{layername}.{weight_type}.{tp_rank}']["weights"][:], is_load(i)) for i in range(layer_num)]
def get_np_data_single(h5f, layername, weight_type, is_loaded, tp_rank=None):
if weight_type is None:
return load_to_torch(h5f[f'model.{layername}']["weights"][:], is_loaded)
if tp_rank is None:
return load_to_torch(h5f[f'model.{layername}.{weight_type}']["weights"][:], is_loaded)
else:
return load_to_torch(h5f[f'model.{layername}.{weight_type}.{tp_rank}']["weights"][:], is_loaded)
import h5py
ckpt_f = h5py.File(ckpt_path, "r")
w.extend(get_np_data(ckpt_f, "input_layernorm", self.layer_num, "weight"))
w.extend(get_np_data(ckpt_f, "input_layernorm", self.layer_num, "bias"))
w.extend(get_np_data(ckpt_f, "attention.query_key_value", self.layer_num, "weight", tp_rank))
w.extend(get_np_data(ckpt_f, "attention.query_key_value", self.layer_num, "bias", tp_rank))
w.extend(get_np_data(ckpt_f, "attention.dense", self.layer_num, "weight", tp_rank))
w.extend(get_np_data(ckpt_f, "attention.dense", self.layer_num, "bias"))
w.extend(get_np_data(ckpt_f, "post_attention_layernorm", self.layer_num, "weight"))
w.extend(get_np_data(ckpt_f, "post_attention_layernorm", self.layer_num, "bias"))
# if moe, load "mlp.moe.experts.dense_h_to_4h"
w.extend(get_np_data(ckpt_f, "mlp.dense_h_to_4h", self.layer_num, "weight", tp_rank))
w.extend(get_np_data(ckpt_f, "mlp.dense_h_to_4h", self.layer_num, "bias", tp_rank))
# if moe, load "mlp.moe.experts.dense_4h_to_h"
w.extend(get_np_data(ckpt_f, "mlp.dense_4h_to_h", self.layer_num, "weight", tp_rank))
w.extend(get_np_data(ckpt_f, "mlp.dense_4h_to_h", self.layer_num, "bias"))
if self.has_pre_decoder_layernorm:
w.append(get_np_data_single(ckpt_f, "pre_decoder_layernorm", "weight", True))
w.append(get_np_data_single(ckpt_f, "pre_decoder_layernorm", "bias", True))
if self.has_post_decoder_layernorm:
w.append(get_np_data_single(ckpt_f, "final_layernorm", "weight", True))
w.append(get_np_data_single(ckpt_f, "final_layernorm", "bias", True))
if self.has_positional_encoding:
wpe = load_to_torch(get_np_data_single(ckpt_f, "wpe", weight_type=None, is_loaded=True)).reshape(-1, self.global_hidden_units)
assert self.max_seq_len <= wpe.size(0), (
f"max_seq_len ({self.max_seq_len} must not exceed "
f"the value of maximum sequence length during training ({wpe.size(0)})."
)
w.append(wpe)
w.append(get_np_data_single(ckpt_f, "wte", weight_type=None, is_loaded=True))
if "model.lm_head.weight" in ckpt_f.keys():
self.share_embed = False
w.append(get_np_data_single(ckpt_f, "lm_head", "weight", True))
else:
self.share_embed = True
w.append(torch.empty(0).to(str_type_map[self.inference_data_type]))
gate_list = []
for i in range(self.layer_num):
if f"model.layers.{i}.mlp.moe.gate.wg.weight" in ckpt_f.keys():
gate_list.append(load_to_torch(
f"{ckpt_path}/model.layers.{i}.mlp.moe.gate.wg.weight.bin", True))
else:
gate_list.append(load_to_torch(
f"{ckpt_path}/model.layers.{i}.mlp.moe.gate.wg.weight.bin", False))
w.extend(gate_list)
"""
if self.has_adapters:
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_h_to_4h.weight.{tp_rank}.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_h_to_4h.weight.{tp_rank}.bin")
else f"{ckpt_path}/model.layers.{i}.after_attention_adapter.moe.experts.dense_h_to_4h.weight.{tp_rank}.bin",
is_load(i)) for i in range(self.layer_num)])
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_h_to_4h.bias.{tp_rank}.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_h_to_4h.bias.{tp_rank}.bin")
else f"{ckpt_path}/model.layers.{i}.after_attention_adapter.moe.experts.dense_h_to_4h.bias.{tp_rank}.bin",
is_load(i)) for i in range(self.layer_num)])
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_4h_to_h.weight.{tp_rank}.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_4h_to_h.weight.{tp_rank}.bin")
else f"{ckpt_path}/model.layers.{i}.after_attention_adapter.moe.experts.dense_4h_to_h.weight.{tp_rank}.bin",
is_load(i)) for i in range(self.layer_num)])
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_4h_to_h.bias.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_attention_adapter.dense_4h_to_h.bias.bin")
else f"{ckpt_path}/model.layers.{i}.after_attention_adapter.moe.experts.dense_4h_to_h.bias.bin",
is_load(i)) for i in range(self.layer_num)])
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_h_to_4h.weight.{tp_rank}.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_h_to_4h.weight.{tp_rank}.bin")
else f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.moe.experts.dense_h_to_4h.weight.{tp_rank}.bin",
is_load(i)) for i in range(self.layer_num)])
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_h_to_4h.bias.{tp_rank}.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_h_to_4h.bias.{tp_rank}.bin")
else f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.moe.experts.dense_h_to_4h.bias.{tp_rank}.bin",
is_load(i)) for i in range(self.layer_num)])
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_4h_to_h.weight.{tp_rank}.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_4h_to_h.weight.{tp_rank}.bin")
else f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.moe.experts.dense_4h_to_h.weight.{tp_rank}.bin",
is_load(i)) for i in range(self.layer_num)])
w.extend([load_to_torch(
f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_4h_to_h.bias.bin"
if os.path.isfile(f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.dense_4h_to_h.bias.bin")
else f"{ckpt_path}/model.layers.{i}.after_ffn_adapter.moe.experts.dense_4h_to_h.bias.bin",
is_load(i)) for i in range(self.layer_num)])
"""
assert len(self.w) == len(w)
# Reshape
try:
for i in range(len(w)):
if w[i].nelement() == self.w[i].nelement():
self.w[i] = w[i].reshape(self.w[i].shape)
else:
self.w[i] = w[i]
except RuntimeError:
raise RuntimeError(
f"head_num, size_per_head, vocab_size, and max_seq_len must be the same as the ones during training "
f"(idx: {i} expected shape: {self.w[i].shape} got shape: {w[i].shape})."
)
# transpose calibrate quantize the kernel
layer_num = self.layer_num
if self.int8_mode != 0:
for i in range(layer_num):
self.int8_w[i + 0 * layer_num], self.scale[i + 0 *
layer_num] = self.weight_transpose_calibrate_quantize(self.w[2 * layer_num + i])
self.int8_w[i + 1 * layer_num], self.scale[i + 1 *
layer_num] = self.weight_transpose_calibrate_quantize(self.w[4 * layer_num + i])
self.int8_w[i + 2 * layer_num], self.scale[i + 2 *
layer_num] = self.weight_transpose_calibrate_quantize(self.w[8 * layer_num + i])
self.int8_w[i + 3 * layer_num], self.scale[i + 3 *
layer_num] = self.weight_transpose_calibrate_quantize(self.w[10 * layer_num + i])
# We clear the original weights since they are no longer needed
if self.int8_mode == 1:
self.w[2 * layer_num +
i] = torch.empty(0).to(str_type_map[self.inference_data_type])
self.w[4 * layer_num +
i] = torch.empty(0).to(str_type_map[self.inference_data_type])
self.w[8 * layer_num +
i] = torch.empty(0).to(str_type_map[self.inference_data_type])
self.w[10 * layer_num +
i] = torch.empty(0).to(str_type_map[self.inference_data_type])
if self.has_adapters:
self.int8_w[i + 4 * layer_num], self.scale[i + 4 * layer_num] = self.weight_transpose_calibrate_quantize(
self.w[12 * layer_num + i + self.adapter_offset])
self.int8_w[i + 5 * layer_num], self.scale[i + 5 * layer_num] = self.weight_transpose_calibrate_quantize(
self.w[14 * layer_num + i + self.adapter_offset])
self.int8_w[i + 6 * layer_num], self.scale[i + 6 * layer_num] = self.weight_transpose_calibrate_quantize(
self.w[16 * layer_num + i + self.adapter_offset])
self.int8_w[i + 7 * layer_num], self.scale[i + 7 * layer_num] = self.weight_transpose_calibrate_quantize(
self.w[18 * layer_num + i + self.adapter_offset])
# Similar to above:
if self.int8_mode == 1:
self.w[12 * layer_num + i + self.adapter_offset] = torch.empty(
0).to(str_type_map[self.inference_data_type])
self.w[14 * layer_num + i + self.adapter_offset] = torch.empty(
0).to(str_type_map[self.inference_data_type])
self.w[16 * layer_num + i + self.adapter_offset] = torch.empty(
0).to(str_type_map[self.inference_data_type])
self.w[18 * layer_num + i + self.adapter_offset] = torch.empty(
0).to(str_type_map[self.inference_data_type])
return True
class BaseBelleModel(nn.Module):
def __init__(self,
head_num, size_per_head,
vocab_size, start_id, end_id, layer_num,
max_seq_len: int,
tensor_para_size: int,
pipeline_para_size: int,
lib_path: typing.Union[str, pathlib.Path],
inference_data_type: str,
inter_size: int = 0,
# gpt_variant_params
layernorm_eps: float = 1e-6,
layernorm_type: typing.Literal['pre_layernorm',
'post_layernorm'] = "pre_layernorm",
activation_type: str = "Gelu",
gpt_with_moe: bool = False,
expert_num: int = 0,
moe_k: int = 0,
moe_layer_index: typing.List = [],
has_positional_encoding: bool = True,
has_pre_decoder_layernorm: bool = False,
has_post_decoder_layernorm: bool = True,
has_adapters: bool = False,
adapter_inter_size: int = 0,
use_attention_linear_bias: bool = False,
int8_mode: int = 0,
weights_data_type: typing.Union[str, np.dtype] = np.float32,
shared_contexts_ratio: float = 1.0):
super().__init__()
self.head_num = head_num
self.size_per_head = size_per_head
self.vocab_size = vocab_size
self.start_id = start_id
self.end_id = end_id
self.layer_num = layer_num
self.inter_size = inter_size if inter_size != 0 else 4 * \
self.head_num * self.size_per_head
# gpt_variant_params
self.layernorm_eps = layernorm_eps
self.layernorm_type = layernorm_type
self.activation_type = activation_type
self.gpt_with_moe = gpt_with_moe
self.expert_num = expert_num
self.moe_k = moe_k
self.moe_layer_index = moe_layer_index
self.has_positional_encoding = has_positional_encoding
self.has_pre_decoder_layernorm = has_pre_decoder_layernorm
self.has_post_decoder_layernorm = has_post_decoder_layernorm
self.has_adapters = has_adapters
self.adapter_inter_size = adapter_inter_size
self.use_attention_linear_bias = use_attention_linear_bias
# multi-gpu params
self.tensor_para_size = tensor_para_size
self.pipeline_para_size = pipeline_para_size
self.use_sparse_gemm = False
self.build_model = False
self.int8_mode = int8_mode
self.weights_data_type = weights_data_type
self.shared_contexts_ratio = shared_contexts_ratio
assert torch.cuda.is_available(), "CUDA is required for this model."
assert head_num % tensor_para_size == 0, "head_num must be a multiple of tensor_para_size."
assert layer_num % pipeline_para_size == 0, "layer_num must be a multiple of pipeline_para_size."
# Load the C++ model into Pytorch model.
torch.classes.load_library(os.path.abspath(lib_path))
# Prepare weights
self.weights = BaseBelleWeights(head_num, size_per_head, layer_num, vocab_size,
max_seq_len, tensor_para_size, pipeline_para_size,
weights_data_type=weights_data_type,
inference_data_type=inference_data_type,
gpt_with_moe=self.gpt_with_moe,
has_positional_encoding=self.has_positional_encoding,
has_pre_decoder_layernorm=self.has_pre_decoder_layernorm,
has_post_decoder_layernorm=self.has_post_decoder_layernorm,
has_adapters=self.has_adapters,
adapter_inter_size=self.adapter_inter_size,
int8_mode=int8_mode,
inter_size=inter_size)
# Prepare for tensor/pipeline parallel
try:
dist.init_process_group(backend='mpi')
except:
print("[INFO] WARNING: Have initialized the process group")
self.rank = dist.get_rank()
self.device_count = torch.cuda.device_count()
self.device = self.rank % self.device_count
torch.cuda.set_device(self.device)
world_size = dist.get_world_size()
assert world_size == tensor_para_size * \
pipeline_para_size, "tensor_para_size * pipeline_para_size must be equal to world_size."
self.tensor_para_rank = self.rank % self.tensor_para_size
self.pipeline_para_rank = self.rank // self.tensor_para_size
def load(self, ckpt_path):
is_load = self.weights.load(ckpt_path, tp_rank=self.tensor_para_rank,
pipeline_para_rank=self.pipeline_para_rank)
self.cuda()
torch.cuda.empty_cache() # clean cache for model weight preprocessing
return is_load
def sparse(self):
if not self.use_sparse_gemm:
self.use_sparse_gemm = True
def cuda(self):
self.weights._map(lambda w: w.cuda(self.device))
if self.int8_mode != 0:
self.weights._map_int8(lambda w: w.cuda(self.device))
if self.build_model:
del self.model
self.build_model = False
self.model = torch.classes.FasterTransformer.GptOp(
self.head_num, self.size_per_head, self.inter_size,
self.layer_num,
self.expert_num,
self.moe_k,
self.moe_layer_index,
self.vocab_size, self.start_id, self.end_id,
self.use_sparse_gemm,
# gpt_variant_params
self.layernorm_eps,
self.layernorm_type,
self.activation_type,
self.has_positional_encoding,
self.has_pre_decoder_layernorm,
self.has_post_decoder_layernorm,
self.has_adapters,
self.adapter_inter_size,
self.use_attention_linear_bias,
self.weights.w)
self.build_model = True
def forward(self,
start_ids: torch.IntTensor,
start_lengths: torch.IntTensor,
output_len: int,
beam_width: int = 1,
top_k: typing.Optional[torch.IntTensor] = None,
top_p: typing.Optional[torch.FloatTensor] = None,
beam_search_diversity_rate: typing.Optional[torch.FloatTensor] = None,
temperature: typing.Optional[torch.FloatTensor] = None,
len_penalty: typing.Optional[torch.FloatTensor] = None,
repetition_penalty: typing.Optional[torch.FloatTensor] = None,
presence_penalty: typing.Optional[torch.FloatTensor] = None,
min_length: typing.Optional[torch.IntTensor] = None,
random_seed: typing.Optional[torch.LongTensor] = None,
bad_words_list: typing.Optional[torch.IntTensor] = None,
return_output_length: bool = False,
return_cum_log_probs: int = 0):
if not self.build_model:
# for the cases we don't load model
self.cuda()
torch.cuda.empty_cache() # clean cache for model weight preprocessing
input_len = start_ids.size(1)
assert input_len > 0, "input len must be larger than zero. For an unconditional case, use start_id as the first token."
# Inputs to device
start_ids = start_ids.cuda(self.device)
start_lengths = start_lengths.cuda(self.device)
# outputs: output_ids, output_lengths, output_cum_log_probs (optional)
outputs = self.model.forward(start_ids,
start_lengths,
output_len,
beam_width, # optional, can be None
top_k, # optional, can be None
top_p, # optional, can be None
beam_search_diversity_rate, # optional, can be None
temperature, # optional, can be None
len_penalty, # optional, can be None
repetition_penalty, # optional, can be None
presence_penalty, # optional, can be None
min_length, # optional, can be None
random_seed, # optional, can be None
bad_words_list, # optional, can be None
return_cum_log_probs) # optional, can be None
if return_cum_log_probs == 0:
output_ids, output_lengths = outputs
else:
output_ids, output_lengths, output_cum_log_probs = outputs
if return_output_length:
if return_cum_log_probs > 0:
return output_ids, output_lengths, output_cum_log_probs
else:
return output_ids, output_lengths
else:
return output_ids
def set_input_tensor(self, input_tensor):
"""Set input tensor to be used instead of forward()'s input.
When doing pipeline parallelism the input from the previous
stage comes from communication, not from the input, so the
model's forward_step_func won't have it. This function is thus
used by internal code to bypass the input provided by the
forward_step_func"""
self.input_tensor = input_tensor
class BaseParallelBelleModel(BaseBelleModel):
def cuda(self):
self.weights._map(lambda w: w.cuda(self.device))
if self.int8_mode != 0:
self.weights._map_int8(lambda w: w.cuda(self.device))
if self.build_model:
del self.model
self.build_model = False
self.model = torch.classes.FasterTransformer.ParallelGptOp(
self.head_num, self.size_per_head, self.inter_size,
self.layer_num,
self.expert_num,
self.moe_k,
self.moe_layer_index,
self.vocab_size, self.start_id, self.end_id,
self.tensor_para_size, self.pipeline_para_size, self.int8_mode,
# GPT variant parameters
self.layernorm_eps,
self.layernorm_type,
self.activation_type,
self.has_positional_encoding,
self.has_pre_decoder_layernorm,
self.has_post_decoder_layernorm,
self.has_adapters,
self.adapter_inter_size,
self.use_attention_linear_bias,
self.weights.w,
self.weights.int8_w,
self.weights.scale,
self.shared_contexts_ratio)
self.build_model = True
class BelleWeight(BaseBelleWeights):
def __init__(self, head_num, size_per_head, layer_num, vocab_size,
tensor_para_size, pipeline_para_size, weights_data_type, inference_data_type,
int8_mode=0):
super().__init__(
head_num, size_per_head, layer_num, vocab_size, 0,
tensor_para_size, pipeline_para_size, weights_data_type,
inference_data_type,
has_adapters=False,
adapter_inter_size=0,
has_positional_encoding=False,
has_pre_decoder_layernorm=True,
has_post_decoder_layernorm=True,
int8_mode=int8_mode)
class BelleModel(BaseParallelBelleModel):
def __init__(self,
head_num, size_per_head,
vocab_size, start_id, end_id, layer_num,
tensor_para_size: int,
pipeline_para_size: int,
lib_path: str | Path,
inference_data_type: str,
weights_data_type: str | np.dtype = np.float32,
layernorm_eps: float = 1e-5,
shared_contexts_ratio: float = 1.0,
int8_mode: int = 0):
super().__init__(
head_num, size_per_head, vocab_size, start_id, end_id, layer_num,
0, tensor_para_size, pipeline_para_size,
lib_path=lib_path,
inference_data_type=inference_data_type,
layernorm_eps=layernorm_eps,
# gpt_variant_params
layernorm_type="pre_layernorm",
activation_type="Gelu",
has_positional_encoding=False,
has_pre_decoder_layernorm=True,
has_post_decoder_layernorm=True,
has_adapters=False,
adapter_inter_size=0,
use_attention_linear_bias=True,
int8_mode=int8_mode,
weights_data_type=weights_data_type,
shared_contexts_ratio=shared_contexts_ratio)
def set_input_tensor(self, input_tensor: Optional[torch.Tensor]):
"""Set input tensor to be used instead of forward()'s input.
When doing pipeline parallelism the input from the previous
stage comes from communication, not from the input, so the
model's forward_step_func won't have it. This function is thus
used by internal code to bypass the input provided by the
forward_step_func
"""
self.input_tensor = input_tensor
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