Understanding Neural Network
Zhiwei:
Could you explain the code?
self.weights = [w-(eta/len(mini_batch))*nw
for w, nw in zip(self.weights, nabla_w)]
self.biases = [b-(eta/len(mini_batch))*nb
for b, nb in zip(self.biases, nabla_b)]
ChatGPT:
Of course! The code you provided seems to be part of a machine learning algorithm, specifically an implementation of stochastic gradient descent (SGD) for updating the weights and biases in a neural network during the training process. Let’s break it down step by step:
-
self.weightsandself.biases: These variables are likely part of a neural network class, representing the weights and biases of the network. In a neural network, weights are parameters that adjust the strength of the connections between neurons in different layers, while biases are parameters that control the neuron’s activation threshold. -
nabla_wandnabla_b: These variables likely store the gradients of the cost function with respect to the weights and biases, respectively. During the training process, gradients are computed to determine how the cost function changes concerning the weights and biases, and this information is used to update the parameters to minimize the cost. -
eta: This is the learning rate, which controls the step size taken in the direction of the gradients during the parameter update. A higher learning rate can lead to faster convergence during training, but it can also cause instability. A smaller learning rate might improve stability but increase the training time. -
mini_batch: This is a subset of the training data used for each iteration of the stochastic gradient descent. Mini-batch gradient descent is a variant of SGD where the entire training dataset is divided into smaller batches, and the parameter updates are performed using each mini-batch.
Now, let’s look at the code itself:
self.weights = [w-(eta/len(mini_batch))*nw
for w, nw in zip(self.weights, nabla_w)]
This line updates the weights of the neural network. It uses a list comprehension to loop through each weight w and its corresponding gradient nw. For each weight, it subtracts a portion of the gradient (scaled by the learning rate eta and the size of the mini-batch) from the original weight. This is how the weights are updated to minimize the cost function.
self.biases = [b-(eta/len(mini_batch))*nb
for b, nb in zip(self.biases, nabla_b)]
This line updates the biases of the neural network in a similar way. It uses a list comprehension to loop through each bias b and its corresponding gradient nb. For each bias, it subtracts a portion of the gradient (scaled by the learning rate eta and the size of the mini-batch) from the original bias.
In summary, these lines of code perform the weight and bias updates for the neural network using stochastic gradient descent. This process is repeated for each mini-batch during the training phase to optimize the network’s parameters and improve its performance on the given task.
Zhiwei: What is SpaCy language model?
ChatGPT: SpaCy is an open-source natural language processing (NLP) library developed by Explosion AI. It is designed to be efficient, fast, and production-ready, making it a popular choice for various NLP tasks. SpaCy provides pre-trained language models that can process and understand text, allowing developers to perform various NLP tasks such as part-of-speech tagging, named entity recognition, sentence parsing, and more.
Key features of SpaCy language model include:
-
Tokenization: SpaCy can split a text into individual words or tokens, taking into account various linguistic rules and complexities.
-
Part-of-speech Tagging: The library can assign part-of-speech tags to each token, indicating whether it is a noun, verb, adjective, etc.
-
Named Entity Recognition (NER): SpaCy can identify and categorize entities such as names of people, organizations, locations, dates, etc.
-
Dependency Parsing: It can analyze the grammatical structure of a sentence and determine the relationships between words.
-
Lemmatization: SpaCy can convert words to their base or root form (lemmas).
-
Word Vectors: The library provides word vectors, also known as word embeddings, which are dense numerical representations of words that capture semantic meaning.
SpaCy supports multiple languages and comes with pre-trained models for different languages, making it widely used in various NLP applications, including information extraction, sentiment analysis, chatbots, language translation, and more. Additionally, it allows developers to train custom models on their specific datasets if needed.
Zhiwei: What is SpaCy language model?
ChatGPT: SpaCy is an open-source natural language processing (NLP) library developed by Explosion AI. It is designed to be efficient, fast, and easy to use, making it a popular choice for various NLP tasks. SpaCy provides pre-trained language models for several languages, allowing users to perform tasks such as tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and more.
The core of SpaCy’s speed and efficiency lies in its optimized Cython implementations and data structures, which make it particularly suitable for production environments and real-world applications. The library is written in Python and comes with pre-trained models for various languages, including English, German, French, Spanish, and more.
SpaCy language models use deep learning techniques to learn the underlying patterns in natural language data. These models are trained on large corpora of text, and the learned representations can then be used for various NLP tasks. The pre-trained models can be further fine-tuned on specific data to achieve better performance on domain-specific tasks.
The architecture and ease of use have made SpaCy a popular choice among developers and researchers in the NLP community. It is often used for tasks like information extraction, text classification, named entity recognition, part-of-speech tagging, and syntactic parsing. Additionally, SpaCy’s modular design allows users to easily integrate it into their existing NLP workflows and pipelines.
Zhiwei: Could you explain?
class Llama:
@staticmethod
def build(
ckpt_dir: str,
tokenizer_path: str,
max_seq_len: int,
max_batch_size: int,
model_parallel_size: Optional[int] = None,
) -> "Llama":
if not torch.distributed.is_initialized():
torch.distributed.init_process_group("nccl")
if not model_parallel_is_initialized():
if model_parallel_size is None:
model_parallel_size = int(os.environ.get("WORLD_SIZE", 1))
initialize_model_parallel(model_parallel_size)
local_rank = int(os.environ.get("LOCAL_RANK", 0))
torch.cuda.set_device(local_rank)
# seed must be the same in all processes
torch.manual_seed(1)
if local_rank > 0:
sys.stdout = open(os.devnull, "w")
start_time = time.time()
checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
assert model_parallel_size == len(
checkpoints
), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
ckpt_path = checkpoints[get_model_parallel_rank()]
checkpoint = torch.load(ckpt_path, map_location="cpu")
with open(Path(ckpt_dir) / "params.json", "r") as f:
params = json.loads(f.read())
Run the command and then get the error log.
% torchrun --nproc_per_node 1 example_text_completion.py \
--ckpt_dir llama-2-7b/ \
--tokenizer_path tokenizer.model \
--max_seq_len 128 --max_batch_size 4
NOTE: Redirects are currently not supported in Windows or MacOs.
Traceback (most recent call last):
File "/Users/lzwjava/projects/llama/example_text_completion.py", line 55, in <module>
fire.Fire(main)
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/fire/core.py", line 141, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/fire/core.py", line 475, in _Fire
component, remaining_args = _CallAndUpdateTrace(
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "/Users/lzwjava/projects/llama/example_text_completion.py", line 18, in main
generator = Llama.build(
File "/Users/lzwjava/projects/llama/llama/generation.py", line 62, in build
torch.distributed.init_process_group("nccl")
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 907, in init_process_group
default_pg = _new_process_group_helper(
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1013, in _new_process_group_helper
raise RuntimeError("Distributed package doesn't have NCCL " "built in")
RuntimeError: Distributed package doesn't have NCCL built in
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 23273) of binary: /Users/lzwjava/anaconda3/bin/python
Traceback (most recent call last):
File "/Users/lzwjava/anaconda3/bin/torchrun", line 33, in <module>
sys.exit(load_entry_point('torch==2.0.1', 'console_scripts', 'torchrun')())
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main
run(args)
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/Users/lzwjava/anaconda3/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
example_text_completion.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-08-03_15:35:37
host : 1.0.0.127.in-addr.arpa
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 23273)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
Learn the error stack trace.
Ask ChatGPT about what Backend.NCCL means in Pytorch?
Zhiwei:
code:
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
model_path = '.'
# model_path = 'openlm-research/open_llama_7b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float16, device_map='auto',
)
prompt = 'Q: What is the largest animal?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=32
)
print(tokenizer.decode(generation_output[0]))
error:
/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/transformers/generation/utils.py:1445: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.
warnings.warn(
Traceback (most recent call last):
File "/home/lzw/Projects/open_llama_3b/run.py", line 17, in <module>
generation_output = model.generate(
^^^^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/transformers/generation/utils.py", line 1515, in generate
return self.greedy_search(
^^^^^^^^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/transformers/generation/utils.py", line 2332, in greedy_search
outputs = self(
^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 688, in forward
outputs = self.model(
^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 531, in forward
inputs_embeds = self.embed_tokens(input_ids)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/torch/nn/modules/sparse.py", line 162, in forward
return F.embedding(
^^^^^^^^^^^^
File "/home/lzw/anaconda3/envs/llama/lib/python3.11/site-packages/torch/nn/functional.py", line 2210, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)
how to fix?
ChatGPT:
The error message you’re encountering indicates that there’s a mismatch in device types between your input tensor (input_ids) and your model (LlamaForCausalLM). The input tensor is on the CPU (cpu), while the model is on the GPU (cuda). To fix this, you need to ensure that the input tensor is on the same device as the model before generating text using the model.
Here’s the modified code to fix the issue:
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
model_path = '.'
# model_path = 'openlm-research/open_llama_7b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float16, device_map='auto',
)
prompt = 'Q: What is the largest animal?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
# Move the input_ids tensor to the same device as the model
input_ids = input_ids.to(model.device)
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=32
)
print(tokenizer.decode(generation_output[0]))
In this code, I’ve added the line input_ids = input_ids.to(model.device) after obtaining the input_ids tensor from the tokenizer. This line moves the input_ids tensor to the same device as the model (either CPU or GPU) before generating text. This should resolve the “Expected all tensors to be on the same device” error.
Remember that if you’re running this code on a GPU-enabled machine and your model is on a GPU, it’s generally more efficient to keep both the model and input data on the GPU to take advantage of parallel processing capabilities.
Zhiwei: Could you explain below codes?
def generate(
self,
prompt_tokens: List[List[int]],
max_gen_len: int,
temperature: float = 0.6,
top_p: float = 0.9,
logprobs: bool = False,
echo: bool = False,
) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
params = self.model.params
bsz = len(prompt_tokens)
assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
min_prompt_len = min(len(t) for t in prompt_tokens)
max_prompt_len = max(len(t) for t in prompt_tokens)
assert max_prompt_len <= params.max_seq_len
total_len = min(params.max_seq_len, max_gen_len + max_prompt_len)
pad_id = self.tokenizer.pad_id
tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
for k, t in enumerate(prompt_tokens):
tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
if logprobs:
token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
prev_pos = 0
eos_reached = torch.tensor([False] * bsz, device="cuda")
input_text_mask = tokens != pad_id
for cur_pos in range(min_prompt_len, total_len):
logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
if logprobs:
token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
input=logits.transpose(1, 2),
target=tokens[:, prev_pos + 1 : cur_pos + 1],
reduction="none",
ignore_index=pad_id,
)
if temperature > 0:
probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
next_token = sample_top_p(probs, top_p)
else:
next_token = torch.argmax(logits[:, -1], dim=-1)
next_token = next_token.reshape(-1)
# only replace token if prompt has already been generated
next_token = torch.where(
input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
)
tokens[:, cur_pos] = next_token
eos_reached |= (~input_text_mask[:, cur_pos]) & (
next_token == self.tokenizer.eos_id
)
prev_pos = cur_pos
if all(eos_reached):
break
if logprobs:
token_logprobs = token_logprobs.tolist()
out_tokens, out_logprobs = [], []
for i, toks in enumerate(tokens.tolist()):
# cut to max gen len
start = 0 if echo else len(prompt_tokens[i])
toks = toks[start : len(prompt_tokens[i]) + max_gen_len]
probs = None
if logprobs:
probs = token_logprobs[i][start : len(prompt_tokens[i]) + max_gen_len]
# cut to eos tok if any
if self.tokenizer.eos_id in toks:
eos_idx = toks.index(self.tokenizer.eos_id)
toks = toks[:eos_idx]
probs = probs[:eos_idx] if logprobs else None
out_tokens.append(toks)
out_logprobs.append(probs)
return (out_tokens, out_logprobs if logprobs else None)
ChatGPT: Certainly! The provided code is a method called generate that takes a prompt (input text) and generates text using a language model. This method is designed to work with a specific model and tokenizer and performs text generation using either greedy sampling or top-p sampling. Here’s a breakdown of how the method works:
- Method Parameters:
prompt_tokens: A list of tokenized prompts, where each prompt is represented as a list of integers (token IDs).max_gen_len: The maximum length of the generated output (in tokens).temperature: A parameter controlling the randomness of the generation. Higher values (e.g., 1.0) make the output more random, while lower values (e.g., 0.6) make it more deterministic.top_p: A parameter controlling the diversity of generated text. Higher values (e.g., 0.9) allow more diverse sampling.logprobs: IfTrue, the method also returns the log-probabilities of generated tokens.echo: IfTrue, the generated output will include the prompt itself.
-
Initialization: The method initializes variables such as
params,bsz(batch size),min_prompt_len, andmax_prompt_len. It also calculatestotal_lenwhich is the total length of generated output including the prompt. -
Token Generation: The method generates tokens for the output text. It uses a loop to iterate over positions in the generated sequence, starting from the end of the prompt and moving forward.
- The logits for each position are obtained by passing the partial sequence to the model’s forward function.
- If
temperatureis greater than 0, the logits are transformed into probabilities using softmax with the given temperature value. Iftemperatureis 0, the token with the highest logit is selected usingtorch.argmax. - The next token is determined based on the generated probabilities or argmax, and it is placed in the corresponding position of the
tokenstensor. - EOS (end-of-sequence) tokens are detected, and if all sequences have reached an EOS token, the generation process is stopped.
- The generated tokens are kept in the
tokenstensor.
- Processing Generated Output:
- The generated tokens are processed to trim the output to the desired maximum length (
max_gen_len). - If
echoisTrue, the prompt is included in the generated output. - If the
eostoken is encountered, the generated text is cut off at that point.
- The generated tokens are processed to trim the output to the desired maximum length (
- Return:
The method returns a tuple containing the generated tokens and, if
logprobsisTrue, the log-probabilities of the generated tokens.
This method generates text by iteratively predicting tokens based on the context provided by the prompt and the previously generated tokens. It uses the specified generation parameters to control the randomness and diversity of the generated text. The method is designed to be used with a specific model architecture and tokenizer, as it relies on their properties and methods for text generation.