Support MiniCPM #5346

runfuture · 2024-02-05T14:23:25Z

Add a new file, convert-minicpm.py, to convert the model (https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16). It is very similar to convert.py, which only supports Llama.
Add a new model architecture in llama.cpp, applying several scaling computations as discussed in MiniCPM 2b model support? #5276.

ggerganov · 2024-02-05T14:41:23Z

convert-minicpm.py

Is it difficult to merge it into convert.py instead of creating a new file?

To merge it into convert.py can actually be achieved by adding args that explicitly indicate the model architecture. Otherwise, it is difficult to differentiate them automatically since the weight storage of MiniCPM is almost the same as Llama. What's your suggestion?

How about in convert-hf-to-gguf.py - would it be easier?

How about in convert-hf-to-gguf.py - would it be easier?

In fact, I've tried to work on that. Copied and pasted a lot of other model code and it almost works, but the hard part is the tokenizer, I haven't finished it yet. The number of lines of code added/modified will be 10x ~ 30x more than convert.py.
I'll try to finish it. Then you could compare which way is better.

How about in convert-hf-to-gguf.py - would it be easier?

It seems to be working now. Not familiar with the vocab, there may be bugs in _set_vocab_hf (convert-hf-to-gguf.py).
If this way better, I'll remove convert-minicpm.py.

I think this looks great. I'm just not sure about the from convert import HfVocab

@cebtenzzre What do you think?

I think it's fine, as long as we're mindful that convert-hf-to-gguf.py is now dependent on convert.py - python is prone to circular dependency issues that can only be resolved by moving code to a shared module.

runfuture · 2024-02-06T11:41:01Z

Improvements have been submitted. :)

ggerganov · 2024-02-06T13:06:56Z

llama.cpp

+        cb(cur, "result_norm", -1);
+
+        // lm_head scaling
+        float scale_lmhead = 1.0f/9.0f; // 1/(dim_model/256)


Maybe keep these constants expanded:

Suggested change

float scale_lmhead = 1.0f/9.0f; // 1/(dim_model/256)

const float scale_lmhead = 256.0f/n_embd;

Done, but leaving a todo for the future. Let's wait for model development? :)

ggerganov · 2024-02-06T17:00:26Z

llama.cpp

+        const int scale_emb = 12;
+        const int dim_model_base = 256;
+        const float scale_depth = 1.4f;


Minor change of the constant names:

Suggested change

const int scale_emb = 12;

const int dim_model_base = 256;

const float scale_depth = 1.4f;

const int64_t n_embd_base = 256;

const float scale_embd = 12.0f;

const float scale_depth = 1.4f;

Minor change of the constant names:

Done with corresponding modifications: this commit

sweetcard · 2024-02-07T06:38:56Z

It doesn't work.
python convert-hf-to-gguf.py "./miniCPM-bf16"
./server -m "./miniCPM-bf16/ggml-model-f16.gguf" -n 256 -c 1024

Any wrong settings?

Check the screenshots please.

lin-calvin · 2024-02-07T06:40:56Z

It doesn't work. python convert-hf-to-gguf.py "./miniCPM-bf16" ./server -m "./miniCPM-bf16/ggml-model-f16.gguf" -n 256 -c 1024

Any wrong settings?

Check the screenshots please.

there seems nothing wrong with it, dont wish a 2b model can do things like coding. If you want a better output, try.tp.enable Mirostat v2 in "More options"

sweetcard · 2024-02-07T06:51:01Z

It doesn't work. python convert-hf-to-gguf.py "./miniCPM-bf16" ./server -m "./miniCPM-bf16/ggml-model-f16.gguf" -n 256 -c 1024
Any wrong settings?
Check the screenshots please.

there seems nothing wrong with it, dont wish a 2b model can do things like coding

The version from LLM Farm can work with python.

Check this:

When Mirostat v2 is enabled, it doesn't work.

lin-calvin · 2024-02-07T07:24:35Z

It doesn't work. python convert-hf-to-gguf.py "./miniCPM-bf16" ./server -m "./miniCPM-bf16/ggml-model-f16.gguf" -n 256 -c 1024
Any wrong settings?
Check the screenshots please.

there seems nothing wrong with it, dont wish a 2b model can do things like coding

The version from LLM Farm can work with python.

Check this:
When Mirostat v2 is enabled, it doesn't work.

Found the problem, your chat prompt templete is wrong,try configure it like this:

sweetcard · 2024-02-07T07:33:39Z

Not lucky. 😭

lin-calvin · 2024-02-07T07:50:53Z

Not lucky. 😭

No way, Is you model the DPO one?

sweetcard · 2024-02-07T07:55:29Z

No way, Is you model the DPO one?

MiniCPM-2B-sft-bf16

lin-calvin · 2024-02-07T08:05:55Z

try this one:https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/

sweetcard · 2024-02-07T08:10:06Z

try this one:https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/

It feels not good.

huangyuxiang03 · 2024-02-07T08:17:09Z

Hi, I'm one of the maintainers of MiniCPM repo. Could you provide a detailed instruction of how to reproduce this problem? We would like to figure this out. Thank you!

sweetcard · 2024-02-07T08:22:43Z

Hi, I'm one of the maintainers of MiniCPM repo. Could you provide a detailed instruction of how to reproduce this problem? We would like to figure this out. Thank you!

python convert-hf-to-gguf.py "./MiniCPM-2B-dpo-fp16"
./server -m "./MiniCPM-2B-dpo-fp16/ggml-model-f16.gguf" -n 256 -c 1024
Go to http://localhost:8080/ , Set the following parameters:

runfuture · 2024-02-07T08:40:19Z

@sweetcard @sweetcard @huangyuxiang03
So sorry that I think there may be bugs in tokenizer process when convert model.
Please use this history version (https://github.com/ggerganov/llama.cpp/tree/550ab5e1c5dc97208e7e148c8f81b1e06f0900b9) and the convert-minicpm.py to convert model first.
I'm trying to fix ASAP.

lin-calvin · 2024-02-07T09:31:47Z

@sweetcard @sweetcard @huangyuxiang03 So sorry that I think there may be bugs in tokenizer process when convert model. Please use this history version (https://github.com/ggerganov/llama.cpp/tree/550ab5e1c5dc97208e7e148c8f81b1e06f0900b9) and the convert-minicpm.py to convert model first. I'm trying to fix ASAP.

The script seems has another problem: The llama.cpp cant run the model because GGML_ASSERT: ggml.c:9630: eps > 0.0f

runfuture · 2024-02-07T10:04:06Z

@sweetcard @sweetcard @huangyuxiang03 So sorry that I think there may be bugs in tokenizer process when convert model. Please use this history version (https://github.com/ggerganov/llama.cpp/tree/550ab5e1c5dc97208e7e148c8f81b1e06f0900b9) and the convert-minicpm.py to convert model first. I'm trying to fix ASAP.

The script seems has another problem: The llama.cpp cant run the model because GGML_ASSERT: ggml.c:9630: eps > 0.0f

Latest commit fixed this issue.
BTW, still suffering from comparing the differences between the models generated by two conversion scripts, and looking for some hints

huangyuxiang03 · 2024-02-07T10:06:53Z

I can get this running by using MiniCPM-dpo-bf16. It seems like everything is correct by using this history version (https://github.com/ggerganov/llama.cpp/tree/550ab5e1c5dc97208e7e148c8f81b1e06f0900b9) and the [convert-minicpm.py](https://github.com/ggerganov/llama.cpp/blob/550ab5e1c5dc97208e7e148c8f81b1e06f0900b9/convert-minicpm.py).

huangyuxiang03 · 2024-02-07T10:08:01Z

Hi, I'm one of the maintainers of MiniCPM repo. Could you provide a detailed instruction of how to reproduce this problem? We would like to figure this out. Thank you!
1. `python convert-hf-to-gguf.py "./MiniCPM-2B-dpo-fp16"`

2. `./server -m "./MiniCPM-2B-dpo-fp16/ggml-model-f16.gguf" -n 256 -c 1024`

3. Go to http://localhost:8080/ , Set the following parameters:

The last line of prompt should be <{{char}}>{{message}}<AI>

runfuture · 2024-02-07T15:15:17Z

@huangyuxiang03 @sweetcard @calvinweb
Thanks for your patience. I have fixed the bug and made a new PR. Please check it out here

raymond-infinitecode · 2024-02-08T11:00:34Z

Download
https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16

convert using
python convert-hf-to-gguf.py d:\MiniCPM --outfile minicpm.gguf

But not able to startup server

llama server listening at http://127.0.0.1:8080

{"timestamp":1707389853,"level":"INFO","function":"main","line":2557,"message":"HTTP server listening","port":"8080","hostname":"127.0.0.1"}
llama_model_loader: loaded meta data with 21 key-value pairs and 362 tensors from minicpm.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = minicpm
llama_model_loader: - kv 1: general.name str = MiniCPM
llama_model_loader: - kv 2: minicpm.context_length u32 = 2048
llama_model_loader: - kv 3: minicpm.embedding_length u32 = 2304
llama_model_loader: - kv 4: minicpm.feed_forward_length u32 = 5760
llama_model_loader: - kv 5: minicpm.block_count u32 = 40
llama_model_loader: - kv 6: minicpm.attention.head_count u32 = 36
llama_model_loader: - kv 7: minicpm.attention.head_count_kv u32 = 36
llama_model_loader: - kv 8: minicpm.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 9: general.file_type u32 = 1
llama_model_loader: - kv 10: minicpm.rope.dimension_count u32 = 64
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,122753] = ["", "~~", "~~", "", "<C...
llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,122753] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,122753] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 18: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 19: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 20: tokenizer.chat_template str = {% for message in messages %}{% if me...
llama_model_loader: - type f32: 81 tensors
llama_model_loader: - type f16: 281 tensors
llm_load_vocab: mismatch in special tokens definition ( 3528/122753 vs 259/122753 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = minicpm
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 122753
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 2048
llm_load_print_meta: n_embd = 2304
llm_load_print_meta: n_head = 36
llm_load_print_meta: n_head_kv = 36
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_embd_head_k = 64
llm_load_print_meta: n_embd_head_v = 64
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 2304
llm_load_print_meta: n_embd_v_gqa = 2304
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 0.0e+00
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 5760
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 2048
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 2B
llm_load_print_meta: model ftype = F16
llm_load_print_meta: model params = 2.72 B
llm_load_print_meta: model size = 5.08 GiB (16.00 BPW)
llm_load_print_meta: general.name = MiniCPM
llm_load_print_meta: BOS token = 1 ''
llm_load_print_meta: EOS token = 2 ''
llm_load_print_meta: UNK token = 0 ''
llm_load_print_meta: LF token = 1099 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/41 layers to GPU
llm_load_tensors: CPU buffer size = 5197.65 MiB
...........................................................................................
llama_new_context_with_model: n_ctx = 1024
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 360.00 MiB
llama_new_context_with_model: KV self size = 360.00 MiB, K (f16): 180.00 MiB, V (f16): 180.00 MiB
llama_new_context_with_model: CPU input buffer size = 6.51 MiB
llama_new_context_with_model: CPU compute buffer size = 268.68 MiB
llama_new_context_with_model: graph splits (measure): 1
GGML_ASSERT: ggml.c:9630: eps > 0.0f
GGML_ASSERT: ggml.c:9630: eps > 0.0f
GGML_ASSERT: ggml.c:9630: eps > 0.0f
GGML_ASSERT: ggml.c:9630: eps > 0.0f
GGML_ASSERT: ggml.c:9630: eps > 0.0f
GGML_ASSERT: ggml.c:9630: eps > 0.0f
GGML_ASSERT: ggml.c:9630: eps > 0.0f
GGML_ASSERT: ggml.c:9630: eps > 0.0f

D:\llama.cpp>

runfuture · 2024-02-08T14:55:57Z

Download https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16

convert using python convert-hf-to-gguf.py d:\MiniCPM --outfile minicpm.gguf

But not able to startup server

llama server listening at http://127.0.0.1:8080

{"timestamp":1707389853,"level":"INFO","function":"main","line":2557,"message":"HTTP server listening","port":"8080","hostname":"127.0.0.1"} llama_model_loader: loaded meta data with 21 key-value pairs and 362 tensors from minicpm.gguf (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = minicpm llama_model_loader: - kv 1: general.name str = MiniCPM llama_model_loader: - kv 2: minicpm.context_length u32 = 2048 llama_model_loader: - kv 3: minicpm.embedding_length u32 = 2304 llama_model_loader: - kv 4: minicpm.feed_forward_length u32 = 5760 llama_model_loader: - kv 5: minicpm.block_count u32 = 40 llama_model_loader: - kv 6: minicpm.attention.head_count u32 = 36 llama_model_loader: - kv 7: minicpm.attention.head_count_kv u32 = 36 llama_model_loader: - kv 8: minicpm.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 9: general.file_type u32 = 1 llama_model_loader: - kv 10: minicpm.rope.dimension_count u32 = 64 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,122753] = ["", "", "", "", "<C... llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,122753] = [-1000.000000, -1000.000000, -1000.00... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,122753] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 18: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 19: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 20: tokenizer.chat_template str = {% for message in messages %}{% if me... llama_model_loader: - type f32: 81 tensors llama_model_loader: - type f16: 281 tensors llm_load_vocab: mismatch in special tokens definition ( 3528/122753 vs 259/122753 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = minicpm llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 122753 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 2048 llm_load_print_meta: n_embd = 2304 llm_load_print_meta: n_head = 36 llm_load_print_meta: n_head_kv = 36 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 64 llm_load_print_meta: n_embd_head_k = 64 llm_load_print_meta: n_embd_head_v = 64 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 2304 llm_load_print_meta: n_embd_v_gqa = 2304 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 0.0e+00 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: n_ff = 5760 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 2048 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: model type = 2B llm_load_print_meta: model ftype = F16 llm_load_print_meta: model params = 2.72 B llm_load_print_meta: model size = 5.08 GiB (16.00 BPW) llm_load_print_meta: general.name = MiniCPM llm_load_print_meta: BOS token = 1 '' llm_load_print_meta: EOS token = 2 '' llm_load_print_meta: UNK token = 0 '' llm_load_print_meta: LF token = 1099 '<0x0A>' llm_load_tensors: ggml ctx size = 0.14 MiB llm_load_tensors: offloading 0 repeating layers to GPU llm_load_tensors: offloaded 0/41 layers to GPU llm_load_tensors: CPU buffer size = 5197.65 MiB ........................................................................................... llama_new_context_with_model: n_ctx = 1024 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 360.00 MiB llama_new_context_with_model: KV self size = 360.00 MiB, K (f16): 180.00 MiB, V (f16): 180.00 MiB llama_new_context_with_model: CPU input buffer size = 6.51 MiB llama_new_context_with_model: CPU compute buffer size = 268.68 MiB llama_new_context_with_model: graph splits (measure): 1 GGML_ASSERT: ggml.c:9630: eps > 0.0f GGML_ASSERT: ggml.c:9630: eps > 0.0f GGML_ASSERT: ggml.c:9630: eps > 0.0f GGML_ASSERT: ggml.c:9630: eps > 0.0f GGML_ASSERT: ggml.c:9630: eps > 0.0f GGML_ASSERT: ggml.c:9630: eps > 0.0f GGML_ASSERT: ggml.c:9630: eps > 0.0f GGML_ASSERT: ggml.c:9630: eps > 0.0f

D:\llama.cpp>

There were bugs which have been fixed by a new PR. Could you please try using the latest release, b2101, to test again?

sweetcard · 2024-02-09T00:55:25Z

@huangyuxiang03 @sweetcard @calvinweb Thanks for your patience. I have fixed the bug and made a new PR. Please check it out here

Thank you for your amazing work. It works now.👍

* support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm

runfuture added 2 commits February 5, 2024 22:10

support minicpm arch.

60ed819

fix tab/space typo.

550ab5e

ggerganov reviewed Feb 5, 2024

View reviewed changes

runfuture added 7 commits February 5, 2024 23:20

convert minicpm model via convert-hf-gguf.py

ec4507a

try to make tokenizer work

3608459

fix bug for quantize minicpm

b698d87

fix for flake8 lint

319ab9d

remove convert-minicpm.py

05aef26

fix for editorconfig

b5d0054

correct minicpm model type (size)

f9cf078

ggerganov reviewed Feb 6, 2024

View reviewed changes

constants expanded for minicpm

a9353ec

ggerganov reviewed Feb 6, 2024

View reviewed changes

Minor change of the constant names for minicpm

390996d

ggerganov approved these changes Feb 7, 2024

View reviewed changes

ggerganov merged commit 316c7fa into ggerganov:master Feb 7, 2024
49 of 53 checks passed

sweetcard mentioned this pull request Feb 7, 2024

MiniCPM 2b model support? #5276

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runfuture mentioned this pull request Feb 7, 2024

Bugfix for MiniCPM model support (undo HF model tensor permute) #5392

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	float scale_lmhead = 1.0f/9.0f; // 1/(dim_model/256)
	const float scale_lmhead = 256.0f/n_embd;

Support MiniCPM #5346

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