mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
feat: Qwen2 Model (#1584)
This commit is contained in:
Cheng Kuan Yong Jason
GitHub
parent
b40e833493
commit
d1d757e676
@ -0,0 +1,84 @@
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"text": " def",
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|
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"generated_text": " for the following function\n\nInputs: def find_max"
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@ -0,0 +1,84 @@
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"text": " to",
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"special": false
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{
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"id": 633,
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"text": " get",
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{
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"id": 264,
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"text": " a",
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"logprob": 0.0,
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"special": false
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},
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{
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"id": 1140,
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"text": " list",
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"logprob": 0.0,
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"special": false
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},
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{
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"id": 315,
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"text": " of",
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"logprob": 0.0,
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"special": false
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},
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{
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"id": 678,
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"text": " all",
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"logprob": 0.0,
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"special": false
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},
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{
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"id": 279,
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"text": " the",
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"logprob": -0.2590332,
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"special": false
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},
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{
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"id": 3847,
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"text": " users",
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"logprob": -0.45239258,
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"special": false
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},
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{
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"id": 304,
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"text": " in",
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"logprob": -0.12322998,
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"special": false
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},
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{
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"id": 419,
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"text": " this",
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"logprob": -1.7275391,
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"special": false
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}
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],
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"top_tokens": null
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},
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"generated_text": "Test request to get a list of all the users in this"
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}
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@ -0,0 +1,338 @@
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[
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{
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"id": 2271,
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"text": "Test",
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"logprob": null
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{
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"id": 1681,
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"text": " request",
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"seed": null,
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"text": " for",
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"special": false
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{
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"id": 279,
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"text": " the",
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"logprob": -2.6210938,
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"special": false
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{
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"id": 2701,
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"text": " following",
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"special": false
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"id": 729,
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"text": " function",
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"special": false
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"id": 271,
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"text": "\n\n",
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"special": false
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"text": "Inputs",
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"special": false
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"id": 25,
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"text": ":",
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"special": false
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{
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"id": 707,
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"text": " def",
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"logprob": -0.5678711,
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"special": false
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{
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"id": 1477,
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"text": " find",
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"special": false
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{
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"id": 6345,
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"text": "_max",
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"logprob": -1.8349609,
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"special": false
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}
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],
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"top_tokens": null
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"generated_text": " for the following function\n\nInputs: def find_max"
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{
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"id": 1681,
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"text": " request",
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],
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"seed": null,
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"tokens": [
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"id": 369,
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"text": " for",
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"logprob": -2.1914062,
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"special": false
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{
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"id": 279,
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"text": " the",
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"logprob": -2.6210938,
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"special": false
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{
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"id": 2701,
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"text": " following",
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"special": false
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{
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"id": 729,
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"text": " function",
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"logprob": -2.9648438,
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"special": false
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{
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"id": 271,
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"text": "\n\n",
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"logprob": -1.9111328,
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"special": false
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},
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{
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"id": 31946,
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"text": "Inputs",
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"logprob": -1.6855469,
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"special": false
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},
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{
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"id": 25,
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"text": ":",
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"logprob": -1.6093254e-05,
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"special": false
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},
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{
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"id": 707,
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"text": " def",
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"logprob": -0.5678711,
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"special": false
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},
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{
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"id": 1477,
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"text": " find",
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"logprob": -2.5917969,
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"special": false
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||||
},
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{
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"id": 6345,
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"text": "_max",
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"logprob": -1.8349609,
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"special": false
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}
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||||
],
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"top_tokens": null
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},
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"generated_text": " for the following function\n\nInputs: def find_max"
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||||
},
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||||
{
|
||||
"details": {
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"best_of_sequences": null,
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"finish_reason": "length",
|
||||
"generated_tokens": 10,
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"prefill": [
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{
|
||||
"id": 2271,
|
||||
"text": "Test",
|
||||
"logprob": null
|
||||
},
|
||||
{
|
||||
"id": 1681,
|
||||
"text": " request",
|
||||
"logprob": -7.0351562
|
||||
}
|
||||
],
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||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 369,
|
||||
"text": " for",
|
||||
"logprob": -2.1914062,
|
||||
"special": false
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},
|
||||
{
|
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"id": 279,
|
||||
"text": " the",
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||||
"logprob": -2.6210938,
|
||||
"special": false
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||||
},
|
||||
{
|
||||
"id": 2701,
|
||||
"text": " following",
|
||||
"logprob": -3.6445312,
|
||||
"special": false
|
||||
},
|
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{
|
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"id": 729,
|
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"text": " function",
|
||||
"logprob": -2.9648438,
|
||||
"special": false
|
||||
},
|
||||
{
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||||
"id": 271,
|
||||
"text": "\n\n",
|
||||
"logprob": -1.9111328,
|
||||
"special": false
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||||
},
|
||||
{
|
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"id": 31946,
|
||||
"text": "Inputs",
|
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"logprob": -1.6855469,
|
||||
"special": false
|
||||
},
|
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{
|
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"id": 25,
|
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"text": ":",
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"logprob": -1.6093254e-05,
|
||||
"special": false
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||||
},
|
||||
{
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"id": 707,
|
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"text": " def",
|
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"logprob": -0.5678711,
|
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"special": false
|
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},
|
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{
|
||||
"id": 1477,
|
||||
"text": " find",
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||||
"logprob": -2.5917969,
|
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"special": false
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},
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{
|
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"id": 6345,
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"text": "_max",
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"logprob": -1.8349609,
|
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"special": false
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}
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],
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"top_tokens": null
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"generated_text": " for the following function\n\nInputs: def find_max"
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{
|
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{
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"id": 2271,
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"text": "Test",
|
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"logprob": null
|
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{
|
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"id": 1681,
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"text": " request",
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"logprob": -7.0351562
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}
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],
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"seed": null,
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"tokens": [
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{
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"id": 369,
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"text": " for",
|
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"logprob": -2.1914062,
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"special": false
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},
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{
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"id": 279,
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"text": " the",
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"logprob": -2.6210938,
|
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"special": false
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},
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{
|
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"id": 2701,
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"text": " following",
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"logprob": -3.6445312,
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"special": false
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{
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"id": 729,
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"text": " function",
|
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"logprob": -2.9648438,
|
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"special": false
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},
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{
|
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"id": 271,
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"text": "\n\n",
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"logprob": -1.9111328,
|
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"special": false
|
||||
},
|
||||
{
|
||||
"id": 31946,
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||||
"text": "Inputs",
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||||
"logprob": -1.6855469,
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"special": false
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||||
},
|
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{
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||||
"id": 25,
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"text": ":",
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"logprob": -1.6093254e-05,
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"special": false
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||||
},
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||||
{
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"id": 707,
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"text": " def",
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||||
"logprob": -0.5678711,
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||||
"special": false
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||||
},
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||||
{
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"id": 1477,
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"text": " find",
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"logprob": -2.5917969,
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"special": false
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||||
},
|
||||
{
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||||
"id": 6345,
|
||||
"text": "_max",
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||||
"logprob": -1.8349609,
|
||||
"special": false
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": " for the following function\n\nInputs: def find_max"
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}
|
||||
]
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61
integration-tests/models/test_flash_qwen2.py
Normal file
61
integration-tests/models/test_flash_qwen2.py
Normal file
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import pytest
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@pytest.fixture(scope="module")
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def flash_qwen2_handle(launcher):
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with launcher("Qwen/Qwen1.5-7B") as handle:
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yield handle
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@pytest.fixture(scope="module")
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async def flash_qwen2(flash_qwen2_handle):
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await flash_qwen2_handle.health(300)
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return flash_qwen2_handle.client
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@pytest.mark.asyncio
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async def test_flash_qwen2(flash_qwen2, response_snapshot):
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response = await flash_qwen2.generate(
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"Test request", max_new_tokens=10, decoder_input_details=True
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)
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assert response.details.generated_tokens == 10
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assert response.generated_text == " for the following function\n\nInputs: def find_max"
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assert response == response_snapshot
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@pytest.mark.asyncio
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async def test_flash_qwen2_all_params(flash_qwen2, response_snapshot):
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response = await flash_qwen2.generate(
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"Test request",
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max_new_tokens=10,
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repetition_penalty=1.2,
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return_full_text=True,
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stop_sequences=["test"],
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temperature=0.5,
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top_p=0.9,
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top_k=10,
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truncate=5,
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typical_p=0.9,
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watermark=True,
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decoder_input_details=True,
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seed=0,
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)
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assert response.details.generated_tokens == 10
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assert response == response_snapshot
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@pytest.mark.asyncio
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async def test_flash_qwen2_load(flash_qwen2, generate_load, response_snapshot):
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responses = await generate_load(
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flash_qwen2, "Test request", max_new_tokens=10, n=4
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)
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assert len(responses) == 4
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assert all(
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||||
[r.generated_text == responses[0].generated_text for r in responses]
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), f"{[r.generated_text for r in responses]}"
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assert responses[0].generated_text == ": Let n = 10 - 1"
|
||||
|
||||
assert responses == response_snapshot
|
||||
@ -54,6 +54,9 @@ try:
|
||||
from text_generation_server.models.flash_llama import (
|
||||
FlashLlama,
|
||||
)
|
||||
from text_generation_server.models.flash_qwen2 import (
|
||||
FlashQwen2,
|
||||
)
|
||||
from text_generation_server.models.flash_gemma import (
|
||||
FlashGemma,
|
||||
)
|
||||
@ -81,6 +84,7 @@ if FLASH_ATTENTION:
|
||||
__all__.append(FlashMistral)
|
||||
__all__.append(FlashMixtral)
|
||||
__all__.append(FlashPhi)
|
||||
__all__.append(FlashQwen2)
|
||||
__all__.append(FlashStarcoder2)
|
||||
|
||||
MAMBA_AVAILABLE = True
|
||||
@ -328,6 +332,27 @@ def get_model(
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif model_type == "qwen2":
|
||||
if FLASH_ATTENTION:
|
||||
return FlashQwen2(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(
|
||||
FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2")
|
||||
)
|
||||
else:
|
||||
return CausalLM(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
if model_type == "gemma":
|
||||
if FLASH_ATTENTION:
|
||||
return FlashGemma(
|
||||
|
||||
@ -0,0 +1,390 @@
|
||||
import torch
|
||||
import torch.distributed
|
||||
|
||||
from torch import nn
|
||||
from transformers.activations import ACT2FN
|
||||
from typing import Optional, List, Tuple
|
||||
|
||||
from text_generation_server.utils import paged_attention, flash_attn
|
||||
from text_generation_server.utils.layers import (
|
||||
TensorParallelRowLinear,
|
||||
TensorParallelColumnLinear,
|
||||
TensorParallelEmbedding,
|
||||
PositionRotaryEmbedding,
|
||||
TensorParallelHead,
|
||||
get_linear,
|
||||
FastRMSNorm,
|
||||
)
|
||||
|
||||
|
||||
def load_attention(config, prefix, weights):
|
||||
if config.num_attention_heads != config.num_key_value_heads:
|
||||
return _load_gqa(config, prefix, weights)
|
||||
else:
|
||||
return TensorParallelColumnLinear.load_multi(
|
||||
config,
|
||||
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
|
||||
dim=0,
|
||||
weights=weights,
|
||||
bias=True,
|
||||
)
|
||||
|
||||
|
||||
def _load_gqa(config, prefix: str, weights):
|
||||
assert config.hidden_size % config.num_attention_heads == 0
|
||||
assert config.num_attention_heads % weights.process_group.size() == 0
|
||||
|
||||
weight = weights.get_multi_weights_col(
|
||||
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
|
||||
quantize=config.quantize,
|
||||
dim=0,
|
||||
)
|
||||
|
||||
if config.quantize not in ["gptq", "awq"]:
|
||||
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
|
||||
|
||||
head_size = config.hidden_size // config.num_attention_heads
|
||||
num_heads = config.num_attention_heads // weights.process_group.size()
|
||||
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
|
||||
assert list(weight.shape) == [
|
||||
(num_heads + 2 * num_key_value_heads) * head_size,
|
||||
config.hidden_size,
|
||||
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
|
||||
|
||||
return TensorParallelColumnLinear(
|
||||
get_linear(weight, bias=None, quantize=config.quantize)
|
||||
)
|
||||
|
||||
|
||||
class Qwen2Attention(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
prefix: str,
|
||||
config,
|
||||
weights,
|
||||
):
|
||||
super().__init__()
|
||||
self.max_past = (
|
||||
config.sliding_window if config.sliding_window is not None else -1
|
||||
)
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.hidden_size = config.hidden_size
|
||||
self.head_size = self.hidden_size // self.num_heads
|
||||
|
||||
self.rotary_emb = PositionRotaryEmbedding.static(
|
||||
config=config,
|
||||
dim=self.head_size,
|
||||
base=config.rope_theta,
|
||||
device=weights.device,
|
||||
)
|
||||
|
||||
self.softmax_scale = self.head_size**-0.5
|
||||
|
||||
if self.num_heads % weights.process_group.size() != 0:
|
||||
raise ValueError(
|
||||
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
|
||||
f"and `num_shards`: {weights.process_group.size()}"
|
||||
)
|
||||
self.num_heads = self.num_heads // weights.process_group.size()
|
||||
self.num_key_value_heads = (
|
||||
config.num_key_value_heads // weights.process_group.size()
|
||||
)
|
||||
|
||||
self.query_key_value = load_attention(config, prefix, weights)
|
||||
|
||||
self.o_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.o_proj",
|
||||
weights=weights,
|
||||
bias=False,
|
||||
)
|
||||
self.num_groups = self.num_heads // self.num_key_value_heads
|
||||
self.kv_head_mapping = torch.arange(
|
||||
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
|
||||
).repeat_interleave(self.num_groups)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
prefill_cache_indices,
|
||||
):
|
||||
qkv = self.query_key_value(hidden_states)
|
||||
query, kv = qkv.split(
|
||||
[
|
||||
self.head_size * self.num_heads,
|
||||
2 * self.head_size * self.num_key_value_heads,
|
||||
],
|
||||
dim=1,
|
||||
)
|
||||
query = query.view(-1, self.num_heads, self.head_size)
|
||||
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
|
||||
|
||||
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
|
||||
|
||||
if prefill_cache_indices is not None:
|
||||
kv_to_cache = kv[prefill_cache_indices]
|
||||
else:
|
||||
kv_to_cache = kv
|
||||
|
||||
paged_attention.reshape_and_cache(
|
||||
kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
|
||||
)
|
||||
|
||||
# output tensor
|
||||
attn_output = torch.empty_like(query)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
# flash attention
|
||||
flash_attn.attention(
|
||||
query,
|
||||
torch.select(kv, dim=1, index=0),
|
||||
torch.select(kv, dim=1, index=1),
|
||||
attn_output,
|
||||
cu_seqlen_prefill,
|
||||
max_s,
|
||||
self.softmax_scale,
|
||||
window_size_left=self.max_past,
|
||||
)
|
||||
# Decode
|
||||
else:
|
||||
paged_attention.attention(
|
||||
attn_output,
|
||||
query,
|
||||
kv_cache[0],
|
||||
kv_cache[1],
|
||||
self.kv_head_mapping,
|
||||
self.softmax_scale,
|
||||
block_tables,
|
||||
input_lengths,
|
||||
max_s,
|
||||
)
|
||||
|
||||
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
|
||||
|
||||
class Qwen2MLP(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
super().__init__()
|
||||
act = config.hidden_act
|
||||
self.act = (
|
||||
ACT2FN[act]
|
||||
if "gelu" not in act
|
||||
else lambda x: torch.nn.functional.gelu(
|
||||
x,
|
||||
approximate=(
|
||||
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
|
||||
),
|
||||
)
|
||||
)
|
||||
# Fuse gate and up proj
|
||||
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
|
||||
config,
|
||||
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
|
||||
weights=weights,
|
||||
dim=0,
|
||||
bias=False,
|
||||
)
|
||||
self.down_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.down_proj",
|
||||
weights=weights,
|
||||
bias=False,
|
||||
)
|
||||
self.intermediate_size = (
|
||||
config.intermediate_size // weights.process_group.size()
|
||||
)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
gate_up_states = self.gate_up_proj(hidden_states)
|
||||
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
|
||||
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
|
||||
|
||||
|
||||
class Qwen2Layer(nn.Module):
|
||||
def __init__(self, layer_id, config, weights):
|
||||
super().__init__()
|
||||
prefix = f"model.layers.{layer_id}"
|
||||
self.self_attn = Qwen2Attention(prefix=f"{prefix}.self_attn", config=config, weights=weights)
|
||||
self.mlp = Qwen2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
||||
self.input_layernorm = FastRMSNorm.load(
|
||||
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
|
||||
)
|
||||
self.post_attention_layernorm = FastRMSNorm.load(
|
||||
prefix=f"{prefix}.post_attention_layernorm",
|
||||
weights=weights,
|
||||
eps=config.rms_norm_eps,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
residual,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
prefill_cache_indices,
|
||||
):
|
||||
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
|
||||
|
||||
# Self Attention
|
||||
attn_output = self.self_attn(
|
||||
normed_hidden_states,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
prefill_cache_indices,
|
||||
)
|
||||
|
||||
# faster post attention rms norm
|
||||
normed_attn_res_output, attn_res = self.post_attention_layernorm(
|
||||
attn_output, res
|
||||
)
|
||||
|
||||
mlp_output = self.mlp(normed_attn_res_output)
|
||||
|
||||
return mlp_output, attn_res
|
||||
|
||||
class Qwen2Model(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
process_group = weights.process_group
|
||||
self.tp_rank = process_group.rank()
|
||||
self.tp_world_size = process_group.size()
|
||||
self.embed_tokens = TensorParallelEmbedding(
|
||||
prefix="model.embed_tokens", weights=weights
|
||||
)
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
Qwen2Layer(
|
||||
layer_id,
|
||||
config,
|
||||
weights,
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = FastRMSNorm.load(
|
||||
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
|
||||
)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
self.head_size = self.layers[0].self_attn.head_size
|
||||
self.num_heads = self.layers[0].self_attn.num_heads
|
||||
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
position_ids: torch.Tensor,
|
||||
cu_seqlen_prefill: Optional[torch.Tensor],
|
||||
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||
block_tables: torch.Tensor,
|
||||
slots: torch.Tensor,
|
||||
input_lengths: torch.Tensor,
|
||||
max_s: int,
|
||||
true_max_s: int,
|
||||
prefill_cache_indices: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
|
||||
# Get rotary cos and sin for this forward
|
||||
# Avoid to index in each layer
|
||||
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
|
||||
position_ids, true_max_s, hidden_states.dtype
|
||||
)
|
||||
|
||||
residual = None
|
||||
for i, layer in enumerate(self.layers):
|
||||
hidden_states, residual = layer(
|
||||
hidden_states,
|
||||
residual,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache[i],
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
prefill_cache_indices,
|
||||
)
|
||||
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class Qwen2ForCausalLM(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
|
||||
self.model = Qwen2Model(config, weights)
|
||||
self.lm_head = TensorParallelHead.load(
|
||||
config,
|
||||
prefix="lm_head",
|
||||
weights=weights,
|
||||
)
|
||||
self.max_past = config.sliding_window
|
||||
self.max_past_tensor = (
|
||||
torch.tensor(config.sliding_window, device=weights.device)
|
||||
if self.max_past is not None
|
||||
else None
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
position_ids: torch.Tensor,
|
||||
cu_seqlen_prefill: Optional[torch.Tensor],
|
||||
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||
block_tables: torch.Tensor,
|
||||
slots: torch.Tensor,
|
||||
input_lengths: torch.Tensor,
|
||||
max_s: int,
|
||||
prefill_cache_indices: Optional[torch.Tensor] = None,
|
||||
lm_head_indices: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
true_max_s = max_s
|
||||
if prefill_cache_indices is not None:
|
||||
# Slots also need to be sliced as it has the same size as the whole kv tensor
|
||||
slots = slots[prefill_cache_indices]
|
||||
elif self.max_past is not None:
|
||||
# Clamp in decode mode as paged attention requires clamped values whereas the flash attention
|
||||
# kernel requires the true values
|
||||
input_lengths = torch.clamp(input_lengths, max=self.max_past_tensor)
|
||||
|
||||
hidden_states = self.model(
|
||||
input_ids,
|
||||
position_ids,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
true_max_s,
|
||||
prefill_cache_indices,
|
||||
)
|
||||
if lm_head_indices is not None:
|
||||
hidden_states = hidden_states[lm_head_indices]
|
||||
logits = self.lm_head(hidden_states)
|
||||
return logits
|
||||
77
server/text_generation_server/models/flash_qwen2.py
Normal file
77
server/text_generation_server/models/flash_qwen2.py
Normal file
@ -0,0 +1,77 @@
|
||||
import torch
|
||||
import torch.distributed
|
||||
|
||||
from opentelemetry import trace
|
||||
from transformers import AutoTokenizer
|
||||
from transformers.models.qwen2 import Qwen2Tokenizer
|
||||
from typing import Optional
|
||||
|
||||
from text_generation_server.models import FlashCausalLM
|
||||
from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
|
||||
Qwen2ForCausalLM,
|
||||
)
|
||||
from transformers.models.qwen2 import Qwen2Config
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
Weights,
|
||||
)
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
|
||||
class FlashQwen2(FlashCausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
self.process_group, rank, world_size = initialize_torch_distributed()
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device(f"cuda:{rank}")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
else:
|
||||
raise NotImplementedError("FlashQwen2 is only available on GPU")
|
||||
|
||||
try:
|
||||
tokenizer = Qwen2Tokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
except Exception:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
config = Qwen2Config.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
config.quantize = quantize
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
|
||||
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
||||
weights = Weights(filenames, device, dtype, process_group=self.process_group)
|
||||
if config.quantize in ["gptq", "awq"]:
|
||||
weights._set_gptq_params(model_id, revision)
|
||||
|
||||
model = Qwen2ForCausalLM(config, weights)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashQwen2, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
num_layers=len(model.model.layers),
|
||||
num_kv_heads=model.model.num_key_value_heads,
|
||||
head_size=model.model.head_size,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
Loading…
Reference in New Issue
Block a user