feat: starcoder2 (#1605)
This commit is contained in:
OlivierDehaene
GitHub
parent
97e22369f4
commit
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@ -0,0 +1,94 @@
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||||
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"text": "_"
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||||
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"id": 7670,
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"logprob": -7.640625,
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"text": "hello"
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"text": "\n "
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{
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"id": 1489,
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"text": " print"
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{
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||||
"id": 459,
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"logprob": -0.6118164,
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"text": "(\""
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{
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||||
"id": 8302,
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"logprob": -0.68652344,
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"special": false,
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"text": "Hello"
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},
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||||
{
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||||
"id": 10914,
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"logprob": -1.4619141,
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"special": false,
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||||
"text": " World"
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},
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{
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"id": 16013,
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"logprob": -0.7993164,
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"special": false,
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"text": "!\")"
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},
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{
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||||
"id": 222,
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"logprob": -0.63134766,
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"special": false,
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||||
"text": "\n"
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||||
},
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{
|
||||
"id": 222,
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"logprob": -0.23278809,
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"special": false,
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"text": "\n"
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},
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{
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"id": 610,
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"logprob": -1.2294922,
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"special": false,
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"text": "def"
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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": "():\n print(\"Hello World!\")\n\ndef"
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}
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@ -0,0 +1,394 @@
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{
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"details": {
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{
|
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"id": 100,
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"logprob": -0.38476562,
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"text": "_"
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},
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{
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"id": 7670,
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"logprob": -7.640625,
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"text": "hello"
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}
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],
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"seed": 0,
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{
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"text": "():"
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},
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{
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"id": 303,
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"logprob": 0.0,
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"special": false,
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"text": "\n "
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},
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{
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"id": 1489,
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"logprob": 0.0,
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"special": false,
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"text": " print"
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},
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{
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"id": 459,
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"logprob": 0.0,
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"special": false,
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"text": "(\""
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},
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{
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"id": 8302,
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"logprob": -0.28125,
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"special": false,
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"text": "Hello"
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},
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{
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"id": 10914,
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"logprob": -0.79248047,
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"special": false,
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"text": " World"
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},
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{
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"id": 16013,
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"logprob": -0.61816406,
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"special": false,
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"text": "!\")"
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},
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{
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"id": 222,
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"logprob": -0.0619812,
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"special": false,
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"text": "\n"
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},
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{
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"id": 222,
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"logprob": 0.0,
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"special": false,
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"text": "\n"
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},
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{
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"id": 610,
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"logprob": -0.4091797,
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"special": false,
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"text": "def"
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},
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{
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"id": 1489,
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"logprob": 0.0,
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"text": " print"
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},
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{
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"id": 100,
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"logprob": 0.0,
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"special": false,
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"text": "_"
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},
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{
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"id": 7670,
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"logprob": 0.0,
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"special": false,
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"text": "hello"
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},
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{
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"id": 100,
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"logprob": 0.0,
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{
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"id": 444,
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"text": "name"
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{
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"id": 45,
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"logprob": 0.0,
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},
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{
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"id": 444,
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"logprob": 0.0,
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"special": false,
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"text": "name"
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},
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{
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"id": 731,
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"logprob": 0.0,
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"special": false,
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"text": "):"
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},
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{
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"id": 303,
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"logprob": 0.0,
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"special": false,
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"text": "\n "
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},
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{
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"id": 1489,
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"logprob": 0.0,
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"special": false,
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"text": " print"
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},
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{
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"id": 459,
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"logprob": 0.0,
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"special": false,
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"text": "(\""
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},
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{
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"id": 8302,
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"logprob": 0.0,
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"special": false,
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"text": "Hello"
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},
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{
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"id": 332,
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"logprob": -0.034698486,
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"special": false,
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"text": " \""
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},
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{
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"id": 494,
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"logprob": 0.0,
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"special": false,
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"text": " +"
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},
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{
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"id": 655,
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"logprob": 0.0,
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"special": false,
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"text": " name"
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},
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{
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"id": 494,
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"logprob": -0.20141602,
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"special": false,
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"text": " +"
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},
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{
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"id": 332,
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"logprob": 0.0,
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"special": false,
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"text": " \""
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},
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{
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"id": 16013,
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"logprob": 0.0,
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"special": false,
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"text": "!\")"
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},
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{
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"id": 222,
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"logprob": 0.0,
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"special": false,
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"text": "\n"
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},
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{
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"id": 222,
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"logprob": 0.0,
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"special": false,
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"text": "\n"
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},
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{
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"id": 610,
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"logprob": 0.0,
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"special": false,
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"text": "def"
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},
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{
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"id": 1489,
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"logprob": 0.0,
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"special": false,
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"text": " print"
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},
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{
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"id": 100,
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"logprob": 0.0,
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"special": false,
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"text": "_"
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},
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{
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"id": 7670,
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"logprob": 0.0,
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"special": false,
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"text": "hello"
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},
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{
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"id": 100,
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"logprob": 0.0,
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"special": false,
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"text": "_"
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},
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{
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"id": 444,
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"logprob": 0.0,
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"special": false,
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"text": "name"
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},
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{
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"id": 100,
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"logprob": 0.0,
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"special": false,
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"text": "_"
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},
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{
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"id": 400,
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"logprob": 0.0,
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"special": false,
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"text": "age"
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},
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{
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"id": 45,
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"logprob": 0.0,
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"special": false,
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"text": "("
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},
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{
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"id": 444,
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"logprob": 0.0,
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"special": false,
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"text": "name"
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},
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{
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"id": 49,
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"logprob": 0.0,
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"special": false,
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"text": ","
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},
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{
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"id": 11505,
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"logprob": 0.0,
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"special": false,
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"text": " age"
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},
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{
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"id": 731,
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"logprob": 0.0,
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"special": false,
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"text": "):"
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},
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{
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"id": 303,
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"logprob": 0.0,
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"special": false,
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"text": "\n "
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},
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{
|
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"id": 1489,
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"logprob": 0.0,
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"special": false,
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"text": " print"
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},
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{
|
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"id": 459,
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"logprob": 0.0,
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"special": false,
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"text": "(\""
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},
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{
|
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"id": 8302,
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"logprob": 0.0,
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"special": false,
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"text": "Hello"
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},
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{
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"id": 332,
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"logprob": 0.0,
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"special": false,
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"text": " \""
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},
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{
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"id": 494,
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"logprob": 0.0,
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"special": false,
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"text": " +"
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},
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{
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"id": 655,
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"logprob": 0.0,
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"special": false,
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"text": " name"
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},
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{
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"id": 494,
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"logprob": 0.0,
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"special": false,
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"text": " +"
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},
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{
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"id": 3021,
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"logprob": -0.5761719,
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"special": false,
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"text": " \","
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},
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{
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"id": 863,
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"logprob": 0.0,
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"special": false,
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"text": " you"
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},
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{
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"id": 904,
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"logprob": 0.0,
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"special": false,
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"text": " are"
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},
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{
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"id": 332,
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"logprob": 0.0,
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"special": false,
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"text": " \""
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},
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{
|
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"id": 494,
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"logprob": 0.0,
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"special": false,
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"text": " +"
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},
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{
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"id": 615,
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"logprob": 0.0,
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"special": false,
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"text": " str"
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},
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{
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"id": 45,
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"logprob": 0.0,
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"special": false,
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"text": "("
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},
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{
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"id": 400,
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"logprob": 0.0,
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"special": false,
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"text": "age"
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},
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{
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"id": 46,
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"logprob": 0.0,
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"special": false,
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"text": ")"
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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": "():\n print(\"Hello World!\")\n\ndef print_hello_name(name):\n print(\"Hello \" + name + \"!\")\n\ndef print_hello_name_age(name, age):\n print(\"Hello \" + name + \", you are \" + str(age)"
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}
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@ -0,0 +1,378 @@
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[
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{
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"details": {
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"best_of_sequences": null,
|
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"finish_reason": "length",
|
||||
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|
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"prefill": [
|
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{
|
||||
"id": 610,
|
||||
"logprob": null,
|
||||
"text": "def"
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -5.2617188,
|
||||
"text": " print"
|
||||
},
|
||||
{
|
||||
"id": 100,
|
||||
"logprob": -0.38476562,
|
||||
"text": "_"
|
||||
},
|
||||
{
|
||||
"id": 7670,
|
||||
"logprob": -7.640625,
|
||||
"text": "hello"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 2284,
|
||||
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|
||||
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|
||||
"text": "():"
|
||||
},
|
||||
{
|
||||
"id": 303,
|
||||
"logprob": -0.40722656,
|
||||
"special": false,
|
||||
"text": "\n "
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -0.27954102,
|
||||
"special": false,
|
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"text": " print"
|
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},
|
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{
|
||||
"id": 459,
|
||||
"logprob": -0.6142578,
|
||||
"special": false,
|
||||
"text": "(\""
|
||||
},
|
||||
{
|
||||
"id": 8302,
|
||||
"logprob": -0.68310547,
|
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"special": false,
|
||||
"text": "Hello"
|
||||
},
|
||||
{
|
||||
"id": 10914,
|
||||
"logprob": -1.4570312,
|
||||
"special": false,
|
||||
"text": " World"
|
||||
},
|
||||
{
|
||||
"id": 16013,
|
||||
"logprob": -0.80126953,
|
||||
"special": false,
|
||||
"text": "!\")"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.6303711,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.23327637,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 610,
|
||||
"logprob": -1.2304688,
|
||||
"special": false,
|
||||
"text": "def"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": "():\n print(\"Hello World!\")\n\ndef"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
"best_of_sequences": null,
|
||||
"finish_reason": "length",
|
||||
"generated_tokens": 10,
|
||||
"prefill": [
|
||||
{
|
||||
"id": 610,
|
||||
"logprob": null,
|
||||
"text": "def"
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -5.2617188,
|
||||
"text": " print"
|
||||
},
|
||||
{
|
||||
"id": 100,
|
||||
"logprob": -0.38476562,
|
||||
"text": "_"
|
||||
},
|
||||
{
|
||||
"id": 7670,
|
||||
"logprob": -7.640625,
|
||||
"text": "hello"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 2284,
|
||||
"logprob": -0.92626953,
|
||||
"special": false,
|
||||
"text": "():"
|
||||
},
|
||||
{
|
||||
"id": 303,
|
||||
"logprob": -0.40722656,
|
||||
"special": false,
|
||||
"text": "\n "
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -0.27954102,
|
||||
"special": false,
|
||||
"text": " print"
|
||||
},
|
||||
{
|
||||
"id": 459,
|
||||
"logprob": -0.6142578,
|
||||
"special": false,
|
||||
"text": "(\""
|
||||
},
|
||||
{
|
||||
"id": 8302,
|
||||
"logprob": -0.68310547,
|
||||
"special": false,
|
||||
"text": "Hello"
|
||||
},
|
||||
{
|
||||
"id": 10914,
|
||||
"logprob": -1.4570312,
|
||||
"special": false,
|
||||
"text": " World"
|
||||
},
|
||||
{
|
||||
"id": 16013,
|
||||
"logprob": -0.80126953,
|
||||
"special": false,
|
||||
"text": "!\")"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.6303711,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.23327637,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 610,
|
||||
"logprob": -1.2304688,
|
||||
"special": false,
|
||||
"text": "def"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": "():\n print(\"Hello World!\")\n\ndef"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
"best_of_sequences": null,
|
||||
"finish_reason": "length",
|
||||
"generated_tokens": 10,
|
||||
"prefill": [
|
||||
{
|
||||
"id": 610,
|
||||
"logprob": null,
|
||||
"text": "def"
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -5.2617188,
|
||||
"text": " print"
|
||||
},
|
||||
{
|
||||
"id": 100,
|
||||
"logprob": -0.38476562,
|
||||
"text": "_"
|
||||
},
|
||||
{
|
||||
"id": 7670,
|
||||
"logprob": -7.640625,
|
||||
"text": "hello"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 2284,
|
||||
"logprob": -0.92626953,
|
||||
"special": false,
|
||||
"text": "():"
|
||||
},
|
||||
{
|
||||
"id": 303,
|
||||
"logprob": -0.40722656,
|
||||
"special": false,
|
||||
"text": "\n "
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -0.27954102,
|
||||
"special": false,
|
||||
"text": " print"
|
||||
},
|
||||
{
|
||||
"id": 459,
|
||||
"logprob": -0.6142578,
|
||||
"special": false,
|
||||
"text": "(\""
|
||||
},
|
||||
{
|
||||
"id": 8302,
|
||||
"logprob": -0.68310547,
|
||||
"special": false,
|
||||
"text": "Hello"
|
||||
},
|
||||
{
|
||||
"id": 10914,
|
||||
"logprob": -1.4570312,
|
||||
"special": false,
|
||||
"text": " World"
|
||||
},
|
||||
{
|
||||
"id": 16013,
|
||||
"logprob": -0.80126953,
|
||||
"special": false,
|
||||
"text": "!\")"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.6303711,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.23327637,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 610,
|
||||
"logprob": -1.2304688,
|
||||
"special": false,
|
||||
"text": "def"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": "():\n print(\"Hello World!\")\n\ndef"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
"best_of_sequences": null,
|
||||
"finish_reason": "length",
|
||||
"generated_tokens": 10,
|
||||
"prefill": [
|
||||
{
|
||||
"id": 610,
|
||||
"logprob": null,
|
||||
"text": "def"
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -5.2617188,
|
||||
"text": " print"
|
||||
},
|
||||
{
|
||||
"id": 100,
|
||||
"logprob": -0.38476562,
|
||||
"text": "_"
|
||||
},
|
||||
{
|
||||
"id": 7670,
|
||||
"logprob": -7.640625,
|
||||
"text": "hello"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 2284,
|
||||
"logprob": -0.92626953,
|
||||
"special": false,
|
||||
"text": "():"
|
||||
},
|
||||
{
|
||||
"id": 303,
|
||||
"logprob": -0.40722656,
|
||||
"special": false,
|
||||
"text": "\n "
|
||||
},
|
||||
{
|
||||
"id": 1489,
|
||||
"logprob": -0.27954102,
|
||||
"special": false,
|
||||
"text": " print"
|
||||
},
|
||||
{
|
||||
"id": 459,
|
||||
"logprob": -0.6142578,
|
||||
"special": false,
|
||||
"text": "(\""
|
||||
},
|
||||
{
|
||||
"id": 8302,
|
||||
"logprob": -0.68310547,
|
||||
"special": false,
|
||||
"text": "Hello"
|
||||
},
|
||||
{
|
||||
"id": 10914,
|
||||
"logprob": -1.4570312,
|
||||
"special": false,
|
||||
"text": " World"
|
||||
},
|
||||
{
|
||||
"id": 16013,
|
||||
"logprob": -0.80126953,
|
||||
"special": false,
|
||||
"text": "!\")"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.6303711,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 222,
|
||||
"logprob": -0.23327637,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 610,
|
||||
"logprob": -1.2304688,
|
||||
"special": false,
|
||||
"text": "def"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": "():\n print(\"Hello World!\")\n\ndef"
|
||||
}
|
||||
]
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
import pytest
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def flash_starcoder2_handle(launcher):
|
||||
with launcher("bigcode/starcoder2-3b", num_shard=2) as handle:
|
||||
yield handle
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
async def flash_starcoder2(flash_starcoder2_handle):
|
||||
await flash_starcoder2_handle.health(300)
|
||||
return flash_starcoder2_handle.client
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.private
|
||||
async def test_flash_starcoder2(flash_starcoder2, response_snapshot):
|
||||
response = await flash_starcoder2.generate(
|
||||
"def print_hello", max_new_tokens=10, decoder_input_details=True
|
||||
)
|
||||
|
||||
assert response.details.generated_tokens == 10
|
||||
assert response == response_snapshot
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.private
|
||||
async def test_flash_starcoder2_default_params(flash_starcoder2, response_snapshot):
|
||||
response = await flash_starcoder2.generate(
|
||||
"def print_hello",
|
||||
max_new_tokens=60,
|
||||
temperature=0.2,
|
||||
top_p=0.95,
|
||||
decoder_input_details=True,
|
||||
seed=0,
|
||||
)
|
||||
|
||||
assert response.details.generated_tokens == 60
|
||||
assert response == response_snapshot
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.private
|
||||
async def test_flash_starcoder2_load(
|
||||
flash_starcoder2, generate_load, response_snapshot
|
||||
):
|
||||
responses = await generate_load(
|
||||
flash_starcoder2, "def print_hello", max_new_tokens=10, n=4
|
||||
)
|
||||
|
||||
assert len(responses) == 4
|
||||
assert all([r.generated_text == responses[0].generated_text for r in responses])
|
||||
|
||||
assert responses == response_snapshot
|
||||
|
|
@ -230,7 +230,6 @@ message WarmupRequest {
|
|||
uint32 max_total_tokens = 4;
|
||||
}
|
||||
|
||||
/// Empty response
|
||||
message WarmupResponse {
|
||||
/// Maximum number of tokens supported by the model
|
||||
optional uint32 max_supported_total_tokens = 1;
|
||||
|
|
|
|||
|
|
@ -64,6 +64,7 @@ try:
|
|||
from text_generation_server.models.flash_mistral import FlashMistral
|
||||
from text_generation_server.models.flash_mixtral import FlashMixtral
|
||||
from text_generation_server.models.flash_phi import FlashPhi
|
||||
from text_generation_server.models.flash_starcoder2 import FlashStarcoder2
|
||||
from text_generation_server.utils.flash_attn import HAS_FLASH_ATTN_V2_CUDA
|
||||
|
||||
except ImportError as e:
|
||||
|
|
@ -80,6 +81,7 @@ if FLASH_ATTENTION:
|
|||
__all__.append(FlashMistral)
|
||||
__all__.append(FlashMixtral)
|
||||
__all__.append(FlashPhi)
|
||||
__all__.append(FlashStarcoder2)
|
||||
|
||||
MAMBA_AVAILABLE = True
|
||||
try:
|
||||
|
|
@ -184,6 +186,16 @@ def get_model(
|
|||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
if model_id.startswith("facebook/galactica"):
|
||||
return GalacticaSharded(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
use_medusa=use_medusa,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
if (
|
||||
model_type == "gpt_bigcode"
|
||||
or model_type == "gpt2"
|
||||
|
|
@ -401,6 +413,18 @@ def get_model(
|
|||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
if model_type == "starcoder2":
|
||||
sliding_window = config_dict.get("sliding_window", -1)
|
||||
if (
|
||||
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
|
||||
) or HAS_FLASH_ATTN_V2_CUDA:
|
||||
return FlashStarcoder2(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
if model_type == "opt":
|
||||
return OPTSharded(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,545 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 Starcoder2 AI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import torch
|
||||
import torch.distributed
|
||||
|
||||
from torch import nn
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
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,
|
||||
SpeculativeHead,
|
||||
get_linear,
|
||||
FastRMSNorm,
|
||||
FastLayerNorm,
|
||||
)
|
||||
|
||||
|
||||
class Starcoder2Config(PretrainedConfig):
|
||||
model_type = "starcoder2"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=49152,
|
||||
hidden_size=3072,
|
||||
intermediate_size=12288,
|
||||
num_hidden_layers=30,
|
||||
num_attention_heads=24,
|
||||
num_key_value_heads=2,
|
||||
mlp_type="default",
|
||||
hidden_act="gelu_pytorch_tanh",
|
||||
max_position_embeddings=4096,
|
||||
initializer_range=0.018042,
|
||||
norm_type="layer_norm",
|
||||
norm_epsilon=1e-5,
|
||||
use_cache=True,
|
||||
bos_token_id=50256,
|
||||
eos_token_id=50256,
|
||||
rope_theta=10000.0,
|
||||
sliding_window=None,
|
||||
attention_dropout=0.0,
|
||||
residual_dropout=0.0,
|
||||
embedding_dropout=0.0,
|
||||
use_bias: bool = True,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.sliding_window = sliding_window
|
||||
self.use_bias = use_bias
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.mlp_type = mlp_type
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.norm_type = norm_type
|
||||
self.norm_epsilon = norm_epsilon
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.attention_dropout = attention_dropout
|
||||
self.residual_dropout = residual_dropout
|
||||
self.embedding_dropout = embedding_dropout
|
||||
|
||||
super().__init__(
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
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=config.use_bias,
|
||||
)
|
||||
|
||||
|
||||
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]}"
|
||||
|
||||
if config.use_bias:
|
||||
w = [
|
||||
weights.get_sharded(f"{p}.bias", dim=0)
|
||||
for p in [f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"]
|
||||
]
|
||||
bias = torch.cat(w, dim=0).to(dtype=weights.dtype).to(device=weights.device)
|
||||
else:
|
||||
bias = None
|
||||
|
||||
return TensorParallelColumnLinear(
|
||||
get_linear(weight, bias=bias, quantize=config.quantize)
|
||||
)
|
||||
|
||||
|
||||
class Starcoder2Attention(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=config.use_bias,
|
||||
)
|
||||
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 Starcoder2MLP(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.c_fc = TensorParallelColumnLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.c_fc",
|
||||
weights=weights,
|
||||
bias=config.use_bias,
|
||||
)
|
||||
self.c_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.c_proj",
|
||||
weights=weights,
|
||||
bias=config.use_bias,
|
||||
)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.c_fc(hidden_states)
|
||||
hidden_states = self.act(hidden_states)
|
||||
return self.c_proj(hidden_states)
|
||||
|
||||
|
||||
class Starcoder2GatedMLP(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=config.use_bias,
|
||||
)
|
||||
self.down_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.down_proj",
|
||||
weights=weights,
|
||||
bias=config.use_bias,
|
||||
)
|
||||
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])
|
||||
|
||||
|
||||
STARCODER2_NORMALIZATION_CLASSES = {
|
||||
"layer_norm": FastLayerNorm,
|
||||
"rms_norm": FastRMSNorm,
|
||||
}
|
||||
|
||||
STARCODER2_MLP_CLASSES = {
|
||||
"default": Starcoder2MLP,
|
||||
"gated": Starcoder2GatedMLP,
|
||||
}
|
||||
|
||||
|
||||
class Starcoder2Layer(nn.Module):
|
||||
def __init__(self, layer_id, config, weights):
|
||||
super().__init__()
|
||||
prefix = f"model.layers.{layer_id}"
|
||||
self.self_attn = Starcoder2Attention(
|
||||
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
||||
)
|
||||
|
||||
self.mlp = STARCODER2_MLP_CLASSES[config.mlp_type](
|
||||
prefix=f"{prefix}.mlp", config=config, weights=weights
|
||||
)
|
||||
|
||||
self.input_layernorm = STARCODER2_NORMALIZATION_CLASSES[config.norm_type].load(
|
||||
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.norm_epsilon
|
||||
)
|
||||
self.post_attention_layernorm = STARCODER2_NORMALIZATION_CLASSES[
|
||||
config.norm_type
|
||||
].load(
|
||||
prefix=f"{prefix}.post_attention_layernorm",
|
||||
weights=weights,
|
||||
eps=config.norm_epsilon,
|
||||
)
|
||||
|
||||
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 Starcoder2Model(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(
|
||||
[
|
||||
Starcoder2Layer(
|
||||
layer_id,
|
||||
config,
|
||||
weights,
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = STARCODER2_NORMALIZATION_CLASSES[config.norm_type].load(
|
||||
prefix="model.norm", weights=weights, eps=config.norm_epsilon
|
||||
)
|
||||
|
||||
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 FlashStarcoder2ForCausalLM(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
|
||||
self.model = Starcoder2Model(config, weights)
|
||||
try:
|
||||
self.lm_head = SpeculativeHead.load(
|
||||
config,
|
||||
prefix="lm_head",
|
||||
weights=weights,
|
||||
)
|
||||
except RuntimeError:
|
||||
self.lm_head = SpeculativeHead.load(
|
||||
config,
|
||||
prefix="model.embed_tokens",
|
||||
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],
|
||||
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
|
||||
|
|
@ -8,7 +8,7 @@ from dataclasses import dataclass
|
|||
from opentelemetry import trace
|
||||
from transformers import PreTrainedTokenizerBase
|
||||
from transformers.models.llama import LlamaTokenizerFast
|
||||
from typing import Optional, Tuple, Type, List
|
||||
from typing import Optional, Tuple, Type
|
||||
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.models import FlashCausalLM
|
||||
|
|
@ -38,6 +38,19 @@ SLIDING_WINDOW_BLOCKS: Optional[int] = None
|
|||
MEM_POOL = torch.cuda.graph_pool_handle()
|
||||
|
||||
|
||||
def set_sliding_window(sliding_window: int, sliding_window_blocks: int):
|
||||
global SLIDING_WINDOW
|
||||
global SLIDING_WINDOW_BLOCKS
|
||||
SLIDING_WINDOW = sliding_window
|
||||
SLIDING_WINDOW_BLOCKS = sliding_window_blocks
|
||||
|
||||
|
||||
def get_sliding_windows() -> Tuple[int, int]:
|
||||
global SLIDING_WINDOW
|
||||
global SLIDING_WINDOW_BLOCKS
|
||||
return SLIDING_WINDOW, SLIDING_WINDOW_BLOCKS
|
||||
|
||||
|
||||
# Adds windowing logic to FlashCausalLMBatch
|
||||
@dataclass
|
||||
class FlashMistralBatch(FlashCausalLMBatch):
|
||||
|
|
@ -53,8 +66,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
) -> "FlashCausalLMBatch":
|
||||
global SLIDING_WINDOW
|
||||
global SLIDING_WINDOW_BLOCKS
|
||||
sliding_window, sliding_window_blocks = get_sliding_windows()
|
||||
|
||||
batch_inputs = []
|
||||
max_truncation = 0
|
||||
|
|
@ -139,8 +151,8 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
|
||||
# Needed blocks can not go over SLIDING_WINDOW_BLOCKS
|
||||
needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
|
||||
if SLIDING_WINDOW_BLOCKS is not None:
|
||||
needed_blocks = min(needed_blocks, SLIDING_WINDOW_BLOCKS)
|
||||
if sliding_window_blocks is not None:
|
||||
needed_blocks = min(needed_blocks, sliding_window_blocks)
|
||||
blocks += needed_blocks
|
||||
|
||||
needed_blocks_slots.append((needed_blocks, total_tokens))
|
||||
|
|
@ -154,9 +166,9 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
slot_indices.append(request_slot_indices)
|
||||
|
||||
# Create tensor to slice into the kv tensor in prefill
|
||||
if SLIDING_WINDOW is not None:
|
||||
if sliding_window is not None:
|
||||
request_prefill_cache_indices = torch.arange(
|
||||
cumulative_length + max(0, input_length - SLIDING_WINDOW),
|
||||
cumulative_length + max(0, input_length - sliding_window),
|
||||
cumulative_length + input_length,
|
||||
dtype=torch.int64,
|
||||
)
|
||||
|
|
@ -212,13 +224,13 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
input_ids = np.concatenate(all_input_ids, dtype=np.int64)
|
||||
position_ids = torch.cat(position_ids)
|
||||
slot_indices = torch.cat(slot_indices)
|
||||
if SLIDING_WINDOW is not None:
|
||||
if sliding_window is not None:
|
||||
prefill_cache_indices = torch.cat(prefill_cache_indices)
|
||||
else:
|
||||
input_ids = all_input_ids[0]
|
||||
position_ids = position_ids[0]
|
||||
slot_indices = slot_indices[0]
|
||||
if SLIDING_WINDOW is not None:
|
||||
if sliding_window is not None:
|
||||
prefill_cache_indices = prefill_cache_indices[0]
|
||||
|
||||
cu_seqlen_prefill = torch.tensor(
|
||||
|
|
@ -228,7 +240,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
position_ids = position_ids.to(device)
|
||||
slot_indices = slot_indices.to(device)
|
||||
prefill_cache_indices = (
|
||||
prefill_cache_indices.to(device) if SLIDING_WINDOW is not None else None
|
||||
prefill_cache_indices.to(device) if sliding_window is not None else None
|
||||
)
|
||||
input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
|
||||
input_lengths_tensor = torch.tensor(
|
||||
|
|
@ -298,9 +310,6 @@ class BaseFlashMistral(FlashCausalLM):
|
|||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
global SLIDING_WINDOW
|
||||
global SLIDING_WINDOW_BLOCKS
|
||||
|
||||
self.process_group, rank, world_size = initialize_torch_distributed()
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device(f"cuda:{rank}")
|
||||
|
|
@ -324,8 +333,9 @@ class BaseFlashMistral(FlashCausalLM):
|
|||
|
||||
# Set context windows
|
||||
if config.sliding_window is not None:
|
||||
SLIDING_WINDOW = config.sliding_window
|
||||
SLIDING_WINDOW_BLOCKS = math.ceil(config.sliding_window / BLOCK_SIZE)
|
||||
set_sliding_window(
|
||||
config.sliding_window, math.ceil(config.sliding_window / BLOCK_SIZE)
|
||||
)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,86 @@
|
|||
import math
|
||||
|
||||
import torch
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from transformers.models.gpt2 import GPT2TokenizerFast
|
||||
|
||||
from text_generation_server.models.cache_manager import BLOCK_SIZE
|
||||
from text_generation_server.models.flash_mistral import (
|
||||
BaseFlashMistral,
|
||||
set_sliding_window,
|
||||
)
|
||||
from text_generation_server.models.custom_modeling.flash_starcoder2_modeling import (
|
||||
Starcoder2Config,
|
||||
FlashStarcoder2ForCausalLM,
|
||||
)
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
Weights,
|
||||
)
|
||||
|
||||
|
||||
# Starcoder2 has the same base as Mistral
|
||||
class FlashStarcoder2(BaseFlashMistral):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
use_medusa: 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("FlashLlama is only available on GPU")
|
||||
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
config = Starcoder2Config.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
config.quantize = quantize
|
||||
config.use_medusa = use_medusa
|
||||
|
||||
# Set context windows
|
||||
if config.sliding_window is not None:
|
||||
set_sliding_window(
|
||||
config.sliding_window, math.ceil(config.sliding_window / BLOCK_SIZE)
|
||||
)
|
||||
|
||||
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 = FlashStarcoder2ForCausalLM(config, weights)
|
||||
|
||||
self.cuda_graphs = {}
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(BaseFlashMistral, 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,
|
||||
sliding_window=config.sliding_window,
|
||||
)
|
||||
Loading…
Reference in New Issue