Fix Phi 3.5 MoE tests (#2684)
PR #2682 also fixed in issue in Phi MoE, but it changes the test outputs a bit. Fix this.
This commit is contained in:
Daniël de Kok
GitHub
parent
1b914f37e7
commit
14a0df3a38
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@ -11,32 +11,32 @@
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@ -44,66 +44,66 @@
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"generated_text": "Gradient descent is a first-order optimization algorithm"
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"generated_text": "Gradient descent is an optimization algorithm commonly used in"
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@ -5,95 +5,95 @@
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"text": "\n"
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"generated_text": "What is gradient descent?\n\nHello! It seems you're addressing a"
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"generated_text": "What is gradient descent?\nGradient Descent (GD) is an"
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}
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@ -12,32 +12,32 @@
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},
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{
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@ -45,68 +45,68 @@
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"id": 29899,
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"logprob": 0.0,
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"special": false,
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{
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"text": " an"
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{
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"id": 13883,
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"logprob": -0.47436523,
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"logprob": -0.010406494,
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"special": false,
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{
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{
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"text": " in"
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"generated_text": "Gradient descent is a first-order optimization algorithm"
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"generated_text": "Gradient descent is an optimization algorithm commonly used in"
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},
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{
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"details": {
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@ -121,32 +121,32 @@
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@ -154,68 +154,68 @@
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|
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|
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|
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|
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"details": {
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@ -230,32 +230,32 @@
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|
||||
"id": 16030,
|
||||
"logprob": -13.9375,
|
||||
"logprob": -13.671875,
|
||||
"text": "gradient"
|
||||
},
|
||||
{
|
||||
"id": 26815,
|
||||
"logprob": -0.049346924,
|
||||
"logprob": -0.0040016174,
|
||||
"text": "descent"
|
||||
},
|
||||
{
|
||||
"id": 29973,
|
||||
"logprob": -3.0078125,
|
||||
"logprob": -2.6230469,
|
||||
"text": "?"
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -2.8242188,
|
||||
"logprob": -6.453125,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -0.86328125,
|
||||
"logprob": -6.6875,
|
||||
"text": "\n"
|
||||
}
|
||||
],
|
||||
|
|
@ -263,68 +263,68 @@
|
|||
"tokens": [
|
||||
{
|
||||
"id": 25584,
|
||||
"logprob": -0.017196655,
|
||||
"logprob": -0.008956909,
|
||||
"special": false,
|
||||
"text": "Grad"
|
||||
},
|
||||
{
|
||||
"id": 993,
|
||||
"logprob": -0.0028438568,
|
||||
"logprob": -8.34465e-07,
|
||||
"special": false,
|
||||
"text": "ient"
|
||||
},
|
||||
{
|
||||
"id": 26815,
|
||||
"logprob": -0.023254395,
|
||||
"logprob": -0.0009407997,
|
||||
"special": false,
|
||||
"text": " descent"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -2.026558e-05,
|
||||
"logprob": -0.0003721714,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 263,
|
||||
"logprob": -0.5229492,
|
||||
"id": 385,
|
||||
"logprob": -0.24499512,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 937,
|
||||
"logprob": -0.17602539,
|
||||
"special": false,
|
||||
"text": " first"
|
||||
},
|
||||
{
|
||||
"id": 29899,
|
||||
"logprob": 0.0,
|
||||
"special": false,
|
||||
"text": "-"
|
||||
},
|
||||
{
|
||||
"id": 2098,
|
||||
"logprob": -0.00011622906,
|
||||
"special": false,
|
||||
"text": "order"
|
||||
"text": " an"
|
||||
},
|
||||
{
|
||||
"id": 13883,
|
||||
"logprob": -0.48608398,
|
||||
"logprob": -0.010406494,
|
||||
"special": false,
|
||||
"text": " optimization"
|
||||
},
|
||||
{
|
||||
"id": 5687,
|
||||
"logprob": -0.00027894974,
|
||||
"logprob": -0.0002501011,
|
||||
"special": false,
|
||||
"text": " algorithm"
|
||||
},
|
||||
{
|
||||
"id": 15574,
|
||||
"logprob": -0.6435547,
|
||||
"special": false,
|
||||
"text": " commonly"
|
||||
},
|
||||
{
|
||||
"id": 1304,
|
||||
"logprob": -0.00092601776,
|
||||
"special": false,
|
||||
"text": " used"
|
||||
},
|
||||
{
|
||||
"id": 297,
|
||||
"logprob": -0.19177246,
|
||||
"special": false,
|
||||
"text": " in"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": "Gradient descent is a first-order optimization algorithm"
|
||||
"generated_text": "Gradient descent is an optimization algorithm commonly used in"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
|
|
@ -339,32 +339,32 @@
|
|||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -0.7192383,
|
||||
"logprob": -0.609375,
|
||||
"text": "is"
|
||||
},
|
||||
{
|
||||
"id": 16030,
|
||||
"logprob": -13.9375,
|
||||
"logprob": -13.6640625,
|
||||
"text": "gradient"
|
||||
},
|
||||
{
|
||||
"id": 26815,
|
||||
"logprob": -0.050445557,
|
||||
"logprob": -0.0038967133,
|
||||
"text": "descent"
|
||||
},
|
||||
{
|
||||
"id": 29973,
|
||||
"logprob": -3.0078125,
|
||||
"logprob": -2.6347656,
|
||||
"text": "?"
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -2.8242188,
|
||||
"logprob": -6.453125,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -0.8276367,
|
||||
"logprob": -6.6875,
|
||||
"text": "\n"
|
||||
}
|
||||
],
|
||||
|
|
@ -372,67 +372,67 @@
|
|||
"tokens": [
|
||||
{
|
||||
"id": 25584,
|
||||
"logprob": -0.01727295,
|
||||
"logprob": -0.008979797,
|
||||
"special": false,
|
||||
"text": "Grad"
|
||||
},
|
||||
{
|
||||
"id": 993,
|
||||
"logprob": -0.0027542114,
|
||||
"logprob": -9.536743e-07,
|
||||
"special": false,
|
||||
"text": "ient"
|
||||
},
|
||||
{
|
||||
"id": 26815,
|
||||
"logprob": -0.023254395,
|
||||
"logprob": -0.0009407997,
|
||||
"special": false,
|
||||
"text": " descent"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -2.0384789e-05,
|
||||
"logprob": -0.00038409233,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 263,
|
||||
"logprob": -0.5229492,
|
||||
"id": 385,
|
||||
"logprob": -0.24499512,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 937,
|
||||
"logprob": -0.17126465,
|
||||
"special": false,
|
||||
"text": " first"
|
||||
},
|
||||
{
|
||||
"id": 29899,
|
||||
"logprob": 0.0,
|
||||
"special": false,
|
||||
"text": "-"
|
||||
},
|
||||
{
|
||||
"id": 2098,
|
||||
"logprob": -0.00011301041,
|
||||
"special": false,
|
||||
"text": "order"
|
||||
"text": " an"
|
||||
},
|
||||
{
|
||||
"id": 13883,
|
||||
"logprob": -0.48608398,
|
||||
"logprob": -0.010414124,
|
||||
"special": false,
|
||||
"text": " optimization"
|
||||
},
|
||||
{
|
||||
"id": 5687,
|
||||
"logprob": -0.00027894974,
|
||||
"logprob": -0.00024354458,
|
||||
"special": false,
|
||||
"text": " algorithm"
|
||||
},
|
||||
{
|
||||
"id": 15574,
|
||||
"logprob": -0.6435547,
|
||||
"special": false,
|
||||
"text": " commonly"
|
||||
},
|
||||
{
|
||||
"id": 1304,
|
||||
"logprob": -0.0009279251,
|
||||
"special": false,
|
||||
"text": " used"
|
||||
},
|
||||
{
|
||||
"id": 297,
|
||||
"logprob": -0.19470215,
|
||||
"special": false,
|
||||
"text": " in"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": "Gradient descent is a first-order optimization algorithm"
|
||||
"generated_text": "Gradient descent is an optimization algorithm commonly used in"
|
||||
}
|
||||
]
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ async def test_flash_phi35_moe(flash_phi35_moe, response_snapshot):
|
|||
assert response.details.generated_tokens == 10
|
||||
assert (
|
||||
response.generated_text
|
||||
== "Gradient descent is a first-order optimization algorithm"
|
||||
== "Gradient descent is an optimization algorithm commonly used in"
|
||||
)
|
||||
assert response == response_snapshot
|
||||
|
||||
|
|
@ -33,7 +33,7 @@ async def test_flash_phi35_moe(flash_phi35_moe, response_snapshot):
|
|||
@pytest.mark.asyncio
|
||||
async def test_flash_phi35_moe_all_params(flash_phi35_moe, response_snapshot):
|
||||
response = await flash_phi35_moe.generate(
|
||||
"What is gradient descent?\n\n",
|
||||
"What is gradient descent?\n",
|
||||
max_new_tokens=10,
|
||||
repetition_penalty=1.2,
|
||||
return_full_text=True,
|
||||
|
|
@ -51,7 +51,7 @@ async def test_flash_phi35_moe_all_params(flash_phi35_moe, response_snapshot):
|
|||
assert response.details.generated_tokens == 10
|
||||
assert (
|
||||
response.generated_text
|
||||
== "What is gradient descent?\n\nHello! It seems you're addressing a"
|
||||
== "What is gradient descent?\nGradient Descent (GD) is an"
|
||||
)
|
||||
assert response == response_snapshot
|
||||
|
||||
|
|
@ -66,7 +66,7 @@ async def test_flash_phi35_moe_load(flash_phi35_moe, generate_load, response_sna
|
|||
assert responses[0].details.generated_tokens == 10
|
||||
assert (
|
||||
responses[0].generated_text
|
||||
== "Gradient descent is a first-order optimization algorithm"
|
||||
== "Gradient descent is an optimization algorithm commonly used in"
|
||||
)
|
||||
assert all(
|
||||
[r.generated_text == responses[0].generated_text for r in responses]
|
||||
|
|
|
|||
Loading…
Reference in New Issue