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hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_t5_sharded.py
|
import pytest
@pytest.fixture(scope="module")
def t5_sharded_handle(launcher):
with launcher("google/flan-t5-xxl", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def t5_sharded(t5_sharded_handle):
await t5_sharded_handle.health(300)
return t5_sharded_handle.client
@pytest.mark.asyncio
async def test_t5_sharded(t5_sharded, response_snapshot):
response = await t5_sharded.generate(
"Please answer the following question. What is the boiling point of Nitrogen?",
max_new_tokens=10,
decoder_input_details=True,
)
assert response == response_snapshot
@pytest.mark.asyncio
async def test_t5_sharded_load(t5_sharded, generate_load, response_snapshot):
responses = await generate_load(
t5_sharded,
"Please answer the following question. What is the boiling point of Nitrogen?",
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
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_llama.py
|
import pytest
@pytest.fixture(scope="module")
def flash_llama_handle(launcher):
with launcher("huggingface/llama-7b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama(flash_llama_handle):
await flash_llama_handle.health(300)
return flash_llama_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama(flash_llama, response_snapshot):
response = await flash_llama.generate(
"Test request", 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_llama_all_params(flash_llama, response_snapshot):
response = await flash_llama.generate(
"Test request",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 5
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_load(flash_llama, generate_load, response_snapshot):
responses = await generate_load(flash_llama, "Test request", 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
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_starcoder_gptq.py
|
import pytest
@pytest.fixture(scope="module")
def flash_starcoder_gptq_handle(launcher):
with launcher("Narsil/starcoder-gptq", num_shard=2, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder_gptq(flash_starcoder_gptq_handle):
await flash_starcoder_gptq_handle.health(300)
return flash_starcoder_gptq_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_starcoder_gptq(flash_starcoder_gptq, generous_response_snapshot):
response = await flash_starcoder_gptq.generate(
"def geometric_mean(L: List[float]):",
max_new_tokens=20,
decoder_input_details=True,
)
assert response.details.generated_tokens == 20
assert response == generous_response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_starcoder_gptq_default_params(
flash_starcoder_gptq, generous_response_snapshot
):
response = await flash_starcoder_gptq.generate(
"def geometric_mean(L: List[float]):",
max_new_tokens=20,
temperature=0.2,
top_p=0.95,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 20
assert response == generous_response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_starcoder_gptq_load(
flash_starcoder_gptq, generate_load, generous_response_snapshot
):
responses = await generate_load(
flash_starcoder_gptq,
"def geometric_mean(L: List[float]):",
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 == generous_response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_bloom_560m_sharded.py
|
import pytest
@pytest.fixture(scope="module")
def bloom_560m_sharded_handle(launcher):
with launcher("bigscience/bloom-560m", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def bloom_560m_sharded(bloom_560m_sharded_handle):
await bloom_560m_sharded_handle.health(240)
return bloom_560m_sharded_handle.client
@pytest.mark.asyncio
async def test_bloom_560m_sharded(bloom_560m_sharded, response_snapshot):
response = await bloom_560m_sharded.generate(
"Pour déguster un ortolan, il faut tout d'abord",
max_new_tokens=10,
top_p=0.9,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_bloom_560m_sharded_load(
bloom_560m_sharded, generate_load, response_snapshot
):
responses = await generate_load(
bloom_560m_sharded,
"Pour déguster un ortolan, il faut tout d'abord",
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
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_neox.py
|
import pytest
@pytest.fixture(scope="module")
def neox_handle(launcher):
with launcher(
"stabilityai/stablelm-tuned-alpha-3b", num_shard=1, use_flash_attention=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def neox(neox_handle):
await neox_handle.health(300)
return neox_handle.client
@pytest.mark.skip
@pytest.mark.asyncio
async def test_neox(neox, response_snapshot):
response = await neox.generate(
"<|USER|>What's your mood today?<|ASSISTANT|>",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.skip
@pytest.mark.asyncio
async def test_neox_load(neox, generate_load, response_snapshot):
responses = await generate_load(
neox,
"<|USER|>What's your mood today?<|ASSISTANT|>",
max_new_tokens=10,
n=4,
)
generated_texts = [r.generated_text for r in responses]
assert len(generated_texts) == 4
assert generated_texts, all(
[text == generated_texts[0] for text in generated_texts]
)
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_awq.py
|
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle(launcher):
with launcher(
"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
num_shard=1,
quantize="awq",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_awq(flash_llama_awq_handle):
await flash_llama_awq_handle.health(300)
return flash_llama_awq_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq(flash_llama_awq, response_snapshot):
response = await flash_llama_awq.generate(
"What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert (
response.generated_text
== "\nWhat is the difference between Deep Learning and Machine"
)
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_all_params(flash_llama_awq, response_snapshot):
response = await flash_llama_awq.generate(
"What is Deep Learning?",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_load(flash_llama_awq, generate_load, response_snapshot):
responses = await generate_load(
flash_llama_awq, "What is Deep Learning?", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all(
[
r.generated_text
== "\nWhat is the difference between Deep Learning and Machine"
for r in responses
]
)
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics.json
|
{
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"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
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},
{
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{
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"text": " "
},
{
"id": 13,
"logprob": -1.7642975e-05,
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"text": "\n"
},
{
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{
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{
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],
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},
"generated_text": " \nAssistant: A rooster stands"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics_load.json
|
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{
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{
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{
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{
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{
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},
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{
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{
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},
{
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},
{
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{
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{
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},
{
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{
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},
{
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}
],
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
"id": 319,
"logprob": -0.9013672,
"special": false,
"text": " A"
},
{
"id": 696,
"logprob": -1.2324219,
"special": false,
"text": " ro"
},
{
"id": 15664,
"logprob": -0.0002477169,
"special": false,
"text": "oster"
},
{
"id": 15028,
"logprob": -1.1660156,
"special": false,
"text": " stands"
}
],
"top_tokens": null
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"generated_text": " \nAssistant: A rooster stands"
}
]
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json
|
{
"details": {
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"finish_reason": "eos_token",
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{
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}
],
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"tokens": [
{
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},
{
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{
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"text": " "
},
{
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"text": "appear"
},
{
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},
{
"id": 281,
"logprob": 0.0,
"special": false,
"text": " in"
},
{
"id": 287,
"logprob": 0.0,
"special": false,
"text": " the"
},
{
"id": 20495,
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},
{
"id": 1,
"logprob": 0.0,
"special": true,
"text": "</s>"
}
]
},
"generated_text": "Why is the sky blue?blue sky appeared in the sky"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base.json
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 5,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": 0,
"tokens": [
{
"id": 926,
"logprob": -4.3554688,
"special": false,
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{
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"logprob": -7.7734375,
"special": false,
"text": " sell"
},
{
"id": 7868,
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"text": " things"
},
{
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{
"id": 1,
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"special": true,
"text": "</s>"
}
]
},
"generated_text": "To sell things."
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json
|
[
{
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"best_of_sequences": null,
"finish_reason": "eos_token",
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"prefill": [
{
"id": 0,
"logprob": null,
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}
],
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},
{
"id": 39261,
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},
{
"id": 609,
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{
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"text": " is"
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{
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},
{
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"prefill": [
{
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{
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"text": " "
},
{
"id": 39261,
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},
{
"id": 609,
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},
{
"id": 339,
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},
{
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},
{
"id": 1,
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"special": true,
"text": "</s>"
}
]
},
"generated_text": "Because it is blue"
},
{
"details": {
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"finish_reason": "eos_token",
"generated_tokens": 6,
"prefill": [
{
"id": 0,
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}
],
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"tokens": [
{
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},
{
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"special": false,
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},
{
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},
{
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{
"id": 16017,
"logprob": -1.6845703,
"special": false,
"text": " blue"
},
{
"id": 1,
"logprob": -0.72753906,
"special": true,
"text": "</s>"
}
]
},
"generated_text": "Because it is blue"
},
{
"details": {
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"finish_reason": "eos_token",
"generated_tokens": 6,
"prefill": [
{
"id": 0,
"logprob": null,
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}
],
"seed": null,
"tokens": [
{
"id": 259,
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"special": false,
"text": " "
},
{
"id": 39261,
"logprob": -0.36279297,
"special": false,
"text": "Because"
},
{
"id": 609,
"logprob": -1.0966797,
"special": false,
"text": " it"
},
{
"id": 339,
"logprob": -0.8276367,
"special": false,
"text": " is"
},
{
"id": 16017,
"logprob": -1.6845703,
"special": false,
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},
{
"id": 1,
"logprob": -0.72753906,
"special": true,
"text": "</s>"
}
]
},
"generated_text": "Because it is blue"
}
]
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder_load.json
|
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
"text": " print"
},
{
"id": 62,
"logprob": -0.24450684,
"text": "_"
},
{
"id": 7196,
"logprob": -6.9609375,
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}
],
"seed": null,
"tokens": [
{
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"special": false,
"text": "():"
},
{
"id": 258,
"logprob": -0.21362305,
"special": false,
"text": "\n "
},
{
"id": 942,
"logprob": -0.44360352,
"special": false,
"text": " print"
},
{
"id": 372,
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"special": false,
"text": "(\""
},
{
"id": 7371,
"logprob": -0.44555664,
"special": false,
"text": "Hello"
},
{
"id": 9956,
"logprob": -1.2441406,
"special": false,
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},
{
"id": 8657,
"logprob": -0.75878906,
"special": false,
"text": "!\")"
},
{
"id": 185,
"logprob": -0.76171875,
"special": false,
"text": "\n"
},
{
"id": 185,
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"text": "\n"
},
{
"id": 1018,
"logprob": -1.2460938,
"special": false,
"text": "print"
}
]
},
"generated_text": "():\n print(\"Hello World!\")\n\nprint"
},
{
"details": {
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"finish_reason": "length",
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"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
"text": " print"
},
{
"id": 62,
"logprob": -0.24450684,
"text": "_"
},
{
"id": 7196,
"logprob": -6.9609375,
"text": "hello"
}
],
"seed": null,
"tokens": [
{
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"logprob": -0.9863281,
"special": false,
"text": "():"
},
{
"id": 258,
"logprob": -0.21362305,
"special": false,
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},
{
"id": 942,
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"special": false,
"text": " print"
},
{
"id": 372,
"logprob": -0.54248047,
"special": false,
"text": "(\""
},
{
"id": 7371,
"logprob": -0.44555664,
"special": false,
"text": "Hello"
},
{
"id": 9956,
"logprob": -1.2441406,
"special": false,
"text": " World"
},
{
"id": 8657,
"logprob": -0.75878906,
"special": false,
"text": "!\")"
},
{
"id": 185,
"logprob": -0.76171875,
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"text": "\n"
},
{
"id": 185,
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"text": "\n"
},
{
"id": 1018,
"logprob": -1.2460938,
"special": false,
"text": "print"
}
]
},
"generated_text": "():\n print(\"Hello World!\")\n\nprint"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
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},
{
"id": 62,
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},
{
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}
],
"seed": null,
"tokens": [
{
"id": 1241,
"logprob": -0.9863281,
"special": false,
"text": "():"
},
{
"id": 258,
"logprob": -0.21362305,
"special": false,
"text": "\n "
},
{
"id": 942,
"logprob": -0.44360352,
"special": false,
"text": " print"
},
{
"id": 372,
"logprob": -0.54248047,
"special": false,
"text": "(\""
},
{
"id": 7371,
"logprob": -0.44555664,
"special": false,
"text": "Hello"
},
{
"id": 9956,
"logprob": -1.2441406,
"special": false,
"text": " World"
},
{
"id": 8657,
"logprob": -0.75878906,
"special": false,
"text": "!\")"
},
{
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"logprob": -0.76171875,
"special": false,
"text": "\n"
},
{
"id": 185,
"logprob": -0.2084961,
"special": false,
"text": "\n"
},
{
"id": 1018,
"logprob": -1.2460938,
"special": false,
"text": "print"
}
]
},
"generated_text": "():\n print(\"Hello World!\")\n\nprint"
},
{
"details": {
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"finish_reason": "length",
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"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
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},
{
"id": 62,
"logprob": -0.24450684,
"text": "_"
},
{
"id": 7196,
"logprob": -6.9609375,
"text": "hello"
}
],
"seed": null,
"tokens": [
{
"id": 1241,
"logprob": -0.9863281,
"special": false,
"text": "():"
},
{
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},
{
"id": 942,
"logprob": -0.44360352,
"special": false,
"text": " print"
},
{
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"text": "(\""
},
{
"id": 7371,
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"special": false,
"text": "Hello"
},
{
"id": 9956,
"logprob": -1.2441406,
"special": false,
"text": " World"
},
{
"id": 8657,
"logprob": -0.75878906,
"special": false,
"text": "!\")"
},
{
"id": 185,
"logprob": -0.76171875,
"special": false,
"text": "\n"
},
{
"id": 185,
"logprob": -0.2084961,
"special": false,
"text": "\n"
},
{
"id": 1018,
"logprob": -1.2460938,
"special": false,
"text": "print"
}
]
},
"generated_text": "():\n print(\"Hello World!\")\n\nprint"
}
]
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder.json
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
"text": " print"
},
{
"id": 62,
"logprob": -0.24450684,
"text": "_"
},
{
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"logprob": -6.9609375,
"text": "hello"
}
],
"seed": null,
"tokens": [
{
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"logprob": -0.9863281,
"special": false,
"text": "():"
},
{
"id": 258,
"logprob": -0.21447754,
"special": false,
"text": "\n "
},
{
"id": 942,
"logprob": -0.43701172,
"special": false,
"text": " print"
},
{
"id": 372,
"logprob": -0.5361328,
"special": false,
"text": "(\""
},
{
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"text": "Hello"
},
{
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"special": false,
"text": " World"
},
{
"id": 8657,
"logprob": -0.7583008,
"special": false,
"text": "!\")"
},
{
"id": 185,
"logprob": -0.76171875,
"special": false,
"text": "\n"
},
{
"id": 185,
"logprob": -0.20837402,
"special": false,
"text": "\n"
},
{
"id": 1018,
"logprob": -1.2470703,
"special": false,
"text": "print"
}
]
},
"generated_text": "():\n print(\"Hello World!\")\n\nprint"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m_sharded/test_bloom_560m_sharded.json
|
{
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{
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"text": "Pour"
},
{
"id": 49833,
"logprob": -10.5390625,
"text": " dég"
},
{
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},
{
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},
{
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"id": 88254,
"logprob": -0.12695312,
"special": false,
"text": "-mar"
},
{
"id": 641,
"logprob": 0.0,
"special": false,
"text": "ie"
},
{
"id": 2940,
"logprob": -3.5175781,
"special": false,
"text": " avec"
}
]
},
"generated_text": " le faire réchauffer au bain-marie avec"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m_sharded/test_bloom_560m_sharded_load.json
|
[
{
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"prefill": [
{
"id": 17934,
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"text": "Pour"
},
{
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},
{
"id": 21543,
"logprob": -0.14758301,
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},
{
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},
{
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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}
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{
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{
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},
{
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},
{
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},
{
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},
{
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"text": " de"
},
{
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},
{
"id": 20226,
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"text": " bou"
},
{
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},
{
"id": 2805,
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"text": " sal"
}
]
},
"generated_text": " le faire cuire dans de l'eau bouillante sal"
},
{
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"prefill": [
{
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{
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},
{
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"text": " ort"
},
{
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"text": "olan"
},
{
"id": 15,
"logprob": -1.4199219,
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},
{
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},
{
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},
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},
{
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}
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{
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},
{
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{
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"text": " l'eau"
},
{
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"text": " bou"
},
{
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},
{
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"special": false,
"text": " sal"
}
]
},
"generated_text": " le faire cuire dans de l'eau bouillante sal"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
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{
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},
{
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{
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},
{
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{
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{
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},
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}
],
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{
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},
{
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},
{
"id": 1273,
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{
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{
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{
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},
{
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},
{
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"special": false,
"text": " sal"
}
]
},
"generated_text": " le faire cuire dans de l'eau bouillante sal"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
},
{
"id": 49833,
"logprob": -10.515625,
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},
{
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},
{
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},
{
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"text": " ort"
},
{
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},
{
"id": 15,
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},
{
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"text": " il"
},
{
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"text": " faut"
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{
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"text": " tout"
},
{
"id": 39261,
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}
],
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"tokens": [
{
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"text": " le"
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{
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"text": " cu"
},
{
"id": 1273,
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},
{
"id": 1486,
"logprob": -1.5175781,
"special": false,
"text": " dans"
},
{
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"logprob": -1.1982422,
"special": false,
"text": " de"
},
{
"id": 40410,
"logprob": -0.11883545,
"special": false,
"text": " l'eau"
},
{
"id": 20226,
"logprob": -0.4909668,
"special": false,
"text": " bou"
},
{
"id": 172483,
"logprob": -0.003047943,
"special": false,
"text": "illante"
},
{
"id": 2805,
"logprob": -1.0185547,
"special": false,
"text": " sal"
}
]
},
"generated_text": " le faire cuire dans de l'eau bouillante sal"
}
]
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_default_params.json
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 60,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6328125,
"text": " print"
},
{
"id": 81,
"logprob": -1.6035156,
"text": "_"
},
{
"id": 7656,
"logprob": -5.9882812,
"text": "hello"
}
],
"seed": 0,
"tokens": [
{
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"special": false,
"text": "():"
},
{
"id": 284,
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},
{
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},
{
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"text": "(\""
},
{
"id": 8279,
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"text": "Hello"
},
{
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"text": " World"
},
{
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"logprob": -0.5229492,
"special": false,
"text": "\")"
},
{
"id": 203,
"logprob": -0.10632324,
"special": false,
"text": "\n"
},
{
"id": 203,
"logprob": 0.0,
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"text": "\n"
},
{
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{
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},
{
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},
{
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"special": false,
"text": "hello"
},
{
"id": 81,
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"text": "_"
},
{
"id": 426,
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"special": false,
"text": "name"
},
{
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"text": "("
},
{
"id": 426,
"logprob": 0.0,
"special": false,
"text": "name"
},
{
"id": 711,
"logprob": 0.0,
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"text": "):"
},
{
"id": 284,
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"text": "\n "
},
{
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},
{
"id": 440,
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},
{
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},
{
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},
{
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"special": false,
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},
{
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},
{
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},
{
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"text": "\n"
},
{
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},
{
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},
{
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{
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{
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{
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},
{
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},
{
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},
{
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"text": "age"
},
{
"id": 26,
"logprob": 0.0,
"special": false,
"text": "("
},
{
"id": 426,
"logprob": 0.0,
"special": false,
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},
{
"id": 30,
"logprob": 0.0,
"special": false,
"text": ","
},
{
"id": 11442,
"logprob": 0.0,
"special": false,
"text": " age"
},
{
"id": 711,
"logprob": 0.0,
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"text": "):"
},
{
"id": 284,
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{
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},
{
"id": 440,
"logprob": 0.0,
"special": false,
"text": "(\""
},
{
"id": 8279,
"logprob": 0.0,
"special": false,
"text": "Hello"
},
{
"id": 313,
"logprob": 0.0,
"special": false,
"text": " \""
},
{
"id": 474,
"logprob": 0.0,
"special": false,
"text": " +"
},
{
"id": 636,
"logprob": 0.0,
"special": false,
"text": " name"
},
{
"id": 474,
"logprob": 0.0,
"special": false,
"text": " +"
},
{
"id": 313,
"logprob": -0.6328125,
"special": false,
"text": " \""
},
{
"id": 313,
"logprob": -1.7011719,
"special": false,
"text": " \""
},
{
"id": 474,
"logprob": 0.0,
"special": false,
"text": " +"
},
{
"id": 596,
"logprob": 0.0,
"special": false,
"text": " str"
},
{
"id": 26,
"logprob": 0.0,
"special": false,
"text": "("
},
{
"id": 381,
"logprob": 0.0,
"special": false,
"text": "age"
},
{
"id": 490,
"logprob": 0.0,
"special": false,
"text": "))"
},
{
"id": 203,
"logprob": 0.0,
"special": false,
"text": "\n"
},
{
"id": 203,
"logprob": 0.0,
"special": false,
"text": "\n"
},
{
"id": 589,
"logprob": 0.0,
"special": false,
"text": "def"
},
{
"id": 1459,
"logprob": 0.0,
"special": false,
"text": " print"
}
]
},
"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 + \" \" + str(age))\n\ndef print"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_load.json
|
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6289062,
"text": " print"
},
{
"id": 81,
"logprob": -1.6005859,
"text": "_"
},
{
"id": 7656,
"logprob": -5.9921875,
"text": "hello"
}
],
"seed": null,
"tokens": [
{
"id": 2262,
"logprob": -0.7705078,
"special": false,
"text": "():"
},
{
"id": 284,
"logprob": -0.2602539,
"special": false,
"text": "\n "
},
{
"id": 1459,
"logprob": -0.39282227,
"special": false,
"text": " print"
},
{
"id": 440,
"logprob": -0.6113281,
"special": false,
"text": "(\""
},
{
"id": 8279,
"logprob": -0.4765625,
"special": false,
"text": "Hello"
},
{
"id": 10896,
"logprob": -1.5068359,
"special": false,
"text": " World"
},
{
"id": 657,
"logprob": -0.8154297,
"special": false,
"text": "\")"
},
{
"id": 203,
"logprob": -0.7319336,
"special": false,
"text": "\n"
},
{
"id": 203,
"logprob": -0.35229492,
"special": false,
"text": "\n"
},
{
"id": 589,
"logprob": -1.0380859,
"special": false,
"text": "def"
}
]
},
"generated_text": "():\n print(\"Hello World\")\n\ndef"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 589,
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"text": "def"
},
{
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"text": " print"
},
{
"id": 81,
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"text": "_"
},
{
"id": 7656,
"logprob": -5.9921875,
"text": "hello"
}
],
"seed": null,
"tokens": [
{
"id": 2262,
"logprob": -0.7705078,
"special": false,
"text": "():"
},
{
"id": 284,
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"special": false,
"text": "\n "
},
{
"id": 1459,
"logprob": -0.39282227,
"special": false,
"text": " print"
},
{
"id": 440,
"logprob": -0.6113281,
"special": false,
"text": "(\""
},
{
"id": 8279,
"logprob": -0.4765625,
"special": false,
"text": "Hello"
},
{
"id": 10896,
"logprob": -1.5068359,
"special": false,
"text": " World"
},
{
"id": 657,
"logprob": -0.8154297,
"special": false,
"text": "\")"
},
{
"id": 203,
"logprob": -0.7319336,
"special": false,
"text": "\n"
},
{
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"text": "\n"
},
{
"id": 589,
"logprob": -1.0380859,
"special": false,
"text": "def"
}
]
},
"generated_text": "():\n print(\"Hello World\")\n\ndef"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6289062,
"text": " print"
},
{
"id": 81,
"logprob": -1.6005859,
"text": "_"
},
{
"id": 7656,
"logprob": -5.9921875,
"text": "hello"
}
],
"seed": null,
"tokens": [
{
"id": 2262,
"logprob": -0.7705078,
"special": false,
"text": "():"
},
{
"id": 284,
"logprob": -0.2602539,
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| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder.json
|
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},
"generated_text": "():\n print(\"Hello World\")\n\ndef"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox_load.json
|
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| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox.json
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| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_all_params.json
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}
],
"top_tokens": null
},
"generated_text": "Test request: Let u be (0 + 3 -"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_load.json
|
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2159,
"logprob": -10.7578125,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 28747,
"logprob": -0.55078125,
"special": false,
"text": ":"
},
{
"id": 3169,
"logprob": -1.4140625,
"special": false,
"text": " Let"
},
{
"id": 307,
"logprob": -3.0273438,
"special": false,
"text": " n"
},
{
"id": 327,
"logprob": -0.94140625,
"special": false,
"text": " ="
},
{
"id": 28705,
"logprob": -0.8173828,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.2978516,
"special": false,
"text": "1"
},
{
"id": 28734,
"logprob": -2.0664062,
"special": false,
"text": "0"
},
{
"id": 387,
"logprob": -1.9560547,
"special": false,
"text": " -"
},
{
"id": 28705,
"logprob": -0.5078125,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.1787109,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": ": Let n = 10 - 1"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2159,
"logprob": -10.7578125,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 28747,
"logprob": -0.54785156,
"special": false,
"text": ":"
},
{
"id": 3169,
"logprob": -1.4111328,
"special": false,
"text": " Let"
},
{
"id": 307,
"logprob": -3.0292969,
"special": false,
"text": " n"
},
{
"id": 327,
"logprob": -0.94433594,
"special": false,
"text": " ="
},
{
"id": 28705,
"logprob": -0.8178711,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.2939453,
"special": false,
"text": "1"
},
{
"id": 28734,
"logprob": -2.0644531,
"special": false,
"text": "0"
},
{
"id": 387,
"logprob": -1.9550781,
"special": false,
"text": " -"
},
{
"id": 28705,
"logprob": -0.5078125,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.1796875,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": ": Let n = 10 - 1"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2159,
"logprob": -10.7578125,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 28747,
"logprob": -0.55078125,
"special": false,
"text": ":"
},
{
"id": 3169,
"logprob": -1.4140625,
"special": false,
"text": " Let"
},
{
"id": 307,
"logprob": -3.0273438,
"special": false,
"text": " n"
},
{
"id": 327,
"logprob": -0.94140625,
"special": false,
"text": " ="
},
{
"id": 28705,
"logprob": -0.8173828,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.2978516,
"special": false,
"text": "1"
},
{
"id": 28734,
"logprob": -2.0664062,
"special": false,
"text": "0"
},
{
"id": 387,
"logprob": -1.9560547,
"special": false,
"text": " -"
},
{
"id": 28705,
"logprob": -0.5078125,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.1787109,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": ": Let n = 10 - 1"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2159,
"logprob": -10.7578125,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 28747,
"logprob": -0.55078125,
"special": false,
"text": ":"
},
{
"id": 3169,
"logprob": -1.4140625,
"special": false,
"text": " Let"
},
{
"id": 307,
"logprob": -3.0273438,
"special": false,
"text": " n"
},
{
"id": 327,
"logprob": -0.94140625,
"special": false,
"text": " ="
},
{
"id": 28705,
"logprob": -0.8173828,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.2978516,
"special": false,
"text": "1"
},
{
"id": 28734,
"logprob": -2.0664062,
"special": false,
"text": "0"
},
{
"id": 387,
"logprob": -1.9560547,
"special": false,
"text": " -"
},
{
"id": 28705,
"logprob": -0.5078125,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.1787109,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": ": Let n = 10 - 1"
}
]
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral.json
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2159,
"logprob": -10.7578125,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 28747,
"logprob": -0.54785156,
"special": false,
"text": ":"
},
{
"id": 3169,
"logprob": -1.4091797,
"special": false,
"text": " Let"
},
{
"id": 307,
"logprob": -3.0273438,
"special": false,
"text": " n"
},
{
"id": 327,
"logprob": -0.94433594,
"special": false,
"text": " ="
},
{
"id": 28705,
"logprob": -0.81347656,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.2958984,
"special": false,
"text": "1"
},
{
"id": 28734,
"logprob": -2.0644531,
"special": false,
"text": "0"
},
{
"id": 387,
"logprob": -1.9580078,
"special": false,
"text": " -"
},
{
"id": 28705,
"logprob": -0.5073242,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -1.1816406,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": ": Let n = 10 - 1"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama.json
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.546875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5351562,
"special": false,
"text": " for"
},
{
"id": 847,
"logprob": -2.5722656,
"special": false,
"text": " /"
},
{
"id": 2754,
"logprob": -2.2714844,
"special": false,
"text": "api"
},
{
"id": 29914,
"logprob": -0.03414917,
"special": false,
"text": "/"
},
{
"id": 29894,
"logprob": -0.95996094,
"special": false,
"text": "v"
},
{
"id": 29896,
"logprob": -0.3635254,
"special": false,
"text": "1"
},
{
"id": 29914,
"logprob": -0.013031006,
"special": false,
"text": "/"
},
{
"id": 16418,
"logprob": -3.1523438,
"special": false,
"text": "projects"
},
{
"id": 29914,
"logprob": -0.43701172,
"special": false,
"text": "/"
},
{
"id": 29896,
"logprob": -1.9394531,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": " for /api/v1/projects/1"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json
|
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.546875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5351562,
"special": false,
"text": " for"
},
{
"id": 847,
"logprob": -2.5566406,
"special": false,
"text": " /"
},
{
"id": 2754,
"logprob": -2.2519531,
"special": false,
"text": "api"
},
{
"id": 29914,
"logprob": -0.03414917,
"special": false,
"text": "/"
},
{
"id": 29894,
"logprob": -0.96240234,
"special": false,
"text": "v"
},
{
"id": 29896,
"logprob": -0.3647461,
"special": false,
"text": "1"
},
{
"id": 29914,
"logprob": -0.012901306,
"special": false,
"text": "/"
},
{
"id": 16418,
"logprob": -3.1542969,
"special": false,
"text": "projects"
},
{
"id": 29914,
"logprob": -0.4362793,
"special": false,
"text": "/"
},
{
"id": 29896,
"logprob": -1.9394531,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": " for /api/v1/projects/1"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.546875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5332031,
"special": false,
"text": " for"
},
{
"id": 847,
"logprob": -2.5625,
"special": false,
"text": " /"
},
{
"id": 2754,
"logprob": -2.2617188,
"special": false,
"text": "api"
},
{
"id": 29914,
"logprob": -0.033996582,
"special": false,
"text": "/"
},
{
"id": 29894,
"logprob": -0.9609375,
"special": false,
"text": "v"
},
{
"id": 29896,
"logprob": -0.36572266,
"special": false,
"text": "1"
},
{
"id": 29914,
"logprob": -0.0129776,
"special": false,
"text": "/"
},
{
"id": 16418,
"logprob": -3.15625,
"special": false,
"text": "projects"
},
{
"id": 29914,
"logprob": -0.4362793,
"special": false,
"text": "/"
},
{
"id": 29896,
"logprob": -1.9394531,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": " for /api/v1/projects/1"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.546875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5332031,
"special": false,
"text": " for"
},
{
"id": 847,
"logprob": -2.5625,
"special": false,
"text": " /"
},
{
"id": 2754,
"logprob": -2.2617188,
"special": false,
"text": "api"
},
{
"id": 29914,
"logprob": -0.033996582,
"special": false,
"text": "/"
},
{
"id": 29894,
"logprob": -0.9609375,
"special": false,
"text": "v"
},
{
"id": 29896,
"logprob": -0.36572266,
"special": false,
"text": "1"
},
{
"id": 29914,
"logprob": -0.0129776,
"special": false,
"text": "/"
},
{
"id": 16418,
"logprob": -3.15625,
"special": false,
"text": "projects"
},
{
"id": 29914,
"logprob": -0.4362793,
"special": false,
"text": "/"
},
{
"id": 29896,
"logprob": -1.9394531,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": " for /api/v1/projects/1"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.546875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5332031,
"special": false,
"text": " for"
},
{
"id": 847,
"logprob": -2.5625,
"special": false,
"text": " /"
},
{
"id": 2754,
"logprob": -2.2617188,
"special": false,
"text": "api"
},
{
"id": 29914,
"logprob": -0.033996582,
"special": false,
"text": "/"
},
{
"id": 29894,
"logprob": -0.9609375,
"special": false,
"text": "v"
},
{
"id": 29896,
"logprob": -0.36572266,
"special": false,
"text": "1"
},
{
"id": 29914,
"logprob": -0.0129776,
"special": false,
"text": "/"
},
{
"id": 16418,
"logprob": -3.15625,
"special": false,
"text": "projects"
},
{
"id": 29914,
"logprob": -0.4362793,
"special": false,
"text": "/"
},
{
"id": 29896,
"logprob": -1.9394531,
"special": false,
"text": "1"
}
],
"top_tokens": null
},
"generated_text": " for /api/v1/projects/1"
}
]
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_all_params.json
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "stop_sequence",
"generated_tokens": 5,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.546875,
"text": "request"
}
],
"seed": 0,
"tokens": [
{
"id": 5229,
"logprob": -2.5839844,
"special": false,
"text": " failed"
},
{
"id": 29901,
"logprob": -0.44970703,
"special": false,
"text": ":"
},
{
"id": 4829,
"logprob": -1.8339844,
"special": false,
"text": " Error"
},
{
"id": 297,
"logprob": -1.0556641,
"special": false,
"text": " in"
},
{
"id": 1243,
"logprob": 0.0,
"special": false,
"text": " test"
}
],
"top_tokens": null
},
"generated_text": "Test request failed: Error in test"
}
| 0
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
|
hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox_load.json
|
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|USER|>"
},
{
"id": 1276,
"logprob": -4.5546875,
"text": "What"
},
{
"id": 434,
"logprob": -4.234375,
"text": "'s"
},
{
"id": 634,
"logprob": -5.21875,
"text": " your"
},
{
"id": 12315,
"logprob": -9.9375,
"text": " mood"
},
{
"id": 3063,
"logprob": -4.1015625,
"text": " today"
},
{
"id": 32,
"logprob": -0.15319824,
"text": "?"
},
{
"id": 50279,
"logprob": -0.2614746,
"text": "<|ASSISTANT|>"
}
],
"seed": null,
"tokens": [
{
"id": 42,
"logprob": -0.8886719,
"special": false,
"text": "I"
},
{
"id": 1353,
"logprob": -0.98046875,
"special": false,
"text": "'m"
},
{
"id": 417,
"logprob": -2.2265625,
"special": false,
"text": " not"
},
{
"id": 2119,
"logprob": -0.3479004,
"special": false,
"text": " sure"
},
{
"id": 13,
"logprob": -1.0117188,
"special": false,
"text": ","
},
{
"id": 534,
"logprob": -0.67871094,
"special": false,
"text": " which"
},
{
"id": 310,
"logprob": -1.421875,
"special": false,
"text": " is"
},
{
"id": 253,
"logprob": -1.7382812,
"special": false,
"text": " the"
},
{
"id": 1682,
"logprob": -0.051330566,
"special": false,
"text": " best"
},
{
"id": 1039,
"logprob": -2.0390625,
"special": false,
"text": " way"
}
]
},
"generated_text": "I'm not sure, which is the best way"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|USER|>"
},
{
"id": 1276,
"logprob": -4.5546875,
"text": "What"
},
{
"id": 434,
"logprob": -4.234375,
"text": "'s"
},
{
"id": 634,
"logprob": -5.1054688,
"text": " your"
},
{
"id": 12315,
"logprob": -9.953125,
"text": " mood"
},
{
"id": 3063,
"logprob": -4.0820312,
"text": " today"
},
{
"id": 32,
"logprob": -0.15148926,
"text": "?"
},
{
"id": 50279,
"logprob": -0.27026367,
"text": "<|ASSISTANT|>"
}
],
"seed": null,
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox.json
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox_load.json
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox.json
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__
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hf_public_repos/text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon_load.json
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{
"id": 752,
"logprob": -2.1679688,
"text": " what"
},
{
"id": 434,
"logprob": -5.6210938,
"text": "'s"
},
{
"id": 253,
"logprob": -0.81103516,
"text": " the"
},
{
"id": 2892,
"logprob": -6.6640625,
"text": " history"
},
{
"id": 3212,
"logprob": -2.265625,
"text": " behind"
},
{
"id": 436,
"logprob": -11.5078125,
"text": " this"
},
{
"id": 3159,
"logprob": -2.1582031,
"text": " word"
},
{
"id": 32,
"logprob": -0.008720398,
"text": "?"
},
{
"id": 0,
"logprob": -2.4726562,
"text": "<|endoftext|>"
},
{
"id": 50281,
"logprob": -18.265625,
"text": "<|assistant|>"
}
],
"seed": null,
"tokens": [
{
"id": 510,
"logprob": -0.63183594,
"special": false,
"text": "The"
},
{
"id": 3159,
"logprob": -0.5390625,
"special": false,
"text": " word"
},
{
"id": 346,
"logprob": -0.045684814,
"special": false,
"text": " \""
},
{
"id": 6441,
"logprob": -0.002090454,
"special": false,
"text": "mem"
},
{
"id": 70,
"logprob": -1.3589859e-05,
"special": false,
"text": "e"
},
{
"id": 3,
"logprob": -0.0009455681,
"special": false,
"text": "\""
},
{
"id": 369,
"logprob": -0.088012695,
"special": false,
"text": " was"
},
{
"id": 806,
"logprob": -0.12585449,
"special": false,
"text": " first"
},
{
"id": 908,
"logprob": -0.017196655,
"special": false,
"text": " used"
},
{
"id": 275,
"logprob": -0.49731445,
"special": false,
"text": " in"
}
]
},
"generated_text": "The word \"meme\" was first used in"
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/proto/generate.proto
|
syntax = "proto3";
package generate.v1;
service TextGenerationService {
/// Model Info
rpc Info (InfoRequest) returns (InfoResponse) {}
/// Service discovery
rpc ServiceDiscovery (ServiceDiscoveryRequest) returns (ServiceDiscoveryResponse) {}
/// Empties batch cache
rpc ClearCache (ClearCacheRequest) returns (ClearCacheResponse);
/// Remove requests from a cached batch
rpc FilterBatch (FilterBatchRequest) returns (FilterBatchResponse);
/// Warmup the model and compute max cache size
rpc Warmup (WarmupRequest) returns (WarmupResponse);
/// Prefill batch and decode first token
rpc Prefill (PrefillRequest) returns (PrefillResponse);
/// Decode token for a list of prefilled batches
rpc Decode (DecodeRequest) returns (DecodeResponse);
/// Health check
rpc Health (HealthRequest) returns (HealthResponse);
}
message HealthRequest {}
message HealthResponse {}
/// Empty request
message InfoRequest {}
message InfoResponse {
bool requires_padding = 1;
string dtype = 2;
string device_type = 3;
optional uint32 window_size = 4;
}
/// Empty request
message ServiceDiscoveryRequest {}
message ServiceDiscoveryResponse {
/// Other shards urls
repeated string urls = 1;
}
message ClearCacheRequest {
/// Optional batch id
optional uint64 id = 1;
}
/// Empty response
message ClearCacheResponse {}
message NextTokenChooserParameters {
/// exponential scaling output probability distribution
float temperature = 1;
/// restricting to the k highest probability elements
uint32 top_k = 2;
/// restricting to top tokens summing to prob_cut_off <= prob_cut_off
float top_p = 3;
/// restricting to top tokens summing to prob_cut_off <= prob_cut_off
float typical_p = 4;
/// apply sampling on the logits
bool do_sample = 5;
/// random seed for sampling
uint64 seed = 6;
/// repetition penalty
float repetition_penalty = 7;
/// token watermarking using "A Watermark for Large Language Models"
bool watermark = 8;
}
message StoppingCriteriaParameters {
/// Maximum number of generated tokens
uint32 max_new_tokens = 1;
/// Optional stopping sequences
repeated string stop_sequences = 2;
/// Ignore end of sequence token
/// used for benchmarking
bool ignore_eos_token = 3;
}
message Request {
/// Request ID
uint64 id = 1;
/// The generation context
string inputs = 2;
/// Context truncation
uint32 truncate = 3;
/// Next Token Chooser Parameters
NextTokenChooserParameters parameters = 4;
/// Stopping Criteria Parameters
StoppingCriteriaParameters stopping_parameters = 5;
/// Return prefill logprobs
bool prefill_logprobs = 6;
/// Return most likely n tokens
uint32 top_n_tokens = 7;
}
message Batch {
/// Batch ID
uint64 id = 1;
/// Individual requests
repeated Request requests = 2;
/// Batch size (==len(requests))
uint32 size = 3;
/// Maximum number of tokens this batch will grow to
uint32 max_tokens = 4;
}
message CachedBatch {
/// Batch ID
uint64 id = 1;
/// Individual requests ids
repeated uint64 request_ids = 2;
/// Batch size (==len(requests))
uint32 size = 3;
/// Maximum number of tokens this batch will grow to
uint32 max_tokens = 4;
}
enum FinishReason {
FINISH_REASON_LENGTH = 0;
FINISH_REASON_EOS_TOKEN = 1;
FINISH_REASON_STOP_SEQUENCE = 2;
}
message GeneratedText {
/// Output
string text = 1;
/// Number of generated tokens
uint32 generated_tokens = 2;
/// Finish reason
FinishReason finish_reason = 3;
/// Seed
optional uint64 seed = 4;
}
message PrefillTokens {
/// Prefill Token IDs
repeated uint32 ids = 1;
/// Prefill Logprobs
repeated float logprobs = 2;
/// Prefill tokens
repeated string texts = 3;
}
message TopTokens {
/// Top Token IDs
repeated uint32 ids = 1;
/// Top Logprobs
repeated float logprobs = 2;
/// Top Token Texts
repeated string texts = 3;
/// If the tokens are special
repeated bool is_special = 6;
}
message Generation {
/// Request ID
uint64 request_id = 1;
/// Prefill tokens (optional)
PrefillTokens prefill_tokens = 2;
/// Token ID
uint32 token_id = 3;
/// Logprob
float token_logprob = 4;
/// Text
string token_text = 5;
/// Is it a special token
bool token_is_special = 6;
/// Complete generated text
optional GeneratedText generated_text = 7;
/// Top tokens
TopTokens top_tokens = 8;
}
message FilterBatchRequest {
/// Batch ID
uint64 batch_id = 1;
/// Requests to keep
repeated uint64 request_ids = 2;
}
message FilterBatchResponse {
/// Filtered Batch (cached)
CachedBatch batch = 1;
}
message PrefillRequest {
/// Batch
Batch batch = 1;
}
message PrefillResponse {
/// Generation
repeated Generation generations = 1;
/// Next batch (cached)
optional CachedBatch batch = 2;
}
message DecodeRequest {
/// Cached batches
repeated CachedBatch batches = 1;
}
message DecodeResponse {
/// Decodes
repeated Generation generations = 1;
/// Next batch (cached)
optional CachedBatch batch = 2;
}
message WarmupRequest {
/// Batch to warmup on
Batch batch = 1;
}
/// Empty response
message WarmupResponse {
/// Maximum number of tokens supported by the model
optional uint32 max_supported_total_tokens = 1;
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/load_tests/starcoder_load.js
|
import {check} from 'k6';
import http from 'k6/http';
import {Trend} from 'k6/metrics';
const host = __ENV.HOST || '127.0.0.1:3000';
const totalTime = new Trend('total_time', true);
const validationTime = new Trend('validation_time', true);
const queueTime = new Trend('queue_time', true);
const inferenceTime = new Trend('inference_time', true);
const timePerToken = new Trend('time_per_token', true);
const example = {
payload: JSON.stringify({
inputs: '# This is a fibonacci function written in the Python programming language.' +
'def fibonacci',
parameters: {
details: true,
max_new_tokens: 60,
temperature: 0.2,
top_p: 0.95,
seed: 0,
},
}),
generated_tokens: 60
};
export const options = {
thresholds: {
http_req_failed: ['rate==0'],
time_per_token: ['p(95)<90'],
queue_time: ['p(95)<1500'],
},
scenarios: {
load_test: {
executor: 'constant-arrival-rate',
duration: '60s',
preAllocatedVUs: 100,
rate: 10,
timeUnit: '1s',
},
},
};
export default function () {
const headers = {'Content-Type': 'application/json'};
const res = http.post(`http://${host}/generate`, example.payload, {
headers,
});
check(res, {
'Post status is 200': (r) => res.status === 200,
'Post response generated tokens': (r) => res.status === 200 && res.json().details.generated_tokens === example.generated_tokens,
});
if (res.status === 200) {
totalTime.add(res.headers["X-Total-Time"]);
validationTime.add(res.headers["X-Validation-Time"]);
queueTime.add(res.headers["X-Queue-Time"]);
inferenceTime.add(res.headers["X-Inference-Time"]);
timePerToken.add(res.headers["X-Time-Per-Token"]);
}
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/load_tests/vllm.js
|
import { get_options, run } from "./common.js";
const reference_latency_ms = 22;
const host = __ENV.HOST || '127.0.0.1:8000';
const max_new_tokens = 50;
function generate_payload(gpt){
const input = gpt["conversations"][0]["value"];
return {"prompt": input, "temperature": 0.5, "ignore_eos": true}
}
export const options = get_options(reference_latency_ms);
export default function(){
run(host, generate_payload, max_new_tokens);
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/load_tests/tgi.js
|
import { get_options, run } from "./common.js";
const reference_latency_ms = 30;
const host = __ENV.HOST || '127.0.0.1:8000';
const max_new_tokens = 50;
function generate_payload(gpt){
const input = gpt["conversations"][0]["value"];
return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "temperature" : 0.5}}
}
export const options = get_options(reference_latency_ms);
export default function(){
run(host, generate_payload, max_new_tokens);
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/load_tests/common.js
|
import { check, randomSeed } from 'k6';
import http from 'k6/http';
import { Trend, Counter } from 'k6/metrics';
import { randomItem } from 'https://jslib.k6.io/k6-utils/1.2.0/index.js';
const seed = 0;
const host = __ENV.HOST || '127.0.0.1:8000';
const timePerToken = new Trend('time_per_token', true);
const throughput = new Counter('tokens_per_s');
randomSeed(seed);
// const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json"))
const shareGPT = JSON.parse(open("small.json"))
export function get_options(reference_latency_ms){
return {
thresholds: {
http_req_failed: ['rate==0'],
time_per_token: [{
threshold: `p(50)<${3 * reference_latency_ms}`,
abortOnFail: true,
delayAbortEval: '10s'
}],
},
scenarios: {
load_test: {
executor: 'constant-arrival-rate',
duration: '60s',
preAllocatedVUs: 100,
rate: 10,
timeUnit: '1s',
},
},
};
}
export function run(host, generate_payload, max_new_tokens) {
const headers = {'Content-Type': 'application/json'};
const query = randomItem(shareGPT);
const payload = JSON.stringify(generate_payload(query));
const res = http.post(`http://${host}/generate`, payload, {
headers,
});
if(res.status >= 400 && res.status < 500){
return;
}
check(res, {
'Post status is 200': (r) => res.status === 200,
});
const n_tokens = max_new_tokens;
const timings = res.timings.duration;
if (res.status === 200) {
const latency_ms_per_token = timings / n_tokens;
timePerToken.add(latency_ms_per_token);
const latency_in_s = latency_ms_per_token / 1000;
const individual_throughput = 1 / latency_in_s;
throughput.add(individual_throughput);
}
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/benchmark/README.md
|
<div align="center">
# Text Generation Inference benchmarking tool
</div>
A lightweight benchmarking tool based inspired by [oha](https://github.com/hatoo/oha)
and powered by [tui](https://github.com/tui-rs-revival/ratatui).
## Install
```shell
make install-benchmark
```
## Run
First, start `text-generation-inference`:
```shell
text-generation-launcher --model-id bigscience/bloom-560m
```
Then run the benchmarking tool:
```shell
text-generation-benchmark --tokenizer-name bigscience/bloom-560m
```
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/benchmark/Cargo.toml
|
[package]
name = "text-generation-benchmark"
description = "Text Generation Benchmarking tool"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[lib]
path = "src/lib.rs"
[[bin]]
name = "text-generation-benchmark"
path = "src/main.rs"
[dependencies]
average = "0.14"
clap = { version = "4.4.5", features = ["derive", "env"] }
crossterm = "0.27"
float-ord = "0.3.2"
serde = {version = "1.0.188", features = ["derive"]}
serde_json = "1.0"
tabled = "0.14.0"
text-generation-client = { path = "../router/client" }
thiserror = "1.0.48"
tokenizers = { version = "0.14.0", features = ["http"] }
tokio = { version = "1.32.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync", "macros"] }
tui = {package = "ratatui", version = "0.23", default-features = false, features = ["crossterm"]}
tracing = "0.1.37"
tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] }
hf-hub = "0.3.1"
| 0
|
hf_public_repos/text-generation-inference/benchmark
|
hf_public_repos/text-generation-inference/benchmark/src/event.rs
|
/// Inspired by https://github.com/orhun/rust-tui-template/blob/472aa515119d4c94903eac12d9784417281dc7f5/src/event.rs
use crossterm::event;
use std::time::{Duration, Instant};
use tokio::sync::{broadcast, mpsc};
/// Events
#[derive(Debug)]
pub(crate) enum Event {
/// Terminal tick.
Tick,
/// Key press.
Key(event::KeyEvent),
/// Terminal resize.
Resize(u16, u16),
}
pub(crate) async fn terminal_event_task(
fps: u32,
event_sender: mpsc::Sender<Event>,
mut shutdown_receiver: broadcast::Receiver<()>,
_shutdown_guard_sender: mpsc::Sender<()>,
) {
// End task if a message is received on shutdown_receiver
// _shutdown_guard_sender will be dropped once the task is finished
tokio::select! {
_ = event_loop(fps, event_sender) => {
},
_ = shutdown_receiver.recv() => {}
}
}
/// Main event loop
async fn event_loop(fps: u32, event_sender: mpsc::Sender<Event>) {
// Frame budget
let per_frame = Duration::from_secs(1) / fps;
// When was last frame executed
let mut last_frame = Instant::now();
loop {
// Sleep to avoid blocking the thread for too long
if let Some(sleep) = per_frame.checked_sub(last_frame.elapsed()) {
tokio::time::sleep(sleep).await;
}
// Get crossterm event and send a new one over the channel
if event::poll(Duration::from_secs(0)).expect("no events available") {
match event::read().expect("unable to read event") {
event::Event::Key(e) => event_sender.send(Event::Key(e)).await.unwrap_or(()),
event::Event::Resize(w, h) => {
event_sender.send(Event::Resize(w, h)).await.unwrap_or(())
}
_ => (),
}
}
// Frame budget exceeded
if last_frame.elapsed() >= per_frame {
// Send tick
event_sender.send(Event::Tick).await.unwrap_or(());
// Rest last_frame time
last_frame = Instant::now();
}
}
}
| 0
|
hf_public_repos/text-generation-inference/benchmark
|
hf_public_repos/text-generation-inference/benchmark/src/table.rs
|
use crate::app::Data;
use tabled::settings::Merge;
use tabled::{builder::Builder, settings::Style, Table};
#[allow(clippy::too_many_arguments)]
pub(crate) fn parameters_table(
tokenizer_name: String,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: usize,
temperature: Option<f32>,
top_k: Option<u32>,
top_p: Option<f32>,
typical_p: Option<f32>,
repetition_penalty: Option<f32>,
watermark: bool,
do_sample: bool,
) -> Table {
let mut builder = Builder::default();
builder.set_header(["Parameter", "Value"]);
builder.push_record(["Model", &tokenizer_name]);
builder.push_record(["Sequence Length", &sequence_length.to_string()]);
builder.push_record(["Decode Length", &decode_length.to_string()]);
builder.push_record(["Top N Tokens", &format!("{top_n_tokens:?}")]);
builder.push_record(["N Runs", &n_runs.to_string()]);
builder.push_record(["Warmups", &warmups.to_string()]);
builder.push_record(["Temperature", &format!("{temperature:?}")]);
builder.push_record(["Top K", &format!("{top_k:?}")]);
builder.push_record(["Top P", &format!("{top_p:?}")]);
builder.push_record(["Typical P", &format!("{typical_p:?}")]);
builder.push_record(["Repetition Penalty", &format!("{repetition_penalty:?}")]);
builder.push_record(["Watermark", &watermark.to_string()]);
builder.push_record(["Do Sample", &do_sample.to_string()]);
let mut table = builder.build();
table.with(Style::markdown());
table
}
pub(crate) fn latency_table(data: &Data) -> Table {
let mut builder = Builder::default();
builder.set_header([
"Step",
"Batch Size",
"Average",
"Lowest",
"Highest",
"p50",
"p90",
"p99",
]);
add_latencies(
&mut builder,
"Prefill",
&data.batch_size,
&data.prefill_latencies,
);
add_latencies(
&mut builder,
"Decode (token)",
&data.batch_size,
&data.decode_token_latencies,
);
add_latencies(
&mut builder,
"Decode (total)",
&data.batch_size,
&data.decode_latencies,
);
let mut table = builder.build();
table.with(Style::markdown()).with(Merge::vertical());
table
}
pub(crate) fn throughput_table(data: &Data) -> Table {
let mut builder = Builder::default();
builder.set_header(["Step", "Batch Size", "Average", "Lowest", "Highest"]);
add_throuhgputs(
&mut builder,
"Prefill",
&data.batch_size,
&data.prefill_throughputs,
);
add_throuhgputs(
&mut builder,
"Decode",
&data.batch_size,
&data.decode_throughputs,
);
let mut table = builder.build();
table.with(Style::markdown()).with(Merge::vertical());
table
}
fn add_latencies(
builder: &mut Builder,
step: &'static str,
batch_size: &[u32],
batch_latencies: &[Vec<f64>],
) {
for (i, b) in batch_size.iter().enumerate() {
let latencies = &batch_latencies[i];
let (avg, min, max) = avg_min_max(latencies);
let row = [
step,
&b.to_string(),
&format_value(avg, "ms"),
&format_value(min, "ms"),
&format_value(max, "ms"),
&format_value(px(latencies, 50), "ms"),
&format_value(px(latencies, 90), "ms"),
&format_value(px(latencies, 99), "ms"),
];
builder.push_record(row);
}
}
fn add_throuhgputs(
builder: &mut Builder,
step: &'static str,
batch_size: &[u32],
batch_throughputs: &[Vec<f64>],
) {
for (i, b) in batch_size.iter().enumerate() {
let throughputs = &batch_throughputs[i];
let (avg, min, max) = avg_min_max(throughputs);
let row = [
step,
&b.to_string(),
&format_value(avg, "tokens/secs"),
&format_value(min, "tokens/secs"),
&format_value(max, "tokens/secs"),
];
builder.push_record(row);
}
}
fn avg_min_max(data: &Vec<f64>) -> (f64, f64, f64) {
let average = data.iter().sum::<f64>() / data.len() as f64;
let min = data
.iter()
.min_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN);
let max = data
.iter()
.max_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN);
(average, *min, *max)
}
fn px(data: &Vec<f64>, p: u32) -> f64 {
let i = (f64::from(p) / 100.0 * data.len() as f64) as usize;
*data.get(i).unwrap_or(&std::f64::NAN)
}
fn format_value(value: f64, unit: &'static str) -> String {
format!("{:.2} {unit}", value)
}
| 0
|
hf_public_repos/text-generation-inference/benchmark
|
hf_public_repos/text-generation-inference/benchmark/src/generation.rs
|
use std::time::{Duration, Instant};
use text_generation_client::{
Batch, CachedBatch, ClientError, NextTokenChooserParameters, Request, ShardedClient,
StoppingCriteriaParameters,
};
use tokenizers::{Tokenizer, TruncationDirection};
use tokio::sync::{broadcast, mpsc};
const LOREM_IPSUM: &str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.";
#[derive(Debug, Clone)]
pub(crate) struct Prefill {
pub(crate) latency: Duration,
pub(crate) throughput: f64,
}
#[derive(Debug, Clone)]
pub(crate) struct Decode {
pub(crate) latency: Duration,
pub(crate) token_latency: Duration,
pub(crate) throughput: f64,
}
#[derive(Debug)]
pub(crate) enum Message {
Warmup,
Prefill(Prefill),
Decode(Decode),
EndRun,
EndBatch,
}
/// Benchmarking task
#[allow(clippy::too_many_arguments)]
pub(crate) async fn generation_task(
tokenizer: Tokenizer,
batch_size: Vec<u32>,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: usize,
parameters: NextTokenChooserParameters,
client: ShardedClient,
run_sender: mpsc::Sender<Result<Message, ClientError>>,
mut shutdown_receiver: broadcast::Receiver<()>,
_shutdown_guard_sender: mpsc::Sender<()>,
) {
// End task if a message is received on shutdown_receiver
// _shutdown_guard_sender will be dropped once the task is finished
tokio::select! {
res = generate_runs(tokenizer, batch_size, sequence_length, decode_length, top_n_tokens, n_runs, warmups, parameters, client, run_sender.clone()) => {
if let Err(err) = res {
run_sender.send(Err(err)).await.unwrap_or(());
}
},
_ = shutdown_receiver.recv() => {}
}
}
/// Benchmark prefill/decode
#[allow(clippy::too_many_arguments)]
async fn generate_runs(
tokenizer: Tokenizer,
batch_size: Vec<u32>,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: usize,
parameters: NextTokenChooserParameters,
mut client: ShardedClient,
run_sender: mpsc::Sender<Result<Message, ClientError>>,
) -> Result<(), ClientError> {
// Create a dummy sequence
let sequence = create_sequence(sequence_length, tokenizer);
for b in batch_size {
// Warmups on batch size
for _ in 0..warmups {
let (_, decode_batch) = prefill(
sequence.clone(),
sequence_length,
b,
decode_length,
parameters.clone(),
top_n_tokens,
&mut client,
)
.await?;
let _ = decode(decode_batch, &mut client).await?;
// Send warmup message
run_sender.send(Ok(Message::Warmup)).await.unwrap_or(());
}
for _ in 0..n_runs {
let (prefill, decode_batch) = prefill(
sequence.clone(),
sequence_length,
b,
decode_length,
parameters.clone(),
top_n_tokens,
&mut client,
)
.await?;
// Send prefill message
run_sender
.send(Ok(Message::Prefill(prefill)))
.await
.unwrap_or(());
let decode = decode(decode_batch, &mut client).await?;
// Send decode message
run_sender
.send(Ok(Message::Decode(decode)))
.await
.unwrap_or(());
// Send run ended message
run_sender.send(Ok(Message::EndRun)).await.unwrap_or(());
}
// Batch ended
run_sender.send(Ok(Message::EndBatch)).await.unwrap_or(());
}
Ok(())
}
// Run a prefill step
async fn prefill(
sequence: String,
sequence_length: u32,
batch_size: u32,
decode_length: u32,
parameters: NextTokenChooserParameters,
top_n_tokens: Option<u32>,
client: &mut ShardedClient,
) -> Result<(Prefill, CachedBatch), ClientError> {
// Create requests
let requests = (0..batch_size)
.map(|id| Request {
id: id.into(),
prefill_logprobs: false,
inputs: sequence.clone(),
truncate: sequence_length,
parameters: Some(parameters.clone()),
stopping_parameters: Some(StoppingCriteriaParameters {
max_new_tokens: decode_length,
stop_sequences: vec![],
ignore_eos_token: true, // Will not stop even if a eos token is generated
}),
top_n_tokens: top_n_tokens.unwrap_or(0),
})
.collect();
let batch = Batch {
id: 0,
requests,
size: batch_size,
max_tokens: batch_size * (sequence_length + decode_length),
};
// Run prefill
let start_time = Instant::now();
let (_, decode_batch) = client.prefill(batch.clone()).await?;
// Get latency
let latency = start_time.elapsed();
// Compute throughput from latency and batch size
let throughput = batch_size as f64 / latency.as_secs_f64();
// Decode batch cannot be empty
let decode_batch = decode_batch.expect("decode_batch is None. This is a bug.");
let step = Prefill {
latency,
throughput,
};
Ok((step, decode_batch))
}
/// Run a full decode
async fn decode(batch: CachedBatch, client: &mut ShardedClient) -> Result<Decode, ClientError> {
let mut decode_length = 0;
let batch_size = batch.size;
let start_time = Instant::now();
// Full decode over decode length
let mut next_batch = Some(batch);
while let Some(batch) = next_batch {
let result = client.decode(vec![batch]).await?;
next_batch = result.1;
decode_length += 1;
}
// Get latency
let latency = start_time.elapsed();
let token_latency = latency / decode_length;
// Compute throughput from latency, batch size and decode length
let throughput = (batch_size * decode_length) as f64 / latency.as_secs_f64();
let step = Decode {
latency,
token_latency,
throughput,
};
Ok(step)
}
/// Create a dummy sequence of the correct length
fn create_sequence(sequence_length: u32, tokenizer: Tokenizer) -> String {
let lorem_ipsum_length = tokenizer.encode(LOREM_IPSUM, true).unwrap().len();
// Repeat lorem ipsum to cover sequence length
let string_sequence =
LOREM_IPSUM.repeat((0..sequence_length).step_by(lorem_ipsum_length).len());
// Encode sequence
let mut encoding = tokenizer.encode(string_sequence, true).unwrap();
// Truncate to sequence_length
encoding.truncate(sequence_length as usize, 0, TruncationDirection::Left);
// Decode
tokenizer.decode(encoding.get_ids(), false).unwrap()
}
| 0
|
hf_public_repos/text-generation-inference/benchmark
|
hf_public_repos/text-generation-inference/benchmark/src/lib.rs
|
mod app;
mod event;
mod generation;
mod table;
mod utils;
use crate::app::App;
use crate::event::Event;
use crossterm::ExecutableCommand;
use std::io;
use text_generation_client::{NextTokenChooserParameters, ShardedClient};
use tokenizers::Tokenizer;
use tokio::sync::{broadcast, mpsc};
use tui::backend::CrosstermBackend;
use tui::Terminal;
/// Run benchmarking app
#[allow(clippy::too_many_arguments)]
pub async fn run(
tokenizer_name: String,
tokenizer: Tokenizer,
batch_size: Vec<u32>,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: usize,
temperature: Option<f32>,
top_k: Option<u32>,
top_p: Option<f32>,
typical_p: Option<f32>,
repetition_penalty: Option<f32>,
watermark: bool,
do_sample: bool,
client: ShardedClient,
) -> Result<(), std::io::Error> {
let parameters = NextTokenChooserParameters {
temperature: temperature.unwrap_or(1.0),
top_k: top_k.unwrap_or(0),
top_p: top_p.unwrap_or(1.0),
typical_p: typical_p.unwrap_or(1.0),
do_sample,
seed: 0,
repetition_penalty: repetition_penalty.unwrap_or(1.0),
watermark,
};
// Initialize terminal properties
crossterm::terminal::enable_raw_mode()?;
io::stdout().execute(crossterm::terminal::EnterAlternateScreen)?;
io::stdout().execute(crossterm::cursor::Hide)?;
// Initialize terminal
let mut terminal = {
let backend = CrosstermBackend::new(io::stdout());
Terminal::new(backend)?
};
// Create message channel between generation_task and app
let (run_sender, run_receiver) = mpsc::channel(8);
// Crossterm event channel
let (event_sender, mut event_receiver) = mpsc::channel(8);
// Shutdown channel to terminate tasks
let (shutdown_sender, _) = broadcast::channel(1);
// Channel to check if tasks terminated
let (shutdown_guard_sender, mut shutdown_guard_receiver) = mpsc::channel(1);
// Create generation task
tokio::spawn(generation::generation_task(
tokenizer,
batch_size.clone(),
sequence_length,
decode_length,
top_n_tokens,
n_runs,
warmups,
parameters,
client,
run_sender,
shutdown_sender.subscribe(),
shutdown_guard_sender.clone(),
));
// Create event task
tokio::spawn(event::terminal_event_task(
250,
event_sender,
shutdown_sender.subscribe(),
shutdown_guard_sender.clone(),
));
// Drop our end of shutdown sender
drop(shutdown_guard_sender);
// Create App
let mut app = App::new(
run_receiver,
tokenizer_name.clone(),
sequence_length,
decode_length,
n_runs,
batch_size,
);
while app.running {
// Draw frame
terminal.draw(|frame| app.render(frame))?;
// Await a new event from event handling task
match event_receiver.recv().await {
None => break,
// Update app state
Some(event) => match event {
Event::Tick => app.tick(),
Event::Key(key_event) => app.handle_key_event(key_event),
_ => {}
},
}
}
// Ask tasks to shutdown
let _ = shutdown_sender.send(());
// Wait for tasks to shutdown
let _ = shutdown_guard_receiver.recv().await;
// Revert terminal to original view
io::stdout().execute(crossterm::terminal::LeaveAlternateScreen)?;
crossterm::terminal::disable_raw_mode()?;
io::stdout().execute(crossterm::cursor::Show)?;
let parameters_table = table::parameters_table(
tokenizer_name,
sequence_length,
decode_length,
top_n_tokens,
n_runs,
warmups,
temperature,
top_k,
top_p,
typical_p,
repetition_penalty,
watermark,
do_sample,
);
println!("\n{parameters_table}\n");
let latency_table = table::latency_table(&app.data);
println!("\n{latency_table}\n");
let throughput_table = table::throughput_table(&app.data);
println!("\n{throughput_table}\n");
Ok(())
}
| 0
|
hf_public_repos/text-generation-inference/benchmark
|
hf_public_repos/text-generation-inference/benchmark/src/main.rs
|
/// Text Generation Inference benchmarking tool
///
/// Inspired by the great Oha app: https://github.com/hatoo/oha
/// and: https://github.com/orhun/rust-tui-template
use clap::Parser;
use std::path::Path;
use text_generation_client::ShardedClient;
use tokenizers::{FromPretrainedParameters, Tokenizer};
use tracing_subscriber::layer::SubscriberExt;
use tracing_subscriber::util::SubscriberInitExt;
use tracing_subscriber::EnvFilter;
/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
/// The name of the tokenizer (as in model_id on the huggingface hub, or local path).
#[clap(short, long, env)]
tokenizer_name: String,
/// The revision to use for the tokenizer if on the hub.
#[clap(default_value = "main", long, env)]
revision: String,
/// The various batch sizes to benchmark for, the idea is to get enough
/// batching to start seeing increased latency, this usually means you're
/// moving from memory bound (usual as BS=1) to compute bound, and this is
/// a sweet spot for the maximum batch size for the model under test
#[clap(short, long)]
batch_size: Option<Vec<u32>>,
/// This is the initial prompt sent to the text-generation-server length
/// in token. Longer prompt will slow down the benchmark. Usually the
/// latency grows somewhat linearly with this for the prefill step.
///
/// Most importantly, the prefill step is usually not the one dominating
/// your runtime, so it's ok to keep it short.
#[clap(default_value = "10", short, long, env)]
sequence_length: u32,
/// This is how many tokens will be generated by the server and averaged out
/// to give the `decode` latency. This is the *critical* number you want to optimize for
/// LLM spend most of their time doing decoding.
///
/// Decode latency is usually quite stable.
#[clap(default_value = "8", short, long, env)]
decode_length: u32,
///How many runs should we average from
#[clap(default_value = "10", short, long, env)]
runs: usize,
/// Number of warmup cycles
#[clap(default_value = "1", short, long, env)]
warmups: usize,
/// The location of the grpc socket. This benchmark tool bypasses the router
/// completely and directly talks to the gRPC processes
#[clap(default_value = "/tmp/text-generation-server-0", short, long, env)]
master_shard_uds_path: String,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
temperature: Option<f32>,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
top_k: Option<u32>,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
top_p: Option<f32>,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
typical_p: Option<f32>,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
repetition_penalty: Option<f32>,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
watermark: bool,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
do_sample: bool,
/// Generation parameter in case you want to specifically test/debug particular
/// decoding strategies, for full doc refer to the `text-generation-server`
#[clap(long, env)]
top_n_tokens: Option<u32>,
}
fn main() -> Result<(), Box<dyn std::error::Error>> {
init_logging();
// Get args
let args = Args::parse();
// Pattern match configuration
let Args {
tokenizer_name,
revision,
batch_size,
sequence_length,
decode_length,
runs,
warmups,
temperature,
top_k,
top_p,
typical_p,
repetition_penalty,
watermark,
do_sample,
master_shard_uds_path,
top_n_tokens,
} = args;
let batch_size = batch_size.unwrap_or(vec![1, 2, 4, 8, 16, 32]);
// Tokenizer instance
// This will only be used to validate payloads
tracing::info!("Loading tokenizer");
let local_path = Path::new(&tokenizer_name);
let tokenizer =
if local_path.exists() && local_path.is_dir() && local_path.join("tokenizer.json").exists()
{
// Load local tokenizer
tracing::info!("Found local tokenizer");
Tokenizer::from_file(local_path.join("tokenizer.json")).unwrap()
} else {
tracing::info!("Downloading tokenizer");
// Parse Huggingface hub token
let auth_token = std::env::var("HUGGING_FACE_HUB_TOKEN").ok();
// Download and instantiate tokenizer
// We need to download it outside of the Tokio runtime
let params = FromPretrainedParameters {
revision,
auth_token,
..Default::default()
};
Tokenizer::from_pretrained(tokenizer_name.clone(), Some(params)).unwrap()
};
tracing::info!("Tokenizer loaded");
// Launch Tokio runtime
tokio::runtime::Builder::new_multi_thread()
.enable_all()
.build()
.unwrap()
.block_on(async {
// Instantiate sharded client from the master unix socket
tracing::info!("Connect to model server");
let mut sharded_client = ShardedClient::connect_uds(master_shard_uds_path)
.await
.expect("Could not connect to server");
// Clear the cache; useful if the webserver rebooted
sharded_client
.clear_cache(None)
.await
.expect("Unable to clear cache");
tracing::info!("Connected");
// Run app
text_generation_benchmark::run(
tokenizer_name,
tokenizer,
batch_size,
sequence_length,
decode_length,
top_n_tokens,
runs,
warmups,
temperature,
top_k,
top_p,
typical_p,
repetition_penalty,
watermark,
do_sample,
sharded_client,
)
.await
.unwrap();
});
Ok(())
}
/// Init logging using LOG_LEVEL
fn init_logging() {
// STDOUT/STDERR layer
let fmt_layer = tracing_subscriber::fmt::layer()
.with_file(true)
.with_line_number(true);
// Filter events with LOG_LEVEL
let env_filter =
EnvFilter::try_from_env("LOG_LEVEL").unwrap_or_else(|_| EnvFilter::new("info"));
tracing_subscriber::registry()
.with(env_filter)
.with(fmt_layer)
.init();
}
| 0
|
hf_public_repos/text-generation-inference/benchmark
|
hf_public_repos/text-generation-inference/benchmark/src/app.rs
|
/// Inspired by https://github.com/hatoo/oha/blob/bb989ea3cd77727e7743e7daa60a19894bb5e901/src/monitor.rs
use crate::generation::{Decode, Message, Prefill};
use crossterm::event::{KeyCode, KeyEvent, KeyModifiers};
use text_generation_client::ClientError;
use tokio::sync::mpsc;
use tui::backend::Backend;
use tui::layout::{Alignment, Constraint, Direction, Layout};
use tui::style::{Color, Modifier, Style};
use tui::text::{Line, Span};
use tui::widgets::{
Axis, BarChart, Block, Borders, Chart, Dataset, Gauge, GraphType, Paragraph, Tabs,
};
use tui::{symbols, Frame};
/// TUI powered App
pub(crate) struct App {
pub(crate) running: bool,
pub(crate) data: Data,
completed_runs: Vec<usize>,
completed_batch: usize,
current_batch: usize,
current_tab: usize,
touched_tab: bool,
zoom: bool,
is_error: bool,
tokenizer_name: String,
sequence_length: u32,
decode_length: u32,
n_run: usize,
receiver: mpsc::Receiver<Result<Message, ClientError>>,
}
impl App {
pub(crate) fn new(
receiver: mpsc::Receiver<Result<Message, ClientError>>,
tokenizer_name: String,
sequence_length: u32,
decode_length: u32,
n_run: usize,
batch_size: Vec<u32>,
) -> Self {
let current_tab = 0;
let completed_runs: Vec<usize> = (0..batch_size.len()).map(|_| 0).collect();
let completed_batch = 0;
let current_batch = 0;
let is_error = false;
let data = Data::new(n_run, batch_size);
Self {
running: true,
data,
completed_runs,
completed_batch,
current_batch,
current_tab,
touched_tab: false,
zoom: false,
is_error,
tokenizer_name,
sequence_length,
decode_length,
n_run,
receiver,
}
}
/// Handle crossterm key events
pub(crate) fn handle_key_event(&mut self, key_event: KeyEvent) {
match key_event {
// Increase and wrap tab
KeyEvent {
code: KeyCode::Right,
..
}
| KeyEvent {
code: KeyCode::Tab, ..
} => {
self.touched_tab = true;
self.current_tab = (self.current_tab + 1) % self.data.batch_size.len();
}
// Decrease and wrap tab
KeyEvent {
code: KeyCode::Left,
..
} => {
self.touched_tab = true;
if self.current_tab > 0 {
self.current_tab -= 1;
} else {
self.current_tab = self.data.batch_size.len() - 1;
}
}
// Zoom on throughput/latency fig
KeyEvent {
code: KeyCode::Char('+'),
..
} => {
self.zoom = true;
}
// Unzoom on throughput/latency fig
KeyEvent {
code: KeyCode::Char('-'),
..
} => {
self.zoom = false;
}
// Quit
KeyEvent {
code: KeyCode::Char('q'),
..
}
| KeyEvent {
code: KeyCode::Char('c'),
modifiers: KeyModifiers::CONTROL,
..
} => {
self.running = false;
}
_ => (),
}
}
/// Get all pending messages from generation task
pub(crate) fn tick(&mut self) {
while let Ok(message) = self.receiver.try_recv() {
match message {
Ok(message) => match message {
Message::Prefill(step) => self.data.push_prefill(step, self.current_batch),
Message::Decode(step) => self.data.push_decode(step, self.current_batch),
Message::EndRun => {
self.completed_runs[self.current_batch] += 1;
}
Message::EndBatch => {
self.data.end_batch(self.current_batch);
self.completed_batch += 1;
if self.current_batch < self.data.batch_size.len() - 1 {
// Only go to next tab if the user never touched the tab keys
if !self.touched_tab {
self.current_tab += 1;
}
self.current_batch += 1;
}
}
Message::Warmup => {}
},
Err(_) => self.is_error = true,
}
}
}
/// Render frame
pub fn render<B: Backend>(&mut self, f: &mut Frame<'_, B>) {
let batch_progress =
(self.completed_batch as f64 / self.data.batch_size.len() as f64).clamp(0.0, 1.0);
let run_progress =
(self.completed_runs[self.current_batch] as f64 / self.n_run as f64).clamp(0.0, 1.0);
// Vertical layout
let row5 = Layout::default()
.direction(Direction::Vertical)
.constraints(
[
Constraint::Length(1),
Constraint::Length(3),
Constraint::Length(3),
Constraint::Length(13),
Constraint::Min(10),
]
.as_ref(),
)
.split(f.size());
// Top row horizontal layout
let top = Layout::default()
.direction(Direction::Horizontal)
.constraints([Constraint::Percentage(50), Constraint::Percentage(50)].as_ref())
.split(row5[2]);
// Mid row horizontal layout
let mid = Layout::default()
.direction(Direction::Horizontal)
.constraints(
[
Constraint::Percentage(25),
Constraint::Percentage(25),
Constraint::Percentage(25),
Constraint::Percentage(25),
]
.as_ref(),
)
.split(row5[3]);
// Left mid row vertical layout
let prefill_text = Layout::default()
.direction(Direction::Vertical)
.constraints([Constraint::Length(8), Constraint::Length(5)].as_ref())
.split(mid[0]);
// Right mid row vertical layout
let decode_text = Layout::default()
.direction(Direction::Vertical)
.constraints([Constraint::Length(8), Constraint::Length(5)].as_ref())
.split(mid[2]);
let decode_text_latency = Layout::default()
.direction(Direction::Horizontal)
.constraints([Constraint::Percentage(50), Constraint::Percentage(50)].as_ref())
.split(decode_text[0]);
// Bottom row horizontal layout
let bottom = Layout::default()
.direction(Direction::Horizontal)
.constraints([Constraint::Percentage(50), Constraint::Percentage(50)].as_ref())
.split(row5[4]);
// Title
let title = Block::default()
.borders(Borders::NONE)
.title(format!(
"Model: {} | Sequence Length: {} | Decode Length: {}",
self.tokenizer_name, self.sequence_length, self.decode_length
))
.style(
Style::default()
.add_modifier(Modifier::BOLD)
.fg(Color::White),
);
f.render_widget(title, row5[0]);
// Helper
let helper = Block::default()
.borders(Borders::NONE)
.title("<- | tab | ->: change batch tab | q / CTRL + c: quit | +/-: zoom")
.title_alignment(Alignment::Right)
.style(Style::default().fg(Color::White));
f.render_widget(helper, row5[0]);
// Batch tabs
let titles = self
.data
.batch_size
.iter()
.map(|b| {
Line::from(vec![Span::styled(
format!("Batch: {b}"),
Style::default().fg(Color::White),
)])
})
.collect();
let tabs = Tabs::new(titles)
.block(Block::default().borders(Borders::ALL).title("Tabs"))
.select(self.current_tab)
.style(Style::default().fg(Color::LightCyan))
.highlight_style(
Style::default()
.add_modifier(Modifier::BOLD)
.bg(Color::Black),
);
f.render_widget(tabs, row5[1]);
// Total progress bar
let color = if self.is_error {
Color::Red
} else {
Color::LightGreen
};
let batch_gauge = progress_gauge(
"Total Progress",
format!("{} / {}", self.completed_batch, self.data.batch_size.len()),
batch_progress,
color,
);
f.render_widget(batch_gauge, top[0]);
// Batch progress Bar
let color = if self.is_error {
Color::Red
} else {
Color::LightBlue
};
let run_gauge = progress_gauge(
"Batch Progress",
format!(
"{} / {}",
self.completed_runs[self.current_batch], self.n_run
),
run_progress,
color,
);
f.render_widget(run_gauge, top[1]);
// Prefill text infos
let prefill_latency_block = latency_paragraph(
&mut self.data.prefill_latencies[self.current_tab],
"Prefill",
);
let prefill_throughput_block =
throughput_paragraph(&self.data.prefill_throughputs[self.current_tab], "Prefill");
f.render_widget(prefill_latency_block, prefill_text[0]);
f.render_widget(prefill_throughput_block, prefill_text[1]);
// Prefill latency histogram
let histo_width = 7;
let bins = if mid[1].width < 2 {
0
} else {
(mid[1].width as usize - 2) / (histo_width + 1)
}
.max(2);
let histo_data =
latency_histogram_data(&self.data.prefill_latencies[self.current_tab], bins);
let histo_data_str: Vec<(&str, u64)> =
histo_data.iter().map(|(l, v)| (l.as_str(), *v)).collect();
let prefill_histogram =
latency_histogram(&histo_data_str, "Prefill").bar_width(histo_width as u16);
f.render_widget(prefill_histogram, mid[1]);
// Decode text info
let decode_latency_block = latency_paragraph(
&mut self.data.decode_latencies[self.current_tab],
"Decode Total",
);
let decode_token_latency_block = latency_paragraph(
&mut self.data.decode_token_latencies[self.current_tab],
"Decode Token",
);
let decode_throughput_block =
throughput_paragraph(&self.data.decode_throughputs[self.current_tab], "Decode");
f.render_widget(decode_latency_block, decode_text_latency[0]);
f.render_widget(decode_token_latency_block, decode_text_latency[1]);
f.render_widget(decode_throughput_block, decode_text[1]);
// Decode latency histogram
let histo_data =
latency_histogram_data(&self.data.decode_latencies[self.current_tab], bins);
let histo_data_str: Vec<(&str, u64)> =
histo_data.iter().map(|(l, v)| (l.as_str(), *v)).collect();
let decode_histogram =
latency_histogram(&histo_data_str, "Decode").bar_width(histo_width as u16);
f.render_widget(decode_histogram, mid[3]);
// Prefill latency/throughput chart
let prefill_latency_throughput_chart = latency_throughput_chart(
&self.data.prefill_batch_latency_throughput,
&self.data.batch_size,
self.zoom,
"Prefill",
);
f.render_widget(prefill_latency_throughput_chart, bottom[0]);
// Decode latency/throughput chart
let decode_latency_throughput_chart = latency_throughput_chart(
&self.data.decode_batch_latency_throughput,
&self.data.batch_size,
self.zoom,
"Decode",
);
f.render_widget(decode_latency_throughput_chart, bottom[1]);
}
}
/// App internal data struct
pub(crate) struct Data {
pub(crate) batch_size: Vec<u32>,
pub(crate) prefill_latencies: Vec<Vec<f64>>,
pub(crate) prefill_throughputs: Vec<Vec<f64>>,
pub(crate) decode_latencies: Vec<Vec<f64>>,
pub(crate) decode_token_latencies: Vec<Vec<f64>>,
pub(crate) decode_throughputs: Vec<Vec<f64>>,
pub(crate) prefill_batch_latency_throughput: Vec<(f64, f64)>,
pub(crate) decode_batch_latency_throughput: Vec<(f64, f64)>,
}
impl Data {
fn new(n_run: usize, batch_size: Vec<u32>) -> Self {
let prefill_latencies: Vec<Vec<f64>> = (0..batch_size.len())
.map(|_| Vec::with_capacity(n_run))
.collect();
let prefill_throughputs: Vec<Vec<f64>> = prefill_latencies.clone();
let decode_latencies: Vec<Vec<f64>> = prefill_latencies.clone();
let decode_token_latencies: Vec<Vec<f64>> = decode_latencies.clone();
let decode_throughputs: Vec<Vec<f64>> = prefill_throughputs.clone();
let prefill_batch_latency_throughput: Vec<(f64, f64)> =
Vec::with_capacity(batch_size.len());
let decode_batch_latency_throughput: Vec<(f64, f64)> =
prefill_batch_latency_throughput.clone();
Self {
batch_size,
prefill_latencies,
prefill_throughputs,
decode_latencies,
decode_token_latencies,
decode_throughputs,
prefill_batch_latency_throughput,
decode_batch_latency_throughput,
}
}
fn push_prefill(&mut self, prefill: Prefill, batch_idx: usize) {
let latency = prefill.latency.as_micros() as f64 / 1000.0;
self.prefill_latencies[batch_idx].push(latency);
self.prefill_throughputs[batch_idx].push(prefill.throughput);
}
fn push_decode(&mut self, decode: Decode, batch_idx: usize) {
let latency = decode.latency.as_micros() as f64 / 1000.0;
let token_latency = decode.token_latency.as_micros() as f64 / 1000.0;
self.decode_latencies[batch_idx].push(latency);
self.decode_token_latencies[batch_idx].push(token_latency);
self.decode_throughputs[batch_idx].push(decode.throughput);
}
fn end_batch(&mut self, batch_idx: usize) {
self.prefill_batch_latency_throughput.push((
self.prefill_latencies[batch_idx].iter().sum::<f64>()
/ self.prefill_latencies[batch_idx].len() as f64,
self.prefill_throughputs[batch_idx].iter().sum::<f64>()
/ self.prefill_throughputs[batch_idx].len() as f64,
));
self.decode_batch_latency_throughput.push((
self.decode_latencies[batch_idx].iter().sum::<f64>()
/ self.decode_latencies[batch_idx].len() as f64,
self.decode_throughputs[batch_idx].iter().sum::<f64>()
/ self.decode_throughputs[batch_idx].len() as f64,
));
}
}
/// Progress bar
fn progress_gauge(title: &str, label: String, progress: f64, color: Color) -> Gauge {
Gauge::default()
.block(Block::default().title(title).borders(Borders::ALL))
.gauge_style(Style::default().fg(color))
.label(Span::raw(label))
.ratio(progress)
}
/// Throughput paragraph
fn throughput_paragraph<'a>(throughput: &Vec<f64>, name: &'static str) -> Paragraph<'a> {
// Throughput average/high/low texts
let throughput_texts = statis_spans(throughput, "tokens/secs");
// Throughput block
Paragraph::new(throughput_texts).block(
Block::default()
.title(Span::raw(format!("{name} Throughput")))
.borders(Borders::ALL),
)
}
/// Latency paragraph
fn latency_paragraph<'a>(latency: &mut Vec<f64>, name: &'static str) -> Paragraph<'a> {
// Latency average/high/low texts
let mut latency_texts = statis_spans(latency, "ms");
// Sort latency for percentiles
float_ord::sort(latency);
let latency_percentiles = crate::utils::percentiles(latency, &[50, 90, 99]);
// Latency p50/p90/p99 texts
let colors = vec![Color::LightGreen, Color::LightYellow, Color::LightRed];
for (i, (name, value)) in latency_percentiles.iter().enumerate() {
let span = Line::from(vec![Span::styled(
format!("{name}: {value:.2} ms"),
Style::default().fg(colors[i]),
)]);
latency_texts.push(span);
}
Paragraph::new(latency_texts).block(
Block::default()
.title(Span::raw(format!("{name} Latency")))
.borders(Borders::ALL),
)
}
/// Average/High/Low spans
fn statis_spans<'a>(data: &Vec<f64>, unit: &'static str) -> Vec<Line<'a>> {
vec![
Line::from(vec![Span::styled(
format!(
"Average: {:.2} {unit}",
data.iter().sum::<f64>() / data.len() as f64
),
Style::default().fg(Color::LightBlue),
)]),
Line::from(vec![Span::styled(
format!(
"Lowest: {:.2} {unit}",
data.iter()
.min_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN)
),
Style::default().fg(Color::Reset),
)]),
Line::from(vec![Span::styled(
format!(
"Highest: {:.2} {unit}",
data.iter()
.max_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN)
),
Style::default().fg(Color::Reset),
)]),
]
}
/// Latency histogram data
fn latency_histogram_data(latency: &[f64], bins: usize) -> Vec<(String, u64)> {
let histo_data: Vec<(String, u64)> = {
let histo = crate::utils::histogram(latency, bins);
histo
.into_iter()
.map(|(label, v)| (format!("{label:.2}"), v as u64))
.collect()
};
histo_data
}
/// Latency Histogram
fn latency_histogram<'a>(
histo_data_str: &'a Vec<(&'a str, u64)>,
name: &'static str,
) -> BarChart<'a> {
BarChart::default()
.block(
Block::default()
.title(format!("{name} latency histogram"))
.style(Style::default().fg(Color::LightYellow).bg(Color::Reset))
.borders(Borders::ALL),
)
.data(histo_data_str.as_slice())
}
/// Latency/Throughput chart
fn latency_throughput_chart<'a>(
latency_throughput: &'a Vec<(f64, f64)>,
batch_sizes: &'a [u32],
zoom: bool,
name: &'static str,
) -> Chart<'a> {
let latency_iter = latency_throughput.iter().map(|(l, _)| l);
let throughput_iter = latency_throughput.iter().map(|(_, t)| t);
// Get extreme values
let min_latency: f64 = *latency_iter
.clone()
.min_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN);
let max_latency: f64 = *latency_iter
.max_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN);
let min_throughput: f64 = *throughput_iter
.clone()
.min_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN);
let max_throughput: f64 = *throughput_iter
.max_by(|a, b| a.total_cmp(b))
.unwrap_or(&std::f64::NAN);
// Char min max values
let min_x = if zoom {
((min_latency - 0.05 * min_latency) / 100.0).floor() * 100.0
} else {
0.0
};
let max_x = ((max_latency + 0.05 * max_latency) / 100.0).ceil() * 100.0;
let step_x = (max_x - min_x) / 4.0;
// Chart min max values
let min_y = if zoom {
((min_throughput - 0.05 * min_throughput) / 100.0).floor() * 100.0
} else {
0.0
};
let max_y = ((max_throughput + 0.05 * max_throughput) / 100.0).ceil() * 100.0;
let step_y = (max_y - min_y) / 4.0;
// Labels
let mut x_labels = vec![Span::styled(
format!("{min_x:.2}"),
Style::default()
.add_modifier(Modifier::BOLD)
.fg(Color::Gray)
.bg(Color::Reset),
)];
for i in 0..3 {
x_labels.push(Span::styled(
format!("{:.2}", min_x + ((i + 1) as f64 * step_x)),
Style::default().fg(Color::Gray).bg(Color::Reset),
));
}
x_labels.push(Span::styled(
format!("{max_x:.2}"),
Style::default()
.add_modifier(Modifier::BOLD)
.fg(Color::Gray)
.bg(Color::Reset),
));
// Labels
let mut y_labels = vec![Span::styled(
format!("{min_y:.2}"),
Style::default()
.add_modifier(Modifier::BOLD)
.fg(Color::Gray)
.bg(Color::Reset),
)];
for i in 0..3 {
y_labels.push(Span::styled(
format!("{:.2}", min_y + ((i + 1) as f64 * step_y)),
Style::default().fg(Color::Gray).bg(Color::Reset),
));
}
y_labels.push(Span::styled(
format!("{max_y:.2}"),
Style::default()
.add_modifier(Modifier::BOLD)
.fg(Color::Gray)
.bg(Color::Reset),
));
// Chart dataset
let colors = color_vec();
let datasets: Vec<Dataset> = (0..latency_throughput.len())
.map(|i| {
let color_idx = i % colors.len();
Dataset::default()
.name(batch_sizes[i].to_string())
.marker(symbols::Marker::Block)
.style(Style::default().fg(colors[color_idx]))
.graph_type(GraphType::Scatter)
.data(&latency_throughput[i..(i + 1)])
})
.collect();
// Chart
Chart::new(datasets)
.style(Style::default().fg(Color::Cyan).bg(Color::Reset))
.block(
Block::default()
.title(Span::styled(
format!("{name} throughput over latency"),
Style::default().fg(Color::Gray).bg(Color::Reset),
))
.borders(Borders::ALL),
)
.x_axis(
Axis::default()
.title("ms")
.style(Style::default().fg(Color::Gray).bg(Color::Reset))
.labels(x_labels)
.bounds([min_x, max_x]),
)
.y_axis(
Axis::default()
.title("tokens/secs")
.style(Style::default().fg(Color::Gray).bg(Color::Reset))
.labels(y_labels)
.bounds([min_y, max_y]),
)
}
// Colors for latency/throughput chart
fn color_vec() -> Vec<Color> {
vec![
Color::Red,
Color::Green,
Color::Yellow,
Color::Blue,
Color::Magenta,
Color::Cyan,
Color::Gray,
Color::DarkGray,
Color::LightRed,
Color::LightGreen,
Color::LightYellow,
Color::LightBlue,
Color::LightMagenta,
Color::LightCyan,
]
}
| 0
|
hf_public_repos/text-generation-inference/benchmark
|
hf_public_repos/text-generation-inference/benchmark/src/utils.rs
|
/// MIT License
//
// Copyright (c) 2020 hatoo
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
use std::collections::BTreeMap;
pub(crate) fn histogram(values: &[f64], bins: usize) -> Vec<(f64, usize)> {
assert!(bins >= 2);
let mut bucket: Vec<usize> = vec![0; bins];
let min = values.iter().collect::<average::Min>().min();
let max = values.iter().collect::<average::Max>().max();
let step = (max - min) / (bins - 1) as f64;
for &v in values {
let i = std::cmp::min(((v - min) / step).ceil() as usize, bins - 1);
bucket[i] += 1;
}
bucket
.into_iter()
.enumerate()
.map(|(i, v)| (min + step * i as f64, v))
.collect()
}
pub(crate) fn percentiles(values: &[f64], pecents: &[i32]) -> BTreeMap<String, f64> {
pecents
.iter()
.map(|&p| {
let i = (f64::from(p) / 100.0 * values.len() as f64) as usize;
(format!("p{p}"), *values.get(i).unwrap_or(&std::f64::NAN))
})
.collect()
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/docs/index.html
|
<html>
<head>
<!-- Load the latest Swagger UI code and style from npm using unpkg.com -->
<script src="https://unpkg.com/swagger-ui-dist@3/swagger-ui-bundle.js"></script>
<link rel="stylesheet" type="text/css" href="https://unpkg.com/swagger-ui-dist@3/swagger-ui.css"/>
<title>Text Generation Inference API</title>
</head>
<body>
<div id="swagger-ui"></div> <!-- Div to hold the UI component -->
<script>
window.onload = function () {
// Begin Swagger UI call region
const ui = SwaggerUIBundle({
url: "openapi.json", //Location of Open API spec in the repo
dom_id: '#swagger-ui',
deepLinking: true,
supportedSubmitMethods: [],
presets: [
SwaggerUIBundle.presets.apis,
SwaggerUIBundle.SwaggerUIStandalonePreset
],
plugins: [
SwaggerUIBundle.plugins.DownloadUrl
],
})
window.ui = ui
}
</script>
</body>
</html>
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/docs/openapi.json
|
{
"openapi": "3.0.3",
"info": {
"title": "Text Generation Inference",
"description": "Text Generation Webserver",
"contact": {
"name": "Olivier Dehaene"
},
"license": {
"name": "Apache 2.0",
"url": "https://www.apache.org/licenses/LICENSE-2.0"
},
"version": "1.1.1"
},
"paths": {
"/": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate tokens if `stream == false` or a stream of token if `stream == true`",
"description": "Generate tokens if `stream == false` or a stream of token if `stream == true`",
"operationId": "compat_generate",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CompatGenerateRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Text",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateResponse"
}
},
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/StreamResponse"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
},
"/generate": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate tokens",
"description": "Generate tokens",
"operationId": "generate",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Text",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateResponse"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
},
"/generate_stream": {
"post": {
"tags": [
"Text Generation Inference"
],
"summary": "Generate a stream of token using Server-Sent Events",
"description": "Generate a stream of token using Server-Sent Events",
"operationId": "generate_stream",
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateRequest"
}
}
},
"required": true
},
"responses": {
"200": {
"description": "Generated Text",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/StreamResponse"
}
}
}
},
"422": {
"description": "Input validation error",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Input validation error"
}
}
}
},
"424": {
"description": "Generation Error",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Request failed during generation"
}
}
}
},
"429": {
"description": "Model is overloaded",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Model is overloaded"
}
}
}
},
"500": {
"description": "Incomplete generation",
"content": {
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "Incomplete generation"
}
}
}
}
}
}
},
"/health": {
"get": {
"tags": [
"Text Generation Inference"
],
"summary": "Health check method",
"description": "Health check method",
"operationId": "health",
"responses": {
"200": {
"description": "Everything is working fine"
},
"503": {
"description": "Text generation inference is down",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
},
"example": {
"error": "unhealthy",
"error_type": "healthcheck"
}
}
}
}
}
}
},
"/info": {
"get": {
"tags": [
"Text Generation Inference"
],
"summary": "Text Generation Inference endpoint info",
"description": "Text Generation Inference endpoint info",
"operationId": "get_model_info",
"responses": {
"200": {
"description": "Served model info",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Info"
}
}
}
}
}
}
},
"/metrics": {
"get": {
"tags": [
"Text Generation Inference"
],
"summary": "Prometheus metrics scrape endpoint",
"description": "Prometheus metrics scrape endpoint",
"operationId": "metrics",
"responses": {
"200": {
"description": "Prometheus Metrics",
"content": {
"text/plain": {
"schema": {
"type": "string"
}
}
}
}
}
}
}
},
"components": {
"schemas": {
"BestOfSequence": {
"type": "object",
"required": [
"generated_text",
"finish_reason",
"generated_tokens",
"prefill",
"tokens"
],
"properties": {
"finish_reason": {
"$ref": "#/components/schemas/FinishReason"
},
"generated_text": {
"type": "string",
"example": "test"
},
"generated_tokens": {
"type": "integer",
"format": "int32",
"example": 1,
"minimum": 0
},
"prefill": {
"type": "array",
"items": {
"$ref": "#/components/schemas/PrefillToken"
}
},
"seed": {
"type": "integer",
"format": "int64",
"example": 42,
"nullable": true,
"minimum": 0
},
"tokens": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Token"
}
},
"top_tokens": {
"type": "array",
"items": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Token"
}
}
}
}
},
"CompatGenerateRequest": {
"type": "object",
"required": [
"inputs"
],
"properties": {
"inputs": {
"type": "string",
"example": "My name is Olivier and I"
},
"parameters": {
"$ref": "#/components/schemas/GenerateParameters"
},
"stream": {
"type": "boolean",
"default": "false"
}
}
},
"Details": {
"type": "object",
"required": [
"finish_reason",
"generated_tokens",
"prefill",
"tokens"
],
"properties": {
"best_of_sequences": {
"type": "array",
"items": {
"$ref": "#/components/schemas/BestOfSequence"
},
"nullable": true
},
"finish_reason": {
"$ref": "#/components/schemas/FinishReason"
},
"generated_tokens": {
"type": "integer",
"format": "int32",
"example": 1,
"minimum": 0
},
"prefill": {
"type": "array",
"items": {
"$ref": "#/components/schemas/PrefillToken"
}
},
"seed": {
"type": "integer",
"format": "int64",
"example": 42,
"nullable": true,
"minimum": 0
},
"tokens": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Token"
}
},
"top_tokens": {
"type": "array",
"items": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Token"
}
}
}
}
},
"ErrorResponse": {
"type": "object",
"required": [
"error",
"error_type"
],
"properties": {
"error": {
"type": "string"
},
"error_type": {
"type": "string"
}
}
},
"FinishReason": {
"type": "string",
"enum": [
"length",
"eos_token",
"stop_sequence"
]
},
"GenerateParameters": {
"type": "object",
"properties": {
"best_of": {
"type": "integer",
"default": "null",
"example": 1,
"nullable": true,
"minimum": 0,
"exclusiveMinimum": 0
},
"decoder_input_details": {
"type": "boolean",
"default": "true"
},
"details": {
"type": "boolean",
"default": "true"
},
"do_sample": {
"type": "boolean",
"default": "false",
"example": true
},
"max_new_tokens": {
"type": "integer",
"format": "int32",
"default": "null",
"example": "20",
"nullable": true,
"minimum": 0
},
"repetition_penalty": {
"type": "number",
"format": "float",
"default": "null",
"example": 1.03,
"nullable": true,
"exclusiveMinimum": 0
},
"return_full_text": {
"type": "boolean",
"default": "null",
"example": false,
"nullable": true
},
"seed": {
"type": "integer",
"format": "int64",
"default": "null",
"example": "null",
"nullable": true,
"minimum": 0,
"exclusiveMinimum": 0
},
"stop": {
"type": "array",
"items": {
"type": "string"
},
"example": [
"photographer"
],
"maxItems": 4
},
"temperature": {
"type": "number",
"format": "float",
"default": "null",
"example": 0.5,
"nullable": true,
"exclusiveMinimum": 0
},
"top_k": {
"type": "integer",
"format": "int32",
"default": "null",
"example": 10,
"nullable": true,
"exclusiveMinimum": 0
},
"top_n_tokens": {
"type": "integer",
"format": "int32",
"default": "null",
"example": 5,
"nullable": true,
"minimum": 0,
"exclusiveMinimum": 0
},
"top_p": {
"type": "number",
"format": "float",
"default": "null",
"example": 0.95,
"nullable": true,
"maximum": 1,
"exclusiveMinimum": 0
},
"truncate": {
"type": "integer",
"default": "null",
"example": "null",
"nullable": true,
"minimum": 0
},
"typical_p": {
"type": "number",
"format": "float",
"default": "null",
"example": 0.95,
"nullable": true,
"maximum": 1,
"exclusiveMinimum": 0
},
"watermark": {
"type": "boolean",
"default": "false",
"example": true
}
}
},
"GenerateRequest": {
"type": "object",
"required": [
"inputs"
],
"properties": {
"inputs": {
"type": "string",
"example": "My name is Olivier and I"
},
"parameters": {
"$ref": "#/components/schemas/GenerateParameters"
}
}
},
"GenerateResponse": {
"type": "object",
"required": [
"generated_text"
],
"properties": {
"details": {
"allOf": [
{
"$ref": "#/components/schemas/Details"
}
],
"nullable": true
},
"generated_text": {
"type": "string",
"example": "test"
}
}
},
"Info": {
"type": "object",
"required": [
"model_id",
"model_dtype",
"model_device_type",
"max_concurrent_requests",
"max_best_of",
"max_stop_sequences",
"max_input_length",
"max_total_tokens",
"waiting_served_ratio",
"max_batch_total_tokens",
"max_waiting_tokens",
"validation_workers",
"version"
],
"properties": {
"docker_label": {
"type": "string",
"example": "null",
"nullable": true
},
"max_batch_total_tokens": {
"type": "integer",
"format": "int32",
"example": "32000",
"minimum": 0
},
"max_best_of": {
"type": "integer",
"example": "2",
"minimum": 0
},
"max_concurrent_requests": {
"type": "integer",
"description": "Router Parameters",
"example": "128",
"minimum": 0
},
"max_input_length": {
"type": "integer",
"example": "1024",
"minimum": 0
},
"max_stop_sequences": {
"type": "integer",
"example": "4",
"minimum": 0
},
"max_total_tokens": {
"type": "integer",
"example": "2048",
"minimum": 0
},
"max_waiting_tokens": {
"type": "integer",
"example": "20",
"minimum": 0
},
"model_device_type": {
"type": "string",
"example": "cuda"
},
"model_dtype": {
"type": "string",
"example": "torch.float16"
},
"model_id": {
"type": "string",
"description": "Model info",
"example": "bigscience/blomm-560m"
},
"model_pipeline_tag": {
"type": "string",
"example": "text-generation",
"nullable": true
},
"model_sha": {
"type": "string",
"example": "e985a63cdc139290c5f700ff1929f0b5942cced2",
"nullable": true
},
"sha": {
"type": "string",
"example": "null",
"nullable": true
},
"validation_workers": {
"type": "integer",
"example": "2",
"minimum": 0
},
"version": {
"type": "string",
"description": "Router Info",
"example": "0.5.0"
},
"waiting_served_ratio": {
"type": "number",
"format": "float",
"example": "1.2"
}
}
},
"PrefillToken": {
"type": "object",
"required": [
"id",
"text",
"logprob"
],
"properties": {
"id": {
"type": "integer",
"format": "int32",
"example": 0,
"minimum": 0
},
"logprob": {
"type": "number",
"format": "float",
"example": -0.34,
"nullable": true
},
"text": {
"type": "string",
"example": "test"
}
}
},
"StreamDetails": {
"type": "object",
"required": [
"finish_reason",
"generated_tokens"
],
"properties": {
"finish_reason": {
"$ref": "#/components/schemas/FinishReason"
},
"generated_tokens": {
"type": "integer",
"format": "int32",
"example": 1,
"minimum": 0
},
"seed": {
"type": "integer",
"format": "int64",
"example": 42,
"nullable": true,
"minimum": 0
}
}
},
"StreamResponse": {
"type": "object",
"required": [
"token"
],
"properties": {
"details": {
"allOf": [
{
"$ref": "#/components/schemas/StreamDetails"
}
],
"default": "null",
"nullable": true
},
"generated_text": {
"type": "string",
"default": "null",
"example": "test",
"nullable": true
},
"token": {
"$ref": "#/components/schemas/Token"
},
"top_tokens": {
"type": "array",
"items": {
"$ref": "#/components/schemas/Token"
}
}
}
},
"Token": {
"type": "object",
"required": [
"id",
"text",
"logprob",
"special"
],
"properties": {
"id": {
"type": "integer",
"format": "int32",
"example": 0,
"minimum": 0
},
"logprob": {
"type": "number",
"format": "float",
"example": -0.34,
"nullable": true
},
"special": {
"type": "boolean",
"example": "false"
},
"text": {
"type": "string",
"example": "test"
}
}
}
}
},
"tags": [
{
"name": "Text Generation Inference",
"description": "Hugging Face Text Generation Inference API"
}
]
}
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hf_public_repos/text-generation-inference/docs/source/supported_models.md
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# Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
## Supported Models
The following models are optimized and can be served with TGI, which uses custom CUDA kernels for better inference. You can add the flag `--disable-custom-kernels` at the end of the `docker run` command if you wish to disable them.
- [BLOOM](https://huggingface.co/bigscience/bloom)
- [FLAN-T5](https://huggingface.co/google/flan-t5-xxl)
- [Galactica](https://huggingface.co/facebook/galactica-120b)
- [GPT-Neox](https://huggingface.co/EleutherAI/gpt-neox-20b)
- [Llama](https://github.com/facebookresearch/llama)
- [OPT](https://huggingface.co/facebook/opt-66b)
- [SantaCoder](https://huggingface.co/bigcode/santacoder)
- [Starcoder](https://huggingface.co/bigcode/starcoder)
- [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
- [Falcon 40B](https://huggingface.co/tiiuae/falcon-40b)
- [MPT](https://huggingface.co/mosaicml/mpt-30b)
- [Llama V2](https://huggingface.co/meta-llama)
- [Code Llama](https://huggingface.co/codellama)
- [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models:
```python
# for causal LMs/text-generation models
AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")`
# or, for text-to-text generation models
AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")
```
If you wish to serve a supported model that already exists on a local folder, just point to the local folder.
```bash
text-generation-launcher --model-id <PATH-TO-LOCAL-BLOOM>
``````
## Supported Hardware
TGI optimized models are supported on NVIDIA [A100](https://www.nvidia.com/en-us/data-center/a100/), [A10G](https://www.nvidia.com/en-us/data-center/products/a10-gpu/) and [T4](https://www.nvidia.com/en-us/data-center/tesla-t4/) GPUs with CUDA 11.8+. Note that you have to install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) to use it. For other NVIDIA GPUs, continuous batching will still apply, but some operations like flash attention and paged attention will not be executed.
TGI also has support of RoCm-enabled AMD Instinct MI210 and MI250 GPUs, with paged attention and flash attention v2 support. The following features are missing from the RoCm version of TGI: quantization and flash [layer norm kernel](https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm).
TGI is also supported on the following AI hardware accelerators:
- *Habana first-gen Gaudi and Gaudi2:* check out this [example](https://github.com/huggingface/optimum-habana/tree/main/text-generation-inference) how to serve models with TGI on Gaudi and Gaudi2 with [Optimum Habana](https://huggingface.co/docs/optimum/habana/index)
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hf_public_repos/text-generation-inference/docs/source/installation.md
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# Installation
This section explains how to install the CLI tool as well as installing TGI from source. **The strongly recommended approach is to use Docker, as it does not require much setup. Check [the Quick Tour](./quicktour) to learn how to run TGI with Docker.**
## Install CLI
You can use TGI command-line interface (CLI) to download weights, serve and quantize models, or get information on serving parameters.
To install the CLI, you need to first clone the TGI repository and then run `make`.
```bash
git clone https://github.com/huggingface/text-generation-inference.git && cd text-generation-inference
make install
```
If you would like to serve models with custom kernels, run
```bash
BUILD_EXTENSIONS=True make install
```
## Local Installation from Source
Before you start, you will need to setup your environment, and install Text Generation Inference. Text Generation Inference is tested on **Python 3.9+**.
Text Generation Inference is available on pypi, conda and GitHub.
To install and launch locally, first [install Rust](https://rustup.rs/) and create a Python virtual environment with at least
Python 3.9, e.g. using conda:
```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
conda create -n text-generation-inference python=3.9
conda activate text-generation-inference
```
You may also need to install Protoc.
On Linux:
```bash
PROTOC_ZIP=protoc-21.12-linux-x86_64.zip
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
rm -f $PROTOC_ZIP
```
On MacOS, using Homebrew:
```bash
brew install protobuf
```
Then run to install Text Generation Inference:
```bash
git clone https://github.com/huggingface/text-generation-inference.git && cd text-generation-inference
BUILD_EXTENSIONS=True make install
```
<Tip warning={true}>
On some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
```bash
sudo apt-get install libssl-dev gcc -y
```
</Tip>
Once installation is done, simply run:
```bash
make run-falcon-7b-instruct
```
This will serve Falcon 7B Instruct model from the port 8080, which we can query.
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hf_public_repos/text-generation-inference/docs/source/_toctree.yml
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- sections:
- local: index
title: Text Generation Inference
- local: quicktour
title: Quick Tour
- local: installation
title: Installation
- local: supported_models
title: Supported Models and Hardware
title: Getting started
- sections:
- local: basic_tutorials/consuming_tgi
title: Consuming TGI
- local: basic_tutorials/preparing_model
title: Preparing Model for Serving
- local: basic_tutorials/gated_model_access
title: Serving Private & Gated Models
- local: basic_tutorials/using_cli
title: Using TGI CLI
- local: basic_tutorials/launcher
title: All TGI CLI options
- local: basic_tutorials/non_core_models
title: Non-core Model Serving
title: Tutorials
- sections:
- local: conceptual/streaming
title: Streaming
- local: conceptual/quantization
title: Quantization
- local: conceptual/tensor_parallelism
title: Tensor Parallelism
- local: conceptual/paged_attention
title: PagedAttention
- local: conceptual/safetensors
title: Safetensors
- local: conceptual/flash_attention
title: Flash Attention
title: Conceptual Guides
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hf_public_repos/text-generation-inference/docs/source/index.md
|
# Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
Text Generation Inference implements many optimizations and features, such as:
- Simple launcher to serve most popular LLMs
- Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
- Tensor Parallelism for faster inference on multiple GPUs
- Token streaming using Server-Sent Events (SSE)
- Continuous batching of incoming requests for increased total throughput
- Optimized transformers code for inference using [Flash Attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323)
- [Safetensors](https://github.com/huggingface/safetensors) weight loading
- Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
- Logits warper (temperature scaling, top-p, top-k, repetition penalty)
- Stop sequences
- Log probabilities
- Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output.
- Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance.
Text Generation Inference is used in production by multiple projects, such as:
- [Hugging Chat](https://github.com/huggingface/chat-ui), an open-source interface for open-access models, such as Open Assistant and Llama
- [OpenAssistant](https://open-assistant.io/), an open-source community effort to train LLMs in the open
- [nat.dev](http://nat.dev/), a playground to explore and compare LLMs.
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# Quick Tour
The easiest way of getting started is using the official Docker container. Install Docker following [their installation instructions](https://docs.docker.com/get-docker/).
Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) model with TGI. Here is an example on how to do that:
```bash
model=tiiuae/falcon-7b-instruct
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 --model-id $model
```
<Tip warning={true}>
To use GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) . We also recommend using NVIDIA drivers with CUDA version 11.8 or higher.
To use TGI on RoCm-enabled AMD GPUs (only MI210 and MI250 are tested), please use the image `ghcr.io/huggingface/text-generation-inference:1.1.1+rocm` instead. For details about the usage on RoCm, please refer to the [Supported Hardware section](./supported_models#supported-hardware) and [AMD documentation](https://rocm.docs.amd.com/en/latest/deploy/docker.html).
</Tip>
Once TGI is running, you can use the `generate` endpoint by doing requests. To learn more about how to query the endpoints, check the [Consuming TGI](./basic_tutorials/consuming_tgi) section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.
<inferencesnippet>
<python>
```python
import requests
headers = {
"Content-Type": "application/json",
}
data = {
'inputs': 'What is Deep Learning?',
'parameters': {
'max_new_tokens': 20,
},
}
response = requests.post('http://127.0.0.1:8080/generate', headers=headers, json=data)
print(response.json())
# {'generated_text': '\n\nDeep Learning is a subset of Machine Learning that is concerned with the development of algorithms that can'}
```
</python>
<js>
```js
async function query() {
const response = await fetch(
'http://127.0.0.1:8080/generate',
{
method: 'POST',
headers: { 'Content-Type': 'application/json'},
body: JSON.stringify({
'inputs': 'What is Deep Learning?',
'parameters': {
'max_new_tokens': 20
}
})
}
);
}
query().then((response) => {
console.log(JSON.stringify(response));
});
/// {"generated_text":"\n\nDeep Learning is a subset of Machine Learning that is concerned with the development of algorithms that can"}
```
</js>
<curl>
```curl
curl 127.0.0.1:8080/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json'
```
</curl>
</inferencesnippet>
<Tip>
To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
```bash
docker run ghcr.io/huggingface/text-generation-inference:1.1.1 --help
```
</Tip>
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|
# Non-core Model Serving
TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced by TGI, such as tensor-parallel sharding or flash attention. However, you can still get many benefits of TGI, such as continuous batching or streaming outputs.
You can serve these models using the same Docker command-line invocation as with fully supported models 👇
```bash
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id gpt2
```
If the model you wish to serve is a custom transformers model, and its weights and implementation are available in the Hub, you can still serve the model by passing the `--trust-remote-code` flag to the `docker run` command like below 👇
```bash
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id <CUSTOM_MODEL_ID> --trust-remote-code
```
Finally, if the model is not on Hugging Face Hub but on your local, you can pass the path to the folder that contains your model like below 👇
```bash
# Make sure your model is in the $volume directory
docker run --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id /data/<PATH-TO-FOLDER>
```
You can refer to [transformers docs on custom models](https://huggingface.co/docs/transformers/main/en/custom_models) for more information.
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|
# Text-generation-launcher arguments
<!-- WRAP CODE BLOCKS -->
```shell
Text Generation Launcher
Usage: text-generation-launcher [OPTIONS]
Options:
```
## MODEL_ID
```shell
--model-id <MODEL_ID>
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`. Or it can be a local directory containing the necessary files as saved by `save_pretrained(...)` methods of transformers
[env: MODEL_ID=]
[default: bigscience/bloom-560m]
```
## REVISION
```shell
--revision <REVISION>
The actual revision of the model if you're referring to a model on the hub. You can use a specific commit id or a branch like `refs/pr/2`
[env: REVISION=]
```
## VALIDATION_WORKERS
```shell
--validation-workers <VALIDATION_WORKERS>
The number of tokenizer workers used for payload validation and truncation inside the router
[env: VALIDATION_WORKERS=]
[default: 2]
```
## SHARDED
```shell
--sharded <SHARDED>
Whether to shard the model across multiple GPUs By default text-generation-inference will use all available GPUs to run the model. Setting it to `false` deactivates `num_shard`
[env: SHARDED=]
[possible values: true, false]
```
## NUM_SHARD
```shell
--num-shard <NUM_SHARD>
The number of shards to use if you don't want to use all GPUs on a given machine. You can use `CUDA_VISIBLE_DEVICES=0,1 text-generation-launcher... --num_shard 2` and `CUDA_VISIBLE_DEVICES=2,3 text-generation-launcher... --num_shard 2` to launch 2 copies with 2 shard each on a given machine with 4 GPUs for instance
[env: NUM_SHARD=]
```
## QUANTIZE
```shell
--quantize <QUANTIZE>
Whether you want the model to be quantized
[env: QUANTIZE=]
Possible values:
- awq: 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=awq. Should replace GPTQ models whereever possible because of the better latency
- eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from https://github.com/NetEase-FuXi/EETQ.git
- gptq: 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. text-generation-inference will use exllama (faster) kernels whereever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels
- bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
```
## DTYPE
```shell
--dtype <DTYPE>
The dtype to be forced upon the model. This option cannot be used with `--quantize`
[env: DTYPE=]
[possible values: float16, bfloat16]
```
## TRUST_REMOTE_CODE
```shell
--trust-remote-code
Whether you want to execute hub modelling code. Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision
[env: TRUST_REMOTE_CODE=]
```
## MAX_CONCURRENT_REQUESTS
```shell
--max-concurrent-requests <MAX_CONCURRENT_REQUESTS>
The maximum amount of concurrent requests for this particular deployment. Having a low limit will refuse clients requests instead of having them wait for too long and is usually good to handle backpressure correctly
[env: MAX_CONCURRENT_REQUESTS=]
[default: 128]
```
## MAX_BEST_OF
```shell
--max-best-of <MAX_BEST_OF>
This is the maximum allowed value for clients to set `best_of`. Best of makes `n` generations at the same time, and return the best in terms of overall log probability over the entire generated sequence
[env: MAX_BEST_OF=]
[default: 2]
```
## MAX_STOP_SEQUENCES
```shell
--max-stop-sequences <MAX_STOP_SEQUENCES>
This is the maximum allowed value for clients to set `stop_sequences`. Stop sequences are used to allow the model to stop on more than just the EOS token, and enable more complex "prompting" where users can preprompt the model in a specific way and define their "own" stop token aligned with their prompt
[env: MAX_STOP_SEQUENCES=]
[default: 4]
```
## MAX_TOP_N_TOKENS
```shell
--max-top-n-tokens <MAX_TOP_N_TOKENS>
This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking
[env: MAX_TOP_N_TOKENS=]
[default: 5]
```
## MAX_INPUT_LENGTH
```shell
--max-input-length <MAX_INPUT_LENGTH>
This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle
[env: MAX_INPUT_LENGTH=]
[default: 1024]
```
## MAX_TOTAL_TOKENS
```shell
--max-total-tokens <MAX_TOTAL_TOKENS>
This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be
[env: MAX_TOTAL_TOKENS=]
[default: 2048]
```
## WAITING_SERVED_RATIO
```shell
--waiting-served-ratio <WAITING_SERVED_RATIO>
This represents the ratio of waiting queries vs running queries where you want to start considering pausing the running queries to include the waiting ones into the same batch. `waiting_served_ratio=1.2` Means when 12 queries are waiting and there's only 10 queries left in the current batch we check if we can fit those 12 waiting queries into the batching strategy, and if yes, then batching happens delaying the 10 running queries by a `prefill` run.
This setting is only applied if there is room in the batch as defined by `max_batch_total_tokens`.
[env: WAITING_SERVED_RATIO=]
[default: 1.2]
```
## MAX_BATCH_PREFILL_TOKENS
```shell
--max-batch-prefill-tokens <MAX_BATCH_PREFILL_TOKENS>
Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent
[env: MAX_BATCH_PREFILL_TOKENS=]
[default: 4096]
```
## MAX_BATCH_TOTAL_TOKENS
```shell
--max-batch-total-tokens <MAX_BATCH_TOTAL_TOKENS>
**IMPORTANT** This is one critical control to allow maximum usage of the available hardware.
This represents the total amount of potential tokens within a batch. When using padding (not recommended) this would be equivalent of `batch_size` * `max_total_tokens`.
However in the non-padded (flash attention) version this can be much finer.
For `max_batch_total_tokens=1000`, you could fit `10` queries of `total_tokens=100` or a single query of `1000` tokens.
Overall this number should be the largest possible amount that fits the remaining memory (after the model is loaded). Since the actual memory overhead depends on other parameters like if you're using quantization, flash attention or the model implementation, text-generation-inference cannot infer this number automatically.
[env: MAX_BATCH_TOTAL_TOKENS=]
```
## MAX_WAITING_TOKENS
```shell
--max-waiting-tokens <MAX_WAITING_TOKENS>
This setting defines how many tokens can be passed before forcing the waiting queries to be put on the batch (if the size of the batch allows for it). New queries require 1 `prefill` forward, which is different from `decode` and therefore you need to pause the running batch in order to run `prefill` to create the correct values for the waiting queries to be able to join the batch.
With a value too small, queries will always "steal" the compute to run `prefill` and running queries will be delayed by a lot.
With a value too big, waiting queries could wait for a very long time before being allowed a slot in the running batch. If your server is busy that means that requests that could run in ~2s on an empty server could end up running in ~20s because the query had to wait for 18s.
This number is expressed in number of tokens to make it a bit more "model" agnostic, but what should really matter is the overall latency for end users.
[env: MAX_WAITING_TOKENS=]
[default: 20]
```
## HOSTNAME
```shell
--hostname <HOSTNAME>
The IP address to listen on
[env: HOSTNAME=]
[default: 0.0.0.0]
```
## PORT
```shell
-p, --port <PORT>
The port to listen on
[env: PORT=]
[default: 3000]
```
## SHARD_UDS_PATH
```shell
--shard-uds-path <SHARD_UDS_PATH>
The name of the socket for gRPC communication between the webserver and the shards
[env: SHARD_UDS_PATH=]
[default: /tmp/text-generation-server]
```
## MASTER_ADDR
```shell
--master-addr <MASTER_ADDR>
The address the master shard will listen on. (setting used by torch distributed)
[env: MASTER_ADDR=]
[default: localhost]
```
## MASTER_PORT
```shell
--master-port <MASTER_PORT>
The address the master port will listen on. (setting used by torch distributed)
[env: MASTER_PORT=]
[default: 29500]
```
## HUGGINGFACE_HUB_CACHE
```shell
--huggingface-hub-cache <HUGGINGFACE_HUB_CACHE>
The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance
[env: HUGGINGFACE_HUB_CACHE=]
```
## WEIGHTS_CACHE_OVERRIDE
```shell
--weights-cache-override <WEIGHTS_CACHE_OVERRIDE>
The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance
[env: WEIGHTS_CACHE_OVERRIDE=]
```
## DISABLE_CUSTOM_KERNELS
```shell
--disable-custom-kernels
For some models (like bloom), text-generation-inference implemented custom cuda kernels to speed up inference. Those kernels were only tested on A100. Use this flag to disable them if you're running on different hardware and encounter issues
[env: DISABLE_CUSTOM_KERNELS=]
```
## CUDA_MEMORY_FRACTION
```shell
--cuda-memory-fraction <CUDA_MEMORY_FRACTION>
Limit the CUDA available memory. The allowed value equals the total visible memory multiplied by cuda-memory-fraction
[env: CUDA_MEMORY_FRACTION=]
[default: 1.0]
```
## ROPE_SCALING
```shell
--rope-scaling <ROPE_SCALING>
Rope scaling will only be used for RoPE models and allow rescaling the position rotary to accomodate for larger prompts.
Goes together with `rope_factor`.
`--rope-factor 2.0` gives linear scaling with a factor of 2.0 `--rope-scaling dynamic` gives dynamic scaling with a factor of 1.0 `--rope-scaling linear` gives linear scaling with a factor of 1.0 (Nothing will be changed basically)
`--rope-scaling linear --rope-factor` fully describes the scaling you want
[env: ROPE_SCALING=]
[possible values: linear, dynamic]
```
## ROPE_FACTOR
```shell
--rope-factor <ROPE_FACTOR>
Rope scaling will only be used for RoPE models See `rope_scaling`
[env: ROPE_FACTOR=]
```
## JSON_OUTPUT
```shell
--json-output
Outputs the logs in JSON format (useful for telemetry)
[env: JSON_OUTPUT=]
```
## OTLP_ENDPOINT
```shell
--otlp-endpoint <OTLP_ENDPOINT>
[env: OTLP_ENDPOINT=]
```
## CORS_ALLOW_ORIGIN
```shell
--cors-allow-origin <CORS_ALLOW_ORIGIN>
[env: CORS_ALLOW_ORIGIN=]
```
## WATERMARK_GAMMA
```shell
--watermark-gamma <WATERMARK_GAMMA>
[env: WATERMARK_GAMMA=]
```
## WATERMARK_DELTA
```shell
--watermark-delta <WATERMARK_DELTA>
[env: WATERMARK_DELTA=]
```
## NGROK
```shell
--ngrok
Enable ngrok tunneling
[env: NGROK=]
```
## NGROK_AUTHTOKEN
```shell
--ngrok-authtoken <NGROK_AUTHTOKEN>
ngrok authentication token
[env: NGROK_AUTHTOKEN=]
```
## NGROK_EDGE
```shell
--ngrok-edge <NGROK_EDGE>
ngrok edge
[env: NGROK_EDGE=]
```
## ENV
```shell
-e, --env
Display a lot of information about your runtime environment
```
## HELP
```shell
-h, --help
Print help (see a summary with '-h')
```
## VERSION
```shell
-V, --version
Print version
```
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# Consuming Text Generation Inference
There are many ways you can consume Text Generation Inference server in your applications. After launching, you can use the `/generate` route and make a `POST` request to get results from the server. You can also use the `/generate_stream` route if you want TGI to return a stream of tokens. You can make the requests using the tool of your preference, such as curl, Python or TypeScrpt. For a final end-to-end experience, we also open-sourced ChatUI, a chat interface for open-source models.
## curl
After the launch, you can query the model using either the `/generate` or `/generate_stream` routes:
```bash
curl 127.0.0.1:8080/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json'
```
## Inference Client
[`huggingface-hub`](https://huggingface.co/docs/huggingface_hub/main/en/index) is a Python library to interact with the Hugging Face Hub, including its endpoints. It provides a nice high-level class, [`~huggingface_hub.InferenceClient`], which makes it easy to make calls to a TGI endpoint. `InferenceClient` also takes care of parameter validation and provides a simple to-use interface.
You can simply install `huggingface-hub` package with pip.
```bash
pip install huggingface-hub
```
Once you start the TGI server, instantiate `InferenceClient()` with the URL to the endpoint serving the model. You can then call `text_generation()` to hit the endpoint through Python.
```python
from huggingface_hub import InferenceClient
client = InferenceClient(model="http://127.0.0.1:8080")
client.text_generation(prompt="Write a code for snake game")
```
You can do streaming with `InferenceClient` by passing `stream=True`. Streaming will return tokens as they are being generated in the server. To use streaming, you can do as follows:
```python
for token in client.text_generation("How do you make cheese?", max_new_tokens=12, stream=True):
print(token)
```
Another parameter you can use with TGI backend is `details`. You can get more details on generation (tokens, probabilities, etc.) by setting `details` to `True`. When it's specified, TGI will return a `TextGenerationResponse` or `TextGenerationStreamResponse` rather than a string or stream.
```python
output = client.text_generation(prompt="Meaning of life is", details=True)
print(output)
# TextGenerationResponse(generated_text=' a complex concept that is not always clear to the individual. It is a concept that is not always', details=Details(finish_reason=<FinishReason.Length: 'length'>, generated_tokens=20, seed=None, prefill=[], tokens=[Token(id=267, text=' a', logprob=-2.0723474, special=False), Token(id=11235, text=' complex', logprob=-3.1272552, special=False), Token(id=17908, text=' concept', logprob=-1.3632495, special=False),..))
```
You can see how to stream below.
```python
output = client.text_generation(prompt="Meaning of life is", stream=True, details=True)
print(next(iter(output)))
# TextGenerationStreamResponse(token=Token(id=267, text=' a', logprob=-2.0723474, special=False), generated_text=None, details=None)
```
You can check out the details of the function [here](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation). There is also an async version of the client, `AsyncInferenceClient`, based on `asyncio` and `aiohttp`. You can find docs for it [here](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.AsyncInferenceClient)
## ChatUI
ChatUI is an open-source interface built for LLM serving. It offers many customization options, such as web search with SERP API and more. ChatUI can automatically consume the TGI server and even provides an option to switch between different TGI endpoints. You can try it out at [Hugging Chat](https://huggingface.co/chat/), or use the [ChatUI Docker Space](https://huggingface.co/new-space?template=huggingchat/chat-ui-template) to deploy your own Hugging Chat to Spaces.
To serve both ChatUI and TGI in same environment, simply add your own endpoints to the `MODELS` variable in `.env.local` file inside the `chat-ui` repository. Provide the endpoints pointing to where TGI is served.
```
{
// rest of the model config here
"endpoints": [{"url": "https://HOST:PORT/generate_stream"}]
}
```
## Gradio
Gradio is a Python library that helps you build web applications for your machine learning models with a few lines of code. It has a `ChatInterface` wrapper that helps create neat UIs for chatbots. Let's take a look at how to create a chatbot with streaming mode using TGI and Gradio. Let's install Gradio and Hub Python library first.
```bash
pip install huggingface-hub gradio
```
Assume you are serving your model on port 8080, we will query through [InferenceClient](consuming_tgi#inference-client).
```python
import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient(model="http://127.0.0.1:8080")
def inference(message, history):
partial_message = ""
for token in client.text_generation(message, max_new_tokens=20, stream=True):
partial_message += token
yield partial_message
gr.ChatInterface(
inference,
chatbot=gr.Chatbot(height=300),
textbox=gr.Textbox(placeholder="Chat with me!", container=False, scale=7),
description="This is the demo for Gradio UI consuming TGI endpoint with LLaMA 7B-Chat model.",
title="Gradio 🤝 TGI",
examples=["Are tomatoes vegetables?"],
retry_btn="Retry",
undo_btn="Undo",
clear_btn="Clear",
).queue().launch()
```
The UI looks like this 👇
<div class="flex justify-center">
<img
class="block dark:hidden"
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/gradio-tgi.png"
/>
<img
class="hidden dark:block"
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/gradio-tgi-dark.png"
/>
</div>
You can try the demo directly here 👇
<div class="block dark:hidden">
<iframe
src="https://merve-gradio-tgi-2.hf.space?__theme=light"
width="850"
height="750"
></iframe>
</div>
<div class="hidden dark:block">
<iframe
src="https://merve-gradio-tgi-2.hf.space?__theme=dark"
width="850"
height="750"
></iframe>
</div>
You can disable streaming mode using `return` instead of `yield` in your inference function, like below.
```python
def inference(message, history):
return client.text_generation(message, max_new_tokens=20)
```
You can read more about how to customize a `ChatInterface` [here](https://www.gradio.app/guides/creating-a-chatbot-fast).
## API documentation
You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route. The Swagger UI is also available [here](https://huggingface.github.io/text-generation-inference).
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# Serving Private & Gated Models
If the model you wish to serve is behind gated access or the model repository on Hugging Face Hub is private, and you have access to the model, you can provide your Hugging Face Hub access token. You can generate and copy a read token from [Hugging Face Hub tokens page](https://huggingface.co/settings/tokens)
If you're using the CLI, set the `HUGGING_FACE_HUB_TOKEN` environment variable. For example:
```
export HUGGING_FACE_HUB_TOKEN=<YOUR READ TOKEN>
```
If you would like to do it through Docker, you can provide your token by specifying `HUGGING_FACE_HUB_TOKEN` as shown below.
```bash
model=meta-llama/Llama-2-7b-chat-hf
volume=$PWD/data
token=<your READ token>
docker run --gpus all \
--shm-size 1g \
-e HUGGING_FACE_HUB_TOKEN=$token \
-p 8080:80 \
-v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 \
--model-id $model
```
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# Using TGI CLI
You can use TGI command-line interface (CLI) to download weights, serve and quantize models, or get information on serving parameters. To install the CLI, please refer to [the installation section](./installation#install-cli).
`text-generation-server` lets you download the model with `download-weights` command like below 👇
```bash
text-generation-server download-weights MODEL_HUB_ID
```
You can also use it to quantize models like below 👇
```bash
text-generation-server quantize MODEL_HUB_ID OUTPUT_DIR
```
You can use `text-generation-launcher` to serve models.
```bash
text-generation-launcher --model-id MODEL_HUB_ID --port 8080
```
There are many options and parameters you can pass to `text-generation-launcher`. The documentation for CLI is kept minimal and intended to rely on self-generating documentation, which can be found by running
```bash
text-generation-launcher --help
```
You can also find it hosted in this [Swagger UI](https://huggingface.github.io/text-generation-inference/).
Same documentation can be found for `text-generation-server`.
```bash
text-generation-server --help
```
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# Preparing the Model
Text Generation Inference improves the model in several aspects.
## Quantization
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq` or `awq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq) when using AWQ quantization, you need to point to one of the models [here](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to [quantization guide](./../conceptual/quantization)
## RoPE Scaling
RoPE scaling can be used to increase the sequence length of the model during the inference time without necessarily fine-tuning it. To enable RoPE scaling, simply pass `--rope-scaling`, `--max-input-length` and `--rope-factors` flags when running through CLI. `--rope-scaling` can take the values `linear` or `dynamic`. If your model is not fine-tuned to a longer sequence length, use `dynamic`. `--rope-factor` is the ratio between the intended max sequence length and the model's original max sequence length. Make sure to pass `--max-input-length` to provide maximum input length for extension.
<Tip>
We recommend using `dynamic` RoPE scaling.
</Tip>
## Safetensors
[Safetensors](https://github.com/huggingface/safetensors) is a fast and safe persistence format for deep learning models, and is required for tensor parallelism. TGI supports `safetensors` model loading under the hood. By default, given a repository with `safetensors` and `pytorch` weights, TGI will always load `safetensors`. If there's no `pytorch` weights, TGI will convert the weights to `safetensors` format.
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# Quantization
TGI offers GPTQ and bits-and-bytes quantization to quantize large language models.
## Quantization with GPTQ
GPTQ is a post-training quantization method to make the model smaller. It quantizes the layers by finding a compressed version of that weight, that will yield a minimum mean squared error like below 👇
Given a layer \\(l\\) with weight matrix \\(W_{l}\\) and layer input \\(X_{l}\\), find quantized weight \\(\\hat{W}_{l}\\):
$$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$
TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing --quantize like below 👇
```bash
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize gptq
```
Note that TGI's GPTQ implementation doesn't use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) under the hood. However, models quantized using AutoGPTQ or Optimum can still be served by TGI.
To quantize a given model using GPTQ with a calibration dataset, simply run
```bash
text-generation-server quantize tiiuae/falcon-40b /data/falcon-40b-gptq
# Add --upload-to-model-id MYUSERNAME/falcon-40b to push the created model to the hub directly
```
This will create a new directory with the quantized files which you can use with,
```bash
text-generation-launcher --model-id /data/falcon-40b-gptq/ --sharded true --num-shard 2 --quantize gptq
```
You can learn more about the quantization options by running `text-generation-server quantize --help`.
If you wish to do more with GPTQ models (e.g. train an adapter on top), you can read about transformers GPTQ integration [here](https://huggingface.co/blog/gptq-integration).
You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf).
## Quantization with bitsandbytes
bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models. Unlike GPTQ quantization, bitsandbytes doesn't require a calibration dataset or any post-processing – weights are automatically quantized on load. However, inference with bitsandbytes is slower than GPTQ or FP16 precision.
8-bit quantization enables multi-billion parameter scale models to fit in smaller hardware without degrading performance too much.
In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
```bash
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes
```
4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
```bash
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes-nf4
```
You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
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# PagedAttention
LLMs struggle with memory limitations during generation. In the decoding part of generation, all the attention keys and values generated for previous tokens are stored in GPU memory for reuse. This is called _KV cache_, and it may take up a large amount of memory for large models and long sequences.
PagedAttention attempts to optimize memory use by partitioning the KV cache into blocks that are accessed through a lookup table. Thus, the KV cache does not need to be stored in contiguous memory, and blocks are allocated as needed. The memory efficiency can increase GPU utilization on memory-bound workloads, so more inference batches can be supported.
The use of a lookup table to access the memory blocks can also help with KV sharing across multiple generations. This is helpful for techniques such as _parallel sampling_, where multiple outputs are generated simultaneously for the same prompt. In this case, the cached KV blocks can be shared among the generations.
TGI's PagedAttention implementation leverages the custom cuda kernels developed by the [vLLM Project](https://github.com/vllm-project/vllm). You can learn more about this technique in the [project's page](https://vllm.ai/).
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# Flash Attention
Scaling the transformer architecture is heavily bottlenecked by the self-attention mechanism, which has quadratic time and memory complexity. Recent developments in accelerator hardware mainly focus on enhancing compute capacities and not memory and transferring data between hardware. This results in attention operation having a memory bottleneck. **Flash Attention** is an attention algorithm used to reduce this problem and scale transformer-based models more efficiently, enabling faster training and inference.
Standard attention mechanism uses High Bandwidth Memory (HBM) to store, read and write keys, queries and values. HBM is large in memory, but slow in processing, meanwhile SRAM is smaller in memory, but faster in operations. In the standard attention implementation, the cost of loading and writing keys, queries, and values from HBM is high. It loads keys, queries, and values from HBM to GPU on-chip SRAM, performs a single step of the attention mechanism, writes it back to HBM, and repeats this for every single attention step. Instead, Flash Attention loads keys, queries, and values once, fuses the operations of the attention mechanism, and writes them back.
It is implemented for supported models. You can check out the complete list of models that support Flash Attention [here](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models), for models with flash prefix.
You can learn more about Flash Attention by reading the paper in this [link](https://arxiv.org/abs/2205.14135).
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hf_public_repos/text-generation-inference/docs/source
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hf_public_repos/text-generation-inference/docs/source/conceptual/safetensors.md
|
# Safetensors
Safetensors is a model serialization format for deep learning models. It is [faster](https://huggingface.co/docs/safetensors/speed) and safer compared to other serialization formats like pickle (which is used under the hood in many deep learning libraries).
TGI depends on safetensors format mainly to enable [tensor parallelism sharding](./tensor_parallelism). For a given model repository during serving, TGI looks for safetensors weights. If there are no safetensors weights, TGI converts the PyTorch weights to safetensors format.
You can learn more about safetensors by reading the [safetensors documentation](https://huggingface.co/docs/safetensors/index).
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hf_public_repos/text-generation-inference/docs/source
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hf_public_repos/text-generation-inference/docs/source/conceptual/streaming.md
|
# Streaming
## What is Streaming?
Token streaming is the mode in which the server returns the tokens one by one as the model generates them. This enables showing progressive generations to the user rather than waiting for the whole generation. Streaming is an essential aspect of the end-user experience as it reduces latency, one of the most critical aspects of a smooth experience.
<div class="flex justify-center">
<img
class="block dark:hidden"
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/streaming-generation-visual_360.gif"
/>
<img
class="hidden dark:block"
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/streaming-generation-visual-dark_360.gif"
/>
</div>
With token streaming, the server can start returning the tokens one by one before having to generate the whole response. Users can have a sense of the generation's quality earlier than the end of the generation. This has different positive effects:
* Users can get results orders of magnitude earlier for extremely long queries.
* Seeing something in progress allows users to stop the generation if it's not going in the direction they expect.
* Perceived latency is lower when results are shown in the early stages.
* When used in conversational UIs, the experience feels more natural.
For example, a system can generate 100 tokens per second. If the system generates 1000 tokens, with the non-streaming setup, users need to wait 10 seconds to get results. On the other hand, with the streaming setup, users get initial results immediately, and although end-to-end latency will be the same, they can see half of the generation after five seconds. Below you can see an interactive demo that shows non-streaming vs streaming side-by-side. Click **generate** below.
<div class="block dark:hidden">
<iframe
src="https://osanseviero-streaming-vs-non-streaming.hf.space?__theme=light"
width="850"
height="350"
></iframe>
</div>
<div class="hidden dark:block">
<iframe
src="https://osanseviero-streaming-vs-non-streaming.hf.space?__theme=dark"
width="850"
height="350"
></iframe>
</div>
## How to use Streaming?
### Streaming with Python
To stream tokens with `InferenceClient`, simply pass `stream=True` and iterate over the response.
```python
from huggingface_hub import InferenceClient
client = InferenceClient("http://127.0.0.1:8080")
for token in client.text_generation("How do you make cheese?", max_new_tokens=12, stream=True):
print(token)
# To
# make
# cheese
#,
# you
# need
# to
# start
# with
# milk
#.
```
If you want additional details, you can add `details=True`. In this case, you get a `TextGenerationStreamResponse` which contains additional information such as the probabilities and the tokens. For the final response in the stream, it also returns the full generated text.
```python
for details in client.text_generation("How do you make cheese?", max_new_tokens=12, details=True, stream=True):
print(details)
#TextGenerationStreamResponse(token=Token(id=193, text='\n', logprob=-0.007358551, special=False), generated_text=None, details=None)
#TextGenerationStreamResponse(token=Token(id=2044, text='To', logprob=-1.1357422, special=False), generated_text=None, details=None)
#TextGenerationStreamResponse(token=Token(id=717, text=' make', logprob=-0.009841919, special=False), generated_text=None, details=None)
#...
#TextGenerationStreamResponse(token=Token(id=25, text='.', logprob=-1.3408203, special=False), generated_text='\nTo make cheese, you need to start with milk.', details=StreamDetails(finish_reason=<FinishReason.Length: 'length'>, generated_tokens=12, seed=None))
```
The `huggingface_hub` library also comes with an `AsyncInferenceClient` in case you need to handle the requests concurrently.
```python
from huggingface_hub import AsyncInferenceClient
client = AsyncInferenceClient("http://127.0.0.1:8080")
async for token in await client.text_generation("How do you make cheese?", stream=True):
print(token)
# To
# make
# cheese
#,
# you
# need
# to
# start
# with
# milk
#.
```
### Streaming with cURL
To use the `generate_stream` endpoint with curl, you can add the `-N` flag, which disables curl default buffering and shows data as it arrives from the server
```curl
curl -N 127.0.0.1:8080/generate_stream \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json'
```
### Streaming with JavaScript
First, we need to install the `@huggingface/inference` library.
`npm install @huggingface/inference`
If you're using the free Inference API, you can use `HfInference`. If you're using inference endpoints, you can use `HfInferenceEndpoint`. Let's
We can create a `HfInferenceEndpoint` providing our endpoint URL and credential.
```js
import { HfInferenceEndpoint } from '@huggingface/inference'
const hf = new HfInferenceEndpoint('https://YOUR_ENDPOINT.endpoints.huggingface.cloud', 'hf_YOUR_TOKEN')
// prompt
const prompt = 'What can you do in Nuremberg, Germany? Give me 3 Tips'
const stream = hf.textGenerationStream({ inputs: prompt })
for await (const r of stream) {
// yield the generated token
process.stdout.write(r.token.text)
}
```
## How does Streaming work under the hood?
Under the hood, TGI uses Server-Sent Events (SSE). In an SSE Setup, a client sends a request with the data, opening an HTTP connection and subscribing to updates. Afterward, the server sends data to the client. There is no need for further requests; the server will keep sending the data. SSEs are unidirectional, meaning the client does not send other requests to the server. SSE sends data over HTTP, making it easy to use.
SSEs are different than:
* Polling: where the client keeps calling the server to get data. This means that the server might return empty responses and cause overhead.
* Webhooks: where there is a bi-directional connection. The server can send information to the client, but the client can also send data to the server after the first request. Webhooks are more complex to operate as they don’t only use HTTP.
If there are too many requests at the same time, TGI returns an HTTP Error with an `overloaded` error type (`huggingface_hub` returns `OverloadedError`). This allows the client to manage the overloaded server (e.g., it could display a busy error to the user or retry with a new request). To configure the maximum number of concurrent requests, you can specify `--max_concurrent_requests`, allowing clients to handle backpressure.
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hf_public_repos/text-generation-inference/docs/source
|
hf_public_repos/text-generation-inference/docs/source/conceptual/tensor_parallelism.md
|
# Tensor Parallelism
Tensor parallelism is a technique used to fit a large model in multiple GPUs. For example, when multiplying the input tensors with the first weight tensor, the matrix multiplication is equivalent to splitting the weight tensor column-wise, multiplying each column with the input separately, and then concatenating the separate outputs. These outputs are then transferred from the GPUs and concatenated together to get the final result, like below 👇
<Tip warning={true}>
Tensor Parallelism only works for [models officially supported](../supported_models), it will not work when falling back to `transformers`. You can get more information about unsupported models [here](../basic_tutorials/non_core_models).
</Tip>
You can learn a lot more details about tensor-parallelism from [the `transformers` docs](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_many#tensor-parallelism).
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hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/router/README.md
|
# Router
Also named `webserver` throughout the docs.
This router is handling most of the logic to handle the "batches" tell
when to pass new `prefill` requests and pausing `decode` requests, which ones etc...
It uses gRPC to communicate with the shards which can therefore be kept
much simpler and focus on having the most efficient forward passes as possible.
## Continuous batching
One important feature of `text-generation-inference` is enabled
by this `router`.
Continuous batching is the act of regularly running queries in the same
`forward` step of the LLM (a "batch") and also removing them when they are
finished.
In order for continuous batching to be useful, you need to have more compute available
with respect to the memory requirements of your model. This is essentially true for
LLMs and the larger the model, the truer it gets (since you have to pool multiple
GPUs to load the model, you effectively have a lot of compute power at your hands).
Static batching is the act of doing several queries at the same time, but usually
this is controlled by the client, and therefore the amount of batching is decided
beforehand.
For text-generation, and LLMs which are memory bound we can try to be much more
efficient with the available compute, by having client sending us single queries,
and let the router mix&match queries into or out of batches to make the use the
compute the most efficiently. This is possible because for LLMs the total compute
for running the model is much bigger than doing mix&match of the batches themselves.
### Simple continuous batching
text-generation works by feeding a prompt to a model, and iteratively calling
`forward` on the model to produce new text, 1 token at a time.
The first idea is simple, when a query arrives, we start working on it directly.
When new queries arrive, we simply wait for the current `forward` to be finished
then batch the current running prompt with the new query, and call `forward`.
Whenever either query is finished: either the model produce EOS (end of sentence) token
or the query reached the allowed limit. We simply drop it from the batch, remove
all the allocated memory and we can continue with the rest until nothing is left.
This simple idea generalizes very well and we could potentially stack many requests
in the same batch.
One thing to note, is that queries can be potentially run with different parameters
meaning different way to choose the next token (sampling, not sampling, temperature, top_k etc..). This is not problematic for the proposed approach we just need to do the sampling
independantly on each member of the batch.
### Prefill, decode and past key values
In order to make LLMs and text-generation efficient, there's actually a very powerful
trick that can be used, which is the "caching" of some attention matrices. [More on that
in the first part of this blog](https://huggingface.co/blog/accelerated-inference#getting-to-the-first-10x-speedup)
What this means, is that the first "pass" of a prompt is different from the subsequent
"forward" passes. Since for the first one we have to compute the entire attention matrix, whereas in the follow-ups only require to compute the new token attention.
The first pass is called `prefill` throughout this codebase where as the follow-ups are called `decode`.
Since `prefill` is much more expensive than `decode` we don't want to do it all the time,
but a currently running query is probably doing `decode`. If we want to do the continuous
batching as explained previously we need to run `prefill` at some point in order to create
the attention matrix required to be able to join the `decode` group.
`text-generation-inference` uses a bunch of different strategies and parameters in
order to enable you to find the sweet spot between exploiting the hardware and perceived latency.
With no continuous batching at all, latency is going to be super good, but throughput (meaning
the total number of requests allowed in a given timeframe) is going to be super bad (since it's essentially 1).
With static batching, you can probably reach the maximum throughput (by using the maximum total batch size applicable to your hardware), but the latency is super bad since in order to have maximum throughput you need to wait for requests to come in before processing.
With continuous batching you can find a sweet spot. In general latency is the most critical
parameter users care about. But a 2x latency slowdown for 10x more users on the same
hardware is an acceptable tradeoff.
## Token streaming
This is a very important aspect of client UX. As mentionned above, latency is the
most critical perceived quality of an LLM API.
With token streaming, the server can start answering after the first `prefill` pass
directly, without waiting for all the generation to be done. For extremely long queries
this means clients can start to see something happening orders of magnitude before
the work is done. Seeing something in progress allows them to cut short if it's not
what's wanted but also it "feels" better.
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|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/router/build.rs
|
use std::error::Error;
use vergen::EmitBuilder;
fn main() -> Result<(), Box<dyn Error>> {
// Try to get the git sha from the local git repository
if EmitBuilder::builder()
.fail_on_error()
.git_sha(false)
.emit()
.is_err()
{
// Unable to get the git sha
if let Ok(sha) = std::env::var("GIT_SHA") {
// Set it from an env var
println!("cargo:rustc-env=VERGEN_GIT_SHA={sha}");
}
}
// Set docker label if present
if let Ok(label) = std::env::var("DOCKER_LABEL") {
// Set it from an env var
println!("cargo:rustc-env=DOCKER_LABEL={label}");
}
Ok(())
}
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/router/Cargo.toml
|
[package]
name = "text-generation-router"
description = "Text Generation Webserver"
build = "build.rs"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[lib]
path = "src/lib.rs"
[[bin]]
name = "text-generation-router"
path = "src/main.rs"
[dependencies]
async-stream = "0.3.5"
axum = { version = "0.6.20", features = ["json"] }
axum-tracing-opentelemetry = "0.14.1"
text-generation-client = { path = "client" }
clap = { version = "4.4.5", features = ["derive", "env"] }
futures = "0.3.28"
metrics = "0.21.1"
metrics-exporter-prometheus = { version = "0.12.1", features = [] }
nohash-hasher = "0.2.0"
opentelemetry = { version = "0.20.0", features = ["rt-tokio"] }
opentelemetry-otlp = "0.13.0"
rand = "0.8.5"
reqwest = { version = "0.11.20", features = [] }
serde = "1.0.188"
serde_json = "1.0.107"
thiserror = "1.0.48"
tokenizers = { version = "0.14.0", features = ["http"] }
tokio = { version = "1.32.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
tokio-stream = "0.1.14"
tower-http = { version = "0.4.4", features = ["cors"] }
tracing = "0.1.37"
tracing-opentelemetry = "0.21.0"
tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] }
utoipa = { version = "3.5.0", features = ["axum_extras"] }
utoipa-swagger-ui = { version = "3.1.5", features = ["axum"] }
ngrok = { version = "0.13.1", features = ["axum"], optional = true }
hf-hub = "0.3.1"
init-tracing-opentelemetry = { version = "0.14.1", features = ["opentelemetry-otlp"] }
[build-dependencies]
vergen = { version = "8.2.5", features = ["build", "git", "gitcl"] }
[features]
default = ["ngrok"]
ngrok = ["dep:ngrok"]
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|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/src/health.rs
|
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;
use text_generation_client::{
Batch, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
};
// Note: Request ids and batch ids cannot collide.
const LIVENESS_ID: u64 = u64::MAX;
const BATCH_ID: u64 = u64::MAX;
#[derive(Clone, Debug)]
pub(crate) struct Health {
client: ShardedClient,
generation_health: Arc<AtomicBool>,
}
impl Health {
pub(crate) fn new(client: ShardedClient, generation_health: Arc<AtomicBool>) -> Self {
Self {
client,
generation_health,
}
}
pub(crate) async fn check(&mut self) -> bool {
if self.generation_health.load(Ordering::SeqCst) {
// Generation is healthy, we only check that the shards are answering gRPC calls
self.client.health().await.is_ok()
} else {
// Generation is unhealthy or have not sent any generation request yet
// Dummy batch of 1 token and 1 generated token
let liveness_request = Request {
id: LIVENESS_ID,
inputs: "liveness".to_string(),
truncate: 10,
prefill_logprobs: false,
parameters: Some(NextTokenChooserParameters {
temperature: 1.0,
top_k: 0,
top_p: 1.0,
typical_p: 1.0,
do_sample: false,
seed: 0,
repetition_penalty: 1.0,
watermark: false,
}),
stopping_parameters: Some(StoppingCriteriaParameters {
max_new_tokens: 1,
stop_sequences: vec![],
ignore_eos_token: false,
}),
top_n_tokens: 0,
};
let batch = Batch {
id: BATCH_ID,
requests: vec![liveness_request],
size: 1,
max_tokens: 2,
};
// Skips the queue
let value = self.client.prefill(batch).await.is_ok();
// Update generation health
self.generation_health.store(value, Ordering::SeqCst);
value
}
}
}
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|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/src/queue.rs
|
use crate::infer::InferError;
use crate::infer::InferStreamResponse;
use crate::validation::ValidGenerateRequest;
use nohash_hasher::{BuildNoHashHasher, IntMap};
use std::cmp::min;
use std::collections::VecDeque;
use text_generation_client::{Batch, Request};
use tokio::sync::{mpsc, oneshot};
use tokio::time::Instant;
use tracing::{info_span, instrument, Span};
/// Queue entry
#[derive(Debug)]
pub(crate) struct Entry {
/// Request
pub request: ValidGenerateRequest,
/// Response sender to communicate between the Infer struct and the batching_task
pub response_tx: mpsc::UnboundedSender<Result<InferStreamResponse, InferError>>,
/// Span that will live as long as entry
pub span: Span,
/// Temporary span used as a guard when logging inference, wait times...
pub temp_span: Option<Span>,
/// Instant when this entry was queued
pub queue_time: Instant,
/// Instant when this entry was added to a batch
pub batch_time: Option<Instant>,
}
/// Request Queue
#[derive(Debug, Clone)]
pub(crate) struct Queue {
/// Channel to communicate with the background queue task
queue_sender: mpsc::UnboundedSender<QueueCommand>,
}
impl Queue {
pub(crate) fn new(requires_padding: bool, block_size: u32, window_size: Option<u32>) -> Self {
// Create channel
let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
// Launch background queue task
tokio::spawn(queue_task(
requires_padding,
block_size,
window_size,
queue_receiver,
));
Self { queue_sender }
}
/// Append an entry to the queue
#[instrument(skip_all)]
pub(crate) fn append(&self, entry: Entry) {
// Send append command to the background task managing the state
// Unwrap is safe here
self.queue_sender
.send(QueueCommand::Append(Box::new(entry), Span::current()))
.unwrap();
}
// Get the next batch
#[instrument(skip(self))]
pub(crate) async fn next_batch(
&self,
min_size: Option<usize>,
prefill_token_budget: u32,
token_budget: u32,
) -> Option<NextBatch> {
// Create response channel
let (response_sender, response_receiver) = oneshot::channel();
// Send next batch command to the background task managing the state
// Unwrap is safe here
self.queue_sender
.send(QueueCommand::NextBatch {
min_size,
prefill_token_budget,
token_budget,
response_sender,
span: Span::current(),
})
.unwrap();
// Await on response channel
// Unwrap is safe here
response_receiver.await.unwrap()
}
}
// Background task responsible of the queue state
async fn queue_task(
requires_padding: bool,
block_size: u32,
window_size: Option<u32>,
mut receiver: mpsc::UnboundedReceiver<QueueCommand>,
) {
let mut state = State::new(requires_padding, block_size, window_size);
while let Some(cmd) = receiver.recv().await {
match cmd {
QueueCommand::Append(entry, span) => {
span.in_scope(|| state.append(*entry));
metrics::increment_gauge!("tgi_queue_size", 1.0);
}
QueueCommand::NextBatch {
min_size,
prefill_token_budget,
token_budget,
response_sender,
span,
} => span.in_scope(|| {
let next_batch = state.next_batch(min_size, prefill_token_budget, token_budget);
response_sender.send(next_batch).unwrap();
metrics::gauge!("tgi_queue_size", state.entries.len() as f64);
}),
}
}
}
/// Queue State
#[derive(Debug)]
struct State {
/// Queue entries organized in a Vec
entries: VecDeque<(u64, Entry)>,
/// Id of the next entry
next_id: u64,
/// Id of the next batch
next_batch_id: u64,
/// Whether the model is using padding
requires_padding: bool,
/// Paged Attention block size
block_size: u32,
/// Sliding window
window_size: Option<u32>,
}
impl State {
fn new(requires_padding: bool, block_size: u32, window_size: Option<u32>) -> Self {
Self {
entries: VecDeque::with_capacity(128),
next_id: 0,
next_batch_id: 0,
requires_padding,
block_size,
window_size,
}
}
/// Append an entry to the queue
fn append(&mut self, mut entry: Entry) {
// Create a span that will live as long as the entry is in the queue waiting to be batched
let queue_span = info_span!(parent: &entry.span, "queued");
entry.temp_span = Some(queue_span);
// Push entry in the queue
self.entries.push_back((self.next_id, entry));
self.next_id += 1;
}
// Get the next batch
fn next_batch(
&mut self,
min_size: Option<usize>,
prefill_token_budget: u32,
token_budget: u32,
) -> Option<NextBatch> {
if self.entries.is_empty() {
return None;
}
// Check if we have enough entries
if let Some(min_size) = min_size {
if self.entries.len() < min_size {
return None;
}
}
// Create span for this batch to add context to inference calls
let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty);
next_batch_span.follows_from(&Span::current());
let mut batch_requests = Vec::with_capacity(self.entries.len());
let mut batch_entries =
IntMap::with_capacity_and_hasher(self.entries.len(), BuildNoHashHasher::default());
let mut max_input_length = 0;
let mut prefill_tokens: u32 = 0;
let mut decode_tokens: u32 = 0;
// Pop entries starting from the front of the queue
while let Some((id, mut entry)) = self.entries.pop_front() {
// Filter entries where the response receiver was dropped (== entries where the request
// was dropped by the client)
if entry.response_tx.is_closed() {
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
continue;
}
if self.requires_padding {
// We pad to max input length in the Python shards
// We need to take these padding tokens into the equation
max_input_length = max_input_length.max(entry.request.input_length);
prefill_tokens = (batch_requests.len() + 1) as u32 * max_input_length
} else {
// pad to block size
prefill_tokens += ((entry.request.input_length + self.block_size - 1)
/ self.block_size)
* self.block_size;
}
if self.requires_padding {
decode_tokens += entry.request.stopping_parameters.max_new_tokens;
} else {
let max_new_tokens = match self.window_size {
None => entry.request.stopping_parameters.max_new_tokens,
Some(window_size) => min(
window_size.saturating_sub(entry.request.input_length),
entry.request.stopping_parameters.max_new_tokens,
),
};
// pad to block size
decode_tokens +=
((max_new_tokens + self.block_size - 1) / self.block_size) * self.block_size;
}
if prefill_tokens > prefill_token_budget
|| (prefill_tokens + decode_tokens) > token_budget
{
// Entry is over budget
// Add it back to the front
self.entries.push_front((id, entry));
break;
}
// Create a new span to link the batch back to this entry
let entry_batch_span = info_span!(parent: &entry.span, "infer");
// Add relationships
next_batch_span.follows_from(&entry_batch_span);
entry_batch_span.follows_from(&next_batch_span);
// Update entry
entry.temp_span = Some(entry_batch_span);
batch_requests.push(Request {
id,
prefill_logprobs: entry.request.decoder_input_details,
inputs: entry.request.inputs.clone(),
truncate: entry.request.truncate,
parameters: Some(entry.request.parameters.clone()),
stopping_parameters: Some(entry.request.stopping_parameters.clone()),
top_n_tokens: entry.request.top_n_tokens,
});
// Set batch_time
entry.batch_time = Some(Instant::now());
// Insert in batch_entries IntMap
batch_entries.insert(id, entry);
}
// Empty batch
if batch_requests.is_empty() {
return None;
}
// Check if our batch is big enough
if let Some(min_size) = min_size {
// Batch is too small
if batch_requests.len() < min_size {
// Add back entries to the queue in the correct order
for r in batch_requests.into_iter().rev() {
let id = r.id;
let entry = batch_entries.remove(&id).unwrap();
self.entries.push_front((id, entry));
}
return None;
}
}
// Final batch size
let size = batch_requests.len() as u32;
next_batch_span.record("batch_size", size);
let batch = Batch {
id: self.next_batch_id,
requests: batch_requests,
size,
max_tokens: (prefill_tokens + decode_tokens),
};
// Increment batch id
self.next_batch_id += 1;
metrics::histogram!("tgi_batch_next_size", batch.size as f64);
Some((batch_entries, batch, next_batch_span))
}
}
type NextBatch = (IntMap<u64, Entry>, Batch, Span);
#[derive(Debug)]
enum QueueCommand {
Append(Box<Entry>, Span),
NextBatch {
min_size: Option<usize>,
prefill_token_budget: u32,
token_budget: u32,
response_sender: oneshot::Sender<Option<NextBatch>>,
span: Span,
},
}
#[cfg(test)]
mod tests {
use super::*;
use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
use tracing::info_span;
fn default_entry() -> (
Entry,
mpsc::UnboundedReceiver<Result<InferStreamResponse, InferError>>,
) {
let (response_tx, receiver_tx) = mpsc::unbounded_channel();
let entry = Entry {
request: ValidGenerateRequest {
inputs: "".to_string(),
input_length: 0,
truncate: 0,
decoder_input_details: false,
parameters: NextTokenChooserParameters {
temperature: 0.0,
top_k: 0,
top_p: 0.0,
typical_p: 0.0,
do_sample: false,
seed: 0,
repetition_penalty: 0.0,
watermark: false,
},
stopping_parameters: StoppingCriteriaParameters {
ignore_eos_token: false,
max_new_tokens: 1,
stop_sequences: vec![],
},
top_n_tokens: 0,
},
response_tx,
span: info_span!("entry"),
temp_span: None,
queue_time: Instant::now(),
batch_time: None,
};
(entry, receiver_tx)
}
#[test]
fn test_append() {
let mut state = State::new(false, 1, None);
let (entry, _guard) = default_entry();
assert_eq!(state.next_id, 0);
assert_eq!(state.entries.len(), 0);
state.append(entry);
assert_eq!(state.next_id, 1);
assert_eq!(state.entries.len(), 1);
let (id, _) = state.entries.remove(0).unwrap();
assert_eq!(id, 0);
}
#[test]
fn test_next_batch_empty() {
let mut state = State::new(false, 1, None);
assert!(state.next_batch(None, 1, 1).is_none());
assert!(state.next_batch(Some(1), 1, 1).is_none());
}
#[test]
fn test_next_batch_min_size() {
let mut state = State::new(false, 1, None);
let (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry();
state.append(entry1);
state.append(entry2);
let (entries, batch, _) = state.next_batch(None, 2, 2).unwrap();
assert_eq!(entries.len(), 2);
assert!(entries.contains_key(&0));
assert!(entries.contains_key(&1));
assert!(entries.get(&0).unwrap().batch_time.is_some());
assert!(entries.get(&1).unwrap().batch_time.is_some());
assert_eq!(batch.id, 0);
assert_eq!(batch.size, 2);
assert_eq!(state.next_id, 2);
assert_eq!(state.entries.len(), 0);
assert_eq!(state.next_batch_id, 1);
let (entry3, _guard3) = default_entry();
state.append(entry3);
assert!(state.next_batch(Some(2), 2, 2).is_none());
assert_eq!(state.next_id, 3);
assert_eq!(state.entries.len(), 1);
let (id, _) = state.entries.remove(0).unwrap();
assert_eq!(id, 2);
}
#[test]
fn test_next_batch_token_budget() {
let mut state = State::new(false, 1, None);
let (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry();
state.append(entry1);
state.append(entry2);
let (entries, batch, _) = state.next_batch(None, 1, 1).unwrap();
assert_eq!(entries.len(), 1);
assert!(entries.contains_key(&0));
assert_eq!(batch.id, 0);
assert_eq!(batch.size, 1);
assert_eq!(state.next_id, 2);
assert_eq!(state.entries.len(), 1);
assert_eq!(state.next_batch_id, 1);
let (entry3, _guard3) = default_entry();
state.append(entry3);
let (entries, batch, _) = state.next_batch(None, 3, 3).unwrap();
assert_eq!(entries.len(), 2);
assert!(entries.contains_key(&1));
assert!(entries.contains_key(&2));
assert_eq!(batch.id, 1);
assert_eq!(batch.size, 2);
assert_eq!(state.next_id, 3);
assert_eq!(state.entries.len(), 0);
assert_eq!(state.next_batch_id, 2);
}
#[tokio::test]
async fn test_queue_append() {
let queue = Queue::new(false, 1, None);
let (entry, _guard) = default_entry();
queue.append(entry);
}
#[tokio::test]
async fn test_queue_next_batch_empty() {
let queue = Queue::new(false, 1, None);
assert!(queue.next_batch(None, 1, 1).await.is_none());
assert!(queue.next_batch(Some(1), 1, 1).await.is_none());
}
#[tokio::test]
async fn test_queue_next_batch_min_size() {
let queue = Queue::new(false, 1, None);
let (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry();
queue.append(entry1);
queue.append(entry2);
let (entries, batch, _) = queue.next_batch(None, 2, 2).await.unwrap();
assert_eq!(entries.len(), 2);
assert!(entries.contains_key(&0));
assert!(entries.contains_key(&1));
assert!(entries.get(&0).unwrap().batch_time.is_some());
assert!(entries.get(&1).unwrap().batch_time.is_some());
assert_eq!(batch.id, 0);
assert_eq!(batch.size, 2);
let (entry3, _guard3) = default_entry();
queue.append(entry3);
// Not enough requests pending
assert!(queue.next_batch(Some(2), 2, 2).await.is_none());
// Not enough token budget
assert!(queue.next_batch(Some(1), 0, 0).await.is_none());
// Ok
let (entries2, batch2, _) = queue.next_batch(Some(1), 2, 2).await.unwrap();
assert_eq!(entries2.len(), 1);
assert!(entries2.contains_key(&2));
assert!(entries2.get(&2).unwrap().batch_time.is_some());
assert_eq!(batch2.id, 1);
assert_eq!(batch2.size, 1);
}
#[tokio::test]
async fn test_queue_next_batch_token_budget() {
let queue = Queue::new(false, 1, None);
let (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry();
queue.append(entry1);
queue.append(entry2);
let (entries, batch, _) = queue.next_batch(None, 1, 1).await.unwrap();
assert_eq!(entries.len(), 1);
assert!(entries.contains_key(&0));
assert_eq!(batch.id, 0);
assert_eq!(batch.size, 1);
let (entry3, _guard3) = default_entry();
queue.append(entry3);
let (entries, batch, _) = queue.next_batch(None, 3, 3).await.unwrap();
assert_eq!(entries.len(), 2);
assert!(entries.contains_key(&1));
assert!(entries.contains_key(&2));
assert_eq!(batch.id, 1);
assert_eq!(batch.size, 2);
}
#[tokio::test]
async fn test_queue_next_batch_dropped_receiver() {
let queue = Queue::new(false, 1, None);
let (entry, _) = default_entry();
queue.append(entry);
assert!(queue.next_batch(None, 1, 1).await.is_none());
}
}
| 0
|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/src/infer.rs
|
/// Batching and inference logic
use crate::validation::{Validation, ValidationError};
use crate::{Entry, Queue, Token};
use crate::{GenerateRequest, PrefillToken};
use futures::future::try_join_all;
use nohash_hasher::IntMap;
use std::sync::{
atomic::{AtomicBool, Ordering},
Arc,
};
use text_generation_client::{
Batch, CachedBatch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
};
use thiserror::Error;
use tokio::sync::mpsc::error::SendError;
use tokio::sync::{mpsc, Notify, OwnedSemaphorePermit, Semaphore, TryAcquireError};
use tokio::time::Instant;
use tokio_stream::wrappers::UnboundedReceiverStream;
use tokio_stream::StreamExt;
use tracing::{info_span, instrument, Instrument, Span};
/// Inference struct
#[derive(Clone)]
pub struct Infer {
/// Validation
validation: Validation,
/// Request queue
queue: Queue,
/// Shared state
shared: Arc<Shared>,
/// Inference limit
limit_concurrent_requests: Arc<Semaphore>,
}
/// Infer shared state
struct Shared {
/// Batching background Tokio task notifier
batching_task: Notify,
}
impl Infer {
#[allow(clippy::too_many_arguments)]
pub(crate) fn new(
client: ShardedClient,
validation: Validation,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
max_concurrent_requests: usize,
requires_padding: bool,
window_size: Option<u32>,
generation_health: Arc<AtomicBool>,
) -> Self {
// Infer shared state
let queue = Queue::new(requires_padding, 16, window_size);
let shared = Arc::new(Shared {
batching_task: Notify::new(),
});
// Spawn batching background task that contains all the inference logic
tokio::spawn(batching_task(
client,
waiting_served_ratio,
max_batch_prefill_tokens,
max_batch_total_tokens,
max_waiting_tokens,
queue.clone(),
shared.clone(),
generation_health,
));
// Inference limit with a semaphore
let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
Self {
validation,
queue,
shared,
limit_concurrent_requests: semaphore,
}
}
/// Add a new request to the queue and return a stream of InferStreamResponse
#[instrument(skip_all)]
pub(crate) async fn generate_stream(
&self,
request: GenerateRequest,
) -> Result<
(
OwnedSemaphorePermit,
UnboundedReceiverStream<Result<InferStreamResponse, InferError>>,
),
InferError,
> {
// Limit concurrent requests by acquiring a permit from the semaphore
let permit = self
.clone()
.limit_concurrent_requests
.try_acquire_owned()
.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
tracing::error!("{err}");
err
})?;
// Validate request
let valid_request = self.validation.validate(request).await.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
err
})?;
// MPSC channel to communicate with the background batching task
let (response_tx, response_rx) = mpsc::unbounded_channel();
// Append the request to the queue
self.queue.append(Entry {
request: valid_request,
response_tx,
span: Span::current(),
temp_span: None,
queue_time: Instant::now(),
batch_time: None,
});
// Notify the background task that we have a new entry in the queue that needs
// to be batched
self.shared.batching_task.notify_one();
// Return stream
Ok((permit, UnboundedReceiverStream::new(response_rx)))
}
/// Add a new request to the queue and return a InferResponse
#[instrument(skip_all)]
pub(crate) async fn generate(
&self,
request: GenerateRequest,
) -> Result<InferResponse, InferError> {
let use_top_tokens = request.parameters.top_n_tokens.is_some_and(|x| x > 0);
// Create stream and keep semaphore permit as long as generate lives
let (_permit, mut stream) = self.generate_stream(request).await?;
// Return values
let mut result_prefill = Vec::new();
let mut result_tokens = Vec::new();
let mut result_top_tokens = Vec::new();
let mut result_generated_text = None;
let mut result_start = None;
let mut result_queued = None;
// Iterate on stream
while let Some(response) = stream.next().await {
match response? {
// Add prefill tokens
InferStreamResponse::Prefill(tokens) => {
// Create Token objects
// We do that here instead of in the Python code as Rust for loops are faster
result_prefill = tokens
.ids
.into_iter()
.zip(tokens.logprobs.into_iter())
.zip(tokens.texts.into_iter())
.map(|((id, logprob), text)| PrefillToken { id, text, logprob })
.collect();
}
// Push last token
InferStreamResponse::Intermediate { token, top_tokens } => {
result_tokens.push(token);
result_top_tokens.push(top_tokens);
}
// Final message
// Set return values
InferStreamResponse::End {
token,
generated_text,
start,
queued,
top_tokens,
} => {
result_tokens.push(token);
result_top_tokens.push(top_tokens);
result_generated_text = Some(generated_text);
result_start = Some(start);
result_queued = Some(queued)
}
}
}
// Check that we received a `InferStreamResponse::End` message
if let (Some(generated_text), Some(queued), Some(start)) =
(result_generated_text, result_queued, result_start)
{
Ok(InferResponse {
prefill: result_prefill,
tokens: result_tokens,
generated_text,
queued,
start,
top_tokens: if use_top_tokens {
result_top_tokens
} else {
Vec::new()
},
})
} else {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
tracing::error!("{err}");
Err(err)
}
}
/// Add best_of new requests to the queue and return a InferResponse of the sequence with
/// the highest log probability per token
#[instrument(skip(self, request))]
pub(crate) async fn generate_best_of(
&self,
request: GenerateRequest,
best_of: usize,
) -> Result<(InferResponse, Vec<InferResponse>), InferError> {
// validate best_of parameter separately
let best_of = self.validation.validate_best_of(best_of)?;
// create multiple generate requests
let mut infer_responses: Vec<InferResponse> =
try_join_all((0..best_of).map(|_| self.generate(request.clone()))).await?;
// get the sequence with the highest log probability per token
let mut max_index = 0;
let mut max_logprob: f32 = f32::MIN;
for (i, response) in infer_responses.iter().enumerate() {
// mean logprobs of the generated tokens
let sequence_logprob = response
.tokens
.iter()
.map(|token| token.logprob)
.sum::<f32>()
/ response.tokens.len() as f32;
// set best sequence
if sequence_logprob > max_logprob {
max_index = i;
max_logprob = sequence_logprob;
}
}
let best_response = infer_responses.remove(max_index);
Ok((best_response, infer_responses))
}
}
/// Batching logic
/// Will be launched in a background Tokio task
///
/// Batches requests and sends them to the inference server
#[allow(clippy::too_many_arguments)]
async fn batching_task(
mut client: ShardedClient,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
queue: Queue,
shared: Arc<Shared>,
generation_health: Arc<AtomicBool>,
) {
// Infinite loop
loop {
// Wait for a notification from the Infer struct
shared.batching_task.notified().await;
// Get the next batch from the queue
// This batch might be smaller than the maximum batch size if there are not enough requests
// waiting in the queue
while let Some((mut entries, batch, span)) = queue
.next_batch(None, max_batch_prefill_tokens, max_batch_total_tokens)
.await
{
let mut cached_batch = prefill(&mut client, batch, &mut entries, &generation_health)
.instrument(span)
.await;
let mut waiting_tokens = 1;
// We loop until we do not receive any cached batch from the inference server (== until
// all requests have met their stopping criteria)
while let Some(batch) = cached_batch {
// Get current batch info
let batch_size = batch.size;
let batch_max_tokens = batch.max_tokens;
let mut batches = vec![batch];
metrics::gauge!("tgi_batch_current_size", batch_size as f64);
metrics::gauge!("tgi_batch_current_max_tokens", batch_max_tokens as f64);
let min_size = if waiting_tokens >= max_waiting_tokens {
// If we didn't onboard any new requests since >= max_waiting_tokens, we try
// to add a new batch even though its size might be small
None
} else {
// Minimum batch size
Some((batch_size as f32 * waiting_served_ratio).floor() as usize)
};
let token_budget = max_batch_total_tokens.saturating_sub(batch_max_tokens);
// Try to get a new batch
if let Some((mut new_entries, new_batch, span)) = queue
.next_batch(min_size, max_batch_prefill_tokens, token_budget)
.await
{
// Tracking metrics
if min_size.is_some() {
metrics::increment_counter!("tgi_batch_concat", "reason" => "backpressure");
} else {
metrics::increment_counter!("tgi_batch_concat", "reason" => "wait_exceeded");
}
entries.iter_mut().for_each(|(_, entry)| {
// Create a new span to add the info that this entry is waiting
// because a new batch is being computed
let entry_waiting_span = info_span!(parent: &entry.span, "waiting");
// Add relationships
span.follows_from(&entry_waiting_span);
entry_waiting_span.follows_from(&span);
// Update entry
entry.temp_span = Some(entry_waiting_span);
});
// Generate one token for this new batch to have the attention past in cache
let new_cached_batch =
prefill(&mut client, new_batch, &mut new_entries, &generation_health)
.instrument(span)
.await;
// Reset waiting counter
waiting_tokens = 1;
// Extend current batch with the new batch
if let Some(new_cached_batch) = new_cached_batch {
entries.extend(new_entries);
batches.push(new_cached_batch);
}
}
// Create span for this batch to add context to inference calls
let next_batch_size = entries.len();
let next_batch_span =
info_span!(parent: None, "batch", batch_size = next_batch_size);
entries.iter_mut().for_each(|(_, entry)| {
// Create a new span to link the batch back to this entry
let entry_batch_span = info_span!(parent: &entry.span, "infer");
// Add relationships
next_batch_span.follows_from(&entry_batch_span);
entry_batch_span.follows_from(&next_batch_span);
// Update entry
entry.temp_span = Some(entry_batch_span);
});
cached_batch = decode(&mut client, batches, &mut entries, &generation_health)
.instrument(next_batch_span)
.await;
waiting_tokens += 1;
}
metrics::gauge!("tgi_batch_current_size", 0.0);
metrics::gauge!("tgi_batch_current_max_tokens", 0.0);
}
}
}
#[instrument(skip_all)]
async fn prefill(
client: &mut ShardedClient,
batch: Batch,
entries: &mut IntMap<u64, Entry>,
generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_id = batch.id;
metrics::increment_counter!("tgi_batch_inference_count", "method" => "prefill");
match client.prefill(batch).await {
Ok((generations, next_batch)) => {
// Update health
generation_health.store(true, Ordering::SeqCst);
// Send generated tokens and filter stopped entries
filter_send_generations(generations, entries);
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "prefill");
next_batch
}
// If we have an error, we discard the whole batch
Err(err) => {
// Update health
generation_health.store(false, Ordering::SeqCst);
let _ = client.clear_cache(Some(batch_id)).await;
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "prefill");
None
}
}
}
#[instrument(skip_all)]
async fn decode(
client: &mut ShardedClient,
batches: Vec<CachedBatch>,
entries: &mut IntMap<u64, Entry>,
generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_ids: Vec<u64> = batches.iter().map(|b| b.id).collect();
metrics::increment_counter!("tgi_batch_inference_count", "method" => "decode");
match client.decode(batches).await {
Ok((generations, next_batch)) => {
// Update health
generation_health.store(true, Ordering::SeqCst);
// Send generated tokens and filter stopped entries
filter_send_generations(generations, entries);
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "decode");
next_batch
}
// If we have an error, we discard the whole batch
Err(err) => {
generation_health.store(false, Ordering::SeqCst);
for id in batch_ids {
let _ = client.clear_cache(Some(id)).await;
}
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "decode");
None
}
}
}
/// Filter a `batch` and remove all requests not present in `entries`
#[instrument(skip_all)]
async fn filter_batch(
client: &mut ShardedClient,
next_batch: Option<CachedBatch>,
entries: &IntMap<u64, Entry>,
) -> Option<CachedBatch> {
let mut batch = next_batch?;
// No need to filter
if batch.size as usize == entries.len() {
return Some(batch);
}
let id = batch.id;
// Retain only requests that are still in entries
batch.request_ids.retain(|id| entries.contains_key(id));
if batch.request_ids.is_empty() {
// All requests have been filtered out
// Next batch is now empty
// Clear it from the Python shards cache
// We unwrap here as we need to panic since we cannot recover if this method fails
client.clear_cache(Some(id)).await.unwrap();
None
} else {
// Filter Python shard cache
// We unwrap here as we need to panic since we cannot recover if this method fails
client.filter_batch(id, batch.request_ids).await.unwrap()
}
}
/// Send one or multiple `InferStreamResponse` to Infer for all `entries`
/// and filter entries
#[instrument(skip_all)]
fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u64, Entry>) {
generations.into_iter().for_each(|generation| {
let id = generation.request_id;
// Get entry
// We can `expect` here as the request id should always be in the entries
let entry = entries
.get(&id)
.expect("ID not found in entries. This is a bug.");
// Create and enter a span to link this function back to the entry
let _span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_generation", generation = ?generation).entered();
// Send generation responses back to the infer task
// If the receive an error from the Flume channel, it means that the client dropped the
// request and we need to stop generating hence why we unwrap_or(true)
let stopped = send_responses(generation, entry).map_err(|err| {
tracing::error!("Entry response channel error.");
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
err
}).unwrap_or(true);
if stopped {
entries.remove(&id).expect("ID not found in entries. This is a bug.");
}
});
}
/// Send responses through the `entry` response channel
fn send_responses(
generation: Generation,
entry: &Entry,
) -> Result<bool, Box<SendError<Result<InferStreamResponse, InferError>>>> {
// Return directly if the channel is disconnected
if entry.response_tx.is_closed() {
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
return Ok(true);
}
let mut stopped = false;
if let Some(prefill_tokens) = generation.prefill_tokens {
// Send message
entry
.response_tx
.send(Ok(InferStreamResponse::Prefill(prefill_tokens)))?;
}
// Create last Token
let token = Token {
id: generation.token_id,
text: generation.token_text,
logprob: generation.token_logprob,
special: generation.token_is_special,
};
// generation.top_tokens
let mut top_tokens = Vec::new();
if let Some(top_tokens_) = generation.top_tokens {
top_tokens.extend(
top_tokens_
.ids
.into_iter()
.zip(top_tokens_.logprobs.into_iter())
.zip(top_tokens_.texts.into_iter())
.zip(top_tokens_.is_special.into_iter())
.map(|(((id, logprob), text), special)| Token {
id,
text,
logprob,
special,
}),
)
}
if let Some(generated_text) = generation.generated_text {
// Generation has ended
stopped = true;
// Send message
entry.response_tx.send(Ok(InferStreamResponse::End {
token,
top_tokens,
generated_text,
queued: entry.queue_time,
start: entry.batch_time.unwrap(),
}))?;
} else {
// Send message
entry
.response_tx
.send(Ok(InferStreamResponse::Intermediate { token, top_tokens }))?;
}
Ok(stopped)
}
/// Send errors to Infer for all `entries`
#[instrument(skip_all)]
fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
entries.drain().for_each(|(_, entry)| {
// Create and enter a span to link this function back to the entry
let _send_error_span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_error").entered();
let err = InferError::GenerationError(error.to_string());
metrics::increment_counter!("tgi_request_failure", "err" => "generation");
tracing::error!("{err}");
// unwrap_or is valid here as we don't care if the receiver is gone.
entry
.response_tx
.send(Err(err))
.unwrap_or(());
});
}
#[derive(Debug)]
pub(crate) enum InferStreamResponse {
// Optional first message
Prefill(PrefillTokens),
// Intermediate messages
Intermediate {
token: Token,
top_tokens: Vec<Token>,
},
// Last message
End {
token: Token,
top_tokens: Vec<Token>,
generated_text: GeneratedText,
start: Instant,
queued: Instant,
},
}
#[derive(Debug)]
pub(crate) struct InferResponse {
pub(crate) prefill: Vec<PrefillToken>,
pub(crate) tokens: Vec<Token>,
pub(crate) generated_text: GeneratedText,
pub(crate) queued: Instant,
pub(crate) start: Instant,
pub(crate) top_tokens: Vec<Vec<Token>>,
}
#[derive(Debug, Error)]
pub enum InferError {
#[error("Request failed during generation: {0}")]
GenerationError(String),
#[error("Model is overloaded")]
Overloaded(#[from] TryAcquireError),
#[error("Input validation error: {0}")]
ValidationError(#[from] ValidationError),
#[error("Incomplete generation")]
IncompleteGeneration,
}
impl InferError {
pub(crate) fn error_type(&self) -> &str {
match self {
InferError::GenerationError(_) => "generation",
InferError::Overloaded(_) => "overloaded",
InferError::ValidationError(_) => "validation",
InferError::IncompleteGeneration => "incomplete_generation",
}
}
}
| 0
|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/src/server.rs
|
/// HTTP Server logic
use crate::health::Health;
use crate::infer::{InferError, InferResponse, InferStreamResponse};
use crate::validation::ValidationError;
use crate::{
BestOfSequence, CompatGenerateRequest, Details, ErrorResponse, FinishReason,
GenerateParameters, GenerateRequest, GenerateResponse, HubModelInfo, Infer, Info, PrefillToken,
StreamDetails, StreamResponse, Token, Validation,
};
use axum::extract::Extension;
use axum::http::{HeaderMap, Method, StatusCode};
use axum::response::sse::{Event, KeepAlive, Sse};
use axum::response::{IntoResponse, Response};
use axum::routing::{get, post};
use axum::{http, Json, Router};
use axum_tracing_opentelemetry::middleware::OtelAxumLayer;
use futures::stream::StreamExt;
use futures::Stream;
use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
use std::convert::Infallible;
use std::net::SocketAddr;
use std::sync::atomic::AtomicBool;
use std::sync::Arc;
use text_generation_client::{ShardInfo, ShardedClient};
use tokenizers::Tokenizer;
use tokio::signal;
use tokio::time::Instant;
use tower_http::cors::{AllowOrigin, CorsLayer};
use tracing::{info_span, instrument, Instrument};
use utoipa::OpenApi;
use utoipa_swagger_ui::SwaggerUi;
/// Generate tokens if `stream == false` or a stream of token if `stream == true`
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/",
request_body = CompatGenerateRequest,
responses(
(status = 200, description = "Generated Text",
content(
("application/json" = GenerateResponse),
("text/event-stream" = StreamResponse),
)),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})),
)
)]
#[instrument(skip(infer, req))]
async fn compat_generate(
Extension(default_return_full_text): Extension<bool>,
infer: Extension<Infer>,
Json(mut req): Json<CompatGenerateRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
// default return_full_text given the pipeline_tag
if req.parameters.return_full_text.is_none() {
req.parameters.return_full_text = Some(default_return_full_text)
}
// switch on stream
if req.stream {
Ok(generate_stream(infer, Json(req.into()))
.await
.into_response())
} else {
let (headers, Json(generation)) = generate(infer, Json(req.into())).await?;
// wrap generation inside a Vec to match api-inference
Ok((headers, Json(vec![generation])).into_response())
}
}
/// Text Generation Inference endpoint info
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/info",
responses((status = 200, description = "Served model info", body = Info))
)]
#[instrument]
async fn get_model_info(info: Extension<Info>) -> Json<Info> {
Json(info.0)
}
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/health",
responses(
(status = 200, description = "Everything is working fine"),
(status = 503, description = "Text generation inference is down", body = ErrorResponse,
example = json ! ({"error": "unhealthy", "error_type": "healthcheck"})),
)
)]
#[instrument(skip(health))]
/// Health check method
async fn health(mut health: Extension<Health>) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
match health.check().await {
true => Ok(()),
false => Err((
StatusCode::SERVICE_UNAVAILABLE,
Json(ErrorResponse {
error: "unhealthy".to_string(),
error_type: "healthcheck".to_string(),
}),
)),
}
}
/// Generate tokens
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/generate",
request_body = GenerateRequest,
responses(
(status = 200, description = "Generated Text", body = GenerateResponse),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})),
)
)]
#[instrument(
skip_all,
fields(
parameters = ? req.parameters,
total_time,
validation_time,
queue_time,
inference_time,
time_per_token,
seed,
)
)]
async fn generate(
infer: Extension<Infer>,
Json(req): Json<GenerateRequest>,
) -> Result<(HeaderMap, Json<GenerateResponse>), (StatusCode, Json<ErrorResponse>)> {
let span = tracing::Span::current();
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
tracing::debug!("Input: {}", req.inputs);
let compute_characters = req.inputs.chars().count();
let mut add_prompt = None;
if req.parameters.return_full_text.unwrap_or(false) {
add_prompt = Some(req.inputs.clone());
}
let details: bool = req.parameters.details || req.parameters.decoder_input_details;
// Inference
let (response, best_of_responses) = match req.parameters.best_of {
Some(best_of) if best_of > 1 => {
let (response, best_of_responses) = infer.generate_best_of(req, best_of).await?;
(response, Some(best_of_responses))
}
_ => (infer.generate(req).await?, None),
};
// Token details
let details = match details {
true => {
// convert best_of_responses
let best_of_sequences = best_of_responses.map(|responses: Vec<InferResponse>| {
responses
.into_iter()
.map(|response: InferResponse| {
// Add prompt if return_full_text
let mut output_text = response.generated_text.text;
if let Some(prompt) = &add_prompt {
output_text = prompt.clone() + &output_text;
}
BestOfSequence {
generated_text: output_text,
finish_reason: FinishReason::from(
response.generated_text.finish_reason,
),
generated_tokens: response.generated_text.generated_tokens,
prefill: response.prefill,
tokens: response.tokens,
top_tokens: response.top_tokens,
seed: response.generated_text.seed,
}
})
.collect()
});
Some(Details {
finish_reason: FinishReason::from(response.generated_text.finish_reason),
generated_tokens: response.generated_text.generated_tokens,
prefill: response.prefill,
tokens: response.tokens,
seed: response.generated_text.seed,
best_of_sequences,
top_tokens: response.top_tokens,
})
}
false => None,
};
// Timings
let total_time = start_time.elapsed();
let validation_time = response.queued - start_time;
let queue_time = response.start - response.queued;
let inference_time = Instant::now() - response.start;
let time_per_token = inference_time / response.generated_text.generated_tokens;
// Tracing metadata
span.record("total_time", format!("{total_time:?}"));
span.record("validation_time", format!("{validation_time:?}"));
span.record("queue_time", format!("{queue_time:?}"));
span.record("inference_time", format!("{inference_time:?}"));
span.record("time_per_token", format!("{time_per_token:?}"));
span.record("seed", format!("{:?}", response.generated_text.seed));
// Headers
let mut headers = HeaderMap::new();
headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
headers.insert(
"x-compute-time",
total_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-compute-characters",
compute_characters.to_string().parse().unwrap(),
);
headers.insert(
"x-total-time",
total_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-validation-time",
validation_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-queue-time",
queue_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-inference-time",
inference_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-time-per-token",
time_per_token.as_millis().to_string().parse().unwrap(),
);
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!(
"tgi_request_validation_duration",
validation_time.as_secs_f64()
);
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!(
"tgi_request_inference_duration",
inference_time.as_secs_f64()
);
metrics::histogram!(
"tgi_request_mean_time_per_token_duration",
time_per_token.as_secs_f64()
);
metrics::histogram!(
"tgi_request_generated_tokens",
response.generated_text.generated_tokens as f64
);
// Send response
let mut output_text = response.generated_text.text;
if let Some(prompt) = add_prompt {
output_text = prompt + &output_text;
}
tracing::debug!("Output: {}", output_text);
tracing::info!("Success");
let response = GenerateResponse {
generated_text: output_text,
details,
};
Ok((headers, Json(response)))
}
/// Generate a stream of token using Server-Sent Events
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/generate_stream",
request_body = GenerateRequest,
responses(
(status = 200, description = "Generated Text", body = StreamResponse,
content_type = "text/event-stream"),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"}),
content_type = "text/event-stream"),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"}),
content_type = "text/event-stream"),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"}),
content_type = "text/event-stream"),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"}),
content_type = "text/event-stream"),
)
)]
#[instrument(
skip_all,
fields(
parameters = ? req.parameters,
total_time,
validation_time,
queue_time,
inference_time,
time_per_token,
seed,
)
)]
async fn generate_stream(
Extension(infer): Extension<Infer>,
Json(req): Json<GenerateRequest>,
) -> (
HeaderMap,
Sse<impl Stream<Item = Result<Event, Infallible>>>,
) {
let span = tracing::Span::current();
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
tracing::debug!("Input: {}", req.inputs);
let compute_characters = req.inputs.chars().count();
let mut headers = HeaderMap::new();
headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
headers.insert(
"x-compute-characters",
compute_characters.to_string().parse().unwrap(),
);
headers.insert("X-Accel-Buffering", "no".parse().unwrap());
let stream = async_stream::stream! {
// Inference
let mut end_reached = false;
let mut error = false;
let mut add_prompt = None;
if req.parameters.return_full_text.unwrap_or(false) {
add_prompt = Some(req.inputs.clone());
}
let details = req.parameters.details;
let best_of = req.parameters.best_of.unwrap_or(1);
if best_of != 1 {
let err = InferError::from(ValidationError::BestOfStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
yield Ok(Event::from(err));
} else if req.parameters.decoder_input_details {
let err = InferError::from(ValidationError::PrefillDetailsStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
yield Ok(Event::from(err));
} else {
match infer.generate_stream(req).instrument(info_span!(parent: &span, "async_stream")).await {
// Keep permit as long as generate_stream lives
Ok((_permit, mut response_stream)) => {
// Server-Sent Event stream
while let Some(response) = response_stream.next().await {
match response {
Ok(response) => {
match response {
// Prefill is ignored
InferStreamResponse::Prefill(_) => {}
// Yield event for every new token
InferStreamResponse::Intermediate{
token,
top_tokens,
} => {
tracing::debug!(parent: &span, "Token: {:?}", token);
// StreamResponse
let stream_token = StreamResponse {
token,
top_tokens,
generated_text: None,
details: None,
};
yield Ok(Event::default().json_data(stream_token).unwrap())
}
// Yield event for last token and compute timings
InferStreamResponse::End {
token,
generated_text,
start,
queued,
top_tokens,
} => {
// Token details
let details = match details {
true => Some(StreamDetails {
finish_reason: FinishReason::from(generated_text.finish_reason),
generated_tokens: generated_text.generated_tokens,
seed: generated_text.seed,
}),
false => None,
};
// Timings
let total_time = start_time.elapsed();
let validation_time = queued - start_time;
let queue_time = start - queued;
let inference_time = Instant::now() - start;
let time_per_token = inference_time / generated_text.generated_tokens;
// Tracing metadata
span.record("total_time", format!("{total_time:?}"));
span.record("validation_time", format!("{validation_time:?}"));
span.record("queue_time", format!("{queue_time:?}"));
span.record("inference_time", format!("{inference_time:?}"));
span.record("time_per_token", format!("{time_per_token:?}"));
span.record("seed", format!("{:?}", generated_text.seed));
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!("tgi_request_validation_duration", validation_time.as_secs_f64());
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!("tgi_request_inference_duration", inference_time.as_secs_f64());
metrics::histogram!("tgi_request_mean_time_per_token_duration", time_per_token.as_secs_f64());
metrics::histogram!("tgi_request_generated_tokens", generated_text.generated_tokens as f64);
// StreamResponse
end_reached = true;
let mut output_text = generated_text.text;
if let Some(prompt) = add_prompt {
output_text = prompt + &output_text;
}
tracing::debug!(parent: &span, "Output: {}", output_text);
tracing::info!(parent: &span, "Success");
let stream_token = StreamResponse {
token,
top_tokens,
generated_text: Some(output_text),
details
};
yield Ok(Event::default().json_data(stream_token).unwrap());
break;
}
}
}
// yield error
Err(err) => {
error = true;
yield Ok(Event::from(err));
break;
}
}
}
},
// yield error
Err(err) => {
error = true;
yield Ok(Event::from(err));
}
}
// Check if generation reached the end
// Skip if we already sent an error
if !end_reached && !error {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
tracing::error!("{err}");
yield Ok(Event::from(err));
}
}
};
(headers, Sse::new(stream).keep_alive(KeepAlive::default()))
}
/// Prometheus metrics scrape endpoint
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/metrics",
responses((status = 200, description = "Prometheus Metrics", body = String))
)]
async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String {
prom_handle.render()
}
/// Serving method
#[allow(clippy::too_many_arguments)]
pub async fn run(
model_info: HubModelInfo,
shard_info: ShardInfo,
compat_return_full_text: bool,
max_concurrent_requests: usize,
max_best_of: usize,
max_stop_sequences: usize,
max_top_n_tokens: u32,
max_input_length: usize,
max_total_tokens: usize,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
client: ShardedClient,
tokenizer: Option<Tokenizer>,
validation_workers: usize,
addr: SocketAddr,
allow_origin: Option<AllowOrigin>,
ngrok: bool,
ngrok_authtoken: Option<String>,
ngrok_edge: Option<String>,
) -> Result<(), axum::BoxError> {
// OpenAPI documentation
#[derive(OpenApi)]
#[openapi(
paths(
health,
get_model_info,
compat_generate,
generate,
generate_stream,
metrics,
),
components(
schemas(
Info,
CompatGenerateRequest,
GenerateRequest,
GenerateParameters,
PrefillToken,
Token,
GenerateResponse,
BestOfSequence,
Details,
FinishReason,
StreamResponse,
StreamDetails,
ErrorResponse,
)
),
tags(
(name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
),
info(
title = "Text Generation Inference",
license(
name = "Apache 2.0",
url = "https://www.apache.org/licenses/LICENSE-2.0"
)
)
)]
struct ApiDoc;
// Create state
let validation = Validation::new(
validation_workers,
tokenizer,
max_best_of,
max_stop_sequences,
max_top_n_tokens,
max_input_length,
max_total_tokens,
);
let generation_health = Arc::new(AtomicBool::new(false));
let health_ext = Health::new(client.clone(), generation_health.clone());
let infer = Infer::new(
client,
validation,
waiting_served_ratio,
max_batch_prefill_tokens,
max_batch_total_tokens,
max_waiting_tokens,
max_concurrent_requests,
shard_info.requires_padding,
shard_info.window_size,
generation_health,
);
// Duration buckets
let duration_matcher = Matcher::Suffix(String::from("duration"));
let n_duration_buckets = 35;
let mut duration_buckets = Vec::with_capacity(n_duration_buckets);
// Minimum duration in seconds
let mut value = 0.0001;
for _ in 0..n_duration_buckets {
// geometric sequence
value *= 1.5;
duration_buckets.push(value);
}
// Input Length buckets
let input_length_matcher = Matcher::Full(String::from("tgi_request_input_length"));
let input_length_buckets: Vec<f64> = (0..100)
.map(|x| (max_input_length as f64 / 100.0) * (x + 1) as f64)
.collect();
// Generated tokens buckets
let generated_tokens_matcher = Matcher::Full(String::from("tgi_request_generated_tokens"));
let generated_tokens_buckets: Vec<f64> = (0..100)
.map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
.collect();
// Input Length buckets
let max_new_tokens_matcher = Matcher::Full(String::from("tgi_request_max_new_tokens"));
let max_new_tokens_buckets: Vec<f64> = (0..100)
.map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
.collect();
// Batch size buckets
let batch_size_matcher = Matcher::Full(String::from("tgi_batch_next_size"));
let batch_size_buckets: Vec<f64> = (0..1024).map(|x| (x + 1) as f64).collect();
// Prometheus handler
let builder = PrometheusBuilder::new()
.set_buckets_for_metric(duration_matcher, &duration_buckets)
.unwrap()
.set_buckets_for_metric(input_length_matcher, &input_length_buckets)
.unwrap()
.set_buckets_for_metric(generated_tokens_matcher, &generated_tokens_buckets)
.unwrap()
.set_buckets_for_metric(max_new_tokens_matcher, &max_new_tokens_buckets)
.unwrap()
.set_buckets_for_metric(batch_size_matcher, &batch_size_buckets)
.unwrap();
let prom_handle = builder
.install_recorder()
.expect("failed to install metrics recorder");
// CORS layer
let allow_origin = allow_origin.unwrap_or(AllowOrigin::any());
let cors_layer = CorsLayer::new()
.allow_methods([Method::GET, Method::POST])
.allow_headers([http::header::CONTENT_TYPE])
.allow_origin(allow_origin);
// Endpoint info
let info = Info {
model_id: model_info.model_id,
model_sha: model_info.sha,
model_dtype: shard_info.dtype,
model_device_type: shard_info.device_type,
model_pipeline_tag: model_info.pipeline_tag,
max_concurrent_requests,
max_best_of,
max_stop_sequences,
max_input_length,
max_total_tokens,
waiting_served_ratio,
max_batch_total_tokens,
max_waiting_tokens,
validation_workers,
version: env!("CARGO_PKG_VERSION"),
sha: option_env!("VERGEN_GIT_SHA"),
docker_label: option_env!("DOCKER_LABEL"),
};
// Create router
let app = Router::new()
.merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", ApiDoc::openapi()))
// Base routes
.route("/", post(compat_generate))
.route("/info", get(get_model_info))
.route("/generate", post(generate))
.route("/generate_stream", post(generate_stream))
// AWS Sagemaker route
.route("/invocations", post(compat_generate))
// Base Health route
.route("/health", get(health))
// Inference API health route
.route("/", get(health))
// AWS Sagemaker health route
.route("/ping", get(health))
// Prometheus metrics route
.route("/metrics", get(metrics))
.layer(Extension(info))
.layer(Extension(health_ext.clone()))
.layer(Extension(compat_return_full_text))
.layer(Extension(infer))
.layer(Extension(prom_handle.clone()))
.layer(OtelAxumLayer::default())
.layer(cors_layer);
if ngrok {
#[cfg(feature = "ngrok")]
{
use ngrok::config::TunnelBuilder;
let _ = addr;
let authtoken =
ngrok_authtoken.expect("`ngrok-authtoken` must be set when using ngrok tunneling");
let edge = ngrok_edge.expect("`ngrok-edge` must be set when using ngrok tunneling");
let tunnel = ngrok::Session::builder()
.authtoken(authtoken)
.connect()
.await
.unwrap()
.labeled_tunnel()
.label("edge", edge);
let listener = tunnel.listen().await.unwrap();
// Run prom metrics and health locally too
tokio::spawn(
axum::Server::bind(&addr)
.serve(
Router::new()
.route("/health", get(health))
.route("/metrics", get(metrics))
.layer(Extension(health_ext))
.layer(Extension(prom_handle))
.into_make_service(),
)
//Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal()),
);
// Run server
axum::Server::builder(listener)
.serve(app.into_make_service())
//Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal())
.await?;
}
#[cfg(not(feature = "ngrok"))]
{
let _ngrok_authtoken = ngrok_authtoken;
let _ngrok_domain = ngrok_domain;
let _ngrok_username = ngrok_username;
let _ngrok_password = ngrok_password;
panic!("`text-generation-router` was compiled without the `ngrok` feature");
}
} else {
// Run server
axum::Server::bind(&addr)
.serve(app.into_make_service())
// Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal())
.await?;
}
Ok(())
}
/// Shutdown signal handler
async fn shutdown_signal() {
let ctrl_c = async {
signal::ctrl_c()
.await
.expect("failed to install Ctrl+C handler");
};
#[cfg(unix)]
let terminate = async {
signal::unix::signal(signal::unix::SignalKind::terminate())
.expect("failed to install signal handler")
.recv()
.await;
};
#[cfg(not(unix))]
let terminate = std::future::pending::<()>();
tokio::select! {
_ = ctrl_c => {},
_ = terminate => {},
}
tracing::info!("signal received, starting graceful shutdown");
opentelemetry::global::shutdown_tracer_provider();
}
impl From<i32> for FinishReason {
fn from(finish_reason: i32) -> Self {
let finish_reason = text_generation_client::FinishReason::try_from(finish_reason).unwrap();
match finish_reason {
text_generation_client::FinishReason::Length => FinishReason::Length,
text_generation_client::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
text_generation_client::FinishReason::StopSequence => FinishReason::StopSequence,
}
}
}
/// Convert to Axum supported formats
impl From<InferError> for (StatusCode, Json<ErrorResponse>) {
fn from(err: InferError) -> Self {
let status_code = match err {
InferError::GenerationError(_) => StatusCode::FAILED_DEPENDENCY,
InferError::Overloaded(_) => StatusCode::TOO_MANY_REQUESTS,
InferError::ValidationError(_) => StatusCode::UNPROCESSABLE_ENTITY,
InferError::IncompleteGeneration => StatusCode::INTERNAL_SERVER_ERROR,
};
(
status_code,
Json(ErrorResponse {
error: err.to_string(),
error_type: err.error_type().to_string(),
}),
)
}
}
impl From<InferError> for Event {
fn from(err: InferError) -> Self {
Event::default()
.json_data(ErrorResponse {
error: err.to_string(),
error_type: err.error_type().to_string(),
})
.unwrap()
}
}
| 0
|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/src/lib.rs
|
mod health;
/// Text Generation Inference Webserver
mod infer;
mod queue;
pub mod server;
mod validation;
use infer::Infer;
use queue::{Entry, Queue};
use serde::{Deserialize, Serialize};
use utoipa::ToSchema;
use validation::Validation;
/// Hub type
#[derive(Clone, Debug, Deserialize)]
pub struct HubModelInfo {
#[serde(rename(deserialize = "id"))]
pub model_id: String,
pub sha: Option<String>,
pub pipeline_tag: Option<String>,
}
#[derive(Clone, Debug, Serialize, ToSchema)]
pub struct Info {
/// Model info
#[schema(example = "bigscience/blomm-560m")]
pub model_id: String,
#[schema(nullable = true, example = "e985a63cdc139290c5f700ff1929f0b5942cced2")]
pub model_sha: Option<String>,
#[schema(example = "torch.float16")]
pub model_dtype: String,
#[schema(example = "cuda")]
pub model_device_type: String,
#[schema(nullable = true, example = "text-generation")]
pub model_pipeline_tag: Option<String>,
/// Router Parameters
#[schema(example = "128")]
pub max_concurrent_requests: usize,
#[schema(example = "2")]
pub max_best_of: usize,
#[schema(example = "4")]
pub max_stop_sequences: usize,
#[schema(example = "1024")]
pub max_input_length: usize,
#[schema(example = "2048")]
pub max_total_tokens: usize,
#[schema(example = "1.2")]
pub waiting_served_ratio: f32,
#[schema(example = "32000")]
pub max_batch_total_tokens: u32,
#[schema(example = "20")]
pub max_waiting_tokens: usize,
#[schema(example = "2")]
pub validation_workers: usize,
/// Router Info
#[schema(example = "0.5.0")]
pub version: &'static str,
#[schema(nullable = true, example = "null")]
pub sha: Option<&'static str>,
#[schema(nullable = true, example = "null")]
pub docker_label: Option<&'static str>,
}
#[derive(Clone, Debug, Deserialize, ToSchema)]
pub(crate) struct GenerateParameters {
#[serde(default)]
#[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 1)]
pub best_of: Option<usize>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
nullable = true,
default = "null",
example = 0.5
)]
pub temperature: Option<f32>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
nullable = true,
default = "null",
example = 1.03
)]
pub repetition_penalty: Option<f32>,
#[serde(default)]
#[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 10)]
pub top_k: Option<i32>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
maximum = 1.0,
nullable = true,
default = "null",
example = 0.95
)]
pub top_p: Option<f32>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
maximum = 1.0,
nullable = true,
default = "null",
example = 0.95
)]
pub typical_p: Option<f32>,
#[serde(default)]
#[schema(default = "false", example = true)]
pub do_sample: bool,
#[serde(default = "default_max_new_tokens")]
#[schema(nullable = true, default = "null", example = "20")]
pub max_new_tokens: Option<u32>,
#[serde(default)]
#[schema(nullable = true, default = "null", example = false)]
pub return_full_text: Option<bool>,
#[serde(default)]
#[schema(inline, max_items = 4, example = json ! (["photographer"]))]
pub stop: Vec<String>,
#[serde(default)]
#[schema(nullable = true, default = "null", example = "null")]
pub truncate: Option<usize>,
#[serde(default)]
#[schema(default = "false", example = true)]
pub watermark: bool,
#[serde(default)]
#[schema(default = "true")]
pub details: bool,
#[serde(default)]
#[schema(default = "true")]
pub decoder_input_details: bool,
#[serde(default)]
#[schema(
exclusive_minimum = 0,
nullable = true,
default = "null",
example = "null"
)]
pub seed: Option<u64>,
#[serde(default)]
#[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 5)]
pub top_n_tokens: Option<u32>,
}
fn default_max_new_tokens() -> Option<u32> {
None
}
fn default_parameters() -> GenerateParameters {
GenerateParameters {
best_of: None,
temperature: None,
repetition_penalty: None,
top_k: None,
top_p: None,
typical_p: None,
do_sample: false,
max_new_tokens: default_max_new_tokens(),
return_full_text: None,
stop: Vec::new(),
truncate: None,
watermark: false,
details: false,
decoder_input_details: false,
seed: None,
top_n_tokens: None,
}
}
#[derive(Clone, Debug, Deserialize, ToSchema)]
pub(crate) struct GenerateRequest {
#[schema(example = "My name is Olivier and I")]
pub inputs: String,
#[serde(default = "default_parameters")]
pub parameters: GenerateParameters,
}
#[derive(Clone, Debug, Deserialize, ToSchema)]
pub(crate) struct CompatGenerateRequest {
#[schema(example = "My name is Olivier and I")]
pub inputs: String,
#[serde(default = "default_parameters")]
pub parameters: GenerateParameters,
#[serde(default)]
#[schema(default = "false")]
pub stream: bool,
}
impl From<CompatGenerateRequest> for GenerateRequest {
fn from(req: CompatGenerateRequest) -> Self {
Self {
inputs: req.inputs,
parameters: req.parameters,
}
}
}
#[derive(Debug, Serialize, ToSchema)]
pub struct PrefillToken {
#[schema(example = 0)]
id: u32,
#[schema(example = "test")]
text: String,
#[schema(nullable = true, example = - 0.34)]
logprob: f32,
}
#[derive(Debug, Serialize, ToSchema)]
pub struct Token {
#[schema(example = 0)]
id: u32,
#[schema(example = "test")]
text: String,
#[schema(nullable = true, example = - 0.34)]
logprob: f32,
#[schema(example = "false")]
special: bool,
}
#[derive(Serialize, ToSchema)]
#[serde(rename_all(serialize = "snake_case"))]
pub(crate) enum FinishReason {
#[schema(rename = "length")]
Length,
#[serde(rename = "eos_token")]
#[schema(rename = "eos_token")]
EndOfSequenceToken,
#[schema(rename = "stop_sequence")]
StopSequence,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct BestOfSequence {
#[schema(example = "test")]
pub generated_text: String,
#[schema(example = "length")]
pub finish_reason: FinishReason,
#[schema(example = 1)]
pub generated_tokens: u32,
#[schema(nullable = true, example = 42)]
pub seed: Option<u64>,
pub prefill: Vec<PrefillToken>,
pub tokens: Vec<Token>,
#[serde(skip_serializing_if = "Vec::is_empty")]
pub top_tokens: Vec<Vec<Token>>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct Details {
#[schema(example = "length")]
pub finish_reason: FinishReason,
#[schema(example = 1)]
pub generated_tokens: u32,
#[schema(nullable = true, example = 42)]
pub seed: Option<u64>,
pub prefill: Vec<PrefillToken>,
pub tokens: Vec<Token>,
#[serde(skip_serializing_if = "Option::is_none")]
pub best_of_sequences: Option<Vec<BestOfSequence>>,
#[serde(skip_serializing_if = "Vec::is_empty")]
pub top_tokens: Vec<Vec<Token>>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct GenerateResponse {
#[schema(example = "test")]
pub generated_text: String,
#[serde(skip_serializing_if = "Option::is_none")]
pub details: Option<Details>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct StreamDetails {
#[schema(example = "length")]
pub finish_reason: FinishReason,
#[schema(example = 1)]
pub generated_tokens: u32,
#[schema(nullable = true, example = 42)]
pub seed: Option<u64>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct StreamResponse {
pub token: Token,
#[serde(skip_serializing_if = "Vec::is_empty")]
pub top_tokens: Vec<Token>,
#[schema(nullable = true, default = "null", example = "test")]
pub generated_text: Option<String>,
#[schema(nullable = true, default = "null")]
pub details: Option<StreamDetails>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct ErrorResponse {
pub error: String,
pub error_type: String,
}
#[cfg(test)]
mod tests {
use std::io::Write;
use tokenizers::Tokenizer;
pub(crate) async fn get_tokenizer() -> Tokenizer {
let filename = std::path::Path::new("tokenizer.json");
if !filename.exists() {
let content = reqwest::get("https://huggingface.co/gpt2/raw/main/tokenizer.json")
.await
.unwrap()
.bytes()
.await
.unwrap();
let tmp_filename = "tokenizer.json.temp";
let mut file = std::fs::File::create(tmp_filename).unwrap();
file.write_all(&content).unwrap();
// Re-check if another process has written this file maybe.
if !filename.exists() {
std::fs::rename(tmp_filename, filename).unwrap()
}
}
Tokenizer::from_file("tokenizer.json").unwrap()
}
}
| 0
|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/src/main.rs
|
/// Text Generation Inference webserver entrypoint
use axum::http::HeaderValue;
use clap::Parser;
use opentelemetry::sdk::propagation::TraceContextPropagator;
use opentelemetry::sdk::trace;
use opentelemetry::sdk::trace::Sampler;
use opentelemetry::sdk::Resource;
use opentelemetry::{global, KeyValue};
use opentelemetry_otlp::WithExportConfig;
use std::net::{IpAddr, Ipv4Addr, SocketAddr};
use std::path::Path;
use std::time::Duration;
use text_generation_client::{ClientError, ShardedClient};
use text_generation_router::{server, HubModelInfo};
use thiserror::Error;
use tokenizers::{FromPretrainedParameters, Tokenizer};
use tower_http::cors::AllowOrigin;
use tracing_subscriber::layer::SubscriberExt;
use tracing_subscriber::util::SubscriberInitExt;
use tracing_subscriber::{EnvFilter, Layer};
/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
#[clap(default_value = "128", long, env)]
max_concurrent_requests: usize,
#[clap(default_value = "2", long, env)]
max_best_of: usize,
#[clap(default_value = "4", long, env)]
max_stop_sequences: usize,
#[clap(default_value = "5", long, env)]
max_top_n_tokens: u32,
#[clap(default_value = "1024", long, env)]
max_input_length: usize,
#[clap(default_value = "2048", long, env)]
max_total_tokens: usize,
#[clap(default_value = "1.2", long, env)]
waiting_served_ratio: f32,
#[clap(default_value = "4096", long, env)]
max_batch_prefill_tokens: u32,
#[clap(long, env)]
max_batch_total_tokens: Option<u32>,
#[clap(default_value = "20", long, env)]
max_waiting_tokens: usize,
#[clap(default_value = "0.0.0.0", long, env)]
hostname: String,
#[clap(default_value = "3000", long, short, env)]
port: u16,
#[clap(default_value = "/tmp/text-generation-server-0", long, env)]
master_shard_uds_path: String,
#[clap(default_value = "bigscience/bloom", long, env)]
tokenizer_name: String,
#[clap(long, env)]
revision: Option<String>,
#[clap(default_value = "2", long, env)]
validation_workers: usize,
#[clap(long, env)]
json_output: bool,
#[clap(long, env)]
otlp_endpoint: Option<String>,
#[clap(long, env)]
cors_allow_origin: Option<Vec<String>>,
#[clap(long, env)]
ngrok: bool,
#[clap(long, env)]
ngrok_authtoken: Option<String>,
#[clap(long, env)]
ngrok_edge: Option<String>,
}
fn main() -> Result<(), RouterError> {
// Get args
let args = Args::parse();
// Pattern match configuration
let Args {
max_concurrent_requests,
max_best_of,
max_stop_sequences,
max_top_n_tokens,
max_input_length,
max_total_tokens,
waiting_served_ratio,
max_batch_prefill_tokens,
max_batch_total_tokens,
max_waiting_tokens,
hostname,
port,
master_shard_uds_path,
tokenizer_name,
revision,
validation_workers,
json_output,
otlp_endpoint,
cors_allow_origin,
ngrok,
ngrok_authtoken,
ngrok_edge,
} = args;
// Validate args
if max_input_length >= max_total_tokens {
return Err(RouterError::ArgumentValidation(
"`max_input_length` must be < `max_total_tokens`".to_string(),
));
}
if max_input_length as u32 > max_batch_prefill_tokens {
return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {max_batch_prefill_tokens} and {max_input_length}")));
}
if validation_workers == 0 {
return Err(RouterError::ArgumentValidation(
"`validation_workers` must be > 0".to_string(),
));
}
if let Some(ref max_batch_total_tokens) = max_batch_total_tokens {
if max_batch_prefill_tokens > *max_batch_total_tokens {
return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {max_batch_prefill_tokens} and {max_batch_total_tokens}")));
}
if max_total_tokens as u32 > *max_batch_total_tokens {
return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_batch_total_tokens}")));
}
}
// CORS allowed origins
// map to go inside the option and then map to parse from String to HeaderValue
// Finally, convert to AllowOrigin
let cors_allow_origin: Option<AllowOrigin> = cors_allow_origin.map(|cors_allow_origin| {
AllowOrigin::list(
cors_allow_origin
.iter()
.map(|origin| origin.parse::<HeaderValue>().unwrap()),
)
});
// Parse Huggingface hub token
let authorization_token = std::env::var("HUGGING_FACE_HUB_TOKEN").ok();
// Tokenizer instance
// This will only be used to validate payloads
let local_path = Path::new(&tokenizer_name);
let local_model = local_path.exists() && local_path.is_dir();
let tokenizer = if local_model {
// Load local tokenizer
Tokenizer::from_file(local_path.join("tokenizer.json")).ok()
} else {
// Download and instantiate tokenizer
// We need to download it outside of the Tokio runtime
let params = FromPretrainedParameters {
revision: revision.clone().unwrap_or("main".to_string()),
auth_token: authorization_token.clone(),
..Default::default()
};
Tokenizer::from_pretrained(tokenizer_name.clone(), Some(params)).ok()
};
// Launch Tokio runtime
tokio::runtime::Builder::new_multi_thread()
.enable_all()
.build()?
.block_on(async {
init_logging(otlp_endpoint, json_output);
if tokenizer.is_none() {
tracing::warn!(
"Could not find a fast tokenizer implementation for {tokenizer_name}"
);
tracing::warn!("Rust input length validation and truncation is disabled");
}
// Get Model info
let model_info = match local_model {
true => HubModelInfo {
model_id: tokenizer_name.clone(),
sha: None,
pipeline_tag: None,
},
false => get_model_info(&tokenizer_name, revision, authorization_token)
.await
.unwrap_or_else(|| {
tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
HubModelInfo {
model_id: tokenizer_name.to_string(),
sha: None,
pipeline_tag: None,
}
}),
};
// if pipeline-tag == text-generation we default to return_full_text = true
let compat_return_full_text = match &model_info.pipeline_tag {
None => {
tracing::warn!("no pipeline tag found for model {tokenizer_name}");
false
}
Some(pipeline_tag) => pipeline_tag.as_str() == "text-generation",
};
// Instantiate sharded client from the master unix socket
let mut sharded_client = ShardedClient::connect_uds(master_shard_uds_path)
.await
.map_err(RouterError::Connection)?;
// Clear the cache; useful if the webserver rebooted
sharded_client
.clear_cache(None)
.await
.map_err(RouterError::Cache)?;
// Get info from the shard
let shard_info = sharded_client.info().await.map_err(RouterError::Info)?;
// Warmup model
tracing::info!("Warming up model");
let max_supported_batch_total_tokens = match sharded_client
.warmup(max_input_length as u32, max_batch_prefill_tokens, max_total_tokens as u32)
.await
.map_err(RouterError::Warmup)?
{
// Older models do not support automatic max-batch-total-tokens
None => {
let max_batch_total_tokens = max_batch_total_tokens.unwrap_or(
16000.max((max_total_tokens as u32).max(max_batch_prefill_tokens)),
);
tracing::warn!("Model does not support automatic max batch total tokens");
max_batch_total_tokens
}
// Flash attention models return their max supported total tokens
Some(max_supported_batch_total_tokens) => {
// Warn if user added his own max-batch-total-tokens as we will ignore it
if max_batch_total_tokens.is_some() {
tracing::warn!(
"`--max-batch-total-tokens` is deprecated for Flash \
Attention models."
);
tracing::warn!(
"Inferred max batch total tokens: {max_supported_batch_total_tokens}"
);
}
if max_total_tokens as u32 > max_supported_batch_total_tokens {
return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_supported_batch_total_tokens}")));
}
max_supported_batch_total_tokens
}
};
tracing::info!("Setting max batch total tokens to {max_supported_batch_total_tokens}");
tracing::info!("Connected");
let addr = match hostname.parse() {
Ok(ip) => SocketAddr::new(ip, port),
Err(_) => {
tracing::warn!("Invalid hostname, defaulting to 0.0.0.0");
SocketAddr::new(IpAddr::V4(Ipv4Addr::new(0, 0, 0, 0)), port)
}
};
// Run server
server::run(
model_info,
shard_info,
compat_return_full_text,
max_concurrent_requests,
max_best_of,
max_stop_sequences,
max_top_n_tokens,
max_input_length,
max_total_tokens,
waiting_served_ratio,
max_batch_prefill_tokens,
max_supported_batch_total_tokens,
max_waiting_tokens,
sharded_client,
tokenizer,
validation_workers,
addr,
cors_allow_origin,
ngrok,
ngrok_authtoken,
ngrok_edge,
)
.await?;
Ok(())
})
}
/// Init logging using env variables LOG_LEVEL and LOG_FORMAT:
/// - otlp_endpoint is an optional URL to an Open Telemetry collector
/// - LOG_LEVEL may be TRACE, DEBUG, INFO, WARN or ERROR (default to INFO)
/// - LOG_FORMAT may be TEXT or JSON (default to TEXT)
fn init_logging(otlp_endpoint: Option<String>, json_output: bool) {
let mut layers = Vec::new();
// STDOUT/STDERR layer
let fmt_layer = tracing_subscriber::fmt::layer()
.with_file(true)
.with_line_number(true);
let fmt_layer = match json_output {
true => fmt_layer.json().flatten_event(true).boxed(),
false => fmt_layer.boxed(),
};
layers.push(fmt_layer);
// OpenTelemetry tracing layer
if let Some(otlp_endpoint) = otlp_endpoint {
global::set_text_map_propagator(TraceContextPropagator::new());
let tracer = opentelemetry_otlp::new_pipeline()
.tracing()
.with_exporter(
opentelemetry_otlp::new_exporter()
.tonic()
.with_endpoint(otlp_endpoint),
)
.with_trace_config(
trace::config()
.with_resource(Resource::new(vec![KeyValue::new(
"service.name",
"text-generation-inference.router",
)]))
.with_sampler(Sampler::AlwaysOn),
)
.install_batch(opentelemetry::runtime::Tokio);
if let Ok(tracer) = tracer {
layers.push(tracing_opentelemetry::layer().with_tracer(tracer).boxed());
init_tracing_opentelemetry::init_propagator().unwrap();
};
}
// Filter events with LOG_LEVEL
let env_filter =
EnvFilter::try_from_env("LOG_LEVEL").unwrap_or_else(|_| EnvFilter::new("info"));
tracing_subscriber::registry()
.with(env_filter)
.with(layers)
.init();
}
/// get model info from the Huggingface Hub
pub async fn get_model_info(
model_id: &str,
revision: Option<String>,
token: Option<String>,
) -> Option<HubModelInfo> {
let revision = match revision {
None => {
tracing::warn!("`--revision` is not set");
tracing::warn!("We strongly advise to set it to a known supported commit.");
"main".to_string()
}
Some(revision) => revision,
};
let client = reqwest::Client::new();
// Poor man's urlencode
let revision = revision.replace('/', "%2F");
let url = format!("https://huggingface.co/api/models/{model_id}/revision/{revision}");
let mut builder = client.get(url).timeout(Duration::from_secs(5));
if let Some(token) = token {
builder = builder.bearer_auth(token);
}
let response = builder.send().await.ok()?;
if response.status().is_success() {
let hub_model_info: HubModelInfo =
serde_json::from_str(&response.text().await.ok()?).ok()?;
if let Some(sha) = &hub_model_info.sha {
tracing::info!(
"Serving revision {sha} of model {}",
hub_model_info.model_id
);
}
Some(hub_model_info)
} else {
None
}
}
#[derive(Debug, Error)]
enum RouterError {
#[error("Argument validation error: {0}")]
ArgumentValidation(String),
#[error("Unable to connect to the Python model shards: {0}")]
Connection(ClientError),
#[error("Unable to clear the Python model shards cache: {0}")]
Cache(ClientError),
#[error("Unable to get the Python model shards info: {0}")]
Info(ClientError),
#[error("Unable to warmup the Python model shards: {0}")]
Warmup(ClientError),
#[error("Tokio runtime failed to start: {0}")]
Tokio(#[from] std::io::Error),
#[error("Axum webserver failed: {0}")]
Axum(#[from] axum::BoxError),
}
| 0
|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/src/validation.rs
|
/// Payload validation logic
use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
use crate::{GenerateParameters, GenerateRequest};
use rand::{thread_rng, Rng};
use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
use thiserror::Error;
use tokenizers::tokenizer::Tokenizer;
use tokenizers::TruncationDirection;
use tokio::sync::mpsc;
use tokio::sync::oneshot;
use tracing::{instrument, Span};
/// Validation
#[derive(Debug, Clone)]
pub struct Validation {
/// Validation parameters
max_best_of: usize,
max_stop_sequences: usize,
max_top_n_tokens: u32,
max_input_length: usize,
max_total_tokens: usize,
/// Channel to communicate with the background tokenization task
sender: Option<mpsc::UnboundedSender<TokenizerRequest>>,
}
impl Validation {
pub(crate) fn new(
workers: usize,
tokenizer: Option<Tokenizer>,
max_best_of: usize,
max_stop_sequences: usize,
max_top_n_tokens: u32,
max_input_length: usize,
max_total_tokens: usize,
) -> Self {
// If we have a fast tokenizer
let sender = if let Some(tokenizer) = tokenizer {
// Create round robin channel
let (validation_sender, validation_round_robin_receiver) = mpsc::unbounded_channel();
let mut senders = Vec::with_capacity(workers);
// Create workers
for _ in 0..workers {
let tokenizer_clone = tokenizer.clone();
let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel();
senders.push(tokenizer_sender);
// Spawn worker
tokio::task::spawn_blocking(move || {
tokenizer_worker(tokenizer_clone, tokenizer_receiver)
});
}
// Create tokenization round robin task
tokio::spawn(round_robin_task(validation_round_robin_receiver, senders));
Some(validation_sender)
} else {
None
};
Self {
max_best_of,
sender,
max_stop_sequences,
max_top_n_tokens,
max_input_length,
max_total_tokens,
}
}
#[instrument(skip(self, inputs))]
async fn validate_input(
&self,
inputs: String,
truncate: Option<usize>,
max_new_tokens: Option<u32>,
) -> Result<(String, usize, u32), ValidationError> {
// If we have a fast tokenizer
if let Some(sender) = &self.sender {
// Create response channel
let (response_sender, response_receiver) = oneshot::channel();
// Send request to the background validation task
// Unwrap is safe here
sender
.send(((inputs, truncate), response_sender, Span::current()))
.unwrap();
// Await on response channel
// Unwrap is safe here
let (inputs, input_length) = response_receiver.await.unwrap()?;
// Get total tokens
let max_new_tokens: u32 = if let Some(max_new_tokens) = max_new_tokens {
max_new_tokens
} else {
self.max_total_tokens.saturating_sub(input_length) as u32
};
let total_tokens = input_length + max_new_tokens as usize;
// Validate MaxTotalTokens
if total_tokens > self.max_total_tokens {
return Err(ValidationError::MaxTotalTokens(
self.max_total_tokens,
input_length,
max_new_tokens,
));
}
// Validate InputLength
if input_length > self.max_input_length {
return Err(ValidationError::InputLength(
self.max_input_length,
input_length,
));
}
metrics::histogram!("tgi_request_input_length", input_length as f64);
Ok((inputs, input_length, max_new_tokens))
}
// Return inputs without validation
else {
// In this case, we don't know the real length in tokens of the inputs
// However, the inputs will be truncated by the python servers
// We make sure that truncate + max_new_tokens <= self.max_total_tokens
let max_new_tokens: u32 = if let Some(max_new_tokens) = max_new_tokens {
max_new_tokens
} else if let Some(truncate) = truncate {
self.max_total_tokens.saturating_sub(truncate) as u32
} else {
return Err(ValidationError::UnsetMaxNewTokens);
};
let input_length = truncate.unwrap_or(self.max_input_length);
// Validate MaxNewTokens
if (input_length as u32 + max_new_tokens) > self.max_total_tokens as u32 {
return Err(ValidationError::MaxNewTokens(
self.max_total_tokens - self.max_input_length,
max_new_tokens,
));
}
Ok((inputs, input_length, max_new_tokens))
}
}
/// Validate a payload and get the number of tokens in the input
#[instrument(skip_all)]
pub(crate) async fn validate(
&self,
request: GenerateRequest,
) -> Result<ValidGenerateRequest, ValidationError> {
let GenerateParameters {
best_of,
temperature,
repetition_penalty,
top_k,
top_p,
typical_p,
do_sample,
max_new_tokens,
stop: stop_sequences,
truncate,
seed,
watermark,
decoder_input_details,
top_n_tokens,
..
} = request.parameters;
// sampling must be true when best_of > 1
let best_of = best_of.unwrap_or(1);
let sampling = do_sample
|| temperature.is_some()
|| top_k.is_some()
|| top_p.is_some()
|| typical_p.is_some();
if best_of > 1 && !sampling {
return Err(BestOfSampling);
}
let temperature = temperature.unwrap_or(1.0);
if temperature <= 0.0 {
return Err(ValidationError::Temperature);
}
let repetition_penalty = repetition_penalty.unwrap_or(1.0);
if repetition_penalty <= 0.0 {
return Err(ValidationError::RepetitionPenalty);
}
// Different because the proto default value is not a valid value
// for the user
let top_p = top_p
.map(|value| {
if value <= 0.0 || value >= 1.0 {
return Err(ValidationError::TopP);
}
Ok(value)
})
.unwrap_or(Ok(1.0))?;
let typical_p = typical_p
.map(|value| {
if value <= 0.0 || value >= 1.0 {
return Err(ValidationError::TypicalP);
}
Ok(value)
})
.unwrap_or(Ok(1.0))?;
let top_k: u32 = top_k
.map(|value| {
if value <= 0 {
return Err(ValidationError::TopK);
}
Ok(value as u32)
})
.unwrap_or(Ok(0))?;
if max_new_tokens == Some(0) {
return Err(ValidationError::NegativeMaxNewTokens);
}
if stop_sequences.len() > self.max_stop_sequences {
return Err(ValidationError::StopSequence(
self.max_stop_sequences,
stop_sequences.len(),
));
}
// If seed is None, assign a random one
let seed = match seed {
None => thread_rng().gen(),
Some(seed) => {
if best_of > 1 {
return Err(BestOfSeed);
}
seed
}
};
let top_n_tokens = top_n_tokens
.map(|value| {
if value > self.max_top_n_tokens {
return Err(ValidationError::TopNTokens(self.max_top_n_tokens, value));
}
Ok(value)
})
.unwrap_or(Ok(0))?;
// Check if inputs is empty
if request.inputs.is_empty() {
return Err(EmptyInput);
}
// Check if truncate is strictly positive and less than max_input_length
let truncate = truncate
.map(|value| {
if value == 0 || value > self.max_input_length {
return Err(ValidationError::Truncate(self.max_input_length, value));
}
Ok(Some(value))
})
.unwrap_or(Ok(None))?;
// Validate inputs
let (inputs, input_length, max_new_tokens) = self
.validate_input(request.inputs, truncate, max_new_tokens)
.await?;
let parameters = NextTokenChooserParameters {
temperature,
repetition_penalty,
top_k,
top_p,
typical_p,
do_sample,
seed,
watermark,
};
let stopping_parameters = StoppingCriteriaParameters {
max_new_tokens,
stop_sequences,
ignore_eos_token: false,
};
metrics::histogram!("tgi_request_max_new_tokens", max_new_tokens as f64);
Ok(ValidGenerateRequest {
inputs,
decoder_input_details,
input_length: input_length as u32,
truncate: truncate.unwrap_or(self.max_input_length) as u32,
parameters,
stopping_parameters,
top_n_tokens,
})
}
/// Validate the best_of parameter
#[instrument(skip_all)]
pub(crate) fn validate_best_of(&self, best_of: usize) -> Result<usize, ValidationError> {
if self.max_best_of == 1 && best_of != 1 {
return Err(ValidationError::BestOfDisabled);
}
if best_of > self.max_best_of {
return Err(ValidationError::BestOf(self.max_best_of, best_of));
}
Ok(best_of)
}
}
/// Round robin tokenization task
async fn round_robin_task(
mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>,
senders: Vec<mpsc::UnboundedSender<TokenizerRequest>>,
) {
loop {
for sender in &senders {
match receiver.recv().await {
None => return,
Some(request) => sender.send(request).unwrap(),
};
}
}
}
/// Start tokenization workers
fn tokenizer_worker(tokenizer: Tokenizer, mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>) {
// Loop over requests
while let Some(((inputs, truncate), response_tx, parent_span)) = receiver.blocking_recv() {
parent_span.in_scope(|| {
response_tx
.send(prepare_input(inputs, truncate, &tokenizer))
.unwrap_or(())
})
}
}
/// Get input length and optionally truncate it
fn prepare_input(
inputs: String,
truncate: Option<usize>,
tokenizer: &Tokenizer,
) -> Result<(String, usize), ValidationError> {
// Get the number of tokens in the input
let mut encoding = tokenizer
.encode(inputs.clone(), true)
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
// Optionally truncate
let (inputs, input_length) = match truncate {
// Truncate is some and < encoding length
Some(truncate) if truncate < encoding.len() => {
// truncate encoding and decode new inputs
encoding.truncate(truncate, 0, TruncationDirection::Left);
let inputs = tokenizer
.decode(encoding.get_ids(), false)
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
(inputs, encoding.len())
}
// Nothing to do
_ => (inputs, encoding.len()),
};
Ok((inputs, input_length))
}
type TokenizerRequest = (
(String, Option<usize>),
oneshot::Sender<Result<(String, usize), ValidationError>>,
Span,
);
#[derive(Debug)]
pub(crate) struct ValidGenerateRequest {
pub inputs: String,
pub input_length: u32,
pub truncate: u32,
pub decoder_input_details: bool,
pub parameters: NextTokenChooserParameters,
pub stopping_parameters: StoppingCriteriaParameters,
pub top_n_tokens: u32,
}
#[derive(Error, Debug)]
pub enum ValidationError {
#[error("`best_of` must be > 0 and <= {0}. Given: {1}")]
BestOf(usize, usize),
#[error("`best_of` != 1 is not allowed for this endpoint")]
BestOfDisabled,
#[error("you must use sampling when `best_of` is > 1")]
BestOfSampling,
#[error("`seed` must not be set when `best_of` > 1")]
BestOfSeed,
#[error("`best_of` != 1 is not supported when streaming tokens")]
BestOfStream,
#[error("`top_n_tokens` must be >= 0 and <= {0}. Given: {1}")]
TopNTokens(u32, u32),
#[error("`top_n_tokens` != 0 is not allowed for this endpoint")]
TopNTokensDisabled,
#[error("`decoder_input_details` == true is not supported when streaming tokens")]
PrefillDetailsStream,
#[error("`temperature` must be strictly positive")]
Temperature,
#[error("`repetition_penalty` must be strictly positive")]
RepetitionPenalty,
#[error("`top_p` must be > 0.0 and < 1.0")]
TopP,
#[error("`top_k` must be strictly positive")]
TopK,
#[error("`truncate` must be strictly positive and less than {0}. Given: {1}")]
Truncate(usize, usize),
#[error("`typical_p` must be > 0.0 and < 1.0")]
TypicalP,
#[error("one of `max_new_tokens` or `truncate` must be set if a fast tokenizer is not in use")]
UnsetMaxNewTokens,
#[error("`max_new_tokens` must be strictly positive")]
NegativeMaxNewTokens,
#[error("`max_new_tokens` must be <= {0}. Given: {1}")]
MaxNewTokens(usize, u32),
#[error("`inputs` tokens + `max_new_tokens` must be <= {0}. Given: {1} `inputs` tokens and {2} `max_new_tokens`")]
MaxTotalTokens(usize, usize, u32),
#[error("`inputs` must have less than {0} tokens. Given: {1}")]
InputLength(usize, usize),
#[error("`inputs` cannot be empty")]
EmptyInput,
#[error("`stop` supports up to {0} stop sequences. Given: {1}")]
StopSequence(usize, usize),
#[error("tokenizer error {0}")]
Tokenizer(String),
}
#[cfg(test)]
mod tests {
use super::*;
use crate::default_parameters;
use crate::tests::get_tokenizer;
#[tokio::test]
async fn test_validation_max_new_tokens() {
let tokenizer = None;
let max_best_of = 2;
let max_stop_sequence = 3;
let max_top_n_tokens = 4;
let max_input_length = 5;
let max_total_tokens = 6;
let workers = 1;
let validation = Validation::new(
workers,
tokenizer,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
max_input_length,
max_total_tokens,
);
let max_new_tokens = 10;
match validation
.validate_input("Hello".to_string(), None, Some(max_new_tokens))
.await
{
Err(ValidationError::MaxNewTokens(1, 10)) => (),
_ => panic!("Unexpected not max new tokens"),
}
}
#[tokio::test]
async fn test_validation_input_length() {
let tokenizer = Some(get_tokenizer().await);
let max_best_of = 2;
let max_stop_sequence = 3;
let max_top_n_tokens = 4;
let max_input_length = 5;
let max_total_tokens = 6;
let workers = 1;
let validation = Validation::new(
workers,
tokenizer,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
max_input_length,
max_total_tokens,
);
let max_new_tokens = 10;
match validation
.validate_input("Hello".to_string(), None, Some(max_new_tokens))
.await
{
Err(ValidationError::MaxTotalTokens(6, 1, 10)) => (),
_ => panic!("Unexpected not max new tokens"),
}
}
#[tokio::test]
async fn test_validation_best_of_sampling() {
let tokenizer = Some(get_tokenizer().await);
let max_best_of = 2;
let max_stop_sequence = 3;
let max_top_n_tokens = 4;
let max_input_length = 5;
let max_total_tokens = 6;
let workers = 1;
let validation = Validation::new(
workers,
tokenizer,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
max_input_length,
max_total_tokens,
);
match validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
best_of: Some(2),
do_sample: false,
..default_parameters()
},
})
.await
{
Err(ValidationError::BestOfSampling) => (),
_ => panic!("Unexpected not best of sampling"),
}
}
#[tokio::test]
async fn test_validation_top_p() {
let tokenizer = Some(get_tokenizer().await);
let max_best_of = 2;
let max_stop_sequence = 3;
let max_top_n_tokens = 4;
let max_input_length = 5;
let max_total_tokens = 6;
let workers = 1;
let validation = Validation::new(
workers,
tokenizer,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
max_input_length,
max_total_tokens,
);
match validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
top_p: Some(1.0),
..default_parameters()
},
})
.await
{
Err(ValidationError::TopP) => (),
_ => panic!("Unexpected top_p"),
}
match validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
top_p: Some(0.99),
..default_parameters()
},
})
.await
{
Ok(_) => (),
_ => panic!("Unexpected top_p error"),
}
let valid_request = validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
top_p: None,
..default_parameters()
},
})
.await
.unwrap();
// top_p == 1.0 is invalid for users to ask for but it's the default resolved value.
assert_eq!(valid_request.parameters.top_p, 1.0);
}
#[tokio::test]
async fn test_validation_top_n_tokens() {
let tokenizer = Some(get_tokenizer().await);
let max_best_of = 2;
let max_stop_sequences = 3;
let max_top_n_tokens = 4;
let max_input_length = 5;
let max_total_tokens = 6;
let workers = 1;
let validation = Validation::new(
workers,
tokenizer,
max_best_of,
max_stop_sequences,
max_top_n_tokens,
max_input_length,
max_total_tokens,
);
match validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
top_n_tokens: Some(5),
..default_parameters()
},
})
.await
{
Err(ValidationError::TopNTokens(4, 5)) => (),
_ => panic!("Unexpected top_n_tokens"),
}
validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
top_n_tokens: Some(4),
..default_parameters()
},
})
.await
.unwrap();
validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
top_n_tokens: Some(0),
..default_parameters()
},
})
.await
.unwrap();
let valid_request = validation
.validate(GenerateRequest {
inputs: "Hello".to_string(),
parameters: GenerateParameters {
top_n_tokens: None,
..default_parameters()
},
})
.await
.unwrap();
assert_eq!(valid_request.top_n_tokens, 0);
}
}
| 0
|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/client/build.rs
|
use std::fs;
fn main() -> Result<(), Box<dyn std::error::Error>> {
println!("cargo:rerun-if-changed=../../proto/generate.proto");
fs::create_dir("src/pb").unwrap_or(());
let mut config = prost_build::Config::new();
config.protoc_arg("--experimental_allow_proto3_optional");
tonic_build::configure()
.build_client(true)
.build_server(false)
.out_dir("src/pb")
.include_file("mod.rs")
.compile_with_config(config, &["../../proto/generate.proto"], &["../../proto"])
.unwrap_or_else(|e| panic!("protobuf compilation failed: {e}"));
Ok(())
}
| 0
|
hf_public_repos/text-generation-inference/router
|
hf_public_repos/text-generation-inference/router/client/Cargo.toml
|
[package]
name = "text-generation-client"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
futures = "^0.3"
grpc-metadata = { path = "../grpc-metadata" }
prost = "^0.12"
thiserror = "^1.0"
tokio = { version = "^1.32", features = ["sync"] }
tonic = "^0.10"
tower = "^0.4"
tracing = "^0.1"
[build-dependencies]
tonic-build = "0.10.1"
prost-build = "0.12.1"
| 0
|
hf_public_repos/text-generation-inference/router/client
|
hf_public_repos/text-generation-inference/router/client/src/sharded_client.rs
|
/// Multi shard Client
use crate::{Batch, CachedBatch, Client, Generation, HealthResponse, ShardInfo};
use crate::{ClientError, Result};
use futures::future::join_all;
use tonic::transport::Uri;
use tracing::instrument;
#[derive(Debug, Clone)]
/// Text Generation Inference gRPC multi client
pub struct ShardedClient {
clients: Vec<Client>,
}
impl ShardedClient {
fn new(clients: Vec<Client>) -> Self {
Self { clients }
}
/// Create a new ShardedClient from a master client. The master client will communicate with
/// the other shards and returns all uris/unix sockets with the `service_discovery` gRPC method.
async fn from_master_client(mut master_client: Client) -> Result<Self> {
// Get all uris/unix sockets from the master client
let uris = master_client.service_discovery().await?;
let futures = uris.into_iter().map(Client::connect_uds);
let clients: Result<Vec<Client>> = join_all(futures).await.into_iter().collect();
Ok(Self::new(clients?))
}
/// Returns a client connected to the given uri
pub async fn connect(uri: Uri) -> Result<Self> {
let master_client = Client::connect(uri).await?;
Self::from_master_client(master_client).await
}
/// Returns a client connected to the given unix socket
pub async fn connect_uds(path: String) -> Result<Self> {
let master_client = Client::connect_uds(path).await?;
Self::from_master_client(master_client).await
}
/// Get the model info
#[instrument(skip(self))]
pub async fn info(&mut self) -> Result<ShardInfo> {
let futures: Vec<_> = self
.clients
.iter_mut()
.map(|client| client.info())
.collect();
join_all(futures).await.pop().unwrap()
}
/// GRPC health check
#[instrument(skip(self))]
pub async fn health(&mut self) -> Result<HealthResponse> {
let futures: Vec<_> = self
.clients
.iter_mut()
.map(|client| client.health())
.collect();
join_all(futures).await.pop().unwrap()
}
/// Clear the past generations cache
#[instrument(skip(self))]
pub async fn clear_cache(&mut self, batch_id: Option<u64>) -> Result<()> {
let futures: Vec<_> = self
.clients
.iter_mut()
.map(|client| client.clear_cache(batch_id))
.collect();
join_all(futures).await.into_iter().collect()
}
/// Filter a cached batch
#[instrument(skip(self))]
pub async fn filter_batch(
&mut self,
batch_id: u64,
request_ids: Vec<u64>,
) -> Result<Option<CachedBatch>> {
let futures: Vec<_> = self
.clients
.iter_mut()
.map(|client| Box::pin(client.filter_batch(batch_id, request_ids.clone())))
.collect();
// all shards return the same message
join_all(futures).await.pop().unwrap()
}
/// Warmup on a max size batch
///
/// Returns the maximum amount of tokens supported by the hardware
#[instrument(skip(self))]
pub async fn warmup(
&mut self,
max_input_length: u32,
max_prefill_tokens: u32,
max_total_tokens: u32,
) -> Result<Option<u32>> {
let futures: Vec<_> = self
.clients
.iter_mut()
.map(|client| {
Box::pin(client.warmup(max_input_length, max_prefill_tokens, max_total_tokens))
})
.collect();
// Take the minimum value
let results = join_all(futures)
.await
.into_iter()
.collect::<Result<Vec<Option<u32>>>>()?;
Ok(results.into_iter().flatten().min())
}
/// Generate one token for each request in the given batch
///
/// Returns Generation for each request in batch
/// and the next cached batch
#[instrument(skip_all, fields(id = &batch.id, size = &batch.size))]
pub async fn prefill(
&mut self,
batch: Batch,
) -> Result<(Vec<Generation>, Option<CachedBatch>)> {
let futures: Vec<_> = self
.clients
.iter_mut()
.map(|client| Box::pin(client.prefill(batch.clone())))
.collect();
let results: Result<Vec<(Vec<Generation>, Option<CachedBatch>)>> =
join_all(futures).await.into_iter().collect();
merge_generations(results?)
}
/// Generate one token for each request in the given cached batches
///
/// Returns Generation for each request in batches
/// and the next cached batch
#[instrument(skip_all, fields(size = batches.iter().map(|batch|{batch.size}).sum::<u32>()))]
pub async fn decode(
&mut self,
batches: Vec<CachedBatch>,
) -> Result<(Vec<Generation>, Option<CachedBatch>)> {
let futures: Vec<_> = self
.clients
.iter_mut()
.map(|client| Box::pin(client.decode(batches.clone())))
.collect();
let results: Result<Vec<(Vec<Generation>, Option<CachedBatch>)>> =
join_all(futures).await.into_iter().collect();
merge_generations(results?)
}
}
/// Merge generations from the different model shards
fn merge_generations(
mut results: Vec<(Vec<Generation>, Option<CachedBatch>)>,
) -> Result<(Vec<Generation>, Option<CachedBatch>)> {
let (mut generations, next_batch) = results.pop().ok_or(ClientError::EmptyResults)?;
for (mut shard_generations, _) in results.into_iter() {
generations.append(&mut shard_generations);
}
Ok((generations, next_batch))
}
| 0
|
hf_public_repos/text-generation-inference/router/client
|
hf_public_repos/text-generation-inference/router/client/src/lib.rs
|
//! Text Generation gRPC client library
mod client;
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod sharded_client;
pub use client::Client;
pub use pb::generate::v1::HealthResponse;
pub use pb::generate::v1::InfoResponse as ShardInfo;
pub use pb::generate::v1::{
Batch, CachedBatch, FinishReason, GeneratedText, Generation, NextTokenChooserParameters,
PrefillTokens, Request, StoppingCriteriaParameters,
};
pub use sharded_client::ShardedClient;
use thiserror::Error;
use tonic::transport;
use tonic::Status;
#[derive(Error, Debug, Clone)]
pub enum ClientError {
#[error("Could not connect to Text Generation server: {0}")]
Connection(String),
#[error("Server error: {0}")]
Generation(String),
#[error("Sharded results are empty")]
EmptyResults,
}
impl From<Status> for ClientError {
fn from(err: Status) -> Self {
let err = Self::Generation(err.message().to_string());
tracing::error!("{err}");
err
}
}
impl From<transport::Error> for ClientError {
fn from(err: transport::Error) -> Self {
let err = Self::Connection(err.to_string());
tracing::error!("{err}");
err
}
}
pub type Result<T> = std::result::Result<T, ClientError>;
| 0
|
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