feat: support phi3.5 moe (#2479)
* feat: support phi3.5 moe model loading * fix: prefer llama base model and improve rotary logic * feat: return reasonable generation and add integration test * fix: run lint and update docs * fix: rerun lint for openapi docs * fix: prefer do_sample false unless temp is set by user, and update chat tests * fix: small typo adjustments * fix: consolidate long rope paths * fix: revert greedy by default and test changes * Vendor configuration so that we don't have to `trust_remote_code` * Use SparseMoELayer * Add support for dense MoE * Some type annotations * Add the usual model tests * Ruff. --------- Co-authored-by: Daniël de Kok <me@danieldk.eu> Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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GitHub
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@ -20,6 +20,7 @@ Text Generation Inference enables serving optimized models on specific hardware
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- [Mixtral](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1)
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- [Mixtral](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1)
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- [Gpt Bigcode](https://huggingface.co/bigcode/gpt_bigcode-santacoder)
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- [Gpt Bigcode](https://huggingface.co/bigcode/gpt_bigcode-santacoder)
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- [Phi](https://huggingface.co/microsoft/phi-1_5)
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- [Phi](https://huggingface.co/microsoft/phi-1_5)
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- [PhiMoe](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct)
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- [Baichuan](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)
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- [Baichuan](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)
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- [Falcon](https://huggingface.co/tiiuae/falcon-7b-instruct)
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- [Falcon](https://huggingface.co/tiiuae/falcon-7b-instruct)
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- [StarCoder 2](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
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- [StarCoder 2](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
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@ -0,0 +1,99 @@
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@ -0,0 +1,438 @@
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|
"text": "order"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13883,
|
||||||
|
"logprob": -0.47436523,
|
||||||
|
"special": false,
|
||||||
|
"text": " optimization"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 5687,
|
||||||
|
"logprob": -0.00027036667,
|
||||||
|
"special": false,
|
||||||
|
"text": " algorithm"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"top_tokens": null
|
||||||
|
},
|
||||||
|
"generated_text": "Gradient descent is a first-order optimization algorithm"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"details": {
|
||||||
|
"best_of_sequences": null,
|
||||||
|
"finish_reason": "length",
|
||||||
|
"generated_tokens": 10,
|
||||||
|
"prefill": [
|
||||||
|
{
|
||||||
|
"id": 1724,
|
||||||
|
"logprob": null,
|
||||||
|
"text": "What"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 338,
|
||||||
|
"logprob": -0.71484375,
|
||||||
|
"text": "is"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 16030,
|
||||||
|
"logprob": -13.9375,
|
||||||
|
"text": "gradient"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 26815,
|
||||||
|
"logprob": -0.049346924,
|
||||||
|
"text": "descent"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 29973,
|
||||||
|
"logprob": -3.0078125,
|
||||||
|
"text": "?"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13,
|
||||||
|
"logprob": -2.8242188,
|
||||||
|
"text": "\n"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13,
|
||||||
|
"logprob": -0.86328125,
|
||||||
|
"text": "\n"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"seed": null,
|
||||||
|
"tokens": [
|
||||||
|
{
|
||||||
|
"id": 25584,
|
||||||
|
"logprob": -0.017196655,
|
||||||
|
"special": false,
|
||||||
|
"text": "Grad"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 993,
|
||||||
|
"logprob": -0.0028438568,
|
||||||
|
"special": false,
|
||||||
|
"text": "ient"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 26815,
|
||||||
|
"logprob": -0.023254395,
|
||||||
|
"special": false,
|
||||||
|
"text": " descent"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 338,
|
||||||
|
"logprob": -2.026558e-05,
|
||||||
|
"special": false,
|
||||||
|
"text": " is"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 263,
|
||||||
|
"logprob": -0.5229492,
|
||||||
|
"special": false,
|
||||||
|
"text": " a"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 937,
|
||||||
|
"logprob": -0.17602539,
|
||||||
|
"special": false,
|
||||||
|
"text": " first"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 29899,
|
||||||
|
"logprob": 0.0,
|
||||||
|
"special": false,
|
||||||
|
"text": "-"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 2098,
|
||||||
|
"logprob": -0.00011622906,
|
||||||
|
"special": false,
|
||||||
|
"text": "order"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13883,
|
||||||
|
"logprob": -0.48608398,
|
||||||
|
"special": false,
|
||||||
|
"text": " optimization"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 5687,
|
||||||
|
"logprob": -0.00027894974,
|
||||||
|
"special": false,
|
||||||
|
"text": " algorithm"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"top_tokens": null
|
||||||
|
},
|
||||||
|
"generated_text": "Gradient descent is a first-order optimization algorithm"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"details": {
|
||||||
|
"best_of_sequences": null,
|
||||||
|
"finish_reason": "length",
|
||||||
|
"generated_tokens": 10,
|
||||||
|
"prefill": [
|
||||||
|
{
|
||||||
|
"id": 1724,
|
||||||
|
"logprob": null,
|
||||||
|
"text": "What"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 338,
|
||||||
|
"logprob": -0.7192383,
|
||||||
|
"text": "is"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 16030,
|
||||||
|
"logprob": -13.9375,
|
||||||
|
"text": "gradient"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 26815,
|
||||||
|
"logprob": -0.050445557,
|
||||||
|
"text": "descent"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 29973,
|
||||||
|
"logprob": -3.0078125,
|
||||||
|
"text": "?"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13,
|
||||||
|
"logprob": -2.8242188,
|
||||||
|
"text": "\n"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13,
|
||||||
|
"logprob": -0.8276367,
|
||||||
|
"text": "\n"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"seed": null,
|
||||||
|
"tokens": [
|
||||||
|
{
|
||||||
|
"id": 25584,
|
||||||
|
"logprob": -0.01727295,
|
||||||
|
"special": false,
|
||||||
|
"text": "Grad"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 993,
|
||||||
|
"logprob": -0.0027542114,
|
||||||
|
"special": false,
|
||||||
|
"text": "ient"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 26815,
|
||||||
|
"logprob": -0.023254395,
|
||||||
|
"special": false,
|
||||||
|
"text": " descent"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 338,
|
||||||
|
"logprob": -2.0384789e-05,
|
||||||
|
"special": false,
|
||||||
|
"text": " is"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 263,
|
||||||
|
"logprob": -0.5229492,
|
||||||
|
"special": false,
|
||||||
|
"text": " a"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 937,
|
||||||
|
"logprob": -0.17126465,
|
||||||
|
"special": false,
|
||||||
|
"text": " first"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 29899,
|
||||||
|
"logprob": 0.0,
|
||||||
|
"special": false,
|
||||||
|
"text": "-"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 2098,
|
||||||
|
"logprob": -0.00011301041,
|
||||||
|
"special": false,
|
||||||
|
"text": "order"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13883,
|
||||||
|
"logprob": -0.48608398,
|
||||||
|
"special": false,
|
||||||
|
"text": " optimization"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 5687,
|
||||||
|
"logprob": -0.00027894974,
|
||||||
|
"special": false,
|
||||||
|
"text": " algorithm"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"top_tokens": null
|
||||||
|
},
|
||||||
|
"generated_text": "Gradient descent is a first-order optimization algorithm"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
@ -0,0 +1,75 @@
|
||||||
|
import pytest
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="module")
|
||||||
|
def flash_phi35_moe_handle(launcher):
|
||||||
|
with launcher(
|
||||||
|
"microsoft/Phi-3.5-MoE-instruct",
|
||||||
|
num_shard=4,
|
||||||
|
) as handle:
|
||||||
|
yield handle
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="module")
|
||||||
|
async def flash_phi35_moe(flash_phi35_moe_handle):
|
||||||
|
await flash_phi35_moe_handle.health(300)
|
||||||
|
return flash_phi35_moe_handle.client
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_flash_phi35_moe(flash_phi35_moe, response_snapshot):
|
||||||
|
response = await flash_phi35_moe.generate(
|
||||||
|
"What is gradient descent?\n\n", max_new_tokens=10, decoder_input_details=True
|
||||||
|
)
|
||||||
|
|
||||||
|
assert response.details.generated_tokens == 10
|
||||||
|
assert (
|
||||||
|
response.generated_text
|
||||||
|
== "Gradient descent is a first-order optimization algorithm"
|
||||||
|
)
|
||||||
|
assert response == response_snapshot
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_flash_phi35_moe_all_params(flash_phi35_moe, response_snapshot):
|
||||||
|
response = await flash_phi35_moe.generate(
|
||||||
|
"What is gradient descent?\n\n",
|
||||||
|
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 == 10
|
||||||
|
assert (
|
||||||
|
response.generated_text
|
||||||
|
== "What is gradient descent?\n\nHello! It seems you're addressing a"
|
||||||
|
)
|
||||||
|
assert response == response_snapshot
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
async def test_flash_phi35_moe_load(flash_phi35_moe, generate_load, response_snapshot):
|
||||||
|
responses = await generate_load(
|
||||||
|
flash_phi35_moe, "What is gradient descent?\n\n", max_new_tokens=10, n=4
|
||||||
|
)
|
||||||
|
|
||||||
|
assert len(responses) == 4
|
||||||
|
assert responses[0].details.generated_tokens == 10
|
||||||
|
assert (
|
||||||
|
responses[0].generated_text
|
||||||
|
== "Gradient descent is a first-order optimization algorithm"
|
||||||
|
)
|
||||||
|
assert all(
|
||||||
|
[r.generated_text == responses[0].generated_text for r in responses]
|
||||||
|
), f"{[r.generated_text for r in responses]}"
|
||||||
|
|
||||||
|
assert responses == response_snapshot
|
||||||
|
|
@ -4,7 +4,9 @@ import pytest
|
||||||
@pytest.fixture(scope="module")
|
@pytest.fixture(scope="module")
|
||||||
def flash_llama_grammar_tools_handle(launcher):
|
def flash_llama_grammar_tools_handle(launcher):
|
||||||
with launcher(
|
with launcher(
|
||||||
"TinyLlama/TinyLlama-1.1B-Chat-v1.0", num_shard=2, disable_grammar_support=False
|
"meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||||
|
num_shard=2,
|
||||||
|
disable_grammar_support=False,
|
||||||
) as handle:
|
) as handle:
|
||||||
yield handle
|
yield handle
|
||||||
|
|
||||||
|
|
@ -208,7 +210,7 @@ async def test_flash_llama_grammar_tools_stream(
|
||||||
async for response in responses:
|
async for response in responses:
|
||||||
count += 1
|
count += 1
|
||||||
|
|
||||||
assert count == 48
|
assert count == 28
|
||||||
assert response == response_snapshot
|
assert response == response_snapshot
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -159,6 +159,7 @@ pub enum Config {
|
||||||
#[serde(rename = "phi-msft")]
|
#[serde(rename = "phi-msft")]
|
||||||
PhiMsft,
|
PhiMsft,
|
||||||
Phi3,
|
Phi3,
|
||||||
|
PhiMoe,
|
||||||
Llama,
|
Llama,
|
||||||
Baichuan,
|
Baichuan,
|
||||||
Paligemma(Paligemma),
|
Paligemma(Paligemma),
|
||||||
|
|
|
||||||
|
|
@ -166,6 +166,20 @@ class PositionRotaryEmbedding(nn.Module):
|
||||||
1 + math.log(scale) / math.log(original_max_position_embeddings)
|
1 + math.log(scale) / math.log(original_max_position_embeddings)
|
||||||
)
|
)
|
||||||
|
|
||||||
|
# if short_mscale and long_mscale are provided we need to scale the freqs
|
||||||
|
# using the Phi3LongRoPEScaledRotaryEmbedding
|
||||||
|
if ("short_mscale" in rope_scaling) and ("long_mscale" in rope_scaling):
|
||||||
|
short_mscale = rope_scaling["short_mscale"]
|
||||||
|
long_mscale = rope_scaling["long_mscale"]
|
||||||
|
return Phi3LongRoPEScaledRotaryEmbedding(
|
||||||
|
short_inv_freq=short_inv_freq,
|
||||||
|
long_inv_freq=long_inv_freq,
|
||||||
|
max_position_embeddings=config.max_position_embeddings,
|
||||||
|
short_mscale=short_mscale,
|
||||||
|
long_mscale=long_mscale,
|
||||||
|
original_max_position_embeddings=original_max_position_embeddings,
|
||||||
|
)
|
||||||
|
|
||||||
return SuRotaryEmbedding(
|
return SuRotaryEmbedding(
|
||||||
short_inv_freq=short_inv_freq,
|
short_inv_freq=short_inv_freq,
|
||||||
long_inv_freq=long_inv_freq,
|
long_inv_freq=long_inv_freq,
|
||||||
|
|
@ -287,6 +301,7 @@ class SuRotaryEmbedding(PositionRotaryEmbedding):
|
||||||
# or if we're on a new device (possibly due to tracing for instance)
|
# or if we're on a new device (possibly due to tracing for instance)
|
||||||
if (
|
if (
|
||||||
seqlen > self._seq_len_cached
|
seqlen > self._seq_len_cached
|
||||||
|
or self._cos_cached is None
|
||||||
or self._cos_cached.device != device
|
or self._cos_cached.device != device
|
||||||
or self._cos_cached.dtype != dtype
|
or self._cos_cached.dtype != dtype
|
||||||
):
|
):
|
||||||
|
|
@ -308,6 +323,63 @@ class SuRotaryEmbedding(PositionRotaryEmbedding):
|
||||||
self._sin_cached = (torch.sin(freqs) * self.scaling_factor).to(dtype)
|
self._sin_cached = (torch.sin(freqs) * self.scaling_factor).to(dtype)
|
||||||
|
|
||||||
|
|
||||||
|
class Phi3LongRoPEScaledRotaryEmbedding(PositionRotaryEmbedding):
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
short_inv_freq: torch.Tensor,
|
||||||
|
long_inv_freq: torch.Tensor,
|
||||||
|
max_position_embeddings: int,
|
||||||
|
short_mscale: float,
|
||||||
|
long_mscale: float,
|
||||||
|
original_max_position_embeddings: int,
|
||||||
|
):
|
||||||
|
super(PositionRotaryEmbedding, self).__init__()
|
||||||
|
self.short_inv_freq = short_inv_freq
|
||||||
|
self.long_inv_freq = long_inv_freq
|
||||||
|
self.max_position_embeddings = max_position_embeddings
|
||||||
|
self.short_mscale = short_mscale
|
||||||
|
self.long_mscale = long_mscale
|
||||||
|
self.original_max_position_embeddings = original_max_position_embeddings
|
||||||
|
|
||||||
|
# cache
|
||||||
|
self._seq_len_cached = 0
|
||||||
|
self._cos_cached = None
|
||||||
|
self._sin_cached = None
|
||||||
|
self._cos_k_cached = None
|
||||||
|
self._sin_k_cached = None
|
||||||
|
self.dynamic_args = None
|
||||||
|
|
||||||
|
def _update_cos_sin_cache(self, dtype, device, seqlen):
|
||||||
|
if (
|
||||||
|
seqlen > self._seq_len_cached
|
||||||
|
or self._cos_cached is None
|
||||||
|
or self._cos_cached.device != device
|
||||||
|
or self._cos_cached.dtype != dtype
|
||||||
|
):
|
||||||
|
self._seq_len_cached = seqlen
|
||||||
|
t = torch.arange(seqlen, device=device, dtype=self.short_inv_freq.dtype)
|
||||||
|
|
||||||
|
short_freqs = torch.outer(
|
||||||
|
t[: self.original_max_position_embeddings],
|
||||||
|
self.short_inv_freq.to(device=t.device),
|
||||||
|
)
|
||||||
|
|
||||||
|
long_freqs = torch.outer(
|
||||||
|
t[self.original_max_position_embeddings :],
|
||||||
|
self.long_inv_freq.to(device=t.device),
|
||||||
|
)
|
||||||
|
|
||||||
|
short_freqs = short_freqs * self.short_mscale
|
||||||
|
long_freqs = long_freqs * self.long_mscale
|
||||||
|
|
||||||
|
freqs = torch.empty((seqlen, short_freqs.shape[1]), device=device)
|
||||||
|
freqs[: self.original_max_position_embeddings] = short_freqs
|
||||||
|
freqs[self.original_max_position_embeddings :] = long_freqs
|
||||||
|
|
||||||
|
self._cos_cached = torch.cos(freqs).to(dtype)
|
||||||
|
self._sin_cached = torch.sin(freqs).to(dtype)
|
||||||
|
|
||||||
|
|
||||||
class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding):
|
class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding):
|
||||||
def __init__(self, dim, max_position_embeddings, base, device, scaling_factor):
|
def __init__(self, dim, max_position_embeddings, base, device, scaling_factor):
|
||||||
inv_freq = _create_inv_freq(dim, base, device)
|
inv_freq = _create_inv_freq(dim, base, device)
|
||||||
|
|
@ -467,7 +539,6 @@ def apply_llama3_scaling(
|
||||||
elif wavelen > low_freq_wavelen:
|
elif wavelen > low_freq_wavelen:
|
||||||
new_freqs.append(freq / scaling_factor)
|
new_freqs.append(freq / scaling_factor)
|
||||||
else:
|
else:
|
||||||
|
|
||||||
assert low_freq_wavelen != high_freq_wavelen
|
assert low_freq_wavelen != high_freq_wavelen
|
||||||
smooth = (original_max_position_embeddings / wavelen - low_freq_factor) / (
|
smooth = (original_max_position_embeddings / wavelen - low_freq_factor) / (
|
||||||
high_freq_factor - low_freq_factor
|
high_freq_factor - low_freq_factor
|
||||||
|
|
|
||||||
|
|
@ -32,6 +32,9 @@ from text_generation_server.models.custom_modeling.phi_modeling import (
|
||||||
PhiConfig,
|
PhiConfig,
|
||||||
PhiForCausalLM,
|
PhiForCausalLM,
|
||||||
)
|
)
|
||||||
|
from text_generation_server.models.custom_modeling.flash_phi_moe_modeling import (
|
||||||
|
PhiMoEConfig,
|
||||||
|
)
|
||||||
from text_generation_server.models.custom_modeling.t5_modeling import (
|
from text_generation_server.models.custom_modeling.t5_modeling import (
|
||||||
T5ForConditionalGeneration,
|
T5ForConditionalGeneration,
|
||||||
)
|
)
|
||||||
|
|
@ -237,6 +240,11 @@ class ModelType(enum.Enum):
|
||||||
"name": "Phi",
|
"name": "Phi",
|
||||||
"url": "https://huggingface.co/microsoft/phi-1_5",
|
"url": "https://huggingface.co/microsoft/phi-1_5",
|
||||||
}
|
}
|
||||||
|
PHI_MOE = {
|
||||||
|
"type": "phimoe",
|
||||||
|
"name": "PhiMoe",
|
||||||
|
"url": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct",
|
||||||
|
}
|
||||||
BAICHUAN = {
|
BAICHUAN = {
|
||||||
"type": "baichuan",
|
"type": "baichuan",
|
||||||
"name": "Baichuan",
|
"name": "Baichuan",
|
||||||
|
|
@ -768,6 +776,29 @@ def get_model(
|
||||||
trust_remote_code=trust_remote_code,
|
trust_remote_code=trust_remote_code,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
elif model_type == PHI_MOE:
|
||||||
|
if FLASH_ATTENTION:
|
||||||
|
return FlashCausalLM(
|
||||||
|
model_id=model_id,
|
||||||
|
model_class=FlashLlamaForCausalLM,
|
||||||
|
config_class=PhiMoEConfig,
|
||||||
|
revision=revision,
|
||||||
|
quantize=quantize,
|
||||||
|
speculator=speculator,
|
||||||
|
dtype=dtype,
|
||||||
|
trust_remote_code=trust_remote_code,
|
||||||
|
lora_adapter_ids=lora_adapter_ids,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
return CausalLM.fallback(
|
||||||
|
model_id,
|
||||||
|
revision,
|
||||||
|
quantize=quantize,
|
||||||
|
speculator=speculator,
|
||||||
|
dtype=dtype,
|
||||||
|
trust_remote_code=trust_remote_code,
|
||||||
|
)
|
||||||
|
|
||||||
elif model_type == "phi-msft":
|
elif model_type == "phi-msft":
|
||||||
if FLASH_ATTENTION:
|
if FLASH_ATTENTION:
|
||||||
raise NotImplementedError(
|
raise NotImplementedError(
|
||||||
|
|
|
||||||
|
|
@ -19,7 +19,7 @@
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
|
|
||||||
from contextlib import contextmanager
|
from contextlib import contextmanager
|
||||||
from typing import List, Optional, Tuple
|
from typing import List, Optional, Tuple, Type
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
import torch.distributed
|
import torch.distributed
|
||||||
|
|
@ -28,6 +28,7 @@ from torch import nn
|
||||||
from transformers.activations import ACT2FN
|
from transformers.activations import ACT2FN
|
||||||
|
|
||||||
from text_generation_server.layers.attention import PREFILL_IN_KV_CACHE
|
from text_generation_server.layers.attention import PREFILL_IN_KV_CACHE
|
||||||
|
from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
|
||||||
from text_generation_server.utils.import_utils import SYSTEM
|
from text_generation_server.utils.import_utils import SYSTEM
|
||||||
from text_generation_server.layers.attention import (
|
from text_generation_server.layers.attention import (
|
||||||
paged_attention,
|
paged_attention,
|
||||||
|
|
@ -46,12 +47,19 @@ from text_generation_server.layers import (
|
||||||
from text_generation_server.layers.rotary import PositionRotaryEmbedding
|
from text_generation_server.layers.rotary import PositionRotaryEmbedding
|
||||||
from text_generation_server.layers.layernorm import (
|
from text_generation_server.layers.layernorm import (
|
||||||
FastRMSNorm,
|
FastRMSNorm,
|
||||||
|
FastLayerNorm,
|
||||||
|
)
|
||||||
|
from text_generation_server.layers import (
|
||||||
|
FastLinear,
|
||||||
)
|
)
|
||||||
from text_generation_server.utils.weights import (
|
from text_generation_server.utils.weights import (
|
||||||
Weights,
|
Weights,
|
||||||
)
|
)
|
||||||
from text_generation_server.layers.fp8 import HybridFP8UnquantLoader
|
from text_generation_server.layers.fp8 import HybridFP8UnquantLoader
|
||||||
|
|
||||||
|
if SYSTEM != "ipex":
|
||||||
|
pass
|
||||||
|
|
||||||
if SYSTEM == "rocm":
|
if SYSTEM == "rocm":
|
||||||
try:
|
try:
|
||||||
from vllm import _custom_C
|
from vllm import _custom_C
|
||||||
|
|
@ -245,6 +253,42 @@ class FlashLlamaAttention(torch.nn.Module):
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class Phi3MoE(nn.Module):
|
||||||
|
def __init__(
|
||||||
|
self, prefix: str, config, moe_layer_cls: Type[MoELayer], weights: Weights
|
||||||
|
):
|
||||||
|
super().__init__()
|
||||||
|
|
||||||
|
# gating
|
||||||
|
self.gate = FastLinear.load(config, f"{prefix}.gate", weights, bias=False)
|
||||||
|
|
||||||
|
self.moe = moe_layer_cls(
|
||||||
|
prefix=f"{prefix}.experts",
|
||||||
|
n_experts=config.num_local_experts,
|
||||||
|
n_expert_group=None,
|
||||||
|
renormalize=True,
|
||||||
|
topk=config.num_experts_per_tok,
|
||||||
|
topk_group=None,
|
||||||
|
weights=weights,
|
||||||
|
gate_proj_name="w1",
|
||||||
|
up_proj_name="w3",
|
||||||
|
down_proj_name="w2",
|
||||||
|
)
|
||||||
|
|
||||||
|
self.process_group = weights.process_group
|
||||||
|
|
||||||
|
def forward(self, x, adapter_data) -> torch.Tensor:
|
||||||
|
# router_logits: (num_tokens, n_experts)
|
||||||
|
router_logits = self.gate(x)
|
||||||
|
out = self.moe(x, gating_output=router_logits)
|
||||||
|
|
||||||
|
# Reduce sum
|
||||||
|
if self.process_group.size() > 1:
|
||||||
|
torch.distributed.all_reduce(out, group=self.process_group)
|
||||||
|
|
||||||
|
return out.view(*x.shape)
|
||||||
|
|
||||||
|
|
||||||
class LlamaMLP(nn.Module):
|
class LlamaMLP(nn.Module):
|
||||||
def __init__(self, prefix, config, weights, index):
|
def __init__(self, prefix, config, weights, index):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
|
|
@ -358,18 +402,40 @@ class FlashLlamaLayer(nn.Module):
|
||||||
weights=weights,
|
weights=weights,
|
||||||
)
|
)
|
||||||
|
|
||||||
self.mlp = LlamaMLP(
|
if config.model_type == "phimoe":
|
||||||
prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
|
moe_layer_cls = (
|
||||||
)
|
SparseMoELayer
|
||||||
|
if SparseMoELayer.is_supported(weights)
|
||||||
self.input_layernorm = FastRMSNorm.load(
|
else DenseMoELayer
|
||||||
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
|
)
|
||||||
)
|
self.dense = Phi3MoE(
|
||||||
self.post_attention_layernorm = FastRMSNorm.load(
|
f"{prefix}.block_sparse_moe", config, moe_layer_cls, weights
|
||||||
prefix=f"{prefix}.post_attention_layernorm",
|
)
|
||||||
weights=weights,
|
# with moe the layernorms are are not rmsnorms and they have bias
|
||||||
eps=config.rms_norm_eps,
|
self.input_layernorm = FastLayerNorm.load(
|
||||||
)
|
prefix=f"{prefix}.input_layernorm",
|
||||||
|
weights=weights,
|
||||||
|
eps=config.rms_norm_eps,
|
||||||
|
)
|
||||||
|
self.post_attention_layernorm = FastLayerNorm.load(
|
||||||
|
prefix=f"{prefix}.post_attention_layernorm",
|
||||||
|
weights=weights,
|
||||||
|
eps=config.rms_norm_eps,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
self.dense = LlamaMLP(
|
||||||
|
prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
|
||||||
|
)
|
||||||
|
self.input_layernorm = FastRMSNorm.load(
|
||||||
|
prefix=f"{prefix}.input_layernorm",
|
||||||
|
weights=weights,
|
||||||
|
eps=config.rms_norm_eps,
|
||||||
|
)
|
||||||
|
self.post_attention_layernorm = FastRMSNorm.load(
|
||||||
|
prefix=f"{prefix}.post_attention_layernorm",
|
||||||
|
weights=weights,
|
||||||
|
eps=config.rms_norm_eps,
|
||||||
|
)
|
||||||
|
|
||||||
def forward(
|
def forward(
|
||||||
self,
|
self,
|
||||||
|
|
@ -406,7 +472,7 @@ class FlashLlamaLayer(nn.Module):
|
||||||
attn_output, res
|
attn_output, res
|
||||||
)
|
)
|
||||||
|
|
||||||
mlp_output = self.mlp(normed_attn_res_output, adapter_data)
|
mlp_output = self.dense(normed_attn_res_output, adapter_data)
|
||||||
|
|
||||||
return mlp_output, attn_res
|
return mlp_output, attn_res
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,254 @@
|
||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
|
||||||
|
"""PyTorch Phi-MoE model."""
|
||||||
|
|
||||||
|
from transformers.configuration_utils import PretrainedConfig
|
||||||
|
from transformers.utils import logging
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
PHIMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||||
|
"microsoft/Phi-3.5-MoE-instruct": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/config.json",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class PhiMoEConfig(PretrainedConfig):
|
||||||
|
r"""
|
||||||
|
This is the configuration class to store the configuration of a [`PhiMoEModel`]. It is used to instantiate a Phi-MoE
|
||||||
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||||
|
defaults will yield a similar configuration to that of the
|
||||||
|
[microsoft/Phi-3.5-MoE-instruct](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct).
|
||||||
|
|
||||||
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||||
|
documentation from [`PretrainedConfig`] for more information.
|
||||||
|
|
||||||
|
|
||||||
|
Args:
|
||||||
|
vocab_size (`int`, *optional*, defaults to 32064):
|
||||||
|
Vocabulary size of the PhiMoE model. Defines the number of different tokens that can be represented by the
|
||||||
|
`inputs_ids` passed when calling [`PhiMoEModel`]
|
||||||
|
hidden_size (`int`, *optional*, defaults to 4096):
|
||||||
|
Dimension of the hidden representations.
|
||||||
|
intermediate_size (`int`, *optional*, defaults to 6400):
|
||||||
|
Dimension of the MLP representations.
|
||||||
|
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||||
|
Number of hidden layers in the Transformer encoder.
|
||||||
|
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||||
|
Number of attention heads for each attention layer in the Transformer encoder.
|
||||||
|
num_key_value_heads (`int`, *optional*, defaults to 8):
|
||||||
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||||
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||||
|
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||||
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||||
|
by meanpooling all the original heads within that group. For more details checkout [this
|
||||||
|
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
|
||||||
|
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||||
|
The non-linear activation function (function or string) in the decoder.
|
||||||
|
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
|
||||||
|
The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
|
||||||
|
allows sequence of up to 4096*32 tokens.
|
||||||
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||||
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||||
|
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||||
|
The epsilon used by the rms normalization layers.
|
||||||
|
use_cache (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||||
|
relevant if `config.is_decoder=True`.
|
||||||
|
pad_token_id (`int`, *optional*):
|
||||||
|
The id of the padding token.
|
||||||
|
bos_token_id (`int`, *optional*, defaults to 1):
|
||||||
|
The id of the "beginning-of-sequence" token.
|
||||||
|
eos_token_id (`int`, *optional*, defaults to 2):
|
||||||
|
The id of the "end-of-sequence" token.
|
||||||
|
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether the model's input and output word embeddings should be tied.
|
||||||
|
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||||
|
The base period of the RoPE embeddings.
|
||||||
|
rope_scaling (`dict`, *optional*):
|
||||||
|
The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
|
||||||
|
contain the following keys: `type`, `short_factor`, `long_factor`, `short_mscale`, `long_mscale` and
|
||||||
|
`original_max_position_embeddings`. The `type` must be `longrope`, the `short_mscale` and `long_scale` must
|
||||||
|
be numbers, the `short_factor` and `long_factor` must be lists of numbers with the same length as half of
|
||||||
|
the attention head size and the `original_max_position_embeddings` must be an integer.
|
||||||
|
sliding_window (`int`, *optional*):
|
||||||
|
Sliding window attention window size. If not specified, will default to `262144`.
|
||||||
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for the attention probabilities.
|
||||||
|
num_experts_per_tok (`int`, *optional*, defaults to 2):
|
||||||
|
The number of experts to root per-token, can be also interpreted as the `top-p` routing
|
||||||
|
parameter
|
||||||
|
num_local_experts (`int`, *optional*, defaults to 16):
|
||||||
|
Number of experts per Sparse MLP layer.
|
||||||
|
output_router_logits (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether or not the router logits should be returned by the model. Enabeling this will also
|
||||||
|
allow the model to output the auxiliary loss. See [here]() for more details
|
||||||
|
router_aux_loss_coef (`float`, *optional*, defaults to 0.0):
|
||||||
|
The aux loss factor for the total loss.
|
||||||
|
router_jitter_noise (`float`, *optional*, defaults to 0.01):
|
||||||
|
Amount of noise to add to the router.
|
||||||
|
|
||||||
|
```python
|
||||||
|
>>> from transformers import PhiMoEModel, PhiMoEConfig
|
||||||
|
|
||||||
|
>>> # Initializing a Phi-3 style configuration
|
||||||
|
>>> configuration = PhiMoEConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
|
||||||
|
|
||||||
|
>>> # Initializing a model from the configuration
|
||||||
|
>>> model = PhiMoEModel(configuration)
|
||||||
|
|
||||||
|
>>> # Accessing the model configuration
|
||||||
|
>>> configuration = model.config
|
||||||
|
```"""
|
||||||
|
|
||||||
|
model_type = "phimoe"
|
||||||
|
keys_to_ignore_at_inference = ["past_key_values"]
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_size=32064,
|
||||||
|
hidden_size=4096,
|
||||||
|
intermediate_size=6400,
|
||||||
|
num_hidden_layers=32,
|
||||||
|
num_attention_heads=32,
|
||||||
|
num_key_value_heads=8,
|
||||||
|
hidden_act="silu",
|
||||||
|
max_position_embeddings=4096 * 32,
|
||||||
|
initializer_range=0.02,
|
||||||
|
rms_norm_eps=1e-5,
|
||||||
|
use_cache=True,
|
||||||
|
pad_token_id=None,
|
||||||
|
bos_token_id=1,
|
||||||
|
eos_token_id=2,
|
||||||
|
tie_word_embeddings=False,
|
||||||
|
rope_theta=1e6,
|
||||||
|
rope_scaling=None,
|
||||||
|
sliding_window=None,
|
||||||
|
attention_dropout=0.0,
|
||||||
|
num_experts_per_tok=2,
|
||||||
|
num_local_experts=16,
|
||||||
|
output_router_logits=False,
|
||||||
|
router_aux_loss_coef=0.001,
|
||||||
|
router_jitter_noise=0.01,
|
||||||
|
input_jitter_noise=0.0,
|
||||||
|
attention_bias=False,
|
||||||
|
lm_head_bias=False,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
self.vocab_size = vocab_size
|
||||||
|
self.max_position_embeddings = max_position_embeddings
|
||||||
|
self.hidden_size = hidden_size
|
||||||
|
self.intermediate_size = intermediate_size
|
||||||
|
self.num_hidden_layers = num_hidden_layers
|
||||||
|
self.num_attention_heads = num_attention_heads
|
||||||
|
self.sliding_window = sliding_window
|
||||||
|
self.attention_bias = attention_bias
|
||||||
|
self.lm_head_bias = lm_head_bias
|
||||||
|
# for backward compatibility
|
||||||
|
if num_key_value_heads is None:
|
||||||
|
num_key_value_heads = num_attention_heads
|
||||||
|
|
||||||
|
self.num_key_value_heads = num_key_value_heads
|
||||||
|
self.hidden_act = hidden_act
|
||||||
|
self.initializer_range = initializer_range
|
||||||
|
self.rms_norm_eps = rms_norm_eps
|
||||||
|
self.use_cache = use_cache
|
||||||
|
self.rope_theta = rope_theta
|
||||||
|
self.attention_dropout = attention_dropout
|
||||||
|
|
||||||
|
self.num_experts_per_tok = num_experts_per_tok
|
||||||
|
self.num_local_experts = num_local_experts
|
||||||
|
self.output_router_logits = output_router_logits
|
||||||
|
self.router_aux_loss_coef = router_aux_loss_coef
|
||||||
|
self.router_jitter_noise = router_jitter_noise
|
||||||
|
self.input_jitter_noise = input_jitter_noise
|
||||||
|
|
||||||
|
self.rope_scaling = rope_scaling
|
||||||
|
self._rope_scaling_validation()
|
||||||
|
|
||||||
|
super().__init__(
|
||||||
|
pad_token_id=pad_token_id,
|
||||||
|
bos_token_id=bos_token_id,
|
||||||
|
eos_token_id=eos_token_id,
|
||||||
|
tie_word_embeddings=tie_word_embeddings,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
def _rope_scaling_validation(self):
|
||||||
|
"""
|
||||||
|
Validate the `rope_scaling` configuration.
|
||||||
|
"""
|
||||||
|
if self.rope_scaling is None:
|
||||||
|
return
|
||||||
|
|
||||||
|
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 6:
|
||||||
|
raise ValueError(
|
||||||
|
"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, "
|
||||||
|
f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, got {self.rope_scaling}"
|
||||||
|
)
|
||||||
|
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||||
|
rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
|
||||||
|
rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
|
||||||
|
rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None)
|
||||||
|
rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None)
|
||||||
|
original_max_position_embeddings = self.rope_scaling.get(
|
||||||
|
"original_max_position_embeddings", None
|
||||||
|
)
|
||||||
|
if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}"
|
||||||
|
)
|
||||||
|
if not (
|
||||||
|
isinstance(rope_scaling_short_factor, list)
|
||||||
|
and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
|
||||||
|
):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
|
||||||
|
)
|
||||||
|
if (
|
||||||
|
not len(rope_scaling_short_factor)
|
||||||
|
== self.hidden_size // self.num_attention_heads // 2
|
||||||
|
):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
|
||||||
|
)
|
||||||
|
if not (
|
||||||
|
isinstance(rope_scaling_long_factor, list)
|
||||||
|
and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
|
||||||
|
):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
|
||||||
|
)
|
||||||
|
if (
|
||||||
|
not len(rope_scaling_long_factor)
|
||||||
|
== self.hidden_size // self.num_attention_heads // 2
|
||||||
|
):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
|
||||||
|
)
|
||||||
|
if not isinstance(rope_scaling_short_mscale, (int, float)):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}"
|
||||||
|
)
|
||||||
|
if not isinstance(rope_scaling_long_mscale, (int, float)):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}"
|
||||||
|
)
|
||||||
|
if not isinstance(original_max_position_embeddings, int):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}"
|
||||||
|
)
|
||||||
Loading…
Reference in New Issue