Updating Phi3 (long context). (#1849)
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This commit is contained in:
Nicolas Patry
GitHub
parent
65539b743e
commit
a25737139d
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@ -3393,7 +3393,7 @@ dependencies = [
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[[package]]
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name = "text-generation-benchmark"
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version = "2.0.1"
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version = "2.0.2"
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dependencies = [
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"average",
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"clap",
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@ -3414,7 +3414,7 @@ dependencies = [
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[[package]]
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name = "text-generation-client"
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version = "2.0.1"
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version = "2.0.2"
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dependencies = [
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"futures",
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"grpc-metadata",
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@ -3430,7 +3430,7 @@ dependencies = [
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[[package]]
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name = "text-generation-launcher"
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version = "2.0.1"
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version = "2.0.2"
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dependencies = [
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"clap",
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"ctrlc",
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@ -3448,7 +3448,7 @@ dependencies = [
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[[package]]
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name = "text-generation-router"
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version = "2.0.1"
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version = "2.0.2"
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dependencies = [
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"async-stream",
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"axum",
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@ -136,6 +136,7 @@ pub enum Config {
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Phi,
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#[serde(rename = "phi-msft")]
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PhiMsft,
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Phi3,
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Llama,
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Baichuan,
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Gemma,
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@ -1029,10 +1029,10 @@ try:
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scaling_factor = None
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rope_scaling = _get_rope_config(config)
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if rope_scaling is not None:
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scaling_factor = rope_scaling["factor"]
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if rope_scaling["type"] == "linear":
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pass
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elif rope_scaling["type"] == "dynamic":
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scaling_factor = rope_scaling["factor"]
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return DynamicPositionRotaryEmbedding(
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dim=dim,
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max_position_embeddings=config.max_position_embeddings,
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@ -1041,6 +1041,7 @@ try:
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scaling_factor=scaling_factor,
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)
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elif rope_scaling["type"] == "yarn":
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scaling_factor = rope_scaling["factor"]
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return YarnPositionRotaryEmbedding(
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dim=2 * inv_freq.shape[0],
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max_position_embeddings=rope_scaling[
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@ -1054,6 +1055,52 @@ try:
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beta_fast=32,
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beta_slow=1,
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)
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elif rope_scaling["type"] == "su":
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short_factor = torch.tensor(
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rope_scaling["short_factor"], dtype=torch.float32, device=device
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)
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short_inv_freq = 1.0 / (
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short_factor
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* base
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** (
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torch.arange(0, dim, 2, device=device, dtype=torch.float32)
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/ dim
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)
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)
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long_factor = torch.tensor(
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rope_scaling["long_factor"], dtype=torch.float32, device=device
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)
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long_inv_freq = 1.0 / (
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long_factor
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* base
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** (
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torch.arange(0, dim, 2, device=device, dtype=torch.float32)
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/ dim
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)
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)
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original_max_position_embeddings = (
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config.original_max_position_embeddings
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)
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max_position_embeddings = config.max_position_embeddings
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if max_position_embeddings <= original_max_position_embeddings:
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scaling_factor = 1.0
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else:
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scale = (
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max_position_embeddings / original_max_position_embeddings
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)
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scaling_factor = math.sqrt(
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1
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+ math.log(scale)
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/ math.log(original_max_position_embeddings)
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)
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return SuRotaryEmbedding(
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short_inv_freq=short_inv_freq,
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long_inv_freq=long_inv_freq,
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scaling_factor=scaling_factor,
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original_max_position_embeddings=original_max_position_embeddings,
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)
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else:
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raise NotImplementedError(
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f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
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@ -1141,6 +1188,49 @@ try:
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# Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow.
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return cos.unsqueeze(1), sin.unsqueeze(1)
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class SuRotaryEmbedding(PositionRotaryEmbedding):
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def __init__(
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self,
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short_inv_freq,
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long_inv_freq,
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scaling_factor,
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original_max_position_embeddings,
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):
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super(PositionRotaryEmbedding, self).__init__()
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self.short_inv_freq = short_inv_freq
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self.long_inv_freq = long_inv_freq
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self.scaling_factor = scaling_factor
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self.original_max_position_embeddings = original_max_position_embeddings
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self._seq_len_cached = 0
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self._cos_cached = None
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self._sin_cached = None
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self._cos_k_cached = None
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self._sin_k_cached = None
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self.dynamic_args = None
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def _update_cos_sin_cache(self, dtype, device, seqlen):
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# Reset the tables if the sequence length has changed,
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# or if we're on a new device (possibly due to tracing for instance)
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if (
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seqlen > self._seq_len_cached
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or self._cos_cached.device != device
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or self._cos_cached.dtype != dtype
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):
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self._seq_len_cached = seqlen
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if seqlen > self.original_max_position_embeddings:
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inv_freq = self.long_inv_freq
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else:
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inv_freq = self.short_inv_freq
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t = torch.arange(seqlen, device=device, dtype=inv_freq.dtype)
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if self.scaling_factor is not None:
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t /= self.scaling_factor
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# Don't do einsum, it converts fp32 to fp16
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# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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freqs = torch.outer(t, inv_freq.to(device=t.device))
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self._cos_cached = torch.cos(freqs).to(dtype)
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self._sin_cached = torch.sin(freqs).to(dtype)
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class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding):
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def __init__(self, dim, max_position_embeddings, base, device, scaling_factor):
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inv_freq = _create_inv_freq(dim, base, device)
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