mirror of
https://github.com/huggingface/text-generation-inference.git
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bab02ff2bc
* feat: add ruff and resolve issue * fix: update client exports and adjust after rebase * fix: adjust syntax to avoid circular import * fix: adjust client ruff settings * fix: lint and refactor import check and avoid model enum as global names * fix: improve fbgemm_gpu check and lints * fix: update lints * fix: prefer comparing model enum over str * fix: adjust lints and ignore specific rules * fix: avoid unneeded quantize check
337 lines
11 KiB
Python
337 lines
11 KiB
Python
# imlementation of the PhiModel and PhiForCausalLM classes
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import torch
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import torch.distributed
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import math
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from torch import nn
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from typing import Optional, List, Tuple
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from transformers.configuration_utils import PretrainedConfig
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from text_generation_server.layers import (
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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SpeculativeHead,
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FastLinear,
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)
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# PhiConfig is the configuration class for the PhiModel.
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class PhiConfig(PretrainedConfig):
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def __init__(
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self,
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vocab_size=51200,
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n_positions=2048,
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n_embd=2560,
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n_layer=32,
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n_inner=None,
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n_head=32,
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rotary_dim=32,
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layer_norm_epsilon=1e-5,
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tie_word_embeddings=False,
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pad_vocab_size_multiple=64,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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no_bias=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_inner = n_inner
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self.n_head = n_head
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self.rotary_dim = rotary_dim
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self.layer_norm_epsilon = layer_norm_epsilon
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self.tie_word_embeddings = tie_word_embeddings
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self.pad_vocab_size_multiple = pad_vocab_size_multiple
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.no_bias = no_bias
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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# RotaryEmbedding is a class that implements the rotary embedding.
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class RotaryEmbedding(nn.Module):
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def __init__(self, dim, max_seq_len):
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super().__init__()
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inv_freq = [1.0 / 10000.0 ** (i / dim) for i in range(0, dim, 2)]
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inv_freq_len = len(inv_freq)
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inv_freq = torch.tensor(inv_freq).view(1, inv_freq_len)
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t = torch.arange(0, max_seq_len, dtype=torch.float).view(max_seq_len, 1)
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freqs = t.matmul(inv_freq)
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self.sin = freqs.sin()
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self.cos = freqs.cos()
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def apply_rotary_emb_qkv(self, qkv, seqlen_offset):
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b_size, seqlen, three, _, _headdim = qkv.shape
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if three != 3:
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raise Exception("unexpected shape for qkv")
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_, rotary_dim = self.cos.shape
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rotary_dim = rotary_dim * 2
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q_rot = qkv[:, :, 0, :, :rotary_dim]
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q_pass = qkv[:, :, 0, :, rotary_dim:]
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k_rot = qkv[:, :, 1, :, :rotary_dim]
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k_pass = qkv[:, :, 1, :, rotary_dim:]
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q12 = torch.chunk(q_rot, 2, dim=-1)
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k12 = torch.chunk(k_rot, 2, dim=-1)
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q1, q2 = q12[0], q12[1]
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k1, k2 = k12[0], k12[1]
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c = self.cos.narrow(0, seqlen_offset, seqlen).unsqueeze(1)
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s = self.sin.narrow(0, seqlen_offset, seqlen).unsqueeze(1)
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q_rot = torch.cat(
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[
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q1 * c - q2 * s,
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q1 * s + q2 * c,
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],
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dim=-1,
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)
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k_rot = torch.cat(
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[
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k1 * c - k2 * s,
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k1 * s + k2 * c,
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],
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dim=-1,
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)
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q = torch.cat([q_rot, q_pass], dim=-1)
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k = torch.cat([k_rot, k_pass], dim=-1)
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v = qkv[:, :, 2]
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return q, k, v
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# PhiCausalLMHead is the head of the PhiModel. It is a linear layer with a layer norm.
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class PhiCausalLMHead(nn.Module):
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def __init__(self, config, weights):
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super().__init__()
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self.ln = nn.LayerNorm.load(
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prefix="lm_head.ln",
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weights=weights,
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eps=config.layer_norm_epsilon,
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)
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self.linear = SpeculativeHead.load(
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config=config, prefix="lm_head.linear", weights=weights
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)
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def forward(self, hidden_states):
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hidden_states = self.ln(hidden_states)
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hidden_states = self.linear(hidden_states)
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return hidden_states
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# PhiMHA is a multi-head attention layer. This layer uses an attention mask to prevent tokens from attending to subsequent tokens.
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class PhiMHA(nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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self.Wqkv = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.Wqkv", weights=weights, bias=not config.no_bias
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)
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self.out_proj = TensorParallelRowLinear.load(
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config,
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prefix=f"{prefix}.out_proj",
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weights=weights,
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bias=not config.no_bias,
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)
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self.op_size = config.n_embd
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self.head_dim = int(config.n_embd / config.n_head)
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self.num_heads = config.n_head
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self.rotary_emb = RotaryEmbedding(
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config.rotary_dim,
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config.n_positions,
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)
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self.softmax_scale = 1.0 / math.sqrt(self.head_dim)
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def forward(
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self,
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hidden_states,
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past_kv_cache,
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attention_mask=None,
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):
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b_size, seq_len, _n_embd = hidden_states.shape
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qkv = self.Wqkv(hidden_states)
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qkv = qkv.view(b_size, seq_len, 3, self.num_heads, self.head_dim)
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seqlen_offset = 0 if past_kv_cache is None else past_kv_cache[0].shape[1]
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q, k, v = self.rotary_emb.apply_rotary_emb_qkv(qkv, seqlen_offset)
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# if there is a kv_cache, then we need to concatenate
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if past_kv_cache is not None:
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prev_k, prev_v = past_kv_cache
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k = torch.cat([prev_k, k], dim=1)
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v = torch.cat([prev_v, v], dim=1)
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past_kv_cache = [k, v]
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attn_weights = torch.einsum("bthd,bshd->bhts", q, k * self.softmax_scale)
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if attention_mask is not None:
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seqlen_k = k.shape[1]
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seqlen_q = q.shape[1]
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causal_mask = torch.triu(
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torch.full((seqlen_q, seqlen_k), -10000.0, device=attn_weights.device),
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1,
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)
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attn_weights = attn_weights + causal_mask.to(dtype=attn_weights.dtype)
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
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attn_output = attn_weights.matmul(v.transpose(1, 2)).squeeze(0)
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attn_output = (
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attn_output.view((b_size, self.num_heads, seq_len, self.head_dim))
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.transpose(1, 2)
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.flatten(-2)
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)
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return self.out_proj(attn_output), past_kv_cache
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# PhiMLP is a multi-layer perceptron. It contains two linear layers with a gelu activation function.
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class PhiMLP(nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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self.n_inner = config.n_inner
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self.fc1 = FastLinear.load(
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config=config,
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prefix=f"{prefix}.fc1",
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weights=weights,
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bias=False,
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)
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self.fc2 = FastLinear.load(
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config=config,
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prefix=f"{prefix}.fc2",
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weights=weights,
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bias=False,
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)
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self.activation = torch.nn.functional.gelu
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def forward(self, hidden_states):
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hidden_states = self.fc1(hidden_states)
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hidden_states = self.activation(hidden_states)
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hidden_states = self.fc2(hidden_states)
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return hidden_states
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# PhiBlock is a single transformer block. It contains a layer norm, a multi-head attention layer and an multi-layer perceptron.
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class PhiBlock(nn.Module):
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def __init__(self, layer_id, config, weights):
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super().__init__()
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self.layer_id = layer_id
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self.layer_norm = nn.LayerNorm.load(
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prefix=f"{layer_id}.ln", weights=weights, eps=config.layer_norm_epsilon
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)
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self.mixer = PhiMHA(prefix=f"{layer_id}.mixer", config=config, weights=weights)
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self.mlp = PhiMLP(prefix=f"{layer_id}.mlp", config=config, weights=weights)
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def forward(
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self,
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hidden_states,
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kv_cache,
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attention_mask,
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):
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residual = hidden_states
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hidden_states = self.layer_norm(hidden_states)
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attn_outputs, past_kv_cache = self.mixer(
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hidden_states, kv_cache, attention_mask
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)
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feed_forward_hidden_states = self.mlp(hidden_states)
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out = attn_outputs + feed_forward_hidden_states + residual
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return out, past_kv_cache
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# PhiModel implements the embedding layer and the transformer blocks.
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class PhiModel(nn.Module):
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def __init__(self, prefix: str, config, weights):
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super().__init__()
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self.tp_rank = weights.process_group.rank()
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self.tp_world_size = weights.process_group.size()
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self.embed_tokens = TensorParallelEmbedding(
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prefix=f"{prefix}.embd.wte", weights=weights
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)
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self.blocks = nn.ModuleList(
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[
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PhiBlock(f"{prefix}.h.{layer_id}", config, weights)
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for layer_id in range(config.n_layer)
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]
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)
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def forward(
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self,
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input_ids: torch.LongTensor,
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past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
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attention_mask: Optional[torch.ByteTensor] = None,
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return_dict: Optional[bool] = None,
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use_cache: Optional[bool] = None,
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) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
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hidden_states = self.embed_tokens(input_ids)
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seq_len = hidden_states.shape[1]
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mask = None if seq_len <= 1 else attention_mask
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past_key_values = (
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[None] * len(self.blocks) if past_key_values is None else past_key_values
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)
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for index, block in enumerate(self.blocks):
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hidden_states, new_key_values = block(
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hidden_states, past_key_values[index], mask
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)
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past_key_values[index] = new_key_values
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return hidden_states, past_key_values
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# PhiForCausalLM wraps the PhiModel and PhiCausalLMHead together and returns a CausalLMOutputWithPast object.
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class PhiForCausalLM(torch.nn.Module):
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def __init__(self, prefix: str, config, weights):
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super().__init__()
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if not prefix:
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prefix = "transformer"
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else:
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prefix = f"{prefix}.transformer"
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self.model = PhiModel(prefix, config, weights)
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self.lm_head = PhiCausalLMHead(config, weights)
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def forward(
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self,
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input_ids: torch.LongTensor,
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past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
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attention_mask: Optional[torch.ByteTensor] = None,
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return_dict: Optional[bool] = None,
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use_cache: Optional[bool] = None,
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labels: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
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model_output = self.model(
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input_ids, past_key_values, attention_mask, return_dict, use_cache
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)
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logits = self.lm_head(model_output[0])
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loss = None
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if labels is not None:
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loss = nn.CrossEntropyLoss()(
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logits[:, :-1].view(-1, logits.size(-1)), labels[:, 1:].view(-1)
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)
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if not return_dict:
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return (
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((loss,) + (logits,) + model_output[1:])
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if loss is not None
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else (logits,) + model_output[1:]
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)
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=model_output[1],
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hidden_states=None,
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attentions=None,
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)
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