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freaking rotary

This commit is contained in:
OlivierDehaene 2024-04-10 15:18:51 +02:00
parent 424e1b41a2
commit 2e7f6e8012
2 changed files with 103 additions and 9 deletions
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2
server/pyproject.toml
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@ -26,7 +26,7 @@ hf-transfer = "^0.1.2"
sentencepiece = "^0.1.97"
tokenizers = "^0.15.0"
huggingface-hub = "^0.19.3"
transformers = "^4.38"
transformers = { git = "https://github.com/huggingface/transformers", rev = "517a3e670d8fc11374895e870dd0dd041467c7fe" }
einops = "^0.6.1"
texttable = { version = "^1.6.7", optional = true }
datasets = { version = "^2.14.0", optional = true }

110
server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
View File

@ -53,8 +53,7 @@ class CohereLayerNorm(nn.Module):
self.eps = eps
def forward(self, hidden_states):
# if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM:
if True:
if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM:
hidden_states = hidden_states.reshape(
-1, self.weight.shape[0], self.weight.shape[1]
)
@ -147,6 +146,93 @@ def _load_gqa(config, prefix: str, weights):
)
class CohereRotaryEmbedding(nn.Module):
def __init__(
self,
dim,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
):
super().__init__()
self.scaling_factor = scaling_factor
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (
self.base
** (
torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
/ self.dim
)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
@torch.no_grad()
def forward(self, device_type, position_ids):
# x: [bs, num_attention_heads, seq_len, head_size]
inv_freq_expanded = (
self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
)
position_ids_expanded = position_ids[None, None, :].float()
# Force float32 since bfloat16 loses precision on long contexts
# See https://github.com/huggingface/transformers/pull/29285
device_type = (
device_type
if isinstance(device_type, str) and device_type != "mps"
else "cpu"
)
with torch.autocast(device_type=device_type, enabled=False):
freqs = (
inv_freq_expanded.float().to(position_ids.device)
@ position_ids_expanded.float()
).transpose(1, 2)
emb = torch.repeat_interleave(freqs, 2, dim=-1)
cos = emb.cos()
sin = emb.sin()
return cos[0], sin[0]
def rotate_half(x):
# Split and rotate
x1 = x[..., ::2]
x2 = x[..., 1::2]
rot_x = torch.stack([-x2, x1], dim=-1).flatten(-2)
return rot_x
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
dtype = q.dtype
q = q.float()
k = k.float()
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed.to(dtype=dtype), k_embed.to(dtype=dtype)
class FlashCohereAttention(torch.nn.Module):
def __init__(
self,
@ -232,14 +318,16 @@ class FlashCohereAttention(torch.nn.Module):
if self.use_qk_norm:
query = query.reshape(-1, self.head_size)
key = key.reshape(-1, self.head_size)
query = self.q_norm(query)
key = self.k_norm(key)
query = self.q_norm(query.contiguous())
key = self.k_norm(key.contiguous())
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_key_value_heads, self.head_size)
value = value.view(-1, self.num_key_value_heads, self.head_size)
self.rotary_emb(query, key, cos, sin)
query, key = apply_rotary_pos_emb(query, key, cos, sin)
# self.rotary_emb(query, key, true_cos.reshape(*cos.shape), true_sin.reshape(*sin.shape))
paged_attention.reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
@ -399,6 +487,11 @@ class FlashCohereModel(torch.nn.Module):
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
self.rotary_true = CohereRotaryEmbedding(
self.head_size,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
)
def forward(
self,
@ -415,9 +508,10 @@ class FlashCohereModel(torch.nn.Module):
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
position_ids, max_s, hidden_states.dtype
)
# cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
# position_ids, max_s, hidden_states.dtype
# )
cos, sin = self.rotary_true(hidden_states.device.type, position_ids)
residual = None
for i, layer in enumerate(self.layers):