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[gaudi] Refine rope memory, do not need to keep sin/cos cache per layer (#3274)

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Wang, Yi 2025-06-23 17:15:39 +08:00 committed by GitHub
parent 238fbd4d50
commit 719907410b
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26 changed files with 315 additions and 3525 deletions
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17
backends/gaudi/server/text_generation_server/layers/rotary.py
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@ -36,7 +36,9 @@ class PositionRotaryEmbedding(nn.Module):
self._sin_k_cached = None
self.scaling_factor = scaling_factor
self.dynamic_args = None
self.max_position_embeddings = max_position_embeddings
self._update_cos_sin_cache(
torch.float32, inv_freq.device, max_position_embeddings
)
def forward(
self,
@ -268,9 +270,7 @@ class PositionRotaryEmbedding(nn.Module):
self._sin_cached = torch.sin(freqs).to(dtype)
def get_cos_sin(self, position_ids: torch.Tensor):
self._update_cos_sin_cache(
torch.float32, position_ids.device, seqlen=self.max_position_embeddings
)
cos = torch.index_select(self._cos_cached, 0, position_ids)
sin = torch.index_select(self._sin_cached, 0, position_ids)
@ -298,6 +298,9 @@ class SuRotaryEmbedding(PositionRotaryEmbedding):
self._cos_k_cached = None
self._sin_k_cached = None
self.dynamic_args = None
self._update_cos_sin_cache(
torch.float32, short_inv_freq.device, max_position_embeddings
)
def _update_cos_sin_cache(self, dtype, device, seqlen):
# Reset the tables if the sequence length has changed,
@ -351,6 +354,9 @@ class Phi3LongRoPEScaledRotaryEmbedding(PositionRotaryEmbedding):
self._cos_k_cached = None
self._sin_k_cached = None
self.dynamic_args = None
self._update_cos_sin_cache(
torch.float32, short_inv_freq.device, max_position_embeddings
)
def _update_cos_sin_cache(self, dtype, device, seqlen):
if (
@ -592,9 +598,6 @@ class RotaryPositionEmbeddingMultimodalSections(PositionRotaryEmbedding):
position_ids: torch.Tensor,
):
slen = position_ids.shape[0]
self._update_cos_sin_cache(
torch.float32, position_ids.device, seqlen=self.max_position_embeddings
)
cos = self._cos_cached[position_ids].gather(1, self._sections[:slen])
sin = self._sin_cached[position_ids].gather(1, self._sections[:slen])

22
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
View File

@ -160,18 +160,14 @@ class FlashCohereAttention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = CohereRotary.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size**-0.5
@ -325,11 +321,14 @@ class CohereMLP(nn.Module):
class FlashCohereLayer(nn.Module):
def __init__(self, prefix: str, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.layers.{layer_id}"
self.self_attn = FlashCohereAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
self.mlp = CohereMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
@ -385,6 +384,12 @@ class FlashCohereModel(torch.nn.Module):
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
rotary_emb = CohereRotary.static(
config=config,
dim=config.hidden_size // config.num_attention_heads,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
FlashCohereLayer(
@ -392,6 +397,7 @@ class FlashCohereModel(torch.nn.Module):
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

28
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
View File

@ -263,6 +263,7 @@ class DbrxAttention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.clip_qkv = config.attn_config.clip_qkv
@ -270,12 +271,7 @@ class DbrxAttention(torch.nn.Module):
self.hidden_size = config.d_model
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.attn_config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size**-0.5
@ -370,13 +366,17 @@ class DbrxNormAttentionNorm(nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.norm_1 = FastLayerNorm.load_no_bias(
prefix=f"{prefix}.norm_1", weights=weights, eps=1e-5
)
self.self_attn = DbrxAttention(
prefix=f"{prefix}.attn", config=config, weights=weights
prefix=f"{prefix}.attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
self.norm_2 = FastLayerNorm.load_no_bias(
prefix=f"{prefix}.norm_2",
@ -601,12 +601,15 @@ class DenseMoE(nn.Module):
class DbrxLayer(nn.Module):
def __init__(self, prefix: str, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.blocks.{layer_id}"
self.attn = DbrxNormAttentionNorm(
prefix=f"{prefix}.norm_attn_norm", config=config, weights=weights
prefix=f"{prefix}.norm_attn_norm",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
moe_cls = BlockSparseMoE if config.quantize is None else DenseMoE
@ -649,6 +652,12 @@ class DbrxModel(torch.nn.Module):
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.wte", weights=weights
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.d_model // config.n_heads,
base=config.attn_config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
@ -657,6 +666,7 @@ class DbrxModel(torch.nn.Module):
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.n_layers)
]

19
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
View File

@ -156,6 +156,7 @@ class DeepseekV2Attention(torch.nn.Module):
prefix: str,
config,
weights: Weights,
rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
@ -167,13 +168,7 @@ class DeepseekV2Attention(torch.nn.Module):
self.head_size = config.qk_nope_head_dim + config.qk_rope_head_dim
self.value_head_size = config.v_head_dim
self.head_pad_size = max(self.head_size, self.value_head_size)
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.qk_rope_head_dim,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
mscale = get_mscale(
self.rotary_emb.scaling_factor, self.rotary_emb.mscale_all_dim
@ -459,7 +454,7 @@ class DeepseekV2MoE(nn.Module):
class DeepseekV2Layer(nn.Module):
def __init__(self, prefix, layer_id, config, weights):
def __init__(self, prefix, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.layers.{layer_id}"
@ -467,6 +462,7 @@ class DeepseekV2Layer(nn.Module):
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
if (
@ -541,6 +537,12 @@ class DeepseekV2Model(torch.nn.Module):
prefix=f"{prefix}.embed_tokens", weights=weights
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.qk_rope_head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
DeepseekV2Layer(
@ -548,6 +550,7 @@ class DeepseekV2Model(torch.nn.Module):
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

19
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_deepseek_v3_modeling.py
View File

@ -169,6 +169,7 @@ class DeepseekV3Attention(torch.nn.Module):
prefix: str,
config,
weights: Weights,
rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
@ -180,13 +181,7 @@ class DeepseekV3Attention(torch.nn.Module):
self.head_size = config.qk_nope_head_dim + config.qk_rope_head_dim
self.value_head_size = config.v_head_dim
self.head_pad_size = max(self.head_size, self.value_head_size)
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.qk_rope_head_dim,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
mscale = get_mscale(
self.rotary_emb.scaling_factor, self.rotary_emb.mscale_all_dim
@ -535,7 +530,7 @@ class DeepseekV3MoE(nn.Module):
class DeepseekV3Layer(nn.Module):
def __init__(self, prefix, layer_id, config, weights):
def __init__(self, prefix, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.layers.{layer_id}"
@ -543,6 +538,7 @@ class DeepseekV3Layer(nn.Module):
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
if (
@ -616,6 +612,12 @@ class DeepseekV3Model(torch.nn.Module):
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.qk_rope_head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
@ -624,6 +626,7 @@ class DeepseekV3Model(torch.nn.Module):
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

35
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
View File

@ -166,7 +166,14 @@ def _load_gqa(config, prefix: str, weights):
class FlashGemma2Attention(torch.nn.Module):
def __init__(
self, prefix: str, config, weights, layer_id, causal: bool, is_sliding: bool
self,
prefix: str,
config,
weights,
layer_id,
causal: bool,
is_sliding: bool,
rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
@ -176,13 +183,7 @@ class FlashGemma2Attention(torch.nn.Module):
self.window_size = config.sliding_window
else:
self.window_size = -1
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
# self.softmax_scale = self.head_size**-0.5
self.softmax_scale = config.query_pre_attn_scalar**-0.5
@ -354,7 +355,14 @@ class Gemma2MLP(nn.Module):
class FlashGemma2Layer(nn.Module):
def __init__(
self, prefix: str, config, weights, layer_id, causal: bool, is_sliding: bool
self,
prefix: str,
config,
weights,
layer_id,
causal: bool,
is_sliding: bool,
rotary_emb,
):
super().__init__()
self.self_attn = FlashGemma2Attention(
@ -364,6 +372,7 @@ class FlashGemma2Layer(nn.Module):
layer_id=layer_id,
causal=causal,
is_sliding=is_sliding,
rotary_emb=rotary_emb,
)
self.mlp = Gemma2MLP(
prefix=f"{prefix}.mlp", config=config, weights=weights, layer_id=layer_id
@ -435,6 +444,13 @@ class FlashGemma2Model(torch.nn.Module):
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
FlashGemma2Layer(
@ -444,6 +460,7 @@ class FlashGemma2Model(torch.nn.Module):
layer_id=layer_id,
causal=causal,
is_sliding=layer_id % 2 == 0,
rotary_emb=rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

50
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gemma3_modeling.py
View File

@ -119,7 +119,15 @@ def _load_gqa(config, prefix: str, weights):
class FlashGemma3Attention(torch.nn.Module):
def __init__(
self, prefix: str, config, weights, layer_id, causal: bool, is_sliding: bool
self,
prefix: str,
config,
weights,
layer_id,
causal: bool,
is_sliding: bool,
local_rotary_emb,
global_rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
@ -130,20 +138,10 @@ class FlashGemma3Attention(torch.nn.Module):
# TODO: remove this hack to support local sliding window
config = copy.deepcopy(config)
config.rope_scaling = dict(rope_type="default")
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.head_dim,
base=config.rope_local_base_freq,
device=weights.device,
)
self.rotary_emb = local_rotary_emb
else:
self.window_size = -1
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.head_dim,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = global_rotary_emb
self.softmax_scale = (
config.query_pre_attn_scalar**-0.5
@ -336,7 +334,15 @@ class Gemma3MLP(nn.Module):
class FlashGemma3Layer(nn.Module):
def __init__(
self, prefix: str, config, weights, layer_id, causal: bool, is_sliding: bool
self,
prefix: str,
config,
weights,
layer_id,
causal: bool,
is_sliding: bool,
local_rotary_emb,
global_rotary_emb,
):
super().__init__()
self.self_attn = FlashGemma3Attention(
@ -346,6 +352,8 @@ class FlashGemma3Layer(nn.Module):
layer_id=layer_id,
causal=causal,
is_sliding=is_sliding,
local_rotary_emb=local_rotary_emb,
global_rotary_emb=global_rotary_emb,
)
self.mlp = Gemma3MLP(
prefix=f"{prefix}.mlp", config=config, weights=weights, layer_id=layer_id
@ -417,6 +425,18 @@ class FlashGemma3Model(torch.nn.Module):
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
local_rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.head_dim,
base=config.rope_local_base_freq,
device=weights.device,
)
global_rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
@ -427,6 +447,8 @@ class FlashGemma3Model(torch.nn.Module):
layer_id=layer_id,
causal=causal,
is_sliding=bool((layer_id + 1) % config.sliding_window_pattern),
local_rotary_emb=local_rotary_emb,
global_rotary_emb=global_rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

27
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View File

@ -163,19 +163,12 @@ def _load_gqa(config, prefix: str, weights):
class FlashGemmaAttention(torch.nn.Module):
def __init__(self, prefix: str, config, weights, causal: bool):
def __init__(self, prefix: str, config, weights, causal: bool, rotary_emb):
super().__init__()
self.num_heads = config.num_attention_heads
self.head_size = config.head_dim
self.causal = causal
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size**-0.5
if self.num_heads % weights.process_group.size() != 0:
@ -300,10 +293,14 @@ class GemmaMLP(nn.Module):
class FlashGemmaLayer(nn.Module):
def __init__(self, prefix: str, config, weights, causal: bool):
def __init__(self, prefix: str, config, weights, causal: bool, rotary_emb):
super().__init__()
self.self_attn = FlashGemmaAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights, causal=causal
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
causal=causal,
rotary_emb=rotary_emb,
)
self.mlp = GemmaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
@ -359,6 +356,13 @@ class FlashGemmaModel(torch.nn.Module):
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
FlashGemmaLayer(
@ -366,6 +370,7 @@ class FlashGemmaModel(torch.nn.Module):
config=config,
weights=weights,
causal=causal,
rotary_emb=rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

23
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
View File

@ -110,6 +110,7 @@ class FlashGPTJAttention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
@ -143,13 +144,7 @@ class FlashGPTJAttention(torch.nn.Module):
self.kv_head_mapping = torch.arange(
0, self.num_heads, dtype=torch.int32, device=weights.device
)
self.rotary_emb = GPTJRotary.static(
config=config,
dim=self.rotary_dim,
base=10000,
device=weights.device,
)
self.rotary_emb = rotary_emb
def forward(
self,
@ -244,10 +239,13 @@ class GPTJMLP(nn.Module):
class FlashGPTJLayer(nn.Module):
def __init__(self, prefix: str, config, weights):
def __init__(self, prefix: str, config, weights, rotary_emb):
super().__init__()
self.self_attn = FlashGPTJAttention(
prefix=f"{prefix}.attn", config=config, weights=weights
prefix=f"{prefix}.attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
self.mlp = GPTJMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
@ -291,6 +289,12 @@ class FlashGPTJModel(torch.nn.Module):
self.config = config
self.wte = TensorParallelEmbedding(prefix=f"{prefix}.wte", weights=weights)
rotary_emb = GPTJRotary.static(
config=config,
dim=config.rotary_dim,
base=10000,
device=weights.device,
)
self.layers = nn.ModuleList(
[
FlashGPTJLayer(
@ -299,6 +303,7 @@ class FlashGPTJModel(torch.nn.Module):
),
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

2
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_llama4_modeling.py
View File

@ -303,7 +303,7 @@ class Llama4TextAttention(FlashLlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, prefix, config, weights, layer_idx):
super().__init__(layer_idx, prefix, config, weights)
super().__init__(layer_idx, prefix, config, weights, None)
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(

22
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
View File

@ -133,6 +133,7 @@ class FlashLlamaAttention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
@ -145,13 +146,7 @@ class FlashLlamaAttention(torch.nn.Module):
config, "num_key_value_heads", config.num_attention_heads
)
if config.model_type != "llama4_text":
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
# `config.attention_multiplier` is used in Granite
self.softmax_scale = getattr(
@ -376,7 +371,7 @@ class LlamaMLP(nn.Module):
class FlashLlamaLayer(nn.Module):
def __init__(self, index, prefix, config, weights):
def __init__(self, index, prefix, config, weights, rotary_emb):
super().__init__()
with no_fp8(weights):
@ -385,6 +380,7 @@ class FlashLlamaLayer(nn.Module):
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
if config.model_type == "phimoe":
@ -480,6 +476,13 @@ class FlashLlamaModel(torch.nn.Module):
# Skip fp8 quant for first and last layers
self.layers = nn.ModuleList()
self.cross_attention_layers = getattr(config, "cross_attention_layers", [])
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.hidden_size // config.num_attention_heads,
base=config.rope_theta,
device=weights.device,
)
with no_fp8(weights):
self.layers.append(
FlashLlamaLayer(
@ -487,6 +490,7 @@ class FlashLlamaModel(torch.nn.Module):
prefix=f"{prefix}.layers.0",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
)
@ -512,6 +516,7 @@ class FlashLlamaModel(torch.nn.Module):
prefix=(f"{prefix}.layers.{layer_id}"),
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
)
@ -523,6 +528,7 @@ class FlashLlamaModel(torch.nn.Module):
prefix=(f"{prefix}.layers.{last_layer_id}"),
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
)

26
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
View File

@ -104,7 +104,7 @@ class MistralConfig(PretrainedConfig):
class MistralAttention(torch.nn.Module):
def __init__(self, prefix: str, config, weights, layer_id):
def __init__(self, prefix: str, config, weights, layer_id, rotary_emb):
super().__init__()
self.max_past = (
config.sliding_window if config.sliding_window is not None else -1
@ -117,12 +117,7 @@ class MistralAttention(torch.nn.Module):
else:
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size**-0.5
@ -300,13 +295,14 @@ class MistralMLP(nn.Module):
class MistralLayer(nn.Module):
def __init__(self, prefix: str, config, weights, layer_id):
def __init__(self, prefix: str, config, weights, layer_id, rotary_emb):
super().__init__()
self.self_attn = MistralAttention(
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
layer_id=layer_id,
rotary_emb=rotary_emb,
)
self.mlp = MistralMLP(
prefix=f"{prefix}.mlp", config=config, weights=weights, layer_id=layer_id
@ -366,6 +362,19 @@ class MistralModel(torch.nn.Module):
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
if getattr(config, "head_dim", None) is not None:
head_dim = config.head_dim
else:
head_dim = config.hidden_size // config.num_attention_heads
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
MistralLayer(
@ -373,6 +382,7 @@ class MistralModel(torch.nn.Module):
config=config,
weights=weights,
layer_id=layer_id,
rotary_emb=rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

23
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
View File

@ -188,6 +188,7 @@ class MixtralAttention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.max_past = (
@ -196,13 +197,7 @@ class MixtralAttention(torch.nn.Module):
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size**-0.5
@ -345,12 +340,15 @@ class MixtralMoE(nn.Module):
class MixtralLayer(nn.Module):
def __init__(self, prefix: str, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.layers.{layer_id}"
self.self_attn = MixtralAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
moe_layer_cls = (
@ -416,6 +414,12 @@ class MixtralModel(torch.nn.Module):
weights=weights,
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.hidden_size // config.num_attention_heads,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
MixtralLayer(
@ -423,6 +427,7 @@ class MixtralModel(torch.nn.Module):
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

29
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
View File

@ -99,7 +99,7 @@ def load_qkv(config, prefix: str, weights, num_heads, head_size, hidden_size):
class FlashNeoxAttention(torch.nn.Module):
def __init__(self, config, prefix, weights):
def __init__(self, config, prefix, weights, rotary_emb):
super().__init__()
num_heads = config.num_attention_heads
hidden_size = config.hidden_size
@ -116,14 +116,7 @@ class FlashNeoxAttention(torch.nn.Module):
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.rotary_dim,
base=config.rotary_emb_base,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size ** (-0.5)
self.query_key_value = load_qkv(
@ -231,7 +224,7 @@ class FlashMLP(nn.Module):
class FlashNeoXLayer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, layer_id, config, weights, rotary_emb):
super().__init__()
layer_norm_eps = config.layer_norm_eps
@ -248,7 +241,10 @@ class FlashNeoXLayer(nn.Module):
eps=layer_norm_eps,
)
self.attention = FlashNeoxAttention(
config, prefix=f"{prefix}.attention", weights=weights
config,
prefix=f"{prefix}.attention",
weights=weights,
rotary_emb=rotary_emb,
)
self.mlp = FlashMLP(config, prefix=f"{prefix}.mlp", weights=weights)
@ -328,9 +324,18 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
prefix=f"{prefix}.embed_in", weights=weights
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=int(
config.rotary_pct * (config.hidden_size // config.num_attention_heads)
),
base=config.rotary_emb_base,
device=weights.device,
)
self.layers = nn.ModuleList(
[
FlashNeoXLayer(layer_id, config, weights)
FlashNeoXLayer(layer_id, config, weights, rotary_emb)
for layer_id in range(config.num_hidden_layers)
]
)

27
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
View File

@ -113,6 +113,7 @@ class FlashPhiAttention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.num_heads = config.num_attention_heads
@ -121,13 +122,7 @@ class FlashPhiAttention(torch.nn.Module):
self.softmax_scale = self.head_size**-0.5
self.rotary_dim = int(config.partial_rotary_factor * self.head_size)
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.rotary_dim,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
@ -259,11 +254,14 @@ class PhiMLP(nn.Module):
class FlashPhiLayer(nn.Module):
def __init__(self, prefix: str, layer_id, config, weights):
def __init__(self, prefix: str, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.layers.{layer_id}"
self.self_attn = FlashPhiAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
self.mlp = PhiMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = FastLayerNorm.load(
@ -315,6 +313,16 @@ class FlashPhiModel(torch.nn.Module):
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=int(
config.partial_rotary_factor
* (config.hidden_size // config.num_attention_heads)
),
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
FlashPhiLayer(
@ -322,6 +330,7 @@ class FlashPhiModel(torch.nn.Module):
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

25
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
View File

@ -58,6 +58,7 @@ class Qwen2Attention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.max_past = (
@ -66,13 +67,7 @@ class Qwen2Attention(torch.nn.Module):
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size**-0.5
@ -199,11 +194,14 @@ class Qwen2MLP(nn.Module):
class Qwen2Layer(nn.Module):
def __init__(self, prefix, layer_id, config, weights):
def __init__(self, prefix, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.layers.{layer_id}"
self.self_attn = Qwen2Attention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
rotary_emb=rotary_emb,
)
self.mlp = Qwen2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = FastRMSNorm.load(
@ -258,6 +256,14 @@ class Qwen2Model(torch.nn.Module):
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.hidden_size // config.num_attention_heads,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
Qwen2Layer(
@ -265,6 +271,7 @@ class Qwen2Model(torch.nn.Module):
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

22
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen3_modeling.py
View File

@ -41,7 +41,7 @@ from text_generation_server.layers.rotary import PositionRotaryEmbedding
class Qwen3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config, prefix, weights, layer_idx):
def __init__(self, config, prefix, weights, layer_idx, rotary_emb):
super().__init__()
self.config = config
self.layer_idx = layer_idx
@ -54,12 +54,7 @@ class Qwen3Attention(nn.Module):
self.num_heads = config.num_attention_heads
self.attention_dropout = config.attention_dropout
self.softmax_scale = self.head_dim**-0.5
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_dim,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
@ -179,7 +174,7 @@ class Qwen3Attention(nn.Module):
class Qwen3DecoderLayer(nn.Module):
def __init__(self, config, prefix, weights, layer_idx: int):
def __init__(self, config, prefix, weights, layer_idx: int, rotary_emb):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Qwen3Attention(
@ -187,6 +182,7 @@ class Qwen3DecoderLayer(nn.Module):
prefix=f"{prefix}.self_attn",
weights=weights,
layer_idx=layer_idx,
rotary_emb=rotary_emb,
)
self.mlp = Qwen2MLP(config=config, prefix=f"{prefix}.mlp", weights=weights)
self.input_layernorm = FastRMSNorm.load(
@ -241,6 +237,15 @@ class Qwen3Model(nn.Module):
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
head_dim = getattr(
config, "head_dim", config.hidden_size // config.num_attention_heads
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
@ -249,6 +254,7 @@ class Qwen3Model(nn.Module):
prefix=f"{prefix}.layers.{layer_idx}",
weights=weights,
layer_idx=layer_idx,
rotary_emb=rotary_emb,
)
for layer_idx in range(config.num_hidden_layers)
]

24
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen3_moe_modeling.py
View File

@ -80,7 +80,7 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
class Qwen3MoeAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config, prefix, weights, layer_idx):
def __init__(self, config, prefix, weights, layer_idx, rotary_emb):
super().__init__()
self.config = config
self.layer_idx = layer_idx
@ -108,13 +108,7 @@ class Qwen3MoeAttention(nn.Module):
self.o_proj = FastLinear.load(
config, f"{prefix}.o_proj", weights, bias=config.attention_bias
)
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_dim,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.q_norm = FastRMSNorm.load(
prefix=f"{prefix}.q_norm",
@ -345,7 +339,7 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
class Qwen3MoeDecoderLayer(nn.Module):
def __init__(self, config, prefix, weights, layer_idx: int):
def __init__(self, config, prefix, weights, layer_idx: int, rotary_emb):
super().__init__()
self.hidden_size = config.hidden_size
@ -355,6 +349,7 @@ class Qwen3MoeDecoderLayer(nn.Module):
prefix=f"{prefix}.self_attn",
weights=weights,
layer_idx=layer_idx,
rotary_emb=rotary_emb,
)
else:
self.self_attn = Qwen3MoeAttention(
@ -362,6 +357,7 @@ class Qwen3MoeDecoderLayer(nn.Module):
prefix=f"{prefix}.self_attn",
weights=weights,
layer_idx=layer_idx,
rotary_emb=rotary_emb,
)
moe_layer_cls = (
@ -433,6 +429,15 @@ class Qwen3MoeModel(nn.Module):
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
head_dim = getattr(
config, "head_dim", config.hidden_size // config.num_attention_heads
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=head_dim,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
@ -441,6 +446,7 @@ class Qwen3MoeModel(nn.Module):
prefix=f"{prefix}.layers.{layer_idx}",
weights=weights,
layer_idx=layer_idx,
rotary_emb=rotary_emb,
)
for layer_idx in range(config.num_hidden_layers)
]

31
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
View File

@ -134,6 +134,7 @@ class FlashRWAttention(torch.nn.Module):
config,
prefix: str,
weights,
rotary_emb,
):
super().__init__()
self.num_heads = config.n_head
@ -141,13 +142,8 @@ class FlashRWAttention(torch.nn.Module):
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rope_theta = config.rope_theta
self.rotary_emb = rotary_emb
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=self.rope_theta,
device=weights.device,
)
self.softmax_scale = self.head_size ** (-0.5)
if self.num_heads % weights.process_group.size() != 0:
@ -243,6 +239,7 @@ class FlashRWLargeAttention(torch.nn.Module):
config,
prefix: str,
weights,
rotary_emb,
):
super().__init__()
@ -255,13 +252,8 @@ class FlashRWLargeAttention(torch.nn.Module):
self.head_size = hidden_size // num_heads
self.num_groups = num_groups
self.rope_theta = config.rope_theta
self.rotary_emb = rotary_emb
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=self.rope_theta,
device=weights.device,
)
self.softmax_scale = self.head_size ** (-0.5)
# self.num_groups = num_heads // (num_heads_kv * 2)
@ -382,6 +374,7 @@ class FlashRWLayer(nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
@ -404,6 +397,7 @@ class FlashRWLayer(nn.Module):
config,
prefix=f"{prefix}.self_attention",
weights=weights,
rotary_emb=rotary_emb,
)
self.post_attention_layernorm = (
FastLayerNorm.load(
@ -526,7 +520,7 @@ class FlashRWLayerNorm(nn.Module):
class FlashRWLargeLayer(nn.Module):
def __init__(self, layer_id, prefix: str, config, weights):
def __init__(self, layer_id, prefix: str, config, weights, rotary_emb):
super().__init__()
prefix = f"{prefix}.h.{layer_id}"
@ -536,6 +530,7 @@ class FlashRWLargeLayer(nn.Module):
config,
prefix=f"{prefix}.self_attention",
weights=weights,
rotary_emb=rotary_emb,
)
assert config.parallel_attn, "This version doesn't support non parallel_attn"
@ -593,11 +588,17 @@ class FlashRWModel(FlashRWPreTrainedModel):
self.word_embeddings = TensorParallelEmbedding(
prefix=f"{prefix}.word_embeddings", weights=weights
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.hidden_size // config.n_head,
base=config.rope_theta,
device=weights.device,
)
if config.new_decoder_architecture:
self.h = nn.ModuleList(
[
FlashRWLargeLayer(layer_id, prefix, config, weights)
FlashRWLargeLayer(layer_id, prefix, config, weights, rotary_emb)
for layer_id in range(config.num_hidden_layers)
]
)
@ -605,7 +606,7 @@ class FlashRWModel(FlashRWPreTrainedModel):
else:
self.h = nn.ModuleList(
[
FlashRWLayer(layer_id, prefix, config, weights)
FlashRWLayer(layer_id, prefix, config, weights, rotary_emb)
for layer_id in range(config.num_hidden_layers)
]
)

24
backends/gaudi/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
View File

@ -180,6 +180,7 @@ class Starcoder2Attention(torch.nn.Module):
prefix: str,
config,
weights,
rotary_emb,
):
super().__init__()
self.max_past = (
@ -188,13 +189,7 @@ class Starcoder2Attention(torch.nn.Module):
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.rotary_emb = rotary_emb
self.softmax_scale = self.head_size**-0.5
@ -411,11 +406,15 @@ STARCODER2_MLP_CLASSES = {
class Starcoder2Layer(nn.Module):
def __init__(self, layer_id, config, weights):
def __init__(self, layer_id, config, weights, rotary_emb):
super().__init__()
prefix = f"model.layers.{layer_id}"
self.self_attn = Starcoder2Attention(
prefix=f"{prefix}.self_attn", config=config, weights=weights, index=layer_id
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
index=layer_id,
rotary_emb=rotary_emb,
)
self.mlp = STARCODER2_MLP_CLASSES[config.mlp_type](
@ -481,12 +480,19 @@ class Starcoder2Model(torch.nn.Module):
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=config.hidden_size // config.num_attention_heads,
base=config.rope_theta,
device=weights.device,
)
self.layers = nn.ModuleList(
[
Starcoder2Layer(
layer_id,
config,
weights,
rotary_emb,
)
for layer_id in range(config.num_hidden_layers)
]

326
backends/gaudi/server/text_generation_server/models/custom_modeling/idefics_config.py
View File

@ -1,326 +0,0 @@
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""Idefics model configuration"""
import copy
from transformers import PretrainedConfig
IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"HuggingFaceM4/idefics-9b": "https://huggingface.co/HuggingFaceM4/idefics-9b/blob/main/config.json",
"HuggingFaceM4/idefics-80b": "https://huggingface.co/HuggingFaceM4/idefics-80b/blob/main/config.json",
}
class IdeficsVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics 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 Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `hidden_size`)
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
intermediate_size (`int`, *optional*, defaults to 5120):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_num_channels (`int`, *optional*, defaults to `3`):
Number of image channels.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
testing).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
"""
model_type = "idefics"
attribute_map = {
"hidden_size": "embed_dim",
}
def __init__(
self,
embed_dim=768,
image_size=224,
intermediate_size=5120,
patch_size=14,
num_hidden_layers=32,
num_attention_heads=16,
num_channels=3,
hidden_act="gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
self.embed_dim = embed_dim
self.image_size = image_size
self.intermediate_size = intermediate_size
self.patch_size = patch_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.layer_norm_eps = layer_norm_eps
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.hidden_act = hidden_act
super().__init__(**kwargs)
class IdeficsPerceiverConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics 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 Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_resampler (`bool`, *optional*, defaults to `False`):
Whether or not to use the resampler
resampler_n_latents (`int`, *optional*, defaults to ):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
resampler_depth (`int`, *optional*, defaults to 6):
Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
resampler_n_heads (`int`, *optional*, defaults to 16):
Number of heads in each Transformer block (for multi-headed self-attention).
resampler_head_dim (`int`, *optional*, defaults to 96):
Dimensionality of each head projection in the Transformer block.
qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`):
Whether or not to use qk layer norms in perceiver
"""
model_type = "idefics"
def __init__(
self,
use_resampler=False,
resampler_n_latents=64,
resampler_depth=6,
resampler_n_heads=16,
resampler_head_dim=96,
qk_layer_norms_perceiver=False,
**kwargs,
):
self.use_resampler = use_resampler
self.resampler_n_latents = resampler_n_latents
self.resampler_depth = resampler_depth
self.resampler_n_heads = resampler_n_heads
self.resampler_head_dim = resampler_head_dim
self.qk_layer_norms_perceiver = qk_layer_norms_perceiver
super().__init__(**kwargs)
class IdeficsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics 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 Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
additional_vocab_size (`int`, *optional`, defaults to 0):
Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens
are always trainable whereas regular vocab tokens can be frozen or not.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~IdeficsModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
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.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
alpha_initializer (`str`, *optional*, defaults to `"zeros"`):
Initialization type for the alphas.
alphas_initializer_range (`float`, *optional*, defaults to 0.0):
The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
Attention.
alpha_type (`str`, *optional*, defaults to `"float"`):
Whether the gating alphas should be vectors or single floats.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
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*, defaults to 0)
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1)
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2)
End of stream token id.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
cross_layer_interval (`int`, *optional*, default to 1)
Interval for cross attention (from text to image) layers.
qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k
freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers
freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing text layers when `freeze_text_layers` is `True`
freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head
freeze_vision_layers (`bool`, *optional*, defaults to `True`): Whether to freeze vision layers
freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing vision layers when `freeze_vision_layers` is `True`
use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler
vision_config (`IdeficsVisionConfig`, *optional*): Custom vision config or dict
perceiver_config (`IdeficsPerceiverConfig`, *optional*): Custom perceiver config or dict
Example:
```python
>>> from transformers import IdeficsModel, IdeficsConfig
>>> # Initializing a Idefics idefics-9b style configuration
>>> configuration = IdeficsConfig()
>>> # Initializing a model from the idefics-9b style configuration
>>> model = IdeficsModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics"
is_composition = True
def __init__(
self,
vocab_size=32000,
additional_vocab_size=0,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
dropout=0.0,
hidden_act="silu",
initializer_range=0.02,
alpha_initializer="zeros",
alphas_initializer_range=0.0,
alpha_type="float",
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
cross_layer_interval=1,
qk_layer_norms=False,
freeze_text_layers=True,
freeze_text_module_exceptions=[],
freeze_lm_head=False,
freeze_vision_layers=True,
freeze_vision_module_exceptions=[],
use_resampler=False,
vision_config=None,
perceiver_config=None,
**kwargs,
):
self.vocab_size = vocab_size
self.additional_vocab_size = additional_vocab_size
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.dropout = dropout
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.alpha_initializer = alpha_initializer
self.alphas_initializer_range = alphas_initializer_range
self.alpha_type = alpha_type
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.cross_layer_interval = cross_layer_interval
self.qk_layer_norms = qk_layer_norms
self.freeze_vision_layers = freeze_vision_layers
self.freeze_text_layers = freeze_text_layers
self.freeze_text_module_exceptions = freeze_text_module_exceptions
self.freeze_vision_module_exceptions = freeze_vision_module_exceptions
self.freeze_lm_head = freeze_lm_head
self.use_resampler = use_resampler
if perceiver_config is None:
self.perceiver_config = IdeficsPerceiverConfig()
elif isinstance(perceiver_config, dict):
self.perceiver_config = IdeficsPerceiverConfig(**perceiver_config)
elif isinstance(perceiver_config, IdeficsPerceiverConfig):
self.perceiver_config = perceiver_config
if vision_config is None:
self.vision_config = IdeficsVisionConfig()
elif isinstance(vision_config, dict):
self.vision_config = IdeficsVisionConfig(**vision_config)
elif isinstance(vision_config, IdeficsVisionConfig):
self.vision_config = vision_config
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,
)
# IMPORTANT: Do not do any __init__ args-based checks in the constructor, since
# PretrainedConfig.from_dict first instantiates the class with the config dict and only then
# updates the config object with `kwargs` from from_pretrained, so during the instantiation
# of this object many attributes have default values and haven't yet been overridden.
# Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run.
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["vision_config"] = self.vision_config.to_dict()
output["perceiver_config"] = self.perceiver_config.to_dict()
output["model_type"] = self.__class__.model_type
return output

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@ -1,297 +0,0 @@
# coding=utf-8
# Copyright 2022 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.
"""Image processor class for Idefics."""
from typing import Callable, Dict, List, Optional, Union, Iterable
import numpy as np
from PIL import Image
import transformers
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import (
resize,
to_channel_dimension_format,
rescale,
normalize,
)
from transformers.image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from io import BytesIO
import base64
import requests
from transformers import TensorType, is_torch_available
IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073]
IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711]
def convert_to_rgb(image):
# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
# for transparent images. The call to `alpha_composite` handles this case
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
class IdeficsImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
image_size (`int`, *optional*, defaults to `224`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `3`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
image_size: int = 224,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_num_channels: Optional[int] = 3,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.image_size = image_size
self.image_num_channels = image_num_channels
self.image_mean = image_mean
self.image_std = image_std
def preprocess(
self,
images: ImageInput,
image_num_channels: Optional[int] = 3,
image_size: Optional[Dict[str, int]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
transform: Callable = None,
**kwargs,
) -> TensorType.PYTORCH:
"""
Preprocess a batch of images.
Args:
images (`ImageInput`):
A list of images to preprocess.
image_size (`int`, *optional*, defaults to `self.image_size`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can
be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess`
method. Can be overridden by the `image_std` parameter in the `preprocess` method.
transform (`Callable`, *optional*, defaults to `None`):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
assumed - and then a preset of inference-specific transforms will be applied to the images
Returns:
a PyTorch tensor of the processed images
"""
image_size = image_size if image_size is not None else self.image_size
image_num_channels = (
image_num_channels
if image_num_channels is not None
else self.image_num_channels
)
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
size = (image_size, image_size)
if len(images) == 0:
return []
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# For training a user needs to pass their own set of transforms as a Callable.
# For reference this is what was used in the original IDEFICS training:
# transform = transforms.Compose([
# convert_to_rgb,
# transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),
# transforms.ToTensor(),
# transforms.Normalize(mean=image_mean, std=image_std),
# ])
if transform is not None:
if not is_torch_available():
raise ImportError("To pass in `transform` torch must be installed")
import torch
images = [transform(x) for x in images]
return torch.stack(images)
# for inference we do the exact transforms that were used to train IDEFICS
images = [convert_to_rgb(x) for x in images]
# further transforms expect numpy arrays
images = [to_numpy_array(x) for x in images]
images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images]
images = [self.rescale(image=image, scale=1 / 255) for image in images]
images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
images = [
to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images
]
# TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available
images = BatchFeature(
data={"pixel_values": images}, tensor_type=TensorType.PYTORCH
)["pixel_values"]
return images
def fetch_images(self, image_url_or_urls: Union[str, List[str]]):
"""
Convert a single or a list of urls into the corresponding `PIL.Image` objects.
If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
returned.
"""
headers = {
"User-Agent": (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0"
" Safari/537.36"
)
}
if isinstance(image_url_or_urls, list):
return [self.fetch_images(x) for x in image_url_or_urls]
elif isinstance(image_url_or_urls, str):
image = image_url_or_urls
if image.startswith("http://") or image.startswith("https://"):
response = requests.get(
image_url_or_urls, stream=True, headers=headers, timeout=(1, 5)
)
response.raise_for_status()
content = response.content
elif image.startswith("data:"):
# https://stackoverflow.com/questions/17090571/is-there-a-way-to-set-background-image-as-a-base64-encoded-image
# data:image/png;base64,xxx
image = image.split(",")[-1]
content = base64.b64decode(image)
else:
raise ValueError(f"Unrecognized image {image}")
try:
image = Image.open(BytesIO(content))
# image.verify()
except Exception:
raise ValueError(f"Could not load image from url {image_url_or_urls}")
return image
else:
raise ValueError(
f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}"
)
def rescale(
self,
image: np.ndarray,
scale: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Rescale an image by a scale factor. image = image * scale.
Args:
image (`np.ndarray`):
Image to rescale.
scale (`float`):
The scaling factor to rescale pixel values by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The rescaled image.
"""
# return rescale(image, scale=scale, data_format=data_format, input_data_format=input_data_format, **kwargs)
# requires 4.32
return rescale(image, scale=scale, data_format=data_format, **kwargs)
def normalize(
self,
image: np.ndarray,
mean: Union[float, Iterable[float]],
std: Union[float, Iterable[float]],
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Normalize an image. image = (image - image_mean) / image_std.
Args:
image (`np.ndarray`):
Image to normalize.
mean (`float` or `Iterable[float]`):
Image mean to use for normalization.
std (`float` or `Iterable[float]`):
Image standard deviation to use for normalization.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The normalized image.
"""
# TODO 4.32
return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs)
transformers.IdeficsImageProcessor = IdeficsImageProcessor

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backends/gaudi/server/text_generation_server/models/custom_modeling/idefics_perceiver.py
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# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# 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.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially
time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note
that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to
prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that
to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore.
References:
- DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
- Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch
"""
from typing import Optional, Tuple
import torch
import torch.nn as nn
from text_generation_server.layers import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
)
EPS = 1e-5
class IdeficsPerceiverResampler(nn.Module):
def __init__(
self,
prefix,
config,
embed_dim: int,
depth: int,
n_heads: int,
head_dim: int,
n_latents: int,
weights,
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed
to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler.
Could be e.g., VIT embed_dim, ResNet pool dim, and so on.
Args:
config (`IdeficsConfig`): config object
embed_dim (`int`): The size of each embedding vector
depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention).
head_dim (`int`): Dimensionality of each head projection in the Transformer block.
n_latents (`int`):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim, self.n_latents = (
embed_dim,
n_heads,
head_dim,
n_latents,
)
self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver
# Create Latents for Perceiver
self.latents = nn.Parameter(weights.get_tensor(f"{prefix}.latents"))
self.intermediate_dim = (
self.embed_dim * 4
if not hasattr(config.vision_config, "embed_dim")
else config.vision_config.embed_dim * 4
)
# Create Transformer Blocks
self.blocks = nn.ModuleList(
[
nn.ModuleList(
[
IdeficsPerceiverAttention(
prefix=f"{prefix}.blocks.{layer_id}.0",
config=config,
embed_dim=self.embed_dim,
n_heads=self.n_heads,
head_dim=self.head_dim,
qk_layer_norms=self.qk_layer_norms,
weights=weights,
),
IdeficsMLP(
prefix=f"{prefix}.blocks.{layer_id}.1",
intermediate_size=self.intermediate_dim,
config=config,
weights=weights,
),
]
)
for layer_id in range(depth)
]
)
self.layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm", weights=weights, eps=EPS
)
def forward(self, context: torch.Tensor) -> torch.Tensor:
"""Resample arbitrary length context & *compress* down to self.n_latents latent embeddings"""
# einsum.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0])
latents = self.latents.repeat(context.shape[0], 1, 1)
# Feed through Perceiver Attention blocks...
for attn, ff in self.blocks:
latents = attn(context, latents) + latents
latents = ff(latents) + latents
return self.layer_norm(latents)
class IdeficsPerceiverAttention(nn.Module):
def __init__(
self,
prefix,
config,
embed_dim: int,
n_heads: int,
head_dim: int,
qk_layer_norms: bool,
weights,
) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim
self.qk_layer_norms = qk_layer_norms
# Normalization & Scaling
self.context_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.context_layer_norm", weights=weights, eps=EPS
)
self.latents_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.latents_layer_norm", weights=weights, eps=EPS
)
if self.qk_layer_norms:
self.q_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.q_layer_norm", weights=weights, eps=EPS
)
self.k_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.k_layer_norm", weights=weights, eps=EPS
)
self.qk_scale = self.head_dim**-0.5
if n_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {n_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.n_heads //= weights.process_group.size()
# Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers).
self.q_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.q_proj", weights=weights, bias=False
)
self.k_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.k_proj", weights=weights, bias=False
)
self.v_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.v_proj", weights=weights, bias=False
)
self.output_proj = TensorParallelRowLinear.load(
config=config, prefix=f"{prefix}.output_proj", weights=weights, bias=False
)
def forward(self, context: torch.Tensor, latents: torch.Tensor) -> torch.Tensor:
"""
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
Args:
context (`torch.Tensor`):
Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample.
latents (`torch.Tensor`):
Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to.
Returns:
`torch.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross
from context.
"""
context = self.context_layer_norm(context)
latents = self.latents_layer_norm(latents)
batch_size, seq_length, embed_dim = context.shape[:3]
# Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn!
# Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents`
q = self.q_proj(latents)
k = self.k_proj(torch.cat([context, latents], dim=-2))
v = self.v_proj(torch.cat([context, latents], dim=-2))
# Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call)
# =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)]
# einsum.rearrange(x, "bsz seq (heads embed) -> bsz heads seq embed", heads=self.n_heads)
q, k, v = [
x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(
1, 2
)
for x in (q, k, v)
]
if self.qk_layer_norms:
q = self.q_layer_norm(q)
k = self.k_layer_norm(k)
scores = torch.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k)
stabilized_scores = scores - (scores.amax(dim=-1, keepdim=True).detach())
attn = stabilized_scores.softmax(dim=-1)
# Attend & project back to output...
resampled = torch.einsum("... i j, ... j d -> ... i d", attn, v)
# einsum.rearrange(resampled, "bsz heads seq embed -> bsz seq (heads embed)", heads=self.n_heads)
return self.output_proj(resampled.transpose(1, 2).flatten(-2))
class IdeficsMLP(nn.Module):
def __init__(
self,
prefix,
intermediate_size,
config,
weights,
):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__()
self.embed_dim = config.vision_config.embed_dim
self.ln = nn.LayerNorm.load(prefix=f"{prefix}.ln", weights=weights, eps=EPS)
self.fc = TensorParallelColumnLinear.load(
config=config,
prefix=f"{prefix}.fc",
weights=weights,
bias=False,
)
self.act = nn.ReLU()
self.c_proj = TensorParallelRowLinear.load(
config=config,
prefix=f"{prefix}.c_proj",
weights=weights,
bias=False,
)
def forward(
self, hidden_states: Optional[Tuple[torch.FloatTensor]]
) -> torch.FloatTensor:
hidden_states = self.ln(hidden_states)
hidden_states = self.fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
return hidden_states

443
backends/gaudi/server/text_generation_server/models/custom_modeling/idefics_processing.py
View File

@ -1,443 +0,0 @@
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""
Processor class for IDEFICS.
"""
from typing import Callable, List, Optional, Union
from urllib.parse import urlparse
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import (
BatchEncoding,
PaddingStrategy,
TextInput,
TruncationStrategy,
)
from transformers.utils import TensorType, is_torch_available
if is_torch_available():
import torch
IMAGE_TOKEN = "<image>"
# copied from m4.training.packing
def incremental_to_binary_attention_mask(incremental_mask, num_classes=-1):
# This function converts: [-1, 0, 1] => [[0, 0], [1, 0], [0, 1]]
# If any of images index are more than num_classes, set them to -1.
# Words after the max number of images allowed have been seen don't attend on anything
if num_classes != -1:
incremental_mask[incremental_mask >= num_classes] = -1
negatives = incremental_mask == -1
incremental_mask[negatives] = 0
attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes)
attn_mask[negatives, :] = 0
return attn_mask
# copied from m4.training.packing
def image_attention_mask_for_packed_input_ids(input_ids, tokenizer):
image_attention_mask = torch.full_like(input_ids, fill_value=-1)
next_image_attention_mask = torch.full_like(input_ids, fill_value=-1)
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
eod_token_id = tokenizer.eos_token_id
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx, token_id in enumerate(input_ids[batch_idx]):
if token_id == image_token_id:
count += 1
image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
image_attention_mask[batch_idx][idx] = count
if seen_eod:
image_attention_mask[batch_idx][idx] = -1
if token_id == eod_token_id:
seen_eod = True
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1):
token_id = input_ids[batch_idx][idx]
if token_id == image_token_id:
count += 1
next_image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
next_image_attention_mask[batch_idx][idx] = count
if token_id == eod_token_id:
seen_eod = True
if seen_eod:
next_image_attention_mask[batch_idx][idx] = -1
non_negative_indices = next_image_attention_mask[batch_idx] != -1
next_image_attention_mask[batch_idx][non_negative_indices] -= count
next_image_attention_mask[batch_idx][non_negative_indices] *= -1
return image_attention_mask, next_image_attention_mask
def is_url(string):
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
invalidated the url"""
if " " in string:
return False
result = urlparse(string)
return all([result.scheme, result.netloc])
def is_image(string):
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
invalidated the url"""
return is_url(string) or string.startswith("data:")
class IdeficsProcessor(ProcessorMixin):
r"""
Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
Args:
image_processor (`IdeficsImageProcessor`):
An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
tokenizer (`LlamaTokenizerFast`):
An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "IdeficsImageProcessor"
tokenizer_class = "LlamaTokenizerFast"
def __init__(
self,
image_processor,
tokenizer=None,
image_size=224,
add_end_of_utterance_token=None,
**kwargs,
):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
self.current_processor = self.image_processor
self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
self.default_image_dims = (
self.image_processor.image_num_channels,
self.image_processor.image_size,
self.image_processor.image_size,
)
self.tokenizer_was_trained_with_end_of_utterance_token = (
True
if "<end_of_utterance>"
in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
else False
)
def __call__(
self,
prompts: Union[List[TextInput], List[List[TextInput]]],
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
transform: Callable = None,
add_eos_token=False,
add_end_of_utterance_token=None,
debug=False,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchEncoding:
"""This method takes batched or non-batched prompts made of text and images and converts them into prompts that
the model was trained on and prepares the image pixel values for the model to process.
Args:
prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
either a single prompt or a batched list of prompts - see the detailed description immediately after
the end of the arguments doc section.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
transform (`Callable`, *optional*):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
set of transforms will be applied to the images
add_eos_token (`bool`, *optional*, defaults to `False`):
Adds `eos_token` at the end of the final prompt if True`
add_end_of_utterance_token (`bool`, *optional*)
Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
image). If `None` the tokenizer will be checked instead and if this token is found in
`additional_special_tokens` then the value will be `True`.
debug (`bool`, *optional*, defaults to `False`):
`True` value will help debug prompt generation by dumping useful information
return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
The type of tensors to return. Can be one of:
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
Returns:
a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
directly passed to `model.generate`
Detailed explanation:
Each entry in `prompts` is either a text to be passed as is or an image that will be processed.
An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
entry into the prompt.
Example:
```python
checkpoint = "HuggingFaceM4/idefics-9b"
processor = AutoProcessor.from_pretrained(checkpoint)
url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
img = processor.image_processor.fetch_images([url])[0]
prompts = [
"User:",
img,
"Describe this image.\nAssistant: An image of two kittens in grass.\n",
"User:",
"https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
"Describe this image.\nAssistant:",
]
inputs = processor(prompts, return_tensors="pt")
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
In this example the `prompts` will be converted into:
```
<s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant: An image of two kittens in grass.
User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant:'
```
and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
`pixel_values` dict entry of the return value.
This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
first image is passed as object and the second one as a url.
To do training do:
```python
image_transform = transforms.Compose(
[
transforms.RandomResizedCrop(
(w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize(mean=self.image_mean, std=self.image_std),
]
)
inputs = processor(prompts, transform=image_transform, return_tensors="pt")
```
In order to help debug prompt generation enable `debug=True` which will show you what's happening.
"""
# if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
if add_end_of_utterance_token is None:
add_end_of_utterance_token = (
self.tokenizer_was_trained_with_end_of_utterance_token
)
# turn non-batched prompts into batched
if not any(isinstance(i, list) for i in prompts):
prompts = [prompts]
fake_token = "<fake_token_around_image>"
image_token = "<image>"
end_of_utterance_token = "<end_of_utterance>"
def image_tokens(last_was_image):
if last_was_image:
return image_token + fake_token
else:
return fake_token + image_token + fake_token
all_texts = []
all_images = []
for sample in prompts:
# the model was trained on samples starting with <s>
full_text = f"{self.tokenizer.bos_token}"
# an image can either be an image object in the item or the url, everything else is a verbatim prompt text
image_objects = []
last_was_image = False
last_was_text = False
for i, item in enumerate(sample):
if i > 0:
last_was_text = True if not last_was_image else False
if isinstance(item, str):
item = item.strip(" ")
if is_image(item):
image = self.image_processor.fetch_images(item)
full_text += image_tokens(last_was_image)
image_objects.append(image)
last_was_image = True
else:
# we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
if add_end_of_utterance_token and last_was_text:
full_text += end_of_utterance_token
full_text += item
last_was_image = False
else:
# must be an image obj
full_text += image_tokens(last_was_image)
image_objects.append(item)
last_was_image = True
if add_eos_token:
full_text += self.tokenizer.eos_token
if debug is True:
print(f"{full_text=}")
image_objects = self.image_processor(image_objects, transform=transform)
text_encoding = self.tokenizer(
text=full_text,
add_special_tokens=False,
padding=padding,
truncation=truncation,
max_length=max_length,
)
all_texts.append(text_encoding["input_ids"])
all_images.append(image_objects)
max_seq_len = max(len(x) for x in all_texts)
# max_num_images has to be at least 1 even when there are no images
max_num_images = max(len(x) for x in all_images)
max_num_images = max(1, max_num_images)
at_least_one_image = sum(len(x) for x in all_images) > 0
output_input_ids = []
output_images = []
output_attention_masks = []
for text, images in zip(all_texts, all_images):
padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len
unpadded_seq_len = len(text)
start = max_seq_len - unpadded_seq_len
padded_input_ids[start:] = text[:max_seq_len]
attention_mask = torch.zeros((max_seq_len,), dtype=torch.long)
attention_mask[start:] = 1
image_count = padded_input_ids.count(self.image_token_id)
local_max_num_images = min(image_count, max_num_images)
current_images = images[:local_max_num_images]
if len(current_images) > 0:
padded_image_tensor = torch.zeros(
max_num_images, *current_images.size()[1:]
)
padded_image_tensor[: current_images.size(0)] = current_images
else:
padded_image_tensor = torch.zeros(
max_num_images, *self.default_image_dims
)
output_images.append(padded_image_tensor)
output_input_ids.append(torch.tensor(padded_input_ids))
output_attention_masks.append(attention_mask)
output_input_ids = torch.stack(output_input_ids)
output_images = torch.stack(output_images)
output_attention_masks = torch.stack(output_attention_masks)
if at_least_one_image:
image_attention_mask, _ = image_attention_mask_for_packed_input_ids(
output_input_ids, self.tokenizer
)
image_attention_mask = incremental_to_binary_attention_mask(
image_attention_mask, num_classes=max_num_images
)
else:
# in full language mode we set the image mask to all-0s
image_attention_mask = torch.zeros(
output_input_ids.shape[0],
output_input_ids.shape[1],
1,
dtype=torch.bool,
)
return BatchFeature(
data={
"input_ids": output_input_ids,
"attention_mask": output_attention_masks,
"pixel_values": output_images,
"image_attention_mask": image_attention_mask,
}
)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

529
backends/gaudi/server/text_generation_server/models/custom_modeling/idefics_vision.py
View File

@ -1,529 +0,0 @@
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace 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 IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from transformers.utils import (
ModelOutput,
logging,
)
from text_generation_server.layers import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
TensorParallelEmbedding,
)
logger = logging.get_logger(__name__)
@dataclass
class IdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
image_embeds: Optional[torch.FloatTensor] = None
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Idefics
class IdeficsVisionEmbeddings(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(
weights.get_tensor(f"{prefix}.class_embedding")
)
self.patch_embedding = nn.Conv2d.load_no_bias(
prefix=f"{prefix}.patch_embedding",
weights=weights,
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = TensorParallelEmbedding(
prefix="model.vision_model.embeddings.position_embedding", weights=weights
)
self.position_ids = (
torch.arange(self.num_positions).expand((1, -1)).to(device=weights.device)
)
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
batch_size = pixel_values.shape[0]
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(
pixel_values.to(dtype=target_dtype)
) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision
class IdeficsVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.embed_dim = self.embed_dim // weights.process_group.size()
self.k_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k_proj", weights=weights, bias=True
)
self.v_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v_proj", weights=weights, bias=True
)
self.q_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q_proj", weights=weights, bias=True
)
self.out_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.out_proj", weights=weights, bias=True
)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return (
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = (
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ causal_attention_mask
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = (
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attention_mask
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(
bsz, self.num_heads, tgt_len, src_len
)
attn_weights = attn_weights_reshaped.view(
bsz * self.num_heads, tgt_len, src_len
)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision
class IdeficsVisionMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.fc1", weights=weights, bias=True
)
self.fc2 = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.fc2", weights=weights, bias=True
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->IdeficsVision
class IdeficsVisionEncoderLayer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IdeficsVisionAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
self.layer_norm1 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm1", weights=weights, eps=config.layer_norm_eps
)
self.mlp = IdeficsVisionMLP(
prefix=f"{prefix}.mlp", config=config, weights=weights
)
self.layer_norm2 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm2", weights=weights, eps=config.layer_norm_eps
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->IdeficsVision
class IdeficsVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`IdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.layers = nn.ModuleList(
[
IdeficsVisionEncoderLayer(
prefix=f"{prefix}.encoder.layers.{layer_id}",
config=config,
weights=weights,
)
for layer_id in range(config.num_hidden_layers)
]
)
# self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# if self.gradient_checkpointing and self.training:
# def create_custom_forward(module):
# def custom_forward(*inputs):
# return module(*inputs, output_attentions)
# return custom_forward
# layer_outputs = torch.utils.checkpoint.checkpoint(
# create_custom_forward(encoder_layer),
# hidden_states,
# attention_mask,
# causal_attention_mask,
# )
# else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, encoder_states, all_attentions]
if v is not None
)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_states,
attentions=all_attentions,
)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer
class IdeficsVisionTransformer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embeddings = IdeficsVisionEmbeddings(
prefix=f"{prefix}.embeddings", config=config, weights=weights
)
self.pre_layrnorm = nn.LayerNorm.load(
prefix=f"{prefix}.pre_layrnorm", weights=weights, eps=config.layer_norm_eps
)
self.encoder = IdeficsVisionEncoder(
prefix=prefix, config=config, weights=weights
)
self.post_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.post_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
# copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)