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[modular] Follow global indexing and attribute setting, and their dependencies (#39180)

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* export global indexing statements

* add example

* style

* examples
This commit is contained in:
Cyril Vallez
2025-07-07 14:36:43 +02:00
committed by GitHub
parent 8570bc29f3
commit 5348fbc005
8 changed files with 254 additions and 85 deletions
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169
examples/modular-transformers/modeling_global_indexing.py Normal file
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@@ -0,0 +1,169 @@
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from examples/modular-transformers/modular_global_indexing.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_global_indexing.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
from typing import Callable, Optional
import torch
from torch import nn
from transformers.modeling_utils import AttentionInterface
from ...cache_utils import Cache
from ...processing_utils import Unpack
from ...utils import TransformersKwargs
from .configuration_global_indexing import GlobalIndexingConfig
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs: Unpack[TransformersKwargs],
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
def custom_flex(x, **kwargs):
"""Dummy function."""
return x
ALL_ATTENTION_FUNCTIONS = AttentionInterface()
# This indexing statement and associated function should be exported correctly!
ALL_ATTENTION_FUNCTIONS["flex_attention"] = custom_flex
class GlobalIndexingAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GlobalIndexingConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.o_proj = nn.Linear(
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor, torch.Tensor]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights

2
examples/modular-transformers/modeling_multimodal2.py
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@@ -289,7 +289,6 @@ class Multimodal2VisionEncoder(nn.Module):
self.layers = nn.ModuleList([Multimodal2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
@can_return_tuple
def forward(
self,
inputs_embeds,
@@ -455,7 +454,6 @@ class Multimodal2VisionTransformer(nn.Module):
self.encoder = Multimodal2VisionEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
@can_return_tuple
@auto_docstring
def forward(
self,

78
examples/modular-transformers/modeling_my_new_model2.py
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@@ -12,13 +12,13 @@ from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...masking_utils import create_causal_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import auto_docstring, can_return_tuple, logging
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.generic import check_model_inputs
from .configuration_my_new_model2 import MyNewModel2Config
@@ -149,7 +149,7 @@ def eager_attention_forward(
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
**kwargs: Unpack[TransformersKwargs],
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
@@ -200,8 +200,8 @@ class MyNewModel2Attention(nn.Module):
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor, torch.Tensor]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
@@ -254,22 +254,19 @@ class MyNewModel2DecoderLayer(GradientCheckpointingLayer):
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
@@ -282,12 +279,7 @@ class MyNewModel2DecoderLayer(GradientCheckpointingLayer):
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
return hidden_states
@auto_docstring
@@ -304,6 +296,10 @@ class MyNewModel2PreTrainedModel(PreTrainedModel):
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
_can_record_outputs = {
"hidden_states": MyNewModel2DecoderLayer,
"attentions": MyNewModel2Attention,
}
def _init_weights(self, module):
std = self.config.initializer_range
@@ -343,7 +339,7 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
def set_input_embeddings(self, value):
self.embed_tokens = value
@can_return_tuple
@check_model_inputs
@auto_docstring
def forward(
self,
@@ -353,26 +349,12 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutputWithPast:
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
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
@@ -394,6 +376,7 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)
# embed positions
@@ -408,42 +391,21 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
hidden_states = hidden_states * normalizer
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = decoder_layer(
hidden_states = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
@@ -488,8 +450,7 @@ class MyNewModel2ForSequenceClassification(MyNewModel2PreTrainedModel):
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
**kwargs: Unpack[TransformersKwargs],
) -> SequenceClassifierOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@@ -505,8 +466,7 @@ class MyNewModel2ForSequenceClassification(MyNewModel2PreTrainedModel):
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
**kwargs,
)
hidden_states = transformer_outputs.last_hidden_state
logits = self.score(hidden_states)

8
examples/modular-transformers/modeling_new_task_model.py
View File

@@ -118,6 +118,8 @@ class NewTaskModelPreTrainedModel(PreTrainedModel):
)
class NewTaskModelModel(NewTaskModelPreTrainedModel):
_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
# we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
accepts_loss_kwargs = False
def __init__(self, config: NewTaskModelConfig):
super().__init__(config)
@@ -313,9 +315,11 @@ class NewTaskModelModel(NewTaskModelPreTrainedModel):
special_image_mask = inputs_embeds == self.get_input_embeddings()(
torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
)
special_image_mask = special_image_mask.all(-1)
else:
special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
special_image_mask = input_ids == self.config.image_token_id
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]

32
examples/modular-transformers/modeling_super.py
View File

@@ -14,12 +14,12 @@ from transformers.modeling_outputs import CausalLMOutputWithPast
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...integrations import use_kernel_forward_from_hub
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import auto_docstring, can_return_tuple
from ...utils import TransformersKwargs, auto_docstring
from ...utils.generic import check_model_inputs
from .configuration_super import SuperConfig
@@ -148,7 +148,7 @@ def eager_attention_forward(
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
**kwargs: Unpack[TransformersKwargs],
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
@@ -199,8 +199,8 @@ class SuperAttention(nn.Module):
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor, torch.Tensor]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
@@ -253,22 +253,19 @@ class SuperDecoderLayer(GradientCheckpointingLayer):
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
@@ -281,12 +278,7 @@ class SuperDecoderLayer(GradientCheckpointingLayer):
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
return hidden_states
@auto_docstring
@@ -303,6 +295,10 @@ class SuperPreTrainedModel(PreTrainedModel):
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
_can_record_outputs = {
"hidden_states": SuperDecoderLayer,
"attentions": SuperAttention,
}
def _init_weights(self, module):
std = self.config.initializer_range
@@ -342,7 +338,7 @@ class SuperModel(SuperPreTrainedModel):
def set_input_embeddings(self, value):
self.embed_tokens = value
@can_return_tuple
@check_model_inputs
@auto_docstring
def forward(
self,

8
examples/modular-transformers/modeling_switch_function.py
View File

@@ -11,9 +11,9 @@ import torch
from torch import nn
from ...cache_utils import Cache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import TransformersKwargs
from .configuration_switch_function import SwitchFunctionConfig
@@ -72,7 +72,7 @@ def eager_attention_forward(
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
**kwargs: Unpack[TransformersKwargs],
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
@@ -123,8 +123,8 @@ class SwitchFunctionAttention(nn.Module):
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor, torch.Tensor]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)

16
examples/modular-transformers/modular_global_indexing.py Normal file
View File

@@ -0,0 +1,16 @@
from transformers.modeling_utils import AttentionInterface
from transformers.models.llama.modeling_llama import LlamaAttention
def custom_flex(x, **kwargs):
"""Dummy function."""
return x
ALL_ATTENTION_FUNCTIONS = AttentionInterface()
# This indexing statement and associated function should be exported correctly!
ALL_ATTENTION_FUNCTIONS["flex_attention"] = custom_flex
class GlobalIndexingAttention(LlamaAttention):
pass

26
utils/modular_model_converter.py
View File

@@ -673,11 +673,24 @@ class ModuleMapper(CSTVisitor, ABC):
simple_top_level_assign_structure = m.SimpleStatementLine(
body=[m.Assign(targets=[m.AssignTarget(target=m.Name())])]
)
simple_top_level_variable_indexing = m.SimpleStatementLine(
body=[m.Assign(targets=[m.AssignTarget(target=m.Subscript(value=m.Name()) | m.Attribute(value=m.Name()))])]
)
if m.matches(parent_node, m.Module()):
if m.matches(node, simple_top_level_assign_structure):
left_hand_side = node.body[0].targets[0].target.value
self.current_assignment = left_hand_side
self.assignments[left_hand_side] = node
# This corresponds to a global variable being indexed or having an attribute look-up
elif m.matches(node, simple_top_level_variable_indexing):
indexed_variable = node.body[0].targets[0].target.value.value
# We should follow any dependencies relative to the variable being indexed
self.current_assignment = indexed_variable
# The indexing node should be directly added as a dependency of the indexed variable (register the node with a "fake" name)
node_name = self.python_module.code_for_node(node)
self.assignments[node_name] = node
self.object_dependency_mapping[indexed_variable].add(node_name)
elif m.matches(node, m.SimpleStatementLine(body=[m.Import() | m.ImportFrom()])):
self.imports.append(node)
@@ -1315,6 +1328,10 @@ class ModularFileMapper(ModuleMapper):
simple_top_level_assign_structure = m.SimpleStatementLine(
body=[m.Assign(targets=[m.AssignTarget(target=m.Name())])]
)
simple_top_level_variable_indexing = m.SimpleStatementLine(
body=[m.Assign(targets=[m.AssignTarget(target=m.Subscript(value=m.Name()) | m.Attribute(value=m.Name()))])]
)
if m.matches(parent_node, m.Module()):
if m.matches(node, m.SimpleStatementLine(body=[m.Import()])):
self.imports.append(node)
@@ -1334,6 +1351,15 @@ class ModularFileMapper(ModuleMapper):
else:
self.current_assignment = assigned_variable
self.assignments[assigned_variable] = node
# This corresponds to a global variable being indexed or having an attribute look-up
elif m.matches(node, simple_top_level_variable_indexing):
indexed_variable = node.body[0].targets[0].target.value.value
# We should follow any dependencies relative to the variable being indexed
self.current_assignment = indexed_variable
# The indexing node should be directly added as a dependency of the indexed variable (register the node with a "fake" name)
node_name = self.python_module.code_for_node(node)
self.assignments[node_name] = node
self.object_dependency_mapping[indexed_variable].add(node_name)
def leave_Module(self, node):
"""When we leave the modular file, we do the following in order: