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""" PyTorch DecisionTransformer model.""" |
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|
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import math |
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import os |
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from dataclasses import dataclass |
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from typing import Optional, Tuple, Union |
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|
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import torch |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.cuda.amp import autocast |
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|
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from ...activations import ACT2FN |
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from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions |
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from ...modeling_utils import PreTrainedModel |
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from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer |
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from ...utils import ( |
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ModelOutput, |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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logging, |
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replace_return_docstrings, |
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) |
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from .configuration_decision_transformer import DecisionTransformerConfig |
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logger = logging.get_logger(__name__) |
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_CHECKPOINT_FOR_DOC = "edbeeching/decision-transformer-gym-hopper-medium" |
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_CONFIG_FOR_DOC = "DecisionTransformerConfig" |
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DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ |
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"edbeeching/decision-transformer-gym-hopper-medium", |
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] |
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def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path): |
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"""Load tf checkpoints in a pytorch model""" |
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try: |
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import re |
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|
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import tensorflow as tf |
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except ImportError: |
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logger.error( |
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"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " |
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"https://www.tensorflow.org/install/ for installation instructions." |
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) |
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raise |
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tf_path = os.path.abspath(gpt2_checkpoint_path) |
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logger.info(f"Converting TensorFlow checkpoint from {tf_path}") |
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|
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init_vars = tf.train.list_variables(tf_path) |
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names = [] |
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arrays = [] |
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for name, shape in init_vars: |
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logger.info(f"Loading TF weight {name} with shape {shape}") |
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array = tf.train.load_variable(tf_path, name) |
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names.append(name) |
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arrays.append(array.squeeze()) |
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|
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for name, array in zip(names, arrays): |
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name = name[6:] |
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name = name.split("/") |
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pointer = model |
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for m_name in name: |
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if re.fullmatch(r"[A-Za-z]+\d+", m_name): |
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scope_names = re.split(r"(\d+)", m_name) |
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else: |
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scope_names = [m_name] |
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if scope_names[0] == "w" or scope_names[0] == "g": |
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pointer = getattr(pointer, "weight") |
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elif scope_names[0] == "b": |
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pointer = getattr(pointer, "bias") |
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elif scope_names[0] == "wpe" or scope_names[0] == "wte": |
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pointer = getattr(pointer, scope_names[0]) |
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pointer = getattr(pointer, "weight") |
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else: |
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pointer = getattr(pointer, scope_names[0]) |
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if len(scope_names) >= 2: |
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num = int(scope_names[1]) |
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pointer = pointer[num] |
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try: |
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if pointer.shape != array.shape: |
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raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") |
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except ValueError as e: |
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e.args += (pointer.shape, array.shape) |
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raise |
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logger.info(f"Initialize PyTorch weight {name}") |
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pointer.data = torch.from_numpy(array) |
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return model |
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class DecisionTransformerGPT2Attention(nn.Module): |
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def __init__(self, config, is_cross_attention=False, layer_idx=None): |
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super().__init__() |
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max_positions = config.max_position_embeddings |
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self.register_buffer( |
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"bias", |
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torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( |
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1, 1, max_positions, max_positions |
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), |
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persistent=False, |
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) |
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self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) |
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self.embed_dim = config.hidden_size |
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self.num_heads = config.num_attention_heads |
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self.head_dim = self.embed_dim // self.num_heads |
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self.split_size = self.embed_dim |
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if self.head_dim * self.num_heads != self.embed_dim: |
|
raise ValueError( |
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f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" |
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f" {self.num_heads})." |
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) |
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self.scale_attn_weights = config.scale_attn_weights |
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self.is_cross_attention = is_cross_attention |
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self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx |
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self.layer_idx = layer_idx |
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self.reorder_and_upcast_attn = config.reorder_and_upcast_attn |
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|
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if self.is_cross_attention: |
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self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim) |
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self.q_attn = Conv1D(self.embed_dim, self.embed_dim) |
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else: |
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self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim) |
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self.c_proj = Conv1D(self.embed_dim, self.embed_dim) |
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self.attn_dropout = nn.Dropout(config.attn_pdrop) |
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self.resid_dropout = nn.Dropout(config.resid_pdrop) |
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self.pruned_heads = set() |
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def prune_heads(self, heads): |
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if len(heads) == 0: |
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return |
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heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) |
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index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) |
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self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) |
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self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) |
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self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) |
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self.num_heads = self.num_heads - len(heads) |
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self.pruned_heads = self.pruned_heads.union(heads) |
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def _attn(self, query, key, value, attention_mask=None, head_mask=None): |
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attn_weights = torch.matmul(query, key.transpose(-1, -2)) |
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if self.scale_attn_weights: |
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attn_weights = attn_weights / torch.full( |
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[], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device |
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) |
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if self.scale_attn_by_inverse_layer_idx: |
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attn_weights = attn_weights / float(self.layer_idx + 1) |
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if not self.is_cross_attention: |
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query_length, key_length = query.size(-2), key.size(-2) |
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causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] |
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mask_value = torch.finfo(attn_weights.dtype).min |
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mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device) |
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attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value) |
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if attention_mask is not None: |
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attn_weights = attn_weights + attention_mask |
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attn_weights = nn.functional.softmax(attn_weights, dim=-1) |
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attn_weights = attn_weights.type(value.dtype) |
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attn_weights = self.attn_dropout(attn_weights) |
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if head_mask is not None: |
|
attn_weights = attn_weights * head_mask |
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attn_output = torch.matmul(attn_weights, value) |
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return attn_output, attn_weights |
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def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None): |
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|
|
bsz, num_heads, q_seq_len, dk = query.size() |
|
_, _, k_seq_len, _ = key.size() |
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attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device) |
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scale_factor = 1.0 |
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if self.scale_attn_weights: |
|
scale_factor /= float(value.size(-1)) ** 0.5 |
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|
|
if self.scale_attn_by_inverse_layer_idx: |
|
scale_factor /= float(self.layer_idx + 1) |
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|
|
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with autocast(enabled=False): |
|
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len) |
|
attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor) |
|
attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) |
|
|
|
if not self.is_cross_attention: |
|
|
|
query_length, key_length = query.size(-2), key.size(-2) |
|
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] |
|
mask_value = torch.finfo(attn_weights.dtype).min |
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|
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mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) |
|
attn_weights = torch.where(causal_mask, attn_weights, mask_value) |
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|
|
if attention_mask is not None: |
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|
|
attn_weights = attn_weights + attention_mask |
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|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1) |
|
|
|
|
|
if attn_weights.dtype != torch.float32: |
|
raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32") |
|
attn_weights = attn_weights.type(value.dtype) |
|
attn_weights = self.attn_dropout(attn_weights) |
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|
|
|
|
if head_mask is not None: |
|
attn_weights = attn_weights * head_mask |
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|
|
attn_output = torch.matmul(attn_weights, value) |
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|
|
return attn_output, attn_weights |
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|
|
def _split_heads(self, tensor, num_heads, attn_head_size): |
|
""" |
|
Splits hidden_size dim into attn_head_size and num_heads |
|
""" |
|
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) |
|
tensor = tensor.view(new_shape) |
|
return tensor.permute(0, 2, 1, 3) |
|
|
|
def _merge_heads(self, tensor, num_heads, attn_head_size): |
|
""" |
|
Merges attn_head_size dim and num_attn_heads dim into hidden_size |
|
""" |
|
tensor = tensor.permute(0, 2, 1, 3).contiguous() |
|
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) |
|
return tensor.view(new_shape) |
|
|
|
def forward( |
|
self, |
|
hidden_states: Optional[Tuple[torch.FloatTensor]], |
|
layer_past: Optional[Tuple[torch.Tensor]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = False, |
|
output_attentions: Optional[bool] = False, |
|
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: |
|
if encoder_hidden_states is not None: |
|
if not hasattr(self, "q_attn"): |
|
raise ValueError( |
|
"If class is used as cross attention, the weights `q_attn` have to be defined. " |
|
"Please make sure to instantiate class with `DecisionTransformerGPT2Attention(..., is_cross_attention=True)`." |
|
) |
|
|
|
query = self.q_attn(hidden_states) |
|
key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) |
|
attention_mask = encoder_attention_mask |
|
else: |
|
query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) |
|
|
|
query = self._split_heads(query, self.num_heads, self.head_dim) |
|
key = self._split_heads(key, self.num_heads, self.head_dim) |
|
value = self._split_heads(value, self.num_heads, self.head_dim) |
|
|
|
if layer_past is not None: |
|
past_key, past_value = layer_past |
|
key = torch.cat((past_key, key), dim=-2) |
|
value = torch.cat((past_value, value), dim=-2) |
|
|
|
if use_cache is True: |
|
present = (key, value) |
|
else: |
|
present = None |
|
|
|
if self.reorder_and_upcast_attn: |
|
attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask) |
|
else: |
|
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) |
|
|
|
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) |
|
attn_output = self.c_proj(attn_output) |
|
attn_output = self.resid_dropout(attn_output) |
|
|
|
outputs = (attn_output, present) |
|
if output_attentions: |
|
outputs += (attn_weights,) |
|
|
|
return outputs |
|
|
|
|
|
|
|
class DecisionTransformerGPT2MLP(nn.Module): |
|
def __init__(self, intermediate_size, config): |
|
super().__init__() |
|
embed_dim = config.hidden_size |
|
self.c_fc = Conv1D(intermediate_size, embed_dim) |
|
self.c_proj = Conv1D(embed_dim, intermediate_size) |
|
self.act = ACT2FN[config.activation_function] |
|
self.dropout = nn.Dropout(config.resid_pdrop) |
|
|
|
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor: |
|
hidden_states = self.c_fc(hidden_states) |
|
hidden_states = self.act(hidden_states) |
|
hidden_states = self.c_proj(hidden_states) |
|
hidden_states = self.dropout(hidden_states) |
|
return hidden_states |
|
|
|
|
|
|
|
class DecisionTransformerGPT2Block(nn.Module): |
|
def __init__(self, config, layer_idx=None): |
|
super().__init__() |
|
hidden_size = config.hidden_size |
|
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size |
|
|
|
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) |
|
self.attn = DecisionTransformerGPT2Attention(config, layer_idx=layer_idx) |
|
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) |
|
|
|
if config.add_cross_attention: |
|
self.crossattention = DecisionTransformerGPT2Attention( |
|
config, is_cross_attention=True, layer_idx=layer_idx |
|
) |
|
self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) |
|
|
|
self.mlp = DecisionTransformerGPT2MLP(inner_dim, config) |
|
|
|
def forward( |
|
self, |
|
hidden_states: Optional[Tuple[torch.FloatTensor]], |
|
layer_past: Optional[Tuple[torch.Tensor]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = False, |
|
output_attentions: Optional[bool] = False, |
|
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: |
|
residual = hidden_states |
|
hidden_states = self.ln_1(hidden_states) |
|
attn_outputs = self.attn( |
|
hidden_states, |
|
layer_past=layer_past, |
|
attention_mask=attention_mask, |
|
head_mask=head_mask, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
) |
|
attn_output = attn_outputs[0] |
|
outputs = attn_outputs[1:] |
|
|
|
hidden_states = attn_output + residual |
|
|
|
if encoder_hidden_states is not None: |
|
|
|
if not hasattr(self, "crossattention"): |
|
raise ValueError( |
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with " |
|
"cross-attention layers by setting `config.add_cross_attention=True`" |
|
) |
|
residual = hidden_states |
|
hidden_states = self.ln_cross_attn(hidden_states) |
|
cross_attn_outputs = self.crossattention( |
|
hidden_states, |
|
attention_mask=attention_mask, |
|
head_mask=head_mask, |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_attention_mask, |
|
output_attentions=output_attentions, |
|
) |
|
attn_output = cross_attn_outputs[0] |
|
|
|
hidden_states = residual + attn_output |
|
outputs = outputs + cross_attn_outputs[2:] |
|
|
|
residual = hidden_states |
|
hidden_states = self.ln_2(hidden_states) |
|
feed_forward_hidden_states = self.mlp(hidden_states) |
|
|
|
hidden_states = residual + feed_forward_hidden_states |
|
|
|
if use_cache: |
|
outputs = (hidden_states,) + outputs |
|
else: |
|
outputs = (hidden_states,) + outputs[1:] |
|
|
|
return outputs |
|
|
|
|
|
class DecisionTransformerGPT2PreTrainedModel(PreTrainedModel): |
|
""" |
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
|
models. |
|
""" |
|
|
|
config_class = DecisionTransformerConfig |
|
load_tf_weights = load_tf_weights_in_gpt2 |
|
base_model_prefix = "transformer" |
|
is_parallelizable = True |
|
supports_gradient_checkpointing = True |
|
|
|
def __init__(self, *inputs, **kwargs): |
|
super().__init__(*inputs, **kwargs) |
|
|
|
def _init_weights(self, module): |
|
"""Initialize the weights.""" |
|
if isinstance(module, (nn.Linear, Conv1D)): |
|
|
|
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
elif isinstance(module, nn.LayerNorm): |
|
module.bias.data.zero_() |
|
module.weight.data.fill_(1.0) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for name, p in module.named_parameters(): |
|
if "c_proj" in name and "weight" in name: |
|
|
|
p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))) |
|
|
|
def _set_gradient_checkpointing(self, module, value=False): |
|
if isinstance(module, DecisionTransformerGPT2Model): |
|
module.gradient_checkpointing = value |
|
|
|
|
|
class DecisionTransformerGPT2Model(DecisionTransformerGPT2PreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
self.embed_dim = config.hidden_size |
|
|
|
self.wte = nn.Embedding(config.vocab_size, self.embed_dim) |
|
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) |
|
|
|
self.drop = nn.Dropout(config.embd_pdrop) |
|
self.h = nn.ModuleList( |
|
[DecisionTransformerGPT2Block(config, layer_idx=i) for i in range(config.num_hidden_layers)] |
|
) |
|
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) |
|
|
|
|
|
self.model_parallel = False |
|
self.device_map = None |
|
self.gradient_checkpointing = False |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.wte |
|
|
|
def set_input_embeddings(self, new_embeddings): |
|
self.wte = new_embeddings |
|
|
|
|
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
token_type_ids: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: |
|
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 |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
if input_ids is not None and inputs_embeds is not None: |
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") |
|
elif input_ids is not None: |
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) |
|
input_shape = input_ids.size() |
|
input_ids = input_ids.view(-1, input_shape[-1]) |
|
batch_size = input_ids.shape[0] |
|
elif inputs_embeds is not None: |
|
input_shape = inputs_embeds.size()[:-1] |
|
batch_size = inputs_embeds.shape[0] |
|
else: |
|
raise ValueError("You have to specify either input_ids or inputs_embeds") |
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device |
|
|
|
if token_type_ids is not None: |
|
token_type_ids = token_type_ids.view(-1, input_shape[-1]) |
|
|
|
if past_key_values is None: |
|
past_length = 0 |
|
past_key_values = tuple([None] * len(self.h)) |
|
else: |
|
past_length = past_key_values[0][0].size(-2) |
|
if position_ids is None: |
|
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) |
|
position_ids = position_ids.unsqueeze(0) |
|
|
|
|
|
if attention_mask is not None: |
|
if batch_size <= 0: |
|
raise ValueError("batch_size has to be defined and > 0") |
|
attention_mask = attention_mask.view(batch_size, -1) |
|
|
|
|
|
|
|
|
|
|
|
attention_mask = attention_mask[:, None, None, :] |
|
|
|
|
|
|
|
|
|
|
|
|
|
attention_mask = attention_mask.to(dtype=self.dtype) |
|
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min |
|
|
|
|
|
|
|
if self.config.add_cross_attention and encoder_hidden_states is not None: |
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() |
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) |
|
if encoder_attention_mask is None: |
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) |
|
encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) |
|
else: |
|
encoder_attention_mask = None |
|
|
|
|
|
|
|
|
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer) |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.wte(input_ids) |
|
position_embeds = self.wpe(position_ids) |
|
hidden_states = inputs_embeds + position_embeds |
|
|
|
if token_type_ids is not None: |
|
token_type_embeds = self.wte(token_type_ids) |
|
hidden_states = hidden_states + token_type_embeds |
|
|
|
hidden_states = self.drop(hidden_states) |
|
|
|
output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),) |
|
|
|
if self.gradient_checkpointing and self.training: |
|
if use_cache: |
|
logger.warning_once( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
|
) |
|
use_cache = False |
|
|
|
presents = () if use_cache else None |
|
all_self_attentions = () if output_attentions else None |
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None |
|
all_hidden_states = () if output_hidden_states else None |
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): |
|
|
|
if self.model_parallel: |
|
torch.cuda.set_device(hidden_states.device) |
|
|
|
if layer_past is not None: |
|
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) |
|
|
|
if attention_mask is not None: |
|
attention_mask = attention_mask.to(hidden_states.device) |
|
if isinstance(head_mask, torch.Tensor): |
|
head_mask = head_mask.to(hidden_states.device) |
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
if self.gradient_checkpointing and self.training: |
|
|
|
def create_custom_forward(module): |
|
def custom_forward(*inputs): |
|
|
|
return module(*inputs, use_cache, output_attentions) |
|
|
|
return custom_forward |
|
|
|
outputs = torch.utils.checkpoint.checkpoint( |
|
create_custom_forward(block), |
|
hidden_states, |
|
None, |
|
attention_mask, |
|
head_mask[i], |
|
encoder_hidden_states, |
|
encoder_attention_mask, |
|
) |
|
else: |
|
outputs = block( |
|
hidden_states, |
|
layer_past=layer_past, |
|
attention_mask=attention_mask, |
|
head_mask=head_mask[i], |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_attention_mask, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
) |
|
|
|
hidden_states = outputs[0] |
|
if use_cache is True: |
|
presents = presents + (outputs[1],) |
|
|
|
if output_attentions: |
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) |
|
if self.config.add_cross_attention: |
|
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) |
|
|
|
|
|
if self.model_parallel: |
|
for k, v in self.device_map.items(): |
|
if i == v[-1] and "cuda:" + str(k) != self.last_device: |
|
hidden_states = hidden_states.to("cuda:" + str(k + 1)) |
|
|
|
hidden_states = self.ln_f(hidden_states) |
|
|
|
hidden_states = hidden_states.view(output_shape) |
|
|
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
if not return_dict: |
|
return tuple( |
|
v |
|
for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] |
|
if v is not None |
|
) |
|
|
|
return BaseModelOutputWithPastAndCrossAttentions( |
|
last_hidden_state=hidden_states, |
|
past_key_values=presents, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attentions, |
|
cross_attentions=all_cross_attentions, |
|
) |
|
|
|
|
|
@dataclass |
|
class DecisionTransformerOutput(ModelOutput): |
|
""" |
|
Base class for model's outputs that also contains a pooling of the last hidden states. |
|
|
|
Args: |
|
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. |
|
state_preds (`torch.FloatTensor` of shape `(batch_size, sequence_length, state_dim)`): |
|
Environment state predictions |
|
action_preds (`torch.FloatTensor` of shape `(batch_size, sequence_length, action_dim)`): |
|
Model action predictions |
|
return_preds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 1)`): |
|
Predicted returns for each state |
|
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 + 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 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. |
|
""" |
|
|
|
state_preds: torch.FloatTensor = None |
|
action_preds: torch.FloatTensor = None |
|
return_preds: torch.FloatTensor = None |
|
hidden_states: torch.FloatTensor = None |
|
attentions: torch.FloatTensor = None |
|
last_hidden_state: torch.FloatTensor = None |
|
|
|
|
|
class DecisionTransformerPreTrainedModel(PreTrainedModel): |
|
""" |
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
|
models. |
|
""" |
|
|
|
config_class = DecisionTransformerConfig |
|
base_model_prefix = "decision_transformer" |
|
main_input_name = "states" |
|
supports_gradient_checkpointing = False |
|
|
|
def _init_weights(self, module): |
|
"""Initialize the weights""" |
|
if isinstance(module, nn.Linear): |
|
|
|
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
elif isinstance(module, nn.LayerNorm): |
|
module.bias.data.zero_() |
|
module.weight.data.fill_(1.0) |
|
|
|
|
|
DECISION_TRANSFORMER_START_DOCSTRING = r""" |
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use |
|
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and |
|
behavior. |
|
|
|
Parameters: |
|
config ([`~DecisionTransformerConfig`]): Model configuration class with all the parameters of the model. |
|
Initializing with a config file does not load the weights associated with the model, only the |
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. |
|
""" |
|
|
|
DECISION_TRANSFORMER_INPUTS_DOCSTRING = r""" |
|
Args: |
|
states (`torch.FloatTensor` of shape `(batch_size, episode_length, state_dim)`): |
|
The states for each step in the trajectory |
|
actions (`torch.FloatTensor` of shape `(batch_size, episode_length, act_dim)`): |
|
The actions taken by the "expert" policy for the current state, these are masked for auto regressive |
|
prediction |
|
rewards (`torch.FloatTensor` of shape `(batch_size, episode_length, 1)`): |
|
The rewards for each state, action |
|
returns_to_go (`torch.FloatTensor` of shape `(batch_size, episode_length, 1)`): |
|
The returns for each state in the trajectory |
|
timesteps (`torch.LongTensor` of shape `(batch_size, episode_length)`): |
|
The timestep for each step in the trajectory |
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, episode_length)`): |
|
Masking, used to mask the actions when performing autoregressive prediction |
|
""" |
|
|
|
|
|
@add_start_docstrings("The Decision Transformer Model", DECISION_TRANSFORMER_START_DOCSTRING) |
|
class DecisionTransformerModel(DecisionTransformerPreTrainedModel): |
|
""" |
|
|
|
The model builds upon the GPT2 architecture to perform autoregressive prediction of actions in an offline RL |
|
setting. Refer to the paper for more details: https://arxiv.org/abs/2106.01345 |
|
|
|
""" |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.config = config |
|
self.hidden_size = config.hidden_size |
|
|
|
|
|
self.encoder = DecisionTransformerGPT2Model(config) |
|
|
|
self.embed_timestep = nn.Embedding(config.max_ep_len, config.hidden_size) |
|
self.embed_return = torch.nn.Linear(1, config.hidden_size) |
|
self.embed_state = torch.nn.Linear(config.state_dim, config.hidden_size) |
|
self.embed_action = torch.nn.Linear(config.act_dim, config.hidden_size) |
|
|
|
self.embed_ln = nn.LayerNorm(config.hidden_size) |
|
|
|
|
|
self.predict_state = torch.nn.Linear(config.hidden_size, config.state_dim) |
|
self.predict_action = nn.Sequential( |
|
*([nn.Linear(config.hidden_size, config.act_dim)] + ([nn.Tanh()] if config.action_tanh else [])) |
|
) |
|
self.predict_return = torch.nn.Linear(config.hidden_size, 1) |
|
|
|
|
|
self.post_init() |
|
|
|
@add_start_docstrings_to_model_forward(DECISION_TRANSFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) |
|
@replace_return_docstrings(output_type=DecisionTransformerOutput, config_class=_CONFIG_FOR_DOC) |
|
def forward( |
|
self, |
|
states: Optional[torch.FloatTensor] = None, |
|
actions: Optional[torch.FloatTensor] = None, |
|
rewards: Optional[torch.FloatTensor] = None, |
|
returns_to_go: Optional[torch.FloatTensor] = None, |
|
timesteps: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple[torch.FloatTensor], DecisionTransformerOutput]: |
|
r""" |
|
Returns: |
|
|
|
Examples: |
|
|
|
```python |
|
>>> from transformers import DecisionTransformerModel |
|
>>> import torch |
|
|
|
>>> model = DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium") |
|
>>> # evaluation |
|
>>> model = model.to(device) |
|
>>> model.eval() |
|
|
|
>>> env = gym.make("Hopper-v3") |
|
>>> state_dim = env.observation_space.shape[0] |
|
>>> act_dim = env.action_space.shape[0] |
|
|
|
>>> state = env.reset() |
|
>>> states = torch.from_numpy(state).reshape(1, 1, state_dim).to(device=device, dtype=torch.float32) |
|
>>> actions = torch.zeros((1, 1, act_dim), device=device, dtype=torch.float32) |
|
>>> rewards = torch.zeros(1, 1, device=device, dtype=torch.float32) |
|
>>> target_return = torch.tensor(TARGET_RETURN, dtype=torch.float32).reshape(1, 1) |
|
>>> timesteps = torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1) |
|
>>> attention_mask = torch.zeros(1, 1, device=device, dtype=torch.float32) |
|
|
|
>>> # forward pass |
|
>>> with torch.no_grad(): |
|
... state_preds, action_preds, return_preds = model( |
|
... states=states, |
|
... actions=actions, |
|
... rewards=rewards, |
|
... returns_to_go=target_return, |
|
... timesteps=timesteps, |
|
... attention_mask=attention_mask, |
|
... return_dict=False, |
|
... ) |
|
```""" |
|
|
|
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 |
|
|
|
batch_size, seq_length = states.shape[0], states.shape[1] |
|
|
|
if attention_mask is None: |
|
|
|
attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long) |
|
|
|
|
|
state_embeddings = self.embed_state(states) |
|
action_embeddings = self.embed_action(actions) |
|
returns_embeddings = self.embed_return(returns_to_go) |
|
time_embeddings = self.embed_timestep(timesteps) |
|
|
|
|
|
state_embeddings = state_embeddings + time_embeddings |
|
action_embeddings = action_embeddings + time_embeddings |
|
returns_embeddings = returns_embeddings + time_embeddings |
|
|
|
|
|
|
|
stacked_inputs = ( |
|
torch.stack((returns_embeddings, state_embeddings, action_embeddings), dim=1) |
|
.permute(0, 2, 1, 3) |
|
.reshape(batch_size, 3 * seq_length, self.hidden_size) |
|
) |
|
stacked_inputs = self.embed_ln(stacked_inputs) |
|
|
|
|
|
stacked_attention_mask = ( |
|
torch.stack((attention_mask, attention_mask, attention_mask), dim=1) |
|
.permute(0, 2, 1) |
|
.reshape(batch_size, 3 * seq_length) |
|
) |
|
device = stacked_inputs.device |
|
|
|
encoder_outputs = self.encoder( |
|
inputs_embeds=stacked_inputs, |
|
attention_mask=stacked_attention_mask, |
|
position_ids=torch.zeros(stacked_attention_mask.shape, device=device, dtype=torch.long), |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
x = encoder_outputs[0] |
|
|
|
|
|
|
|
x = x.reshape(batch_size, seq_length, 3, self.hidden_size).permute(0, 2, 1, 3) |
|
|
|
|
|
return_preds = self.predict_return(x[:, 2]) |
|
state_preds = self.predict_state(x[:, 2]) |
|
action_preds = self.predict_action(x[:, 1]) |
|
if not return_dict: |
|
return (state_preds, action_preds, return_preds) |
|
|
|
return DecisionTransformerOutput( |
|
last_hidden_state=encoder_outputs.last_hidden_state, |
|
state_preds=state_preds, |
|
action_preds=action_preds, |
|
return_preds=return_preds, |
|
hidden_states=encoder_outputs.hidden_states, |
|
attentions=encoder_outputs.attentions, |
|
) |
|
|