def parallel_lm_logits(input_,
word_embeddings_weight,
parallel_output,
bias=None,
weight_tying=True):
"""LM logits using word embedding weights."""
# Parallel logits.
input_parallel = mpu.copy_to_model_parallel_region(input_)
if weight_tying:
# Matrix multiply.
if bias is None:
logits_parallel = F.linear(input_parallel, word_embeddings_weight)
else:
logits_parallel = F.linear(input_parallel, word_embeddings_weight,
bias)
else:
args = get_args()
logits_fn = mpu.RowParallelLinear(args.hidden_size,
args.padded_vocab_size,
bias=False,
input_is_parallel=True,
skip_bias_add=False)
logits_parallel = logits_fn(input_parallel)
# Gather if needed.
if parallel_output:
return logits_parallel
return mpu.gather_from_model_parallel_region(logits_parallel)
def forward(self,
layernorm_input,
attention_mask,
presents=None,
layer_past=None,
get_key_value=False):
# hidden_states: [b, s, h]
# Collect the scattered result from the fused dropout.
if self.scattered_attn_output:
layernorm_input = mpu.gather_from_model_parallel_region(
layernorm_input)
# Attention output/bias are not used again, so no need to gather
# Layer norm post the self attention.
layernorm_output = self.post_attention_layernorm(layernorm_input)
# MLP.
mlp_output, mlp_bias = self.mlp(layernorm_output)
# Second residual connection.
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = layernorm_input
# jit scripting for a nn.module (with dropout) is not
# trigerring the fusion kernel. For now, we use two
# different nn.functional routines to account for varying
# dropout semantics during training and inference phases.
if self.bias_dropout_fusion:
if self.training:
bias_dropout_add_func = bias_dropout_add_fused_train
else:
bias_dropout_add_func = bias_dropout_add_fused_inference
else:
bias_dropout_add_func = get_bias_dropout_add(self.training)
#re-enable torch grad to enable fused optimization.
with torch.enable_grad():
output = bias_dropout_add_func(mlp_output,
mlp_bias.expand_as(residual),
residual, self.hidden_dropout)
if get_key_value:
output = [output, presents]
return output
def parallel_lm_logits(input_,
word_embeddings_weight,
parallel_output,
bias=None):
"""LM logits using word embedding weights."""
# Parallel logits.
input_parallel = mpu.copy_to_model_parallel_region(input_)
# Matrix multiply.
if bias is None:
logits_parallel = F.linear(input_parallel, word_embeddings_weight)
else:
logits_parallel = F.linear(input_parallel, word_embeddings_weight,
bias)
# Gather if needed.
if parallel_output:
return logits_parallel
return mpu.gather_from_model_parallel_region(logits_parallel)