Merge branch 'transformer_refactoring_from_pretrain_refactoring' into 'master' (beb3e0d3) · Commits · candle / Megatron-LM

docker/Dockerfile

+1 −10

Original line number	Diff line number	Diff line
		# ===========
		# base images
		# ===========
		FROM nvcr.io/nvidia/pytorch:19.05-py3
		FROM nvcr.io/nvidia/pytorch:19.09-py3


		# ===============
		@@ -27,12 +27,3 @@ RUN pip install --upgrade pip && \
		COPY requirements.txt /tmp/
		RUN pip install --upgrade --ignore-installed -r /tmp/requirements.txt


		# ===========
		# latest apex
		# ===========
		RUN pip uninstall -y apex && \
		git clone https://github.com/NVIDIA/apex.git ~/apex && \
		cd ~/apex && \
		pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .

docker/README.md

deleted100644 → 0

+0 −1

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		Note that as of now you need to have PySOL cloned to the directory here before building the container.

evaluate_gpt2.py

+0 −1

Original line number	Diff line number	Diff line
		@@ -29,7 +29,6 @@ from megatron.fp16 import FP16_Module
		from megatron.fp16 import FP16_Optimizer
		from megatron.learning_rates import AnnealingLR
		from megatron.model import GPT2Model
		from megatron.model import gpt2_get_params_for_weight_decay_optimization
		from megatron.model import DistributedDataParallel as DDP
		from megatron import mpu
		from apex.optimizers import FusedAdam as Adam

generate_samples.py

+13 −8

Original line number	Diff line number	Diff line
		@@ -26,9 +26,8 @@ import argparse
		import time
		from arguments import get_args
		from megatron.utils import Timers
		from pretrain_gpt2 import initialize_distributed
		from pretrain_gpt2 import set_random_seed
		from pretrain_gpt2 import get_train_val_test_data
		from megatron.utils import initialize_distributed
		from megatron.utils import set_random_seed
		from pretrain_gpt2 import get_masks_and_position_ids
		from megatron.utils import load_checkpoint
		from megatron.data_utils import make_tokenizer
		@@ -96,7 +95,8 @@ def get_batch(context_tokens, args):
		tokens,
		args.eod_token,
		args.reset_position_ids,
		args.reset_attention_mask)
		args.reset_attention_mask,
		False)

		return tokens, attention_mask, position_ids

		@@ -361,7 +361,7 @@ def switch(val1, val2, boolean):
		boolean = boolean.type_as(val1)
		return (1-boolean)val1 + booleanval2

		def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask, position_ids, tokenizer, args, maxlen=None):
		def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask, position_ids, tokenizer, args, maxlen=None, type_ids=None):
		model.eval()
		with torch.no_grad():
		context_length = context_lengths.min().item()
		@@ -384,16 +384,21 @@ def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask
		while context_length <= (maxlen):

		if args.recompute:
		logits = model(tokens, position_ids, attention_mask)
		logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids)
		logits = logits[:, context_length - 1, :]
		else:
		types2use = None
		if counter == 0:
		tokens2use = tokens[:, :context_length]
		positions2use = position_ids[:, :context_length]
		if type_ids is not None:
		types2use = type_ids[:, :context_length]
		else:
		tokens2use = tokens[:, context_length - 1].view(batch_size, -1)
		positions2use = position_ids[:, context_length - 1].view(batch_size, -1)
		logits, layer_past = model(tokens2use, positions2use, attention_mask, layer_past=layer_past, get_present=True)
		if type_ids is not None:
		types2use = type_ids[:, context_length - 1].view(batch_size, -1)
		logits, layer_past = model(tokens2use, positions2use, attention_mask, layer_past=layer_past, get_key_value=True, tokentype_ids=types2use)
		logits = logits[:, -1].view(batch_size,-1).contiguous()

		if args.greedy:

megatron/fp16/fp16.py

+9 −1

Original line number	Diff line number	Diff line
		@@ -22,6 +22,9 @@ from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
		from .loss_scaler import DynamicLossScaler, LossScaler
		from .fp16util import model_grads_to_master_grads, master_params_to_model_params, clip_grad_norm


		from megatron.module import MegatronModule

		FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
		HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)

		@@ -56,7 +59,7 @@ def fp16_to_fp32(val):
		return val
		return conversion_helper(val, float_conversion)

		class FP16_Module(nn.Module):
		class FP16_Module(MegatronModule):
		def __init__(self, module):
		super(FP16_Module, self).__init__()
		self.add_module('module', module.half())
		@@ -67,6 +70,11 @@ class FP16_Module(nn.Module):
		def state_dict(self, destination=None, prefix='', keep_vars=False):
		return self.module.state_dict(destination, prefix, keep_vars)

		def state_dict_for_save_checkpoint(self, destination=None, prefix='',
		keep_vars=False):
		return self.module.state_dict_for_save_checkpoint(destination, prefix,
		keep_vars)

		def load_state_dict(self, state_dict, strict=True):
		self.module.load_state_dict(state_dict, strict=strict)