Merge branch 'main' into next-best-lm/merge-rope-main

.gitlab-ci.yml

@@ -35,6 +35,7 @@ unit_tests:
   stage: test
   script: &selene-test-launcher-script
     - echo "Running selene resume from checkpoint test. "
+    - echo "In case of error check ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${CI_JOB_NAME}/results directory for result logs."
     - pwd
     - export BUILD_DIR=`pwd`
     - export RUN_NAME=resume_${RUN_MODEL}_tp${TP_SIZE}_pp${PP_SIZE}_${NUM_NODES}nodes
@@ -66,6 +67,7 @@ unit_tests:
     - echo "Finished job"  
     - source $PYTHON_VIRTUAL_ENV
     - pytest $BUILD_DIR/tests/functional_tests/python_test_utils/test_resume_checkpoint_pipeline.py
+    - if [ $? -ne 0 ]; then echo "Pytest failed. See ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${CI_JOB_NAME}/results directory for result logs."; fi
     - echo "Completed the job"
   rules:
     - if: $TEST_LEVEL =~ $TESTS_TO_RUN_ON_THIS_COMMIT || $CI_JOB_NAME =~ $TESTS_TO_RUN_ON_THIS_COMMIT || $CI_JOB_NAME =~ $TEST_REGEX_ON_THIS_COMMIT
@@ -82,11 +84,13 @@ unit_tests:
   stage: test
   script: &selene-test-launcher-script
     - echo "Running selene test"
+    - echo "In case of error check ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${CI_JOB_NAME}/results directory for result logs."
     - echo "$CI_MERGE_REQUEST_APPROVED"
     - pwd
     - export BUILD_DIR=`pwd`
     - export RUN_NAME=${RUN_MODEL}_tp${TP_SIZE}_pp${PP_SIZE}_${NUM_NODES}nodes_${MAX_STEPS}steps
     - export TP_SIZE PP_SIZE NUM_NODES MAX_STEPS VP_SIZE
+    - export MBS GBS
     - export DATA_DIR=$DATA_DIR
     - echo "Run name is $RUN_NAME"
     - mkdir -p $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/checkpoints
@@ -100,7 +104,7 @@ unit_tests:
     - export RESULTS_DIR=$BASE_DIR/results
     - export CHECKPOINTS_DIR=$BASE_DIR/checkpoints
     - echo "Submitting job"
-    - sbatch_submission=`sbatch $BUILD_DIR/tests/functional_tests/test_scripts/$RUN_MODEL/sbatch_${RUN_MODEL}_distributed_test.sh --export=BASE_DIR,BUILD_DIR,DATA_DIR,TP_SIZE,PP_SIZE,NUM_NODES,MAX_STEPS,VP_SIZE`
+    - sbatch_submission=`sbatch $BUILD_DIR/tests/functional_tests/test_scripts/$RUN_MODEL/sbatch_${RUN_MODEL}_distributed_test.sh --export=BASE_DIR,BUILD_DIR,DATA_DIR,TP_SIZE,PP_SIZE,NUM_NODES,MAX_STEPS,VP_SIZE,MBS,GBS`
     - export SLURM_JOBID=$(echo $sbatch_submission| grep 'Submitted batch job' | awk '{ print $4 }');
     - bash $BUILD_DIR/tests/functional_tests/shell_test_utils/jobwait.sh $SLURM_JOBID
     - \[ ! -z ${SLURM_JOBID} \] && echo -e " --------------------------------------------------\n"
@@ -167,6 +171,19 @@ train.gpt3.345m_tp1_pp2_1node_50steps:
     TIME_LIMIT: "20:00"
     TEST_LEVEL: L0 
 
+train.gpt3.345m_tp1_pp4_1node_50steps:
+  <<: *selene-test-launcher
+  variables:
+    <<: [*VARS]
+    RUN_MODEL: gpt3
+    TP_SIZE: 1
+    PP_SIZE: 4
+    VP_SIZE: 1
+    NUM_NODES: 1
+    MAX_STEPS: 50
+    TIME_LIMIT: "20:00"
+    TEST_LEVEL: L0
+
 resume.checkpoint.gpt3.345m_tp1_pp2_1node:
   <<: *selene-test-resume-checkpoint-launcher
   variables:
@@ -245,8 +262,8 @@ cleanup.selene:
   variables:
     <<: [*VARS]
   script:
-    - NUM_CLEANUP=`find ${SELENE_ADLR_CI_PATH}/* -type d -ctime +20 | wc -l`
-    - find ${SELENE_ADLR_CI_PATH}/* -type d -ctime +20 | xargs rm -rf
+    - NUM_CLEANUP=`find ${SELENE_ADLR_CI_PATH}/* -type d -ctime +20 | grep -v data | wc -l`
+    - find ${SELENE_ADLR_CI_PATH}/* -type d -ctime +20 | grep -v data | xargs rm -rf
     - echo "Finished cleaning $NUM_CLEANUP directories older than 20 days everything in Selene"
   allow_failure: true
   rules:

megatron/arguments.py

@@ -250,6 +250,14 @@ def validate_args(args, defaults={}):
     if args.ffn_hidden_size is None:
         args.ffn_hidden_size = 4 * args.hidden_size
 
+    if args.swiglu:
+        # reduce the dimnesion for MLP since projections happens on
+        # two linear layers. this keeps the number of paramters in
+        # the same ballpark as the counterpart with 4*h size
+        # we keep it a multiple of 64, which means the actual tensor size
+        # will be a multiple of 64 / tp_size
+        args.ffn_hidden_size = int((4 * args.hidden_size * 2 / 3) / 64) * 64
+
     if args.kv_channels is None:
         assert args.hidden_size % args.num_attention_heads == 0
         args.kv_channels = args.hidden_size // args.num_attention_heads
@@ -349,6 +357,10 @@ def validate_args(args, defaults={}):
                 "Using async gradient all reduce requires setting the environment "
                 "variable CUDA_DEVICE_MAX_CONNECTIONS to 1")
 
+    # Disable bias gelu fusion if we are disabling bias altogether
+    if not args.add_bias_linear:
+        args.bias_gelu_fusion = False
+
     # Load retro args.
     if args.retro_workdir:
         retro_args_path = get_retro_args_path(args.retro_workdir)
@@ -530,6 +542,10 @@ def _add_network_size_args(parser):
                        help='Use OpenAIs GeLU implementation. This option'
                        'should not be used unless for backward compatibility'
                        'reasons.')
+    group.add_argument('--squared-relu', action='store_true',
+                       help='Use squared relu activation instead of default gelu')
+    group.add_argument('--swiglu', action='store_true',
+                       help='Use gated linear units and SiLU activation instead of default gelu')
     group.add_argument('--onnx-safe', type=bool, required=False,
                        help='Use workarounds for known problems with '
                        'Torch ONNX exporter')
@@ -736,6 +752,9 @@ def _add_training_args(parser):
     group.add_argument('--use-flash-attn', action='store_true',
                        help='use FlashAttention implementation of attention. '
                        'https://arxiv.org/abs/2205.14135')
+    group.add_argument('--disable-bias-linear', action='store_false',
+                       help='Disable bias in the linear layers',
+                       dest='add_bias_linear')
     group.add_argument('--optimizer', type=str, default='adam',
                        choices=['adam', 'sgd'],
                        help='Optimizer function')

megatron/core/pipeline_parallel/schedules.py

@@ -593,7 +593,7 @@ def forward_backward_pipelining_with_interleaving(*,
     if not forward_only:
         if all_warmup_microbatches:
             output_tensor_grads[num_model_chunks-1].append(
-                p2p_communication.recv_backward(tensor_shape, timers=timers))
+                p2p_communication.recv_backward(tensor_shape, dtype=dtype, timers=timers))
         for k in range(num_microbatches_remaining, total_num_microbatches):
             input_tensor_grad = backward_step_helper(k)
             next_backward_model_chunk_id = get_model_chunk_id(k+1, forward=False)

megatron/model/transformer.py

@@ -88,28 +88,45 @@ class ParallelMLP(MegatronModule):
         super(ParallelMLP, self).__init__()
         args = get_args()
 
+        self.add_bias = args.add_bias_linear
 
-        # Project to 4h.
+        # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
         self.dense_h_to_4h = tensor_parallel.ColumnParallelLinear(
             args.hidden_size,
-            args.ffn_hidden_size,
+            args.ffn_hidden_size * 2 if args.swiglu else args.ffn_hidden_size,
+            bias=self.add_bias,
             gather_output=False,
             init_method=init_method,
             skip_bias_add=True,
             async_tensor_model_parallel_allreduce=args.async_tensor_model_parallel_allreduce,
             **_args_to_kwargs())
 
-        self.bias_gelu_fusion = args.bias_gelu_fusion
-        self.activation_func = F.gelu
+        self.bias_gelu_fusion = False
+        self.activation_func = None
+        self.swiglu = args.swiglu
+
         if args.openai_gelu:
             self.activation_func = openai_gelu
         elif args.onnx_safe:
             self.activation_func = erf_gelu
+        elif args.swiglu:
+            def swiglu(x):
+                x = torch.chunk(x, 2, dim=-1)
+                return F.silu(x[0]) * x[1]
+            self.activation_func = swiglu
+        elif args.squared_relu:
+            def squared_relu(x):
+                return torch.pow(F.relu(x), 2)
+            self.activation_func = squared_relu
+        else:
+            self.bias_gelu_fusion = args.bias_gelu_fusion
+            self.activation_func = F.gelu
 
         # Project back to h.
         self.dense_4h_to_h = tensor_parallel.RowParallelLinear(
             args.ffn_hidden_size,
             args.hidden_size,
+            bias=self.add_bias,
             input_is_parallel=True,
             init_method=output_layer_init_method,
             skip_bias_add=True,
@@ -121,11 +138,13 @@ class ParallelMLP(MegatronModule):
         intermediate_parallel, bias_parallel = self.dense_h_to_4h(hidden_states)
 
         if self.bias_gelu_fusion:
-             intermediate_parallel = \
-                     bias_gelu_impl(intermediate_parallel, bias_parallel)
+            assert self.add_bias is True
+            assert self.activation_func == F.gelu
+            intermediate_parallel = bias_gelu_impl(intermediate_parallel, bias_parallel)
         else:
-            intermediate_parallel = \
-                self.activation_func(intermediate_parallel + bias_parallel)
+            if bias_parallel is not None:
+                intermediate_parallel = intermediate_parallel + bias_parallel
+            intermediate_parallel = self.activation_func(intermediate_parallel)
 
         # [s, b, h]
         output, output_bias = self.dense_4h_to_h(intermediate_parallel)
@@ -403,6 +422,7 @@ class ParallelAttention(MegatronModule):
             self.query_key_value = tensor_parallel.ColumnParallelLinear(
                 args.hidden_size,
                 3 * projection_size,
+                bias=args.add_bias_linear,
                 gather_output=False,
                 init_method=init_method,
                 async_tensor_model_parallel_allreduce=args.async_tensor_model_parallel_allreduce,
@@ -412,6 +432,7 @@ class ParallelAttention(MegatronModule):
             self.query = tensor_parallel.ColumnParallelLinear(
                 args.hidden_size,
                 projection_size,
+                bias=args.add_bias_linear,
                 gather_output=False,
                 init_method=init_method,
                 async_tensor_model_parallel_allreduce=args.async_tensor_model_parallel_allreduce,
@@ -421,6 +442,7 @@ class ParallelAttention(MegatronModule):
             self.key_value = tensor_parallel.ColumnParallelLinear(
                 args.hidden_size,
                 2 * projection_size,
+                bias=args.add_bias_linear,
                 gather_output=False,
                 init_method=init_method,
                 async_tensor_model_parallel_allreduce=args.async_tensor_model_parallel_allreduce,
@@ -439,6 +461,7 @@ class ParallelAttention(MegatronModule):
         self.dense = tensor_parallel.RowParallelLinear(
             projection_size,
             args.hidden_size,
+            bias=args.add_bias_linear,
             input_is_parallel=True,
             init_method=output_layer_init_method,
             skip_bias_add=True,
@@ -632,7 +655,9 @@ class ParallelAttention(MegatronModule):
 
 def bias_dropout_add(x, bias, residual, prob, training):
     # type: (Tensor, Tensor, Tensor, float, bool) -> Tensor
-    out = torch.nn.functional.dropout(x + bias, p=prob, training=training)
+    if bias is not None:
+        x = x + bias
+    out = torch.nn.functional.dropout(x, p=prob, training=training)
     out = residual + out
     return out
 
@@ -767,10 +792,12 @@ class ParallelTransformerLayer(MegatronModule):
             else:
                 bias_dropout_add_func = get_bias_dropout_add(self.training)
 
+            if attention_bias is not None:
+                attention_bias = attention_bias.expand_as(residual)
             with self.bias_dropout_add_exec_handler():
                 layernorm_input = bias_dropout_add_func(
                     attention_output,
-                    attention_bias.expand_as(residual),
+                    attention_bias,
                     residual,
                     self.hidden_dropout)
         else:
@@ -793,10 +820,13 @@ class ParallelTransformerLayer(MegatronModule):
             else:
                 residual = layernorm_input
 
+            if attention_bias is not None:
+                attention_bias = attention_bias.expand_as(residual)
+
             with self.bias_dropout_add_exec_handler():
                 layernorm_input = bias_dropout_add_func(
                     attention_output,
-                    attention_bias.expand_as(residual),
+                    attention_bias,
                     residual,
                     self.hidden_dropout)
 
@@ -813,10 +843,12 @@ class ParallelTransformerLayer(MegatronModule):
             residual = layernorm_input
 
         if self.drop_path is None:
+            if mlp_bias is not None:
+                mlp_bias = mlp_bias.expand_as(residual)
             with self.bias_dropout_add_exec_handler():
                 output = bias_dropout_add_func(
                     mlp_output,
-                    mlp_bias.expand_as(residual),
+                    mlp_bias,
                     residual,
                     self.hidden_dropout)
 
@@ -831,7 +863,9 @@ class ParallelTransformerLayer(MegatronModule):
                                                      keep_graph = True)
 
         else:
-            out = torch.nn.functional.dropout(mlp_output + mlp_bias,
+            if mlp_bias is not None:
+                mlp_output = mlp_output + mlp_bias
+            out = torch.nn.functional.dropout(mlp_output,
                                               p=self.hidden_dropout,
                                               training=self.training)
             output = residual + self.drop_path(out)
@@ -1085,9 +1119,10 @@ class ParallelTransformer(MegatronModule):
         """Forward method with activation checkpointing."""
         def custom(start, end, is_transformer_engine=False):
             def custom_forward(*args, **kwargs):
+                x_, *args = args
                 for index in range(start, end):
                     layer = self._get_layer(index)
-                    x_ = layer(*args, **kwargs)
+                    x_ = layer(x_, *args, **kwargs)
                 return x_
             def custom_forward_transformer_engine(*args, **kwargs):
                 return custom_forward(*args, is_first_microbatch=is_first_microbatch, **kwargs)

tests/functional_tests/python_test_utils/get_test_results_from_tensorboard_logs.py

@@ -25,6 +25,7 @@ def read_tb_logs_as_list(path, summary_name):
         ea.Reload()
         summary = ea.Scalars(summary_name)
         summary_list = [round(x.value, 5) for x in summary]
+        print(f'\nObtained the following list for {summary_name} ------------------')
         print(summary_list)
         return summary_list
     raise FileNotFoundError(f"File not found matching: {path}/events*")    

tests/functional_tests/python_test_utils/test_ci_pipeline.py

@@ -34,6 +34,7 @@ def read_tb_logs_as_list(path, summary_name):
         ea.Reload()
         summary = ea.Scalars(summary_name)
         summary_list = [round(x.value, 5) for x in summary]
+        print(f'\nObtained the following list for {summary_name} ------------------')
         print(summary_list)
         return summary_list
     raise FileNotFoundError(f"File not found matching: {path}/events*")
@@ -53,9 +54,11 @@ class TestCIPipeline:
             raise FileNotFoundError("Expected data is none")
         expected = self.expected[loss_type]
         expected_list = expected["values"]
+        print(expected_list)
         actual_list = read_tb_logs_as_list(LOGS_DIR, loss_type)
         assert actual_list is not None, f"No TensorBoard events file was found in the logs for {loss_type}."
         for i, step in enumerate(range(expected["start_step"], expected["end_step"], expected["step_interval"])):
+            print(f"Checking step {step} against expected {i}")
             if test_type == TypeOfTest.APPROX:
                 assert actual_list[step] == pytest.approx(expected=expected_list[i], rel=self.margin_loss), f"{self.job_name} : The loss at step {step} should be approximately {expected_list[i]} but it is {actual_list[step]}."
             else:

tests/functional_tests/test_results/gpt3/gpt3_tp1_pp4_1nodes_50steps.json

+{"lm loss": {"start_step": 0, "end_step": 45, "step_interval": 5, "values": [10.7947, 10.85294, 10.87058, 10.83388, 10.83012, 10.78726, 10.56378, 10.57311, 10.48692]}, "num-zeros": {"start_step": 0, "end_step": 29, "step_interval": 5, "values": [2452.0, 2818.0, 2036.0, 2662.0, 2651.0, 2422.0]}, "iteration_timing_avg": 0.1187023333333333}

tests/functional_tests/test_scripts/gpt3/pretrain_gpt3_distributed_test.sh

@@ -7,7 +7,9 @@ TP_SIZE=$4
 PP_SIZE=$5
 NNODES=$6
 MAX_STEPS=$7
-
+VP_SIZE=$8
+MBS=$9
+GBS=${10}
 GPUS_PER_NODE=8
 # Change for multinode config
 MASTER_ADDR=localhost
@@ -30,8 +32,8 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS \
        --log-validation-ppl-to-tensorboard \
        --log-timers-to-tensorboard \
        --tensorboard-dir ${TENSORBOARD_DIR} \
-       --micro-batch-size 4 \
-       --global-batch-size 32 \
+       --micro-batch-size ${MBS:-4} \
+       --global-batch-size ${GBS:-32} \
        --seq-length 1024 \
        --max-position-embeddings 1024 \
        --train-iters $MAX_STEPS \
@@ -57,5 +59,6 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS \
        --eval-iters 10 \
        --tensor-model-parallel-size $TP_SIZE \
        --pipeline-model-parallel-size $PP_SIZE \
+       ${VP_SIZE:+--num-layers-per-virtual-pipeline-stage "$VP_SIZE"} \
        --no-gradient-accumulation-fusion \
        --fp16

tests/functional_tests/test_scripts/gpt3/sbatch_gpt3_distributed_test.sh

@@ -13,4 +13,4 @@ TENSORBOARD_DIR=/workspace/logs
 srun --output $BASE_DIR/results/slurm-%j.out --error $BASE_DIR/results/slurm-%j.out --container-image gitlab-master.nvidia.com/dl/dgx/pytorch:21.12-py3-devel --container-mounts $BASE_DIR/logs:/workspace/logs,$BASE_DIR/checkpoints:/workspace/checkpoints,$BUILD_DIR:/workspace/megatron-lm,$DATA_DIR:/workspace/data --no-container-mount-home bash -c "
   ls 
   cd /workspace/megatron-lm
-  ./tests/functional_tests/test_scripts/gpt3/pretrain_gpt3_distributed_test.sh $DATA_PATH $CHECKPOINT_PATH $TENSORBOARD_DIR $TP_SIZE $PP_SIZE $NUM_NODES $MAX_STEPS"
+  ./tests/functional_tests/test_scripts/gpt3/pretrain_gpt3_distributed_test.sh $DATA_PATH $CHECKPOINT_PATH $TENSORBOARD_DIR $TP_SIZE $PP_SIZE $NUM_NODES $MAX_STEPS $VP_SIZE $MBS $GBS"
\ No newline at end of file