WildfireDetection/apex/tests/L0/run_transformer/test_layers.py

import logging
import unittest
import typing

import torch
import torch.nn as nn
from torch.testing._internal import common_utils

from apex.transformer import parallel_state
from apex.transformer.tensor_parallel import layers
from apex.transformer.testing.commons import set_random_seed
from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
from apex.transformer.testing.distributed_test_base import UccDistributedTestBase


logging.getLogger("torch").setLevel(logging.WARNING)
logging.getLogger("apex").setLevel(logging.WARNING)


# N.B.(mkozuki): Disable TF32 matrix multiply.
# Matrices used in this test are so small that TF32 matmul
# can be less precise so that `self.assertEqual` raises.
torch.backends.cuda.matmul.allow_tf32 = False


class TensorParallelLayerTestBase:

    BATCH_SIZE: int = 8
    SEQUENCE_LENGTH: int = 128
    VOCAB_SIZE: int = 1024
    HIDDEN_SIZE: int = 256
    INPUT_SIZE_COEFF: int = 256
    OUTPUT_SIZE_COEFF: int = 256
    SEED: int = 123456

    @property
    def tensor_shape(self) -> typing.Sequence[int]:
        return [self.SEQUENCE_LENGTH, self.BATCH_SIZE, self.HIDDEN_SIZE]

    @torch.no_grad()
    @unittest.skipIf(torch.cuda.device_count() < 2, "Requires >=2 GPUs")
    def test_all_gather_parity(self) -> None:
        if self.DISTRIBUTED_BACKEND == "ucc":
            self.skipTest("torch_ucc does NOT support `torch.distributed._all_gather_base` as of 2022/06/15")
        from torch.distributed.distributed_c10d import all_gather, _all_gather_base  # NOQA

        for tensor_model_parallel_world_size in range(1, self.world_size + 1):
            if self.world_size % tensor_model_parallel_world_size:
                continue
            parallel_state.initialize_model_parallel(
                tensor_model_parallel_size_=tensor_model_parallel_world_size,
            )
            tensor_model_parallel_rank = parallel_state.get_tensor_model_parallel_rank()
            cur_tensor_model_device = torch.device(f"cuda:{tensor_model_parallel_rank}")
            with torch.no_grad():
                tensor = tensor_model_parallel_rank * torch.ones(
                    self.tensor_shape, dtype=torch.float32, device=cur_tensor_model_device)
            numel = tensor.numel()
            numel_gathered = tensor_model_parallel_world_size * numel
            gathered = torch.empty(
                torch.Size((numel_gathered,)),
                device=cur_tensor_model_device,
                dtype=torch.float32,
                requires_grad=False,
            )
            chunks = [
                gathered[i * numel : (i + 1) * numel]
                for i in range(tensor_model_parallel_world_size)
            ]
            all_gather(chunks, tensor, group=parallel_state.get_tensor_model_parallel_group())

            gathered_for_base = torch.empty(
                torch.Size((numel_gathered,)),
                device=cur_tensor_model_device,
                dtype=torch.float32,
                requires_grad=False,
            )
            _all_gather_base(
                gathered_for_base,
                tensor,
                group=parallel_state.get_tensor_model_parallel_group(),
            )

            msg = f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}"
            self.assertEqual(gathered, gathered_for_base, msg=msg)
            parallel_state.destroy_model_parallel()

    @torch.no_grad()
    @unittest.skipIf(torch.cuda.device_count() < 2, "Requires >=2 GPUs")
    def test_reduce_scatter_parity(self) -> None:
        if self.DISTRIBUTED_BACKEND == "ucc":
            self.skipTest("torch_ucc does NOT support `torch.distributed._reduce_scatter_base` as of 2022/06/15")
        from torch.distributed.distributed_c10d import reduce_scatter, _reduce_scatter_base  # NOQA

        for tensor_model_parallel_world_size in range(2, self.world_size + 1):
            if self.world_size % tensor_model_parallel_world_size:
                continue
            parallel_state.initialize_model_parallel(
                tensor_model_parallel_size_=tensor_model_parallel_world_size,
            )
            tensor_model_parallel_rank = parallel_state.get_tensor_model_parallel_rank()
            cur_tensor_model_device = torch.device(f"cuda:{tensor_model_parallel_rank}")
            with torch.no_grad():
                input = torch.cat([
                    i * torch.ones(self.tensor_shape, dtype=torch.float32, device=cur_tensor_model_device)
                    for i in range(tensor_model_parallel_world_size)
                ])
                input_list = [t.clone() for t in input.chunk(tensor_model_parallel_world_size)]
            output = torch.empty(
                self.tensor_shape,
                device=cur_tensor_model_device,
                dtype=torch.float32,
                requires_grad=False,
            )
            reduce_scatter(
                output, input_list,
                group=parallel_state.get_tensor_model_parallel_group(),
            )

            output_for_base = torch.empty(
                self.tensor_shape,
                device=cur_tensor_model_device,
                dtype=torch.float32,
                requires_grad=False,
            )
            _reduce_scatter_base(
                output_for_base,
                input,
                group=parallel_state.get_tensor_model_parallel_group(),
            )

            msg = f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}"
            self.assertEqual(output, output_for_base, msg=msg)
            self.assertEqual(input, torch.cat(input_list), msg=msg)
            parallel_state.destroy_model_parallel()

    def test_parallel_embedding(self) -> None:
        for tensor_model_parallel_world_size in range(1, self.world_size + 1):
            if self.world_size % tensor_model_parallel_world_size:
                continue
            parallel_state.initialize_model_parallel(
                tensor_model_parallel_size_=tensor_model_parallel_world_size,
            )
            set_random_seed(self.SEED + 1)
            input_tensor = torch.randint(
                0,
                self.VOCAB_SIZE,
                (
                    self.BATCH_SIZE,
                    self.SEQUENCE_LENGTH,
                ),
                device="cuda",
            )
            loss_weight = torch.randn(
                (
                    self.BATCH_SIZE,
                    self.SEQUENCE_LENGTH,
                    self.HIDDEN_SIZE,
                ),
                device="cuda",
            )

            set_random_seed(self.SEED)
            embedding_torch = nn.Embedding(
                self.VOCAB_SIZE,
                self.HIDDEN_SIZE,
            ).cuda()
            output_torch = embedding_torch(input_tensor)
            loss_torch = torch.mul(output_torch, loss_weight).sum()
            loss_torch.backward()

            # N.B.(mkozuki): With affine weight initialization on GPU,
            # it's super difficult to keep the consistency with nn.Embedding.
            # Thus, turning on `use_cpu_initialization`.
            set_random_seed(self.SEED)
            embedding_vocab_parallel = layers.VocabParallelEmbedding(
                self.VOCAB_SIZE,
                self.HIDDEN_SIZE,
                init_method=nn.init.normal_,
                use_cpu_initialization=True,
            ).cuda()
            output_vocab_parallel = embedding_vocab_parallel(input_tensor)
            loss_vocab_parallel = torch.mul(
                output_vocab_parallel, loss_weight
            ).sum()
            loss_vocab_parallel.backward()

            msg = f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}"
            self.assertEqual(output_torch, output_vocab_parallel, msg=msg)
            self.assertEqual(loss_torch, loss_vocab_parallel, msg=msg)

            splitted_weight_torch = torch.split(
                embedding_torch.weight.grad,
                self.VOCAB_SIZE
                // tensor_model_parallel_world_size,
                0,
            )[parallel_state.get_tensor_model_parallel_rank()]
            self.assertEqual(
                splitted_weight_torch, embedding_vocab_parallel.weight.grad, msg=msg,
            )

            parallel_state.destroy_model_parallel()

    def _affine_weight_init_test_impl(
        self, init_device: str, is_column_parallel: bool
    ) -> None:
        dim = int(not is_column_parallel)
        for tensor_model_parallel_world_size in range(1, self.world_size + 1):
            if self.world_size % tensor_model_parallel_world_size:
                continue
            parallel_state.initialize_model_parallel(
                tensor_model_parallel_size_=tensor_model_parallel_world_size
            )
            input_size: int = self.INPUT_SIZE_COEFF * tensor_model_parallel_world_size
            output_size: int = self.OUTPUT_SIZE_COEFF * tensor_model_parallel_world_size

            weight_shape = (
                (self.OUTPUT_SIZE_COEFF, input_size)
                if is_column_parallel
                else (output_size, self.INPUT_SIZE_COEFF)
            )
            weight = torch.empty(weight_shape)
            set_random_seed(self.SEED)

            sharding_dim_size = (
                self.OUTPUT_SIZE_COEFF
                if is_column_parallel
                else self.INPUT_SIZE_COEFF
            )

            if init_device == "cpu":
                layers._initialize_affine_weight_cpu(
                    weight,
                    output_size,
                    input_size,
                    sharding_dim_size,
                    dim,
                    nn.init.normal_,
                    params_dtype=torch.float32,
                )
            else:
                layers._initialize_affine_weight_gpu(
                    weight, torch.nn.init.normal_, dim
                )
            # Target
            set_random_seed(self.SEED)
            if init_device == "cpu":
                main_weight = torch.empty(output_size, input_size)
                nn.init.normal_(main_weight)
                curr_weight = torch.split(main_weight, sharding_dim_size, dim=dim)[
                    parallel_state.get_tensor_model_parallel_rank()
                ]
            else:
                curr_weight = torch.empty(*weight_shape)
                nn.init.normal_(curr_weight)

            self.assertEqual(
                curr_weight, weight, msg=f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}")
            parallel_state.destroy_model_parallel()

    def test_affine_weight_init_column_parallel_cpu(self) -> None:
        self._affine_weight_init_test_impl(init_device="cpu", is_column_parallel=True)

    def test_affine_weight_init_column_parallel_gpu(self) -> None:
        self._affine_weight_init_test_impl(init_device="gpu", is_column_parallel=True)

    def test_affine_weight_init_row_parallel_cpu(self) -> None:
        self._affine_weight_init_test_impl(init_device="cpu", is_column_parallel=False)

    def test_affine_weight_init_row_parallel_gpu(self) -> None:
        self._affine_weight_init_test_impl(init_device="gpu", is_column_parallel=False)

    def test_row_parallel_linear(self) -> None:
        self._row_parallel_linear_test_impl(False, False, False)

    def test_row_parallel_linear_gradient_accumulation_fusion(self) -> None:
        self._row_parallel_linear_test_impl(True, False, False)

    def test_row_parallel_linear_gradient_accumulation_fusion_in_fp16(self) -> None:
        self._row_parallel_linear_test_impl(True, True, False)

    # fails on native ucc and torch ucc: ucc does not support reduce scatter
    @unittest.skipIf(torch.cuda.device_count() < 2, "Sequence Parallel requires >=2 GPUs")
    def test_row_parallel_linear_sequence_parallel(self) -> None:
        self._row_parallel_linear_test_impl(False, False, True)

    # TODO(mkozuki): Merge this with `_column_parallel_linear_test_impl`
    # Note that `input_is_parallel` is unique to `RowParallelLinear` which could make the merge complicated.
    def _row_parallel_linear_test_impl(
        self,
        gradient_accumulation_fusion: bool,
        accumulation_in_fp16: bool,
        sequence_parallel_enabled: bool,
    ) -> None:
        tensor_shape = (
            self.SEQUENCE_LENGTH,
            self.BATCH_SIZE,
            self.HIDDEN_SIZE,
        )
        for tensor_model_parallel_world_size in range(
            1 + int(sequence_parallel_enabled), self.world_size + 1
        ):
            if self.world_size % tensor_model_parallel_world_size:
                continue
            parallel_state.initialize_model_parallel(
                tensor_model_parallel_size_=tensor_model_parallel_world_size,
            )
            set_random_seed(self.SEED)

            linear = layers.RowParallelLinear(
                self.HIDDEN_SIZE,
                self.HIDDEN_SIZE,
                keep_master_weight_for_test=True,
                params_dtype=torch.float32,
                use_cpu_initialization=True,
                gradient_accumulation_fusion=gradient_accumulation_fusion,
                accumulation_in_fp16=accumulation_in_fp16,
                sequence_parallel_enabled=sequence_parallel_enabled,
                # n.b.(mkozuki): RowParallelLinear is constructed with `input_is_parallel=True`
                # by default, e.g. https://github.com/NVIDIA/NeMo/blob/782b4e1652aaa43c8be390d9\
                # db0dc89544afa080/nemo/collections/nlp/modules/common/megatron/transformer.py#L204
                input_is_parallel=True,
            ).cuda()
            if accumulation_in_fp16:
                linear = linear.half()
            # Simulate the situation where fusion of weight grad calculation and gradient accumulation is enabled.
            if gradient_accumulation_fusion:
                with torch.no_grad():
                    linear.weight.main_grad = torch.zeros_like(linear.weight)

            msg = f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}"

            with torch.no_grad():
                orig_input_tensor = torch.randn(tensor_shape, requires_grad=True, device="cuda")
                orig_loss_weight = torch.randn(tensor_shape, device="cuda")
                input_tensor = orig_input_tensor.chunk(
                    chunks=tensor_model_parallel_world_size,
                    dim=2,
                )[parallel_state.get_tensor_model_parallel_rank()].contiguous()
                if sequence_parallel_enabled:
                    loss_weight = orig_loss_weight.chunk(
                        chunks=tensor_model_parallel_world_size,
                        dim=0,
                    )[parallel_state.get_tensor_model_parallel_rank()]
                else:
                    loss_weight = orig_loss_weight
                if accumulation_in_fp16:
                    orig_input_tensor = orig_input_tensor.half()
                    input_tensor = input_tensor.half()
                    loss_weight = loss_weight.half()
            input_tensor.requires_grad_()
            output, _ = linear(input_tensor)
            loss = torch.mul(output, loss_weight).sum()
            loss.backward()
            self.assertIsNotNone(input_tensor.grad, msg=msg)

            ref_linear = nn.Linear(
                in_features=self.HIDDEN_SIZE,
                out_features=self.HIDDEN_SIZE,
                bias=False,
                device="cuda",
            )
            with torch.no_grad():
                dldy = orig_loss_weight.clone()
                x = orig_input_tensor.clone()
                ref_linear.weight.copy_(linear.master_weight)
                if accumulation_in_fp16:
                    ref_linear = ref_linear.half()
            x.requires_grad_()
            expected_output = ref_linear(x)
            expected_loss = torch.mul(expected_output, dldy).sum()
            expected_loss.backward()

            if not accumulation_in_fp16:
                if sequence_parallel_enabled:
                    self.assertEqual(
                        x=output,
                        y=expected_output.chunk(
                            chunks=tensor_model_parallel_world_size,
                            dim=0,
                        )[parallel_state.get_tensor_model_parallel_rank()],
                        msg=msg,
                    )
                else:
                    self.assertEqual(
                        x=output,
                        y=expected_output,
                        msg=msg,
                    )

            grad_attr_name = "main_grad" if gradient_accumulation_fusion else "grad"
            # NOTE(mkozuki): Numerical errors seems to be enlarged by tensor model parallel.
            if tensor_model_parallel_world_size == 1:
                self.assertEqual(
                    x=getattr(linear.weight, grad_attr_name),
                    y=ref_linear.weight.grad.chunk(
                        chunks=tensor_model_parallel_world_size,
                        dim=0,
                    )[parallel_state.get_tensor_model_parallel_rank()],
                    msg=msg,
                )

            parallel_state.destroy_model_parallel()

    def test_column_parallel_linear(self):
        self._column_parallel_linear_test_impl(False, False, False, False)

    def test_column_parallel_linear_async(self):
        self._column_parallel_linear_test_impl(True, False, False, False)

    def test_column_parallel_linear_gradient_accumulation_fusion(self):
        self._column_parallel_linear_test_impl(False, True, False, False)

    def test_column_parallel_linear_gradient_accumulation_fusion_in_fp16(self):
        self._column_parallel_linear_test_impl(False, True, True, False)

    def test_column_parallel_linear_sequence_parallel(self):
        if self.DISTRIBUTED_BACKEND == "ucc":
            self.skipTest("Backward's reduce_scatter fails. as of 2022/06/15")
        self._column_parallel_linear_test_impl(False, False, False, True)

    @unittest.skipIf(torch.cuda.device_count() < 2, "Sequence Parallel requires >= 2 GPUs")
    def test_column_parallel_linear_exception(self):
        with self.assertRaisesRegex(
            RuntimeError,
            "`async_tensor_model_parallel_allreduce` and `sequence_parallel_enabled` cannot be enabled at the same time.",
        ):
            self._column_parallel_linear_test_impl(True, False, False, True)

    def _column_parallel_linear_test_impl(
        self,
        async_tensor_model_parallel_allreduce: bool,
        gradient_accumulation_fusion: bool,
        accumulation_in_fp16: bool,
        sequence_parallel_enabled: bool,
    ):
        for tensor_model_parallel_world_size in range(1, self.world_size + 1):
            if async_tensor_model_parallel_allreduce and sequence_parallel_enabled:
                if tensor_model_parallel_world_size == 1:
                    continue
            if self.world_size % tensor_model_parallel_world_size:
                continue
            msg = f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}"
            parallel_state.initialize_model_parallel(
                tensor_model_parallel_size_=tensor_model_parallel_world_size,
            )

            input_tensor_shape = self.tensor_shape
            expected_output_shape = self.tensor_shape
            # When sequence parallel, `gather_output` is disabled, i.e.,
            # output of matmul isn't gathered in dimension of feature/hidden (last dim).
            if sequence_parallel_enabled:
                expected_output_shape[-1] //= tensor_model_parallel_world_size

            # tensor's shape is [sequence length, batch size, hidden size]
            set_random_seed(self.SEED)
            linear = layers.ColumnParallelLinear(
                self.HIDDEN_SIZE,
                self.HIDDEN_SIZE,
                bias=False,
                keep_master_weight_for_test=True,
                params_dtype=torch.float32,
                use_cpu_initialization=True,
                gather_output=not sequence_parallel_enabled,
                no_async_tensor_model_parallel_allreduce=not async_tensor_model_parallel_allreduce,
                gradient_accumulation_fusion=gradient_accumulation_fusion,
                accumulation_in_fp16=accumulation_in_fp16,
                sequence_parallel_enabled=sequence_parallel_enabled,
            ).cuda()
            if accumulation_in_fp16:
                linear = linear.half()

            # Simulate the situation where fusion of weight grad calculation and gradient accumulation happens.
            if gradient_accumulation_fusion:
                with torch.no_grad():
                    linear.weight.main_grad = torch.zeros_like(linear.weight)

            orig_input_tensor = torch.randn(input_tensor_shape, device="cuda", requires_grad=True)
            if accumulation_in_fp16:
                orig_input_tensor = orig_input_tensor.half()
            if sequence_parallel_enabled:
                input_tensor = list(
                    orig_input_tensor.chunk(tensor_model_parallel_world_size, dim=0)
                )[parallel_state.get_tensor_model_parallel_rank()]
            else:
                input_tensor = orig_input_tensor
            output, _ = linear(input_tensor)
            # The order of dimension is expected to be (sequence, batch, hidden)
            self.assertEqual(output.shape, expected_output_shape, msg=msg)

            orig_loss_weight = torch.randn(input_tensor_shape, device="cuda")
            if accumulation_in_fp16:
                orig_loss_weight = orig_loss_weight.half()
            if sequence_parallel_enabled:
                loss_weight = orig_loss_weight.chunk(
                    tensor_model_parallel_world_size, dim=2,
                )[parallel_state.get_tensor_model_parallel_rank()]
            else:
                loss_weight = orig_loss_weight
            loss = torch.mul(output, loss_weight).sum()
            loss.backward()

            with torch.no_grad():
                dldy = orig_loss_weight.clone()
                x = orig_input_tensor.clone()
                ref_linear = nn.Linear(
                    in_features=self.HIDDEN_SIZE,
                    out_features=self.HIDDEN_SIZE,
                    bias=False,
                    device="cuda",
                )
                if accumulation_in_fp16:
                    ref_linear = ref_linear.half()
                # NOTE(mkozuki): `master_weight` is available because `keep_master_weight_for_test` is set.
                ref_linear.weight.copy_(linear.master_weight)
            x.requires_grad_()
            expected_output = ref_linear(x)
            if sequence_parallel_enabled:
                chunk = expected_output.chunk(
                    tensor_model_parallel_world_size,
                    dim=2,
                )[parallel_state.get_tensor_model_parallel_rank()]
                self.assertEqual(
                    x=output,
                    y=chunk,
                    msg=msg,
                )
            else:
                self.assertEqual(
                    x=output,
                    y=expected_output,
                    msg=msg,
                )

            expected_loss = torch.mul(expected_output, dldy).sum()
            expected_loss.backward()
            grad_attr_name = "main_grad" if gradient_accumulation_fusion else "grad"
            # NOTE(mkozuki): Numerical errors seems to be enlarged by tensor model parallel.
            if tensor_model_parallel_world_size == 1:
                self.assertEqual(
                    x=getattr(linear.weight, grad_attr_name),
                    y=ref_linear.weight.grad.chunk(
                        chunks=tensor_model_parallel_world_size,
                        dim=0,
                    )[parallel_state.get_tensor_model_parallel_rank()],
                    msg=msg,
                )

            parallel_state.destroy_model_parallel()


class NcclTensorParallelLayerTest(TensorParallelLayerTestBase, NcclDistributedTestBase):
    pass


class UccTensorParallelLayerTest(TensorParallelLayerTestBase, UccDistributedTestBase):
    pass


if __name__ == "__main__":
    common_utils.run_tests()