def _run_sharded_linear(self, spec, input_size, linear_size, sharded_dim,
dtype):
# Use same seed.
torch.manual_seed(0)
local_linear = torch.nn.Linear(*linear_size,
dtype=dtype).cuda(self.rank)
sharded_linear = torch.nn.Linear(*linear_size, dtype=dtype)
# Copy the weights and bias from local linear
sharded_linear.weight = clone_module_parameter(local_linear, "weight")
sharded_linear.bias = clone_module_parameter(local_linear, "bias")
# Shard the parameter.
shard_parameter(sharded_linear, "weight", spec)
# Run sharded computation
torch.manual_seed(self.rank) # inputs different on each rank
inp = torch.rand(*input_size, dtype=dtype).cuda(self.rank)
reshard_spec = copy.deepcopy(spec)
reshard_spec.dim = 0
reshard_spec.placements.sort(key=lambda placement: placement.rank())
sharded_linear = _collect_local_shard(
_reshard_output(sharded_linear, reshard_spec))
sharded_output = sharded_linear(inp)
# Run local computation
local_output = local_linear(inp)
# Verify
self.assertEqual(local_output, sharded_output, atol=1e-3, rtol=1e-3)
# Validate for torch.nn.functional.linear version.
local_output = torch.nn.functional.linear(inp, local_linear.weight,
local_linear.bias)
sharded_output = torch.nn.functional.linear(inp, sharded_linear.weight,
sharded_linear.bias)
sharded_output = sharded_output.reshard(reshard_spec).local_tensor()
# When local tensor only has one dimension, we increase one more dimension
# for reshard. We need to squeeze the # of dimensions manually.
if inp.dim() == 1:
sharded_output = sharded_output.squeeze(reshard_spec.dim)
self.assertEqual(local_output, sharded_output, atol=1e-3, rtol=1e-3)
# Compute loss and run backward pass.
local_output.sum().backward()
sharded_output.sum().backward()
local_grad = local_linear.weight.grad
# Verify that both weight and bias in the sharded linear has non-None grad.
sharded_weight = sharded_linear.weight.local_tensor()
self.assertNotEqual(sharded_linear.bias.grad, None)
self.assertNotEqual(sharded_weight.grad, None)
# Shard the local linear's weight grad so that we can compare.
dist.all_reduce(local_grad)
(start_pos,
chunk_size) = generate_local_weight_sharding_params_for_test(
local_linear.weight, sharded_dim, TEST_GPU_NUM, spec, self.rank)
local_grad_narrowed = local_grad.narrow(sharded_dim, start_pos,
chunk_size)
local_bias_grad = local_linear.bias.grad
dist.all_reduce(local_bias_grad)
# Test backward gradient calculation.
self.assertEqual(sharded_linear.bias.grad,
local_bias_grad,
atol=1e-3,
rtol=1e-3)
self.assertEqual(sharded_weight.grad,
local_grad_narrowed,
atol=1e-3,
rtol=1e-3)
# Test optimizer.
previous = local_linear.weight.clone().detach()
optim = torch.optim.SGD(local_linear.parameters(), lr=0.1)
optim.step()
self.assertNotEqual(previous, local_linear.weight)
previous_sharded_weight = sharded_weight.clone()
previous_sharded_bias = sharded_linear.bias.clone()
sharded_optim = ShardedOptimizer(
dict(named_params_with_sharded_tensor(sharded_linear)),
torch.optim.SGD,
lr=0.1,
)
sharded_optim.step()
sharded_weight = sharded_linear.weight.local_tensor()
local_weight_narrowed = local_linear.weight.narrow(
sharded_dim, start_pos, chunk_size)
self.assertEqual(sharded_weight.size(), local_weight_narrowed.size())
self.assertNotEqual(previous_sharded_weight, sharded_weight)
self.assertEqual(sharded_weight,
local_weight_narrowed,
atol=1e-3,
rtol=1e-3)
self.assertNotEqual(previous_sharded_bias, sharded_linear.bias)
self.assertEqual(sharded_linear.bias,
local_linear.bias,
atol=1e-3,
rtol=1e-3)
def test_sharding_plan_simple_megatron(self):
colwise_sharding_spec = generate_chunk_sharding_specs_for_test(0)
rowwise_sharding_spec = generate_chunk_sharding_specs_for_test(1)
for spec in zip(colwise_sharding_spec, rowwise_sharding_spec):
# test each sharding spec pair and see if we can apply sharding
reshard_spec = copy.deepcopy(spec[1])
reshard_spec.placements.sort(key=lambda placement: placement.rank())
reshard_spec.dim = 0
sharding_plan = ShardingPlan(
plan={
"fc1.weight": spec[0],
"fc2.weight": spec[1]
},
output_plan={
"": reshard_spec
},
return_local_tensor=[""])
# Use same seed.
torch.manual_seed(0)
local_megatron_lm = SimpleMegatronLM([[17, 12], [12, 29]]).cuda(self.rank)
megatron_lm = copy.deepcopy(local_megatron_lm)
# shard the module with the provided sharding plan
shard_module(megatron_lm, sharding_plan)
# check to make sure the module already been sharded
self.assertTrue(isinstance(megatron_lm.fc1.weight, ShardedTensor))
self.assertTrue(isinstance(megatron_lm.fc2.weight, ShardedTensor))
self.assertEqual(megatron_lm.fc1.weight.sharding_spec(), spec[0])
self.assertEqual(megatron_lm.fc2.weight.sharding_spec(), spec[1])
# make sure we can run sharded computation
input = torch.rand(22, 17).cuda(self.rank)
sharded_output = megatron_lm(input)
local_output = local_megatron_lm(input)
# verify and make sure local and sharded output matches
self.assertEqual(local_output, sharded_output)
# Compute loss and run backward pass.
local_output.sum().backward()
sharded_output.sum().backward()
(
local_weight_grad_fc1,
local_weight_grad_fc2,
) = local_megatron_lm.get_weight_grads()
local_bias_grad_fc1, local_bias_grad_fc2 = local_megatron_lm.get_bias_grads()
# Verify that weights in both layers and biases in the sharded linear has non-None grad.
(
sharded_weight_fc1,
sharded_weight_fc2,
) = megatron_lm.get_weights()
bias_grad_fc1, bias_grad_fc2 = megatron_lm.get_bias_grads()
self.assertNotEqual(sharded_weight_fc1.grad, None)
self.assertNotEqual(sharded_weight_fc2.grad, None)
self.assertNotEqual(bias_grad_fc1, None)
self.assertNotEqual(bias_grad_fc2, None)
# Shard the local linear's weight grad so that we can compare.
dist.all_reduce(local_weight_grad_fc1)
dist.all_reduce(local_weight_grad_fc2)
dist.all_reduce(local_bias_grad_fc1)
dist.all_reduce(local_bias_grad_fc2)
local_weight_fc1, local_weight_fc2 = local_megatron_lm.get_weights()
(
start_pos_fc1,
chunk_size_fc1,
) = generate_local_weight_sharding_params_for_test(
local_weight_fc1, 0, TEST_GPU_NUM, spec[0], self.rank
)
local_grad_narrowed_fc1 = local_weight_grad_fc1.narrow(
0, start_pos_fc1, chunk_size_fc1
)
(
start_pos_fc2,
chunk_size_fc2,
) = generate_local_weight_sharding_params_for_test(
local_weight_fc2, 1, TEST_GPU_NUM, spec[1], self.rank
)
local_grad_narrowed_fc2 = local_weight_grad_fc2.narrow(
1, start_pos_fc2, chunk_size_fc2
)
# Test backward gradient calculation.
self.assertEqual(sharded_weight_fc1.grad, local_grad_narrowed_fc1)
self.assertEqual(sharded_weight_fc2.grad, local_grad_narrowed_fc2)
self.assertEqual(bias_grad_fc1, local_bias_grad_fc1)
self.assertEqual(bias_grad_fc2, local_bias_grad_fc2)
# Test optimizer.
bias_fc1, bias_fc2 = megatron_lm.get_biases()
local_bias_fc1, local_bias_fc2 = local_megatron_lm.get_biases()
self.assertEqual(bias_fc1, local_bias_fc1)
self.assertEqual(bias_fc2, local_bias_fc2)
self.assertEqual(bias_fc1.grad, local_bias_fc1.grad)
self.assertEqual(bias_fc2.grad, local_bias_fc2.grad)
previous_sharded_weight_fc1 = sharded_weight_fc1.clone()
previous_sharded_weight_fc2 = sharded_weight_fc2.clone()
previous_bias_fc1 = bias_fc1.clone()
previous_bias_fc2 = bias_fc2.clone()
optim = torch.optim.SGD(local_megatron_lm.parameters(), lr=0.1)
optim.step()
sharded_optim = ShardedOptimizer(
dict(named_params_with_sharded_tensor(megatron_lm)),
torch.optim.SGD,
lr=0.1,
)
sharded_optim.step()
local_weight_fc1_narrowed = local_weight_fc1.narrow(
0, start_pos_fc1, chunk_size_fc1
)
local_weight_fc2_narrowed = local_weight_fc2.narrow(
1, start_pos_fc2, chunk_size_fc2
)
# Test weight value after optimizer.
self.assertEqual(sharded_weight_fc1.size(), local_weight_fc1_narrowed.size())
self.assertEqual(sharded_weight_fc2.size(), local_weight_fc2_narrowed.size())
self.assertNotEqual(previous_sharded_weight_fc1, sharded_weight_fc1)
self.assertNotEqual(previous_sharded_weight_fc2, sharded_weight_fc2)
self.assertEqual(sharded_weight_fc1, local_weight_fc1_narrowed)
self.assertEqual(sharded_weight_fc2, local_weight_fc2_narrowed)
# Test bias value after optimizer.
local_bias_fc1, local_bias_fc2 = local_megatron_lm.get_biases()
self.assertNotEqual(previous_bias_fc1, bias_fc1)
self.assertEqual(bias_fc1, local_bias_fc1)
self.assertNotEqual(previous_bias_fc2, bias_fc2)
self.assertEqual(bias_fc2, local_bias_fc2)
def _run_megatron_linear(self, spec, input_size, linear_size, dtype):
def _weight_override(module_dst, module_src):
module_dst.fc1.weight = clone_module_parameter(module_src.fc1, "weight")
module_dst.fc1.bias = clone_module_parameter(module_src.fc1, "bias")
module_dst.fc2.weight = clone_module_parameter(module_src.fc2, "weight")
module_dst.fc2.bias = clone_module_parameter(module_src.fc2, "bias")
def _shard_parameter(module, spec):
shard_parameter(module.fc1, "weight", spec[0])
shard_parameter(module.fc2, "weight", spec[1])
# Use same seed.
torch.manual_seed(0)
local_megatron_lm = SimpleMegatronLM(linear_size, rank=self.rank, dtype=dtype)
sharded_megatron_lm = SimpleMegatronLM(linear_size, dtype=dtype)
_weight_override(sharded_megatron_lm, local_megatron_lm)
# Shard the parameter. First col-wise sharding and then row-wise
_shard_parameter(sharded_megatron_lm, spec)
# Setup resharding of output.
reshard_spec = copy.deepcopy(spec[1])
reshard_spec.placements.sort(key=lambda placement: placement.rank())
reshard_spec.dim = 0
sharded_megatron_lm = _collect_local_shard(
_reshard_output(sharded_megatron_lm, reshard_spec)
)
torch.manual_seed(self.rank) # inputs different on each rank
inp = torch.rand(*input_size, requires_grad=True, device=self.rank, dtype=dtype)
# Run local computation
local_output = local_megatron_lm(inp)
# Compute loss and run backward pass.
local_output.sum().backward()
# Save and reset input grads.
local_input_grad = inp.grad
self.assertIsNotNone(inp.grad)
inp.grad = None
# Run sharded computation
sharded_output = sharded_megatron_lm(inp)
# Verify local and sharded results
self.assertEqual(local_output, sharded_output, atol=1e-3, rtol=1e-6)
sharded_output.sum().backward()
sharded_input_grad = inp.grad
self.assertIsNotNone(inp.grad)
# Verify sharded and local grads.
self.assertEqual(local_input_grad, sharded_input_grad, atol=1e-3, rtol=1e-6)
(
local_weight_grad_fc1,
local_weight_grad_fc2,
) = local_megatron_lm.get_weight_grads()
local_bias_grad_fc1, local_bias_grad_fc2 = local_megatron_lm.get_bias_grads()
# Verify that weights in both layers and biases in the sharded linear has non-None grad.
(
sharded_weight_fc1,
sharded_weight_fc2,
) = sharded_megatron_lm.get_weights()
bias_grad_fc1, bias_grad_fc2 = sharded_megatron_lm.get_bias_grads()
self.assertNotEqual(sharded_weight_fc1.grad, None)
self.assertNotEqual(sharded_weight_fc2.grad, None)
self.assertNotEqual(bias_grad_fc1, None)
self.assertNotEqual(bias_grad_fc2, None)
# Shard the local linear's weight grad so that we can compare.
dist.all_reduce(local_weight_grad_fc1)
dist.all_reduce(local_weight_grad_fc2)
dist.all_reduce(local_bias_grad_fc1)
dist.all_reduce(local_bias_grad_fc2)
local_weight_fc1, local_weight_fc2 = local_megatron_lm.get_weights()
(
start_pos_fc1,
chunk_size_fc1,
) = generate_local_weight_sharding_params_for_test(
local_weight_fc1, 0, TEST_GPU_NUM, spec[0], self.rank
)
local_grad_narrowed_fc1 = local_weight_grad_fc1.narrow(
0, start_pos_fc1, chunk_size_fc1
)
(
start_pos_fc2,
chunk_size_fc2,
) = generate_local_weight_sharding_params_for_test(
local_weight_fc2, 1, TEST_GPU_NUM, spec[1], self.rank
)
local_grad_narrowed_fc2 = local_weight_grad_fc2.narrow(
1, start_pos_fc2, chunk_size_fc2
)
# Test backward gradient calculation.
self.assertEdistNorm(sharded_weight_fc1.grad, local_grad_narrowed_fc1)
self.assertEdistNorm(sharded_weight_fc2.grad, local_grad_narrowed_fc2)
self.assertEdistNorm(bias_grad_fc1, local_bias_grad_fc1)
self.assertEdistNorm(bias_grad_fc2, local_bias_grad_fc2)
# Test optimizer.
bias_fc1, bias_fc2 = sharded_megatron_lm.get_biases()
local_bias_fc1, local_bias_fc2 = local_megatron_lm.get_biases()
self.assertEdistNorm(bias_fc1, local_bias_fc1)
self.assertEdistNorm(bias_fc2, local_bias_fc2)
self.assertEdistNorm(bias_fc1.grad, local_bias_fc1.grad)
self.assertEdistNorm(bias_fc2.grad, local_bias_fc2.grad)
previous_sharded_weight_fc1 = sharded_weight_fc1.clone()
previous_sharded_weight_fc2 = sharded_weight_fc2.clone()
previous_bias_fc1 = bias_fc1.clone()
previous_bias_fc2 = bias_fc2.clone()
optim = torch.optim.SGD(local_megatron_lm.parameters(), lr=0.1)
optim.step()
sharded_optim = ShardedOptimizer(
dict(named_params_with_sharded_tensor(sharded_megatron_lm)),
torch.optim.SGD,
lr=0.1,
)
sharded_optim.step()
local_weight_fc1_narrowed = local_weight_fc1.narrow(
0, start_pos_fc1, chunk_size_fc1
)
local_weight_fc2_narrowed = local_weight_fc2.narrow(
1, start_pos_fc2, chunk_size_fc2
)
# Test weight value after optimizer.
self.assertEqual(sharded_weight_fc1.size(), local_weight_fc1_narrowed.size())
self.assertEqual(sharded_weight_fc2.size(), local_weight_fc2_narrowed.size())
self.assertNotEqual(previous_sharded_weight_fc1, sharded_weight_fc1)
self.assertNotEqual(previous_sharded_weight_fc2, sharded_weight_fc2)
self.assertEdistNorm(sharded_weight_fc1, local_weight_fc1_narrowed)
self.assertEdistNorm(sharded_weight_fc2, local_weight_fc2_narrowed)
# Test bias value after optimizer.
local_bias_fc1, local_bias_fc2 = local_megatron_lm.get_biases()
self.assertNotEqual(previous_bias_fc1, bias_fc1)
self.assertEdistNorm(bias_fc1, local_bias_fc1)
self.assertNotEqual(previous_bias_fc2, bias_fc2)
self.assertEdistNorm(bias_fc2, local_bias_fc2)
def _run_sharded_embedding(
self,
spec,
input_size,
num_embeddings,
embedding_dim,
max_norm=None,
norm_type=2.0,
padding_idx=None,
):
# Use same seed.
torch.manual_seed(0)
local_embedding = torch.nn.Embedding(
num_embeddings,
embedding_dim,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
).cuda(self.rank)
sharded_embedding = torch.nn.Embedding(
num_embeddings,
embedding_dim,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
# Copy the weights from local embedding
sharded_embedding.weight = clone_module_parameter(
local_embedding, "weight")
# Shard the parameter.
shard_parameter(sharded_embedding, "weight", spec)
# Run sharded computation
torch.manual_seed(self.rank) # inputs different on each rank
inp = torch.randint(0, num_embeddings,
tuple(input_size)).cuda(self.rank)
sharded_output = sharded_embedding(inp)
# If max_norm is set, we need to ensure that the renorm has been applied across
# inputs from all ranks.
if max_norm is not None:
gathered_inputs = [
torch.zeros_like(inp) for _ in range(TEST_GPU_NUM)
]
dist.all_gather(gathered_inputs, inp)
unique_inp = torch.unique(torch.cat(gathered_inputs))
local_embedding(unique_inp)
# Run local computation
local_output = local_embedding(inp)
# Compare local weight and shared one to ensure the renorm
# as expected.
if max_norm is not None:
sharded_dim = spec.dim
sharded_weight = sharded_embedding.weight.local_shards()[0].tensor
(start_pos,
chunk_size) = generate_local_weight_sharding_params_for_test(
local_embedding.weight, sharded_dim, TEST_GPU_NUM, spec,
self.rank)
local_weight_narrowed = local_embedding.weight.narrow(
sharded_dim, start_pos, chunk_size)
self.assertEqual(local_weight_narrowed, sharded_weight)
# Verify
self.assertEqual(local_output, sharded_output)
# Validate for torch.nn.functional.embedding version.
local_output = torch.nn.functional.embedding(
inp,
local_embedding.weight,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
sharded_output = torch.nn.functional.embedding(
inp,
sharded_embedding.weight,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
self.assertEqual(local_output, sharded_output)
def _run_sharded_embedding_bag(
self,
spec,
input_size,
num_embeddings,
embedding_dim,
mode,
sharded_dim=None,
include_last_offset=False,
offset_size=None,
max_norm=None,
norm_type=2.0,
padding_idx=None,
):
# Use same seed.
torch.manual_seed(0)
local_embedding_bag = torch.nn.EmbeddingBag(
num_embeddings,
embedding_dim,
mode=mode,
max_norm=max_norm,
norm_type=norm_type,
include_last_offset=include_last_offset,
padding_idx=padding_idx,
).cuda(self.rank)
sharded_embedding_bag = torch.nn.EmbeddingBag(
num_embeddings,
embedding_dim,
mode=mode,
max_norm=max_norm,
norm_type=norm_type,
include_last_offset=include_last_offset,
padding_idx=padding_idx,
)
# Copy the weights from local embedding bag.
sharded_embedding_bag.weight = torch.nn.Parameter(
local_embedding_bag.weight.detach().clone())
# Shard the parameter.
shard_parameter(sharded_embedding_bag, "weight", spec)
# Run sharded computation
torch.manual_seed(self.rank) # inputs different on each rank
inp = torch.randint(0, num_embeddings,
tuple(input_size)).cuda(self.rank)
per_sample_weights = None
if mode == "sum":
per_sample_weights = torch.rand(*input_size).cuda(self.rank)
offsets = None
if len(input_size) == 1:
# We need to generate certain length offset for each rank.
# The current implementation and dist API does not support
# the case when the offset has different lengths.
# input_size[0] >> offset_size, so the while loop will not
# for too long.
while offsets is None or (offsets.size(0) != offset_size):
offsets = torch.randint(input_size[0], (offset_size, ))
offsets[0] = 0
if include_last_offset:
offsets[-1] = input_size[0]
offsets = (torch.unique(
offsets, sorted=True).contiguous().cuda(self.rank))
# If max_norm is set, we need to ensure that the renorm has been applied across
# inputs from all ranks.
if max_norm is not None:
gathered_inputs = [
torch.zeros_like(inp) for _ in range(TEST_GPU_NUM)
]
dist.all_gather(gathered_inputs, inp)
unique_inp = torch.unique(torch.cat(gathered_inputs))
offsets_dummy = torch.tensor([len(unique_inp) // 2
]).cuda(self.rank)
local_embedding_bag(unique_inp, offsets=offsets_dummy)
sharded_output = sharded_embedding_bag(
inp,
offsets=offsets,
per_sample_weights=per_sample_weights,
)
# Run local computation
local_output = local_embedding_bag(
inp,
offsets=offsets,
per_sample_weights=per_sample_weights,
)
# Compare local weight and shared one to ensure the renorm
# as expected.
if max_norm is not None:
sharded_weight = sharded_embedding_bag.weight.local_shards(
)[0].tensor
(start_pos,
chunk_size) = generate_local_weight_sharding_params_for_test(
local_embedding_bag.weight, sharded_dim, TEST_GPU_NUM, spec,
self.rank)
local_weight_narrowed = local_embedding_bag.weight.narrow(
sharded_dim, start_pos, chunk_size)
self.assertEqual(local_weight_narrowed, sharded_weight)
# Verify
self.assertEqual(local_output, sharded_output)
# Validate for torch.nn.functional.embedding_bag version.
local_output = torch.nn.functional.embedding_bag(
inp,
local_embedding_bag.weight,
offsets=offsets,
mode=mode,
per_sample_weights=per_sample_weights,
include_last_offset=include_last_offset,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
sharded_output = torch.nn.functional.embedding_bag(
inp,
sharded_embedding_bag.weight,
offsets=offsets,
mode=mode,
per_sample_weights=per_sample_weights,
include_last_offset=include_last_offset,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
self.assertEqual(local_output, sharded_output)
def _run_sharded_linear(self, spec, input_size, linear_size, sharded_dim):
# Use same seed.
torch.manual_seed(0)
local_linear = torch.nn.Linear(*linear_size).cuda(self.rank)
sharded_linear = torch.nn.Linear(*linear_size)
# Copy the weights and bias from local linear
sharded_linear.weight = torch.nn.Parameter(
local_linear.weight.detach().clone())
sharded_linear.bias = torch.nn.Parameter(
local_linear.bias.detach().clone())
# Shard the parameter.
shard_parameter(sharded_linear, "weight", spec)
# Run sharded computation
torch.manual_seed(self.rank) # inputs different on each rank
inp = torch.rand(*input_size).cuda(self.rank)
sharded_output = sharded_linear(inp)
# Run local computation
local_output = local_linear(inp)
# Verify
self.assertEqual(local_output, sharded_output)
# Validate for torch.nn.functional.linear version.
local_output = torch.nn.functional.linear(inp, local_linear.weight,
local_linear.bias)
sharded_output = torch.nn.functional.linear(inp, sharded_linear.weight,
sharded_linear.bias)
self.assertEqual(local_output, sharded_output)
# Compute loss and run backward pass.
local_output.sum().backward()
sharded_output.sum().backward()
local_grad = local_linear.weight.grad
# Verify that both weight and bias in the sharded linear has non-None grad.
sharded_weight = sharded_linear.weight.local_shards()[0].tensor
self.assertNotEqual(sharded_linear.bias.grad, None)
self.assertNotEqual(sharded_weight.grad, None)
# Shard the local linear's weight grad so that we can compare.
dist.all_reduce(local_grad)
(start_pos,
chunk_size) = generate_local_weight_sharding_params_for_test(
local_linear.weight, sharded_dim, TEST_GPU_NUM, spec, self.rank)
local_grad_narrowed = local_grad.narrow(sharded_dim, start_pos,
chunk_size)
# Test backward gradient calculation.
self.assertEqual(sharded_linear.bias.grad, local_linear.bias.grad)
self.assertEqual(sharded_weight.grad, local_grad_narrowed)
# Test optimizer.
previous = local_linear.weight.clone().detach()
optim = torch.optim.SGD(local_linear.parameters(), lr=0.1)
optim.step()
self.assertNotEqual(previous, local_linear.weight)
previous_sharded_weight = sharded_weight.clone()
previous_sharded_bias = sharded_linear.bias.clone()
sharded_optim = ShardedOptimizer(dict(
named_params_with_sharded_tensor(sharded_linear)),
torch.optim.SGD,
lr=0.1)
sharded_optim.step()
sharded_weight = sharded_linear.weight.local_shards()[0].tensor
local_weight_narrowed = local_linear.weight.narrow(
sharded_dim, start_pos, chunk_size)
self.assertEqual(sharded_weight.size(), local_weight_narrowed.size())
self.assertNotEqual(previous_sharded_weight, sharded_weight)
self.assertEqual(sharded_weight, local_weight_narrowed)
self.assertNotEqual(previous_sharded_bias, sharded_linear.bias)
self.assertEqual(sharded_linear.bias, local_linear.bias)
