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ColossalAI/tests/test_layers/test_2d/test_operation.py

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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.nn.layer.parallel_2d import Matmul_AB_2D, Matmul_ABT_2D, Matmul_ATB_2D
from colossalai.utils import get_current_device
from colossalai.utils import print_rank_0
from common import check_equal, BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE, DEPTH
def check_AB():
data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(ParallelMode.DATA)
pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
ParallelMode.PIPELINE)
pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
ParallelMode.PIPELINE)
tensor_parallel_size = gpc.get_world_size(ParallelMode.TENSOR)
dtype = torch.float
j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
A_master = torch.randn(A_shape, dtype=dtype, device=get_current_device())
torch.distributed.broadcast(A_master, src=0)
A = torch.chunk(A_master, DEPTH, dim=0)[i]
A = torch.chunk(A, DEPTH, dim=-1)[j]
A = A.clone()
A.requires_grad = True
B_shape = (HIDDEN_SIZE, 4 * HIDDEN_SIZE)
B_master = torch.randn(B_shape, dtype=dtype, device=get_current_device())
torch.distributed.broadcast(B_master, src=0)
B = torch.chunk(B_master, DEPTH, dim=0)[i]
B = torch.chunk(B, DEPTH, dim=-1)[j]
B = B.clone()
B.requires_grad = True
out_shape = (BATCH_SIZE // DEPTH, SEQ_LENGTH, 4 * HIDDEN_SIZE // DEPTH)
out = Matmul_AB_2D.apply(
A, B,
DEPTH,
out_shape,
i, j,
ParallelMode.PARALLEL_2D_ROW,
ParallelMode.PARALLEL_2D_COL,
data_parallel_rank,
pipeline_parallel_rank,
pipeline_parallel_size,
tensor_parallel_size
)
C_shape = (BATCH_SIZE, SEQ_LENGTH, 4 * HIDDEN_SIZE)
A_master = A_master.clone()
A_master.requires_grad = True
B_master = B_master.clone()
B_master.requires_grad = True
C_master = torch.matmul(A_master, B_master)
C = torch.chunk(C_master, DEPTH, dim=0)[i]
C = torch.chunk(C, DEPTH, dim=-1)[j]
# check forward correctness
check_equal(out, C)
print_rank_0('AB forward: pass')
grad_shape = C_master.shape
grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
torch.distributed.broadcast(grad_master, src=0)
grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
grad = torch.chunk(grad, DEPTH, dim=-1)[j]
out.backward(grad)
C_master.backward(grad_master)
A_grad = A_master.grad
A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i]
A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[j]
# check backward correctness
check_equal(A_grad, A.grad)
B_grad = B_master.grad
B_grad = torch.chunk(B_grad, DEPTH, dim=0)[i]
B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[j]
# check backward correctness
check_equal(B_grad, B.grad)
print_rank_0('AB backward: pass')
def check_ABT():
data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(ParallelMode.DATA)
pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
ParallelMode.PIPELINE)
pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
ParallelMode.PIPELINE)
tensor_parallel_size = gpc.get_world_size(ParallelMode.TENSOR)
dtype = torch.float
device = get_current_device()
j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
C_shape = (BATCH_SIZE, SEQ_LENGTH, 4 * HIDDEN_SIZE)
C_master = torch.randn(C_shape, dtype=dtype, device=device)
torch.distributed.broadcast(C_master, src=0)
C = torch.chunk(C_master, DEPTH, dim=0)[i]
C = torch.chunk(C, DEPTH, dim=-1)[j]
C = C.clone()
C.requires_grad = True
B_shape = (HIDDEN_SIZE, 4 * HIDDEN_SIZE)
B_master = torch.randn(B_shape, dtype=dtype, device=device)
torch.distributed.broadcast(B_master, src=0)
B = torch.chunk(B_master, DEPTH, dim=0)[i]
B = torch.chunk(B, DEPTH, dim=-1)[j]
B = B.clone()
B.requires_grad = True
out = Matmul_ABT_2D.apply(
C, B,
DEPTH, (BATCH_SIZE // DEPTH, SEQ_LENGTH, HIDDEN_SIZE // DEPTH),
i, j,
ParallelMode.PARALLEL_2D_ROW,
ParallelMode.PARALLEL_2D_COL,
data_parallel_rank,
pipeline_parallel_rank,
pipeline_parallel_size,
tensor_parallel_size
)
A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
C_master = C_master.clone()
C_master.requires_grad = True
B_master = B_master.clone()
B_master.requires_grad = True
A_master = torch.matmul(C_master, B_master.transpose(0, 1))
A = torch.chunk(A_master, DEPTH, dim=0)[i]
A = torch.chunk(A, DEPTH, dim=-1)[j]
check_equal(out, A)
print_rank_0('ABT forward: pass')
grad_shape = A_master.shape
grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
torch.distributed.broadcast(grad_master, src=0)
grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
grad = torch.chunk(grad, DEPTH, dim=-1)[j]
# backward
out.backward(grad)
A_master.backward(grad_master)
C_grad = C_master.grad
C_grad = torch.chunk(C_grad, DEPTH, dim=0)[i]
C_grad = torch.chunk(C_grad, DEPTH, dim=-1)[j]
check_equal(C_grad, C.grad)
B_grad = B_master.grad
B_grad = torch.chunk(B_grad, DEPTH, dim=0)[i]
B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[j]
check_equal(B_grad, B.grad)
print_rank_0('ABT backward: pass')
def check_ATB():
data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(ParallelMode.DATA)
pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
ParallelMode.PIPELINE)
pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
ParallelMode.PIPELINE)
tensor_parallel_size = gpc.get_world_size(ParallelMode.TENSOR)
device = get_current_device()
dtype = torch.float
j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
A_master = torch.randn(A_shape, dtype=dtype, device=device)
torch.distributed.broadcast(A_master, src=0)
A = torch.chunk(A_master, DEPTH, dim=0)[i]
A = torch.chunk(A, DEPTH, dim=-1)[j]
A = A.clone()
A.requires_grad = True
C_shape = (BATCH_SIZE, SEQ_LENGTH, 4 * HIDDEN_SIZE)
C_master = torch.randn(C_shape, dtype=dtype, device=device)
torch.distributed.broadcast(C_master, src=0)
C = torch.chunk(C_master, DEPTH, dim=0)[i]
C = torch.chunk(C, DEPTH, dim=-1)[j]
C = C.clone()
C.requires_grad = True
out = Matmul_ATB_2D.apply(
A, C,
DEPTH, (HIDDEN_SIZE // DEPTH, 4 * HIDDEN_SIZE // DEPTH),
i, j,
ParallelMode.PARALLEL_2D_ROW,
ParallelMode.PARALLEL_2D_COL,
data_parallel_rank,
pipeline_parallel_rank,
pipeline_parallel_size,
tensor_parallel_size
)
B_shape = (HIDDEN_SIZE, 4 * HIDDEN_SIZE)
A_master = A_master.clone()
A_master.requires_grad = True
C_master = C_master.clone()
C_master.requires_grad = True
B_master = torch.matmul(
A_master.view(-1, A_master.shape[-1]).transpose(0, 1),
C_master.view(-1, C_master.shape[-1]))
B = torch.chunk(B_master, DEPTH, dim=0)[i]
B = torch.chunk(B, DEPTH, dim=-1)[j]
check_equal(out, B)
print_rank_0('ATB forward: pass')
grad_shape = B_master.shape
grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
torch.distributed.broadcast(grad_master, src=0)
grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
grad = torch.chunk(grad, DEPTH, dim=-1)[j]
out.backward(grad)
B_master.backward(grad_master)
A_grad = A_master.grad
A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i]
A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[j]
check_equal(A_grad, A.grad)
C_grad = C_master.grad
C_grad = torch.chunk(C_grad, DEPTH, dim=0)[i]
C_grad = torch.chunk(C_grad, DEPTH, dim=-1)[j]
check_equal(C_grad, C.grad)
print_rank_0('ATB backward: pass')
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