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Unverified Commit 9f899769 authored by Hubert Lu's avatar Hubert Lu Committed by GitHub
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Merge pull request #56 from ROCmSoftwarePlatform/dev/hubertlu/multihead_attn 

Enable multihead atten
parents 325246e4 62f06964
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Showing with 112 additions and 335 deletions
apex/contrib/csrc/optimizers/fused_adam_cuda_kernel.cu
View file @ 9f899769
......@@ -234,12 +234,12 @@ void fused_adam_cuda(
}
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
if (g.scalar_type() == at::ScalarType::Half) {
if (g.scalar_type() == at::ScalarType::Half || g.scalar_type() == at::ScalarType::BFloat16) {
//all other values should be fp32 for half gradients
AT_ASSERTM(p.scalar_type() == at::ScalarType::Float, "expected parameter to be of float type");
//dispatch is done on the gradient type
using namespace at; // prevents "toString is undefined" errors
DISPATCH_FLOAT_AND_HALF(g.scalar_type(), 0, "adam_cuda_kernel",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(g.scalar_type(), 0, "adam_cuda_kernel",
using accscalar_t = at::acc_type<scalar_t_0, true>;
adam_cuda_kernel<accscalar_t, scalar_t_0><<<blocks,threadsPerBlock, 0, stream>>>(
p.DATA_PTR<accscalar_t>(),
......@@ -308,12 +308,12 @@ void fused_adam_cuda_mt(
size_t tl_sz = tensor_lists.size();
AT_ASSERTM(tl_sz == 4 || tl_sz == 5, "expected tensor lists of size 4 or 5");
if (tensor_lists[3][0].scalar_type() == at::ScalarType::Half) {
if (tensor_lists[3][0].scalar_type() == at::ScalarType::Half || tensor_lists[3][0].scalar_type() == at::ScalarType::BFloat16) {
//alher values should be fp32 for half gradients
AT_ASSERTM(tensor_lists[0][0].scalar_type() == at::ScalarType::Float, "expected parameter to be of float type");
//dich is done on the gradient type
if (tl_sz == 5) {
DISPATCH_FLOAT_AND_HALF(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
using accscalar_t = at::acc_type<scalar_t_0, true>;
multi_tensor_apply<5>(
BLOCK_SIZE,
......@@ -330,7 +330,7 @@ void fused_adam_cuda_mt(
decay);
);
} else {
DISPATCH_FLOAT_AND_HALF(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(tensor_lists[3][0].scalar_type(), 0, "adam_cuda_mt_kernel",
using accscalar_t = at::acc_type<scalar_t_0, true>;
multi_tensor_apply<4>(
BLOCK_SIZE,
......@@ -846,13 +846,13 @@ void fused_reversible_adam_cuda(
}
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
if (g.scalar_type() == at::ScalarType::Half) {
if (g.scalar_type() == at::ScalarType::Half || g.scalar_type() == at::ScalarType::BFloat16) {
//all other values should be fp32 for half gradients
AT_ASSERTM(p.scalar_type() == at::ScalarType::Float, "expected parameter to be of float type");
//dispatch is done on the gradient type
using namespace at; // prevents "toString is undefined" errors
if (p_copy.numel() == 0 || p_copy.scalar_type() == g.scalar_type()) {
DISPATCH_FLOAT_AND_HALF(g.scalar_type(), 0, "adam_cuda_kernel",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(g.scalar_type(), 0, "adam_cuda_kernel",
using accscalar_t = at::acc_type<scalar_t_0, true>;
reversible_adam_cuda_kernel<accscalar_t, scalar_t_0, scalar_t_0><<<blocks,threadsPerBlock, 0, stream>>>(
p.DATA_PTR<accscalar_t>(),
......@@ -871,7 +871,7 @@ void fused_reversible_adam_cuda(
);
} else {
AT_ASSERTM(p_copy.scalar_type() == at::ScalarType::Byte, "expected parameter to be of byte type");
DISPATCH_FLOAT_AND_HALF(g.scalar_type(), 0, "adam_cuda_e5m2_kernel",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(g.scalar_type(), 0, "adam_cuda_e5m2_kernel",
using accscalar_t = at::acc_type<scalar_t_0, true>;
reversible_adam_cuda_kernel<accscalar_t, scalar_t_0, uint8_t><<<blocks,threadsPerBlock, 0, stream>>>(
p.DATA_PTR<accscalar_t>(),
......@@ -991,12 +991,12 @@ void fused_maybe_adam_undo_cuda(
}
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
if (g.scalar_type() == at::ScalarType::Half) {
if (g.scalar_type() == at::ScalarType::Half || g.scalar_type() == at::ScalarType::BFloat16) {
//all other values should be fp32 for half gradients
AT_ASSERTM(p.scalar_type() == at::ScalarType::Float, "expected parameter to be of float type");
//dispatch is done on the gradient type
using namespace at; // prevents "toString is undefined" errors
DISPATCH_FLOAT_AND_HALF(g.scalar_type(), 0, "adam_cuda_kernel",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(g.scalar_type(), 0, "adam_cuda_kernel",
using accscalar_t = at::acc_type<scalar_t_0, true>;
maybe_adam_undo_cuda_kernel<accscalar_t, scalar_t_0><<<blocks,threadsPerBlock, 0, stream>>>(
overflow_flag.numel() ? overflow_flag.DATA_PTR<int>() : NULL,
......
apex/contrib/csrc/optimizers/multi_tensor_distopt_adam_kernel.cu
View file @ 9f899769
......@@ -187,7 +187,7 @@ void multi_tensor_fused_adam_cuda(
AT_ASSERTM(tl_sz == 4 || tl_sz == 5, "expected tensor lists of size 4 or 5");
if (tl_sz == 5) {
DISPATCH_FLOAT_AND_HALF(tensor_lists[3][0].scalar_type(), 0, "dist_adam_cuda_kernel", // g
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(tensor_lists[3][0].scalar_type(), 0, "dist_adam_cuda_kernel", // g
using accscalar_t = at::acc_type<scalar_t_0, true>;
multi_tensor_apply<5>(
BLOCK_SIZE,
......@@ -206,7 +206,7 @@ void multi_tensor_fused_adam_cuda(
(adamMode_t) mode);
);
} else {
DISPATCH_FLOAT_AND_HALF(tensor_lists[3][0].scalar_type(), 0, "dist_adam_cuda_kernel", // g
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(tensor_lists[3][0].scalar_type(), 0, "dist_adam_cuda_kernel", // g
using accscalar_t = at::acc_type<scalar_t_0, true>;
multi_tensor_apply<4>(
BLOCK_SIZE,
......
apex/contrib/csrc/xentropy/xentropy_kernel.cu
View file @ 9f899769
......@@ -586,7 +586,7 @@ std::vector<Tensor> host_softmax_xentropy(
const Tensor & labels_,
const float smoothing,
const bool half_to_float){
if (half_to_float) AT_ASSERTM(input_.type().scalarType() == ScalarType::Half,"conversion is supported for Half type only");
if (half_to_float) AT_ASSERTM(input_.type().scalarType() == ScalarType::Half || input_.type().scalarType() == ScalarType::BFloat16,"conversion is supported for Half and BFloat16 type only");
AT_ASSERTM(labels_.type().scalarType() == ScalarType::Long,"Label type should be CUDA Long");
auto input = input_.contiguous();
......@@ -617,7 +617,7 @@ std::vector<Tensor> host_softmax_xentropy(
dim3 grid(outer_size);
using namespace at;
DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "host_softmax_xentropy",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(input.scalar_type(), 0, "host_softmax_xentropy",
using accscalar_t = at::acc_type<scalar_t_0, true>;
const int ILP = sizeof(float4)/sizeof(scalar_t_0);
dim3 block = SoftMax_getBlockSize(ILP, dim_size);
......@@ -685,7 +685,7 @@ Tensor host_softmax_xentropy_backward(
dim3 grid(outer_size);
DISPATCH_FLOAT_AND_HALF(gI.scalar_type(), 0, "host_softmax_xentropy_backward",
DISPATCH_FLOAT_AND_HALF_AND_BFLOAT16(gI.scalar_type(), 0, "host_softmax_xentropy_backward",
using accscalar_t = acc_type<scalar_t_0, true>;
const int ILP = sizeof(float4)/sizeof(scalar_t_0);
dim3 block = SoftMax_getBlockSize(ILP, dim_size);
......@@ -724,7 +724,7 @@ at::Tensor softmax_xentropy_backward_cuda(
const float smoothing) {
bool half_to_float = grad_loss.type().scalarType() != logits.type().scalarType();
if (half_to_float) {
AT_ASSERTM((grad_loss.type().scalarType() == ScalarType::Float && logits.type().scalarType() == ScalarType::Half), "expected input and grad types to match, or input to be at::Half and grad to be at::Float");
AT_ASSERTM((grad_loss.type().scalarType() == ScalarType::Float && (logits.type().scalarType() == ScalarType::Half || logits.type().scalarType() == ScalarType::BFloat16)), "expected input and grad types to match, or input to be at::Half or at::Bfloat16 and grad to be at::Float");
}
return host_softmax_xentropy_backward<LogSoftMaxBackwardEpilogue>(grad_loss, logits, max_log_sum_exp, labels, smoothing, half_to_float);
}
apex/contrib/multihead_attn/encdec_multihead_attn_func.py
View file @ 9f899769
......@@ -263,6 +263,6 @@ class EncdecAttnFunc(torch.autograd.Function):
input_q_grads, input_kv_grads, \
input_weight_q_grads, input_weight_kv_grads, output_weight_grads, \
input_bias_grads_q, input_bias_grads_kv, output_bias_grads, \
None, None
None, None, None
encdec_attn_func = EncdecAttnFunc.apply
apex/contrib/multihead_attn/fast_self_multihead_attn_norm_add_func.py
View file @ 9f899769
......@@ -9,7 +9,7 @@ class FastSelfAttnNormAddFunc(torch.autograd.Function):
dropout_prob_t = torch.tensor([dropout_prob])
null_tensor = torch.tensor([])
use_mask = (pad_mask is not None)
print("---use_mask-----",use_mask)
lyr_nrm_results, \
lyr_nrm_mean, \
lyr_nrm_invvar, \
......
apex/contrib/multihead_attn/self_multihead_attn_func.py
View file @ 9f899769
......@@ -230,6 +230,6 @@ class SelfAttnFunc(torch.autograd.Function):
input_grads, \
input_weight_grads, output_weight_grads, \
input_bias_grads, output_bias_grads, \
None, None
None, None, None
self_attn_func = SelfAttnFunc.apply
setup.py
View file @ 9f899769
......@@ -144,17 +144,19 @@ if "--distributed_adam" in sys.argv:
from torch.utils.cpp_extension import BuildExtension
cmdclass['build_ext'] = BuildExtension
if torch.utils.cpp_extension.CUDA_HOME is None:
if torch.utils.cpp_extension.CUDA_HOME is None and not IS_ROCM_PYTORCH:
raise RuntimeError("--distributed_adam was requested, but nvcc was not found. Are you sure your environment has nvcc available? If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.")
else:
nvcc_args_adam = ['-O3', '--use_fast_math'] + version_dependent_macros
hipcc_args_adam = ['-O3'] + version_dependent_macros
ext_modules.append(
CUDAExtension(name='distributed_adam_cuda',
sources=['apex/contrib/csrc/optimizers/multi_tensor_distopt_adam.cpp',
'apex/contrib/csrc/optimizers/multi_tensor_distopt_adam_kernel.cu'],
include_dirs=[os.path.join(this_dir, 'csrc')],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/optimizers')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros,
'nvcc':['-O3',
'--use_fast_math'] + version_dependent_macros}))
'nvcc':nvcc_args_adam if not IS_ROCM_PYTORCH else hipcc_args_adam}))
if "--distributed_lamb" in sys.argv:
from torch.utils.cpp_extension import CUDAExtension
......@@ -275,7 +277,8 @@ if "--xentropy" in sys.argv:
CUDAExtension(name='xentropy_cuda',
sources=['apex/contrib/csrc/xentropy/interface.cpp',
'apex/contrib/csrc/xentropy/xentropy_kernel.cu'],
include_dirs=[os.path.join(this_dir, 'csrc')],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/xentropy')],
extra_compile_args={'cxx': ['-O3'] + version_dependent_macros,
'nvcc':['-O3'] + version_dependent_macros}))
......@@ -297,7 +300,8 @@ if "--deprecated_fused_adam" in sys.argv:
CUDAExtension(name='fused_adam_cuda',
sources=['apex/contrib/csrc/optimizers/fused_adam_cuda.cpp',
'apex/contrib/csrc/optimizers/fused_adam_cuda_kernel.cu'],
include_dirs=[os.path.join(this_dir, 'csrc')],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/optimizers')],
extra_compile_args={'cxx': ['-O3'] + version_dependent_macros,
'nvcc' : nvcc_args_fused_adam if not IS_ROCM_PYTORCH else hipcc_args_fused_adam}))
......@@ -356,7 +360,7 @@ if "--fast_layer_norm" in sys.argv:
'-gencode', 'arch=compute_70,code=sm_70',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'-I./apex/contrib/csrc/layer_norm/',
'-Iapex/contrib/csrc/layer_norm',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
......@@ -368,121 +372,98 @@ if "--fast_multihead_attn" in sys.argv:
from torch.utils.cpp_extension import BuildExtension
cmdclass['build_ext'] = BuildExtension.with_options(use_ninja=False)
if torch.utils.cpp_extension.CUDA_HOME is None:
if torch.utils.cpp_extension.CUDA_HOME is None and not IS_ROCM_PYTORCH:
raise RuntimeError("--fast_multihead_attn was requested, but nvcc was not found. Are you sure your environment has nvcc available? If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.")
else:
# Check, if CUDA11 is installed for compute capability 8.0
cc_flag = []
_, bare_metal_major, _ = get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
if int(bare_metal_major) >= 11:
cc_flag.append('-gencode')
cc_flag.append('arch=compute_80,code=sm_80')
if not IS_ROCM_PYTORCH:
_, bare_metal_major, _ = get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
if int(bare_metal_major) >= 11:
cc_flag.append('-gencode')
cc_flag.append('arch=compute_80,code=sm_80')
subprocess.run(["git", "submodule", "update", "--init", "apex/contrib/csrc/multihead_attn/cutlass"])
nvcc_args_mha = ['-O3',
'-gencode',
'arch=compute_70,code=sm_70',
'-Iapex/contrib/csrc/multihead_attn/cutlass',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag
hipcc_args_mha = ['-O3',
'-Iapex/contrib/csrc/multihead_attn/cutlass',
'-I/opt/rocm/include/hiprand',
'-I/opt/rocm/include/rocrand',
'-U__HIP_NO_HALF_OPERATORS__',
'-U__HIP_NO_HALF_CONVERSIONS__'] + version_dependent_macros + generator_flag
ext_modules.append(
CUDAExtension(name='fast_additive_mask_softmax_dropout',
sources=['apex/contrib/csrc/multihead_attn/additive_masked_softmax_dropout.cpp',
sources=['apex/contrib/csrc/multihead_attn/additive_masked_softmax_dropout_cpp.cpp',
'apex/contrib/csrc/multihead_attn/additive_masked_softmax_dropout_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
ext_modules.append(
CUDAExtension(name='fast_mask_softmax_dropout',
sources=['apex/contrib/csrc/multihead_attn/masked_softmax_dropout.cpp',
sources=['apex/contrib/csrc/multihead_attn/masked_softmax_dropout_cpp.cpp',
'apex/contrib/csrc/multihead_attn/masked_softmax_dropout_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
ext_modules.append(
CUDAExtension(name='fast_self_multihead_attn_bias_additive_mask',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_additive_mask.cpp',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_additive_mask_cpp.cpp',
'apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_additive_mask_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
ext_modules.append(
CUDAExtension(name='fast_self_multihead_attn_bias',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn_bias.cpp',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_cpp.cpp',
'apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
ext_modules.append(
CUDAExtension(name='fast_self_multihead_attn',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn.cpp',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn_cpp.cpp',
'apex/contrib/csrc/multihead_attn/self_multihead_attn_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
ext_modules.append(
CUDAExtension(name='fast_self_multihead_attn_norm_add',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn_norm_add.cpp',
sources=['apex/contrib/csrc/multihead_attn/self_multihead_attn_norm_add_cpp.cpp',
'apex/contrib/csrc/multihead_attn/self_multihead_attn_norm_add_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
ext_modules.append(
CUDAExtension(name='fast_encdec_multihead_attn',
sources=['apex/contrib/csrc/multihead_attn/encdec_multihead_attn.cpp',
sources=['apex/contrib/csrc/multihead_attn/encdec_multihead_attn_cpp.cpp',
'apex/contrib/csrc/multihead_attn/encdec_multihead_attn_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
ext_modules.append(
CUDAExtension(name='fast_encdec_multihead_attn_norm_add',
sources=['apex/contrib/csrc/multihead_attn/encdec_multihead_attn_norm_add.cpp',
sources=['apex/contrib/csrc/multihead_attn/encdec_multihead_attn_norm_add_cpp.cpp',
'apex/contrib/csrc/multihead_attn/encdec_multihead_attn_norm_add_cuda.cu'],
include_dirs=[os.path.join(this_dir, 'csrc'),
os.path.join(this_dir, 'apex/contrib/csrc/multihead_attn')],
extra_compile_args={'cxx': ['-O3',] + version_dependent_macros + generator_flag,
'nvcc':['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'-I./apex/contrib/csrc/multihead_attn/cutlass/',
'-U__CUDA_NO_HALF_OPERATORS__',
'-U__CUDA_NO_HALF_CONVERSIONS__',
'--expt-relaxed-constexpr',
'--expt-extended-lambda',
'--use_fast_math'] + version_dependent_macros + generator_flag + cc_flag}))
'nvcc':nvcc_args_mha if not IS_ROCM_PYTORCH else hipcc_args_mha}))
setup(
name='apex',
......
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