[Quant][Inductor] Enable quantization dynamic batch size support #108550

leslie-fang-intel · 2023-09-05T02:43:44Z

Stack from ghstack (oldest at bottom):

-> [Quant][Inductor] Enable quantization dynamic batch size support #108550

Summary
This Diff enables dynamic batch size support for quantization use case in Inductor. Take the UT in this PR as example, after this PR, the generated code will have assumption of dynamic input batch size.

cpp_fused_quantize_per_tensor_0 = async_compile.cpp('''
#include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h"
extern "C" void kernel(const float* in_ptr0,
                       unsigned char* out_ptr0,
                       const long ks0,
                       const long ks1)
{
    {
        #pragma GCC ivdep
        for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L))
        {
            #pragma GCC ivdep
            for(long i1=static_cast<long>(0L); i1<static_cast<long>(3L); i1+=static_cast<long>(1L))
            {
                #pragma GCC ivdep
                for(long i2=static_cast<long>(0L); i2<static_cast<long>(static_cast<long>(ks1*ks1)); i2+=static_cast<long>(1L))
                {
                    auto tmp0 = in_ptr0[static_cast<long>(i2 + (i1*(static_cast<long>(ks1*ks1))) + (3L*i0*(static_cast<long>(ks1*ks1))))];
                    auto tmp1 = static_cast<float>(40.36037717834931);
                    auto tmp2 = decltype(tmp0)(tmp0 * tmp1);
                    auto tmp3 = std::nearbyint(tmp2);
                    auto tmp4 = static_cast<float>(97.0);
                    auto tmp5 = tmp3 + tmp4;
                    auto tmp6 = static_cast<float>(0.0);
                    auto tmp7 = max_propagate_nan(tmp5, tmp6);
                    auto tmp8 = static_cast<float>(255.0);
                    auto tmp9 = min_propagate_nan(tmp7, tmp8);
                    auto tmp10 = static_cast<unsigned char>(tmp9);
                    out_ptr0[static_cast<long>(i1 + (3L*i2) + (3L*i0*(static_cast<long>(ks1*ks1))))] = tmp10;
                }
            }
        }
    }
}
''')


cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1 = async_compile.cpp('''
#include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h"
extern "C" void kernel(const unsigned char* in_ptr0,
                       float* out_ptr0,
                       unsigned char* out_ptr1,
                       const long ks0,
                       const long ks1)
{
    {
        #pragma GCC ivdep
        for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L))
        {
            for(long i1=static_cast<long>(0L); i1<static_cast<long>(16L); i1+=static_cast<long>(16L))
            {
                {
                    #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={at::vec::Vectorized<float>(0)})
                    float tmp_acc0 = 0;
                    at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0);
                    for(long i2=static_cast<long>(0L); i2<static_cast<long>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); i2+=static_cast<long>(1L))
                    {
                        auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i1 + (16L*i0) + (16L*i2) + (16L*i0*(static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L))))) + (32L*i0*(at::native::div_floor_integer(ks1, 2L)))));
                        auto tmp1 = at::vec::convert_uint8_to_float(tmp0);
                        auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.0));
                        auto tmp3 = tmp1 - tmp2;
                        auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.010429476387798786));
                        auto tmp5 = tmp3 * tmp4;
                        tmp_acc0_vec = tmp_acc0_vec + tmp5;
                    }
                    tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (16L*i0)));
                }
            }
        }
    }
    {
        #pragma GCC ivdep
        for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(1L))
        {
            auto tmp0 = out_ptr0[static_cast<long>(i0)];
            auto tmp1 = static_cast<float>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L))));
            auto tmp2 = tmp0 / tmp1;
            auto tmp3 = static_cast<float>(168.09128392896545);
            auto tmp4 = decltype(tmp2)(tmp2 * tmp3);
            auto tmp5 = std::nearbyint(tmp4);
            auto tmp6 = static_cast<float>(0.0);
            auto tmp7 = tmp5 + tmp6;
            auto tmp8 = max_propagate_nan(tmp7, tmp6);
            auto tmp9 = static_cast<float>(255.0);
            auto tmp10 = min_propagate_nan(tmp8, tmp9);
            auto tmp11 = static_cast<unsigned char>(tmp10);
            out_ptr1[static_cast<long>(i0)] = tmp11;
        }
    }
}
''')


cpp_fused_dequantize_per_tensor_2 = async_compile.cpp('''
#include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h"
extern "C" void kernel(const unsigned char* in_ptr0,
                       float* out_ptr0,
                       const long ks0)
{
    {
        for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(16L))
        {
            auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i0));
            auto tmp1 = at::vec::convert_uint8_to_float(tmp0);
            auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0));
            auto tmp3 = tmp1 - tmp2;
            auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.0056716203689575195));
            auto tmp5 = tmp3 * tmp4;
            tmp5.store(out_ptr0 + static_cast<long>(i0));
        }
    }
}
''')


async_compile.wait(globals())
del async_compile

def call(args):
    arg8_1, arg9_1, arg10_1 = args
    args.clear()
    s0 = arg8_1
    s2 = arg9_1
    assert_size_stride(arg10_1, (s0, 3, s2, s2), (3*(s2*s2), s2*s2, s2, 1))
    buf0 = empty_strided((s0, 3, s2, s2), (3*(s2*s2), 1, 3*s2, 3), device='cpu', dtype=torch.uint8)
    cpp_fused_quantize_per_tensor_0(c_void_p(arg10_1.data_ptr()), c_void_p(buf0.data_ptr()), c_long(s0), c_long(s2))
    del arg10_1
    buf1 = torch.ops.onednn.qconv2d_pointwise(buf0, 0.024776775389909744, 97, constant5, constant2, constant3, constant0, [1, 1], [1, 1], [1, 1], 1, 95.88209060714476, 0, False, 'relu', [], '')
    assert_size_stride(buf1, (s0, 16, 1 + s2, 1 + s2), (16 + (16*(s2*s2)) + (32*s2), 1, 16 + (16*s2), 16))
    del buf0
    # Source Nodes: [quantize_per_tensor_default_2], Original ATen: [quantized_decomposed.quantize_per_tensor]
    buf2 = torch.ops.quantized.max_pool2d(buf1, [3, 3], [2, 2], [1, 1], [1, 1], False)
    del buf1
    buf3 = buf2
    assert_size_stride(buf3, (s0, 16, 1 + (s2 // 2), 1 + (s2 // 2)), (16 + (16*((s2 // 2)*(s2 // 2))) + (32*(s2 // 2)), 1, 16 + (16*(s2 // 2)), 16))
    del buf2
    buf4 = empty_strided((s0, 16, 1, 1), (16, 1, 16*s0, 16*s0), device='cpu', dtype=torch.float32)
    buf5 = empty_strided((s0, 16), (16, 1), device='cpu', dtype=torch.uint8)
    cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_long(s0), c_long(s2))
    del buf3
    buf6 = torch.ops.onednn.qlinear_pointwise(buf5, 0.005949148442596197, 0, constant6, constant4, constant3, constant1, 176.31645543014483, 100, False, 'none', [], '')
    assert_size_stride(buf6, (s0, 16), (16, 1))
    del buf5
    buf7 = reinterpret_tensor(buf4, (s0, 16), (16, 1)); del buf4  # reuse
    cpp_fused_dequantize_per_tensor_2(c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_long(s0))
    return (buf7, )

TestPlan

python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_maxpool2d_linear_dynamic

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov

pytorch-bot · 2023-09-05T02:43:46Z

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[ghstack-poisoned]

ghstack-source-id: 3837eee Pull Request resolved: #108550

…e support" cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]

ghstack-source-id: 38bf165 Pull Request resolved: #108550

**Summary** This Diff enables dynamic batch size support for quantization use case in Inductor. Take the UT in this PR as example, after this PR, the generated code will have assumption of dynamic input batch size. ``` cpp_fused_quantize_per_tensor_0 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const float* in_ptr0, unsigned char* out_ptr0, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i1=static_cast<long>(0L); i1<static_cast<long>(3L); i1+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i2=static_cast<long>(0L); i2<static_cast<long>(static_cast<long>(ks1*ks1)); i2+=static_cast<long>(1L)) { auto tmp0 = in_ptr0[static_cast<long>(i2 + (i1*(static_cast<long>(ks1*ks1))) + (3L*i0*(static_cast<long>(ks1*ks1))))]; auto tmp1 = static_cast<float>(40.36037717834931); auto tmp2 = decltype(tmp0)(tmp0 * tmp1); auto tmp3 = std::nearbyint(tmp2); auto tmp4 = static_cast<float>(97.0); auto tmp5 = tmp3 + tmp4; auto tmp6 = static_cast<float>(0.0); auto tmp7 = max_propagate_nan(tmp5, tmp6); auto tmp8 = static_cast<float>(255.0); auto tmp9 = min_propagate_nan(tmp7, tmp8); auto tmp10 = static_cast<unsigned char>(tmp9); out_ptr0[static_cast<long>(i1 + (3L*i2) + (3L*i0*(static_cast<long>(ks1*ks1))))] = tmp10; } } } } } ''') cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, unsigned char* out_ptr1, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { for(long i1=static_cast<long>(0L); i1<static_cast<long>(16L); i1+=static_cast<long>(16L)) { { #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={at::vec::Vectorized<float>(0)}) float tmp_acc0 = 0; at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0); for(long i2=static_cast<long>(0L); i2<static_cast<long>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); i2+=static_cast<long>(1L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i1 + (16L*i0) + (16L*i2) + (16L*i0*(static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L))))) + (32L*i0*(at::native::div_floor_integer(ks1, 2L))))); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.010429476387798786)); auto tmp5 = tmp3 * tmp4; tmp_acc0_vec = tmp_acc0_vec + tmp5; } tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (16L*i0))); } } } } { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(1L)) { auto tmp0 = out_ptr0[static_cast<long>(i0)]; auto tmp1 = static_cast<float>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); auto tmp2 = tmp0 / tmp1; auto tmp3 = static_cast<float>(168.09128392896545); auto tmp4 = decltype(tmp2)(tmp2 * tmp3); auto tmp5 = std::nearbyint(tmp4); auto tmp6 = static_cast<float>(0.0); auto tmp7 = tmp5 + tmp6; auto tmp8 = max_propagate_nan(tmp7, tmp6); auto tmp9 = static_cast<float>(255.0); auto tmp10 = min_propagate_nan(tmp8, tmp9); auto tmp11 = static_cast<unsigned char>(tmp10); out_ptr1[static_cast<long>(i0)] = tmp11; } } } ''') cpp_fused_dequantize_per_tensor_2 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, const long ks0) { { for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(16L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i0)); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.0056716203689575195)); auto tmp5 = tmp3 * tmp4; tmp5.store(out_ptr0 + static_cast<long>(i0)); } } } ''') async_compile.wait(globals()) del async_compile def call(args): arg8_1, arg9_1, arg10_1 = args args.clear() s0 = arg8_1 s2 = arg9_1 assert_size_stride(arg10_1, (s0, 3, s2, s2), (3*(s2*s2), s2*s2, s2, 1)) buf0 = empty_strided((s0, 3, s2, s2), (3*(s2*s2), 1, 3*s2, 3), device='cpu', dtype=torch.uint8) cpp_fused_quantize_per_tensor_0(c_void_p(arg10_1.data_ptr()), c_void_p(buf0.data_ptr()), c_long(s0), c_long(s2)) del arg10_1 buf1 = torch.ops.onednn.qconv2d_pointwise(buf0, 0.024776775389909744, 97, constant5, constant2, constant3, constant0, [1, 1], [1, 1], [1, 1], 1, 95.88209060714476, 0, False, 'relu', [], '') assert_size_stride(buf1, (s0, 16, 1 + s2, 1 + s2), (16 + (16*(s2*s2)) + (32*s2), 1, 16 + (16*s2), 16)) del buf0 # Source Nodes: [quantize_per_tensor_default_2], Original ATen: [quantized_decomposed.quantize_per_tensor] buf2 = torch.ops.quantized.max_pool2d(buf1, [3, 3], [2, 2], [1, 1], [1, 1], False) del buf1 buf3 = buf2 assert_size_stride(buf3, (s0, 16, 1 + (s2 // 2), 1 + (s2 // 2)), (16 + (16*((s2 // 2)*(s2 // 2))) + (32*(s2 // 2)), 1, 16 + (16*(s2 // 2)), 16)) del buf2 buf4 = empty_strided((s0, 16, 1, 1), (16, 1, 16*s0, 16*s0), device='cpu', dtype=torch.float32) buf5 = empty_strided((s0, 16), (16, 1), device='cpu', dtype=torch.uint8) cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_long(s0), c_long(s2)) del buf3 buf6 = torch.ops.onednn.qlinear_pointwise(buf5, 0.005949148442596197, 0, constant6, constant4, constant3, constant1, 176.31645543014483, 100, False, 'none', [], '') assert_size_stride(buf6, (s0, 16), (16, 1)) del buf5 buf7 = reinterpret_tensor(buf4, (s0, 16), (16, 1)); del buf4 # reuse cpp_fused_dequantize_per_tensor_2(c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_long(s0)) return (buf7, ) ``` **TestPlan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_maxpool2d_linear_dynamic ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]

test/inductor/test_mkldnn_pattern_matcher.py

ghstack-source-id: 5ef3c27 Pull Request resolved: #108550

**Summary** This Diff enables dynamic batch size support for quantization use case in Inductor. Take the UT in this PR as example, after this PR, the generated code will have assumption of dynamic input batch size. ``` cpp_fused_quantize_per_tensor_0 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const float* in_ptr0, unsigned char* out_ptr0, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i1=static_cast<long>(0L); i1<static_cast<long>(3L); i1+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i2=static_cast<long>(0L); i2<static_cast<long>(static_cast<long>(ks1*ks1)); i2+=static_cast<long>(1L)) { auto tmp0 = in_ptr0[static_cast<long>(i2 + (i1*(static_cast<long>(ks1*ks1))) + (3L*i0*(static_cast<long>(ks1*ks1))))]; auto tmp1 = static_cast<float>(40.36037717834931); auto tmp2 = decltype(tmp0)(tmp0 * tmp1); auto tmp3 = std::nearbyint(tmp2); auto tmp4 = static_cast<float>(97.0); auto tmp5 = tmp3 + tmp4; auto tmp6 = static_cast<float>(0.0); auto tmp7 = max_propagate_nan(tmp5, tmp6); auto tmp8 = static_cast<float>(255.0); auto tmp9 = min_propagate_nan(tmp7, tmp8); auto tmp10 = static_cast<unsigned char>(tmp9); out_ptr0[static_cast<long>(i1 + (3L*i2) + (3L*i0*(static_cast<long>(ks1*ks1))))] = tmp10; } } } } } ''') cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, unsigned char* out_ptr1, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { for(long i1=static_cast<long>(0L); i1<static_cast<long>(16L); i1+=static_cast<long>(16L)) { { #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={at::vec::Vectorized<float>(0)}) float tmp_acc0 = 0; at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0); for(long i2=static_cast<long>(0L); i2<static_cast<long>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); i2+=static_cast<long>(1L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i1 + (16L*i0) + (16L*i2) + (16L*i0*(static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L))))) + (32L*i0*(at::native::div_floor_integer(ks1, 2L))))); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.010429476387798786)); auto tmp5 = tmp3 * tmp4; tmp_acc0_vec = tmp_acc0_vec + tmp5; } tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (16L*i0))); } } } } { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(1L)) { auto tmp0 = out_ptr0[static_cast<long>(i0)]; auto tmp1 = static_cast<float>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); auto tmp2 = tmp0 / tmp1; auto tmp3 = static_cast<float>(168.09128392896545); auto tmp4 = decltype(tmp2)(tmp2 * tmp3); auto tmp5 = std::nearbyint(tmp4); auto tmp6 = static_cast<float>(0.0); auto tmp7 = tmp5 + tmp6; auto tmp8 = max_propagate_nan(tmp7, tmp6); auto tmp9 = static_cast<float>(255.0); auto tmp10 = min_propagate_nan(tmp8, tmp9); auto tmp11 = static_cast<unsigned char>(tmp10); out_ptr1[static_cast<long>(i0)] = tmp11; } } } ''') cpp_fused_dequantize_per_tensor_2 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, const long ks0) { { for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(16L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i0)); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.0056716203689575195)); auto tmp5 = tmp3 * tmp4; tmp5.store(out_ptr0 + static_cast<long>(i0)); } } } ''') async_compile.wait(globals()) del async_compile def call(args): arg8_1, arg9_1, arg10_1 = args args.clear() s0 = arg8_1 s2 = arg9_1 assert_size_stride(arg10_1, (s0, 3, s2, s2), (3*(s2*s2), s2*s2, s2, 1)) buf0 = empty_strided((s0, 3, s2, s2), (3*(s2*s2), 1, 3*s2, 3), device='cpu', dtype=torch.uint8) cpp_fused_quantize_per_tensor_0(c_void_p(arg10_1.data_ptr()), c_void_p(buf0.data_ptr()), c_long(s0), c_long(s2)) del arg10_1 buf1 = torch.ops.onednn.qconv2d_pointwise(buf0, 0.024776775389909744, 97, constant5, constant2, constant3, constant0, [1, 1], [1, 1], [1, 1], 1, 95.88209060714476, 0, False, 'relu', [], '') assert_size_stride(buf1, (s0, 16, 1 + s2, 1 + s2), (16 + (16*(s2*s2)) + (32*s2), 1, 16 + (16*s2), 16)) del buf0 # Source Nodes: [quantize_per_tensor_default_2], Original ATen: [quantized_decomposed.quantize_per_tensor] buf2 = torch.ops.quantized.max_pool2d(buf1, [3, 3], [2, 2], [1, 1], [1, 1], False) del buf1 buf3 = buf2 assert_size_stride(buf3, (s0, 16, 1 + (s2 // 2), 1 + (s2 // 2)), (16 + (16*((s2 // 2)*(s2 // 2))) + (32*(s2 // 2)), 1, 16 + (16*(s2 // 2)), 16)) del buf2 buf4 = empty_strided((s0, 16, 1, 1), (16, 1, 16*s0, 16*s0), device='cpu', dtype=torch.float32) buf5 = empty_strided((s0, 16), (16, 1), device='cpu', dtype=torch.uint8) cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_long(s0), c_long(s2)) del buf3 buf6 = torch.ops.onednn.qlinear_pointwise(buf5, 0.005949148442596197, 0, constant6, constant4, constant3, constant1, 176.31645543014483, 100, False, 'none', [], '') assert_size_stride(buf6, (s0, 16), (16, 1)) del buf5 buf7 = reinterpret_tensor(buf4, (s0, 16), (16, 1)); del buf4 # reuse cpp_fused_dequantize_per_tensor_2(c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_long(s0)) return (buf7, ) ``` **TestPlan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_maxpool2d_linear_dynamic ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]

…upport" **Summary** This Diff enables dynamic batch size support for quantization use case in Inductor. Take the UT in this PR as example, after this PR, the generated code will have assumption of dynamic input batch size. ``` cpp_fused_quantize_per_tensor_0 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const float* in_ptr0, unsigned char* out_ptr0, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i1=static_cast<long>(0L); i1<static_cast<long>(3L); i1+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i2=static_cast<long>(0L); i2<static_cast<long>(static_cast<long>(ks1*ks1)); i2+=static_cast<long>(1L)) { auto tmp0 = in_ptr0[static_cast<long>(i2 + (i1*(static_cast<long>(ks1*ks1))) + (3L*i0*(static_cast<long>(ks1*ks1))))]; auto tmp1 = static_cast<float>(40.36037717834931); auto tmp2 = decltype(tmp0)(tmp0 * tmp1); auto tmp3 = std::nearbyint(tmp2); auto tmp4 = static_cast<float>(97.0); auto tmp5 = tmp3 + tmp4; auto tmp6 = static_cast<float>(0.0); auto tmp7 = max_propagate_nan(tmp5, tmp6); auto tmp8 = static_cast<float>(255.0); auto tmp9 = min_propagate_nan(tmp7, tmp8); auto tmp10 = static_cast<unsigned char>(tmp9); out_ptr0[static_cast<long>(i1 + (3L*i2) + (3L*i0*(static_cast<long>(ks1*ks1))))] = tmp10; } } } } } ''') cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, unsigned char* out_ptr1, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { for(long i1=static_cast<long>(0L); i1<static_cast<long>(16L); i1+=static_cast<long>(16L)) { { #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={at::vec::Vectorized<float>(0)}) float tmp_acc0 = 0; at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0); for(long i2=static_cast<long>(0L); i2<static_cast<long>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); i2+=static_cast<long>(1L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i1 + (16L*i0) + (16L*i2) + (16L*i0*(static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L))))) + (32L*i0*(at::native::div_floor_integer(ks1, 2L))))); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.010429476387798786)); auto tmp5 = tmp3 * tmp4; tmp_acc0_vec = tmp_acc0_vec + tmp5; } tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (16L*i0))); } } } } { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(1L)) { auto tmp0 = out_ptr0[static_cast<long>(i0)]; auto tmp1 = static_cast<float>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); auto tmp2 = tmp0 / tmp1; auto tmp3 = static_cast<float>(168.09128392896545); auto tmp4 = decltype(tmp2)(tmp2 * tmp3); auto tmp5 = std::nearbyint(tmp4); auto tmp6 = static_cast<float>(0.0); auto tmp7 = tmp5 + tmp6; auto tmp8 = max_propagate_nan(tmp7, tmp6); auto tmp9 = static_cast<float>(255.0); auto tmp10 = min_propagate_nan(tmp8, tmp9); auto tmp11 = static_cast<unsigned char>(tmp10); out_ptr1[static_cast<long>(i0)] = tmp11; } } } ''') cpp_fused_dequantize_per_tensor_2 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, const long ks0) { { for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(16L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i0)); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.0056716203689575195)); auto tmp5 = tmp3 * tmp4; tmp5.store(out_ptr0 + static_cast<long>(i0)); } } } ''') async_compile.wait(globals()) del async_compile def call(args): arg8_1, arg9_1, arg10_1 = args args.clear() s0 = arg8_1 s2 = arg9_1 assert_size_stride(arg10_1, (s0, 3, s2, s2), (3*(s2*s2), s2*s2, s2, 1)) buf0 = empty_strided((s0, 3, s2, s2), (3*(s2*s2), 1, 3*s2, 3), device='cpu', dtype=torch.uint8) cpp_fused_quantize_per_tensor_0(c_void_p(arg10_1.data_ptr()), c_void_p(buf0.data_ptr()), c_long(s0), c_long(s2)) del arg10_1 buf1 = torch.ops.onednn.qconv2d_pointwise(buf0, 0.024776775389909744, 97, constant5, constant2, constant3, constant0, [1, 1], [1, 1], [1, 1], 1, 95.88209060714476, 0, False, 'relu', [], '') assert_size_stride(buf1, (s0, 16, 1 + s2, 1 + s2), (16 + (16*(s2*s2)) + (32*s2), 1, 16 + (16*s2), 16)) del buf0 # Source Nodes: [quantize_per_tensor_default_2], Original ATen: [quantized_decomposed.quantize_per_tensor] buf2 = torch.ops.quantized.max_pool2d(buf1, [3, 3], [2, 2], [1, 1], [1, 1], False) del buf1 buf3 = buf2 assert_size_stride(buf3, (s0, 16, 1 + (s2 // 2), 1 + (s2 // 2)), (16 + (16*((s2 // 2)*(s2 // 2))) + (32*(s2 // 2)), 1, 16 + (16*(s2 // 2)), 16)) del buf2 buf4 = empty_strided((s0, 16, 1, 1), (16, 1, 16*s0, 16*s0), device='cpu', dtype=torch.float32) buf5 = empty_strided((s0, 16), (16, 1), device='cpu', dtype=torch.uint8) cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_long(s0), c_long(s2)) del buf3 buf6 = torch.ops.onednn.qlinear_pointwise(buf5, 0.005949148442596197, 0, constant6, constant4, constant3, constant1, 176.31645543014483, 100, False, 'none', [], '') assert_size_stride(buf6, (s0, 16), (16, 1)) del buf5 buf7 = reinterpret_tensor(buf4, (s0, 16), (16, 1)); del buf4 # reuse cpp_fused_dequantize_per_tensor_2(c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_long(s0)) return (buf7, ) ``` **TestPlan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_maxpool2d_linear_dynamic ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]

ghstack-source-id: e2dfdcc Pull Request resolved: #108550

…upport" **Summary** This Diff enables dynamic batch size support for quantization use case in Inductor. Take the UT in this PR as example, after this PR, the generated code will have assumption of dynamic input batch size. ``` cpp_fused_quantize_per_tensor_0 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const float* in_ptr0, unsigned char* out_ptr0, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i1=static_cast<long>(0L); i1<static_cast<long>(3L); i1+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i2=static_cast<long>(0L); i2<static_cast<long>(static_cast<long>(ks1*ks1)); i2+=static_cast<long>(1L)) { auto tmp0 = in_ptr0[static_cast<long>(i2 + (i1*(static_cast<long>(ks1*ks1))) + (3L*i0*(static_cast<long>(ks1*ks1))))]; auto tmp1 = static_cast<float>(40.36037717834931); auto tmp2 = decltype(tmp0)(tmp0 * tmp1); auto tmp3 = std::nearbyint(tmp2); auto tmp4 = static_cast<float>(97.0); auto tmp5 = tmp3 + tmp4; auto tmp6 = static_cast<float>(0.0); auto tmp7 = max_propagate_nan(tmp5, tmp6); auto tmp8 = static_cast<float>(255.0); auto tmp9 = min_propagate_nan(tmp7, tmp8); auto tmp10 = static_cast<unsigned char>(tmp9); out_ptr0[static_cast<long>(i1 + (3L*i2) + (3L*i0*(static_cast<long>(ks1*ks1))))] = tmp10; } } } } } ''') cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, unsigned char* out_ptr1, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { for(long i1=static_cast<long>(0L); i1<static_cast<long>(16L); i1+=static_cast<long>(16L)) { { #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={at::vec::Vectorized<float>(0)}) float tmp_acc0 = 0; at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0); for(long i2=static_cast<long>(0L); i2<static_cast<long>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); i2+=static_cast<long>(1L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i1 + (16L*i0) + (16L*i2) + (16L*i0*(static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L))))) + (32L*i0*(at::native::div_floor_integer(ks1, 2L))))); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.010429476387798786)); auto tmp5 = tmp3 * tmp4; tmp_acc0_vec = tmp_acc0_vec + tmp5; } tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (16L*i0))); } } } } { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(1L)) { auto tmp0 = out_ptr0[static_cast<long>(i0)]; auto tmp1 = static_cast<float>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); auto tmp2 = tmp0 / tmp1; auto tmp3 = static_cast<float>(168.09128392896545); auto tmp4 = decltype(tmp2)(tmp2 * tmp3); auto tmp5 = std::nearbyint(tmp4); auto tmp6 = static_cast<float>(0.0); auto tmp7 = tmp5 + tmp6; auto tmp8 = max_propagate_nan(tmp7, tmp6); auto tmp9 = static_cast<float>(255.0); auto tmp10 = min_propagate_nan(tmp8, tmp9); auto tmp11 = static_cast<unsigned char>(tmp10); out_ptr1[static_cast<long>(i0)] = tmp11; } } } ''') cpp_fused_dequantize_per_tensor_2 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, const long ks0) { { for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(16L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i0)); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.0056716203689575195)); auto tmp5 = tmp3 * tmp4; tmp5.store(out_ptr0 + static_cast<long>(i0)); } } } ''') async_compile.wait(globals()) del async_compile def call(args): arg8_1, arg9_1, arg10_1 = args args.clear() s0 = arg8_1 s2 = arg9_1 assert_size_stride(arg10_1, (s0, 3, s2, s2), (3*(s2*s2), s2*s2, s2, 1)) buf0 = empty_strided((s0, 3, s2, s2), (3*(s2*s2), 1, 3*s2, 3), device='cpu', dtype=torch.uint8) cpp_fused_quantize_per_tensor_0(c_void_p(arg10_1.data_ptr()), c_void_p(buf0.data_ptr()), c_long(s0), c_long(s2)) del arg10_1 buf1 = torch.ops.onednn.qconv2d_pointwise(buf0, 0.024776775389909744, 97, constant5, constant2, constant3, constant0, [1, 1], [1, 1], [1, 1], 1, 95.88209060714476, 0, False, 'relu', [], '') assert_size_stride(buf1, (s0, 16, 1 + s2, 1 + s2), (16 + (16*(s2*s2)) + (32*s2), 1, 16 + (16*s2), 16)) del buf0 # Source Nodes: [quantize_per_tensor_default_2], Original ATen: [quantized_decomposed.quantize_per_tensor] buf2 = torch.ops.quantized.max_pool2d(buf1, [3, 3], [2, 2], [1, 1], [1, 1], False) del buf1 buf3 = buf2 assert_size_stride(buf3, (s0, 16, 1 + (s2 // 2), 1 + (s2 // 2)), (16 + (16*((s2 // 2)*(s2 // 2))) + (32*(s2 // 2)), 1, 16 + (16*(s2 // 2)), 16)) del buf2 buf4 = empty_strided((s0, 16, 1, 1), (16, 1, 16*s0, 16*s0), device='cpu', dtype=torch.float32) buf5 = empty_strided((s0, 16), (16, 1), device='cpu', dtype=torch.uint8) cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_long(s0), c_long(s2)) del buf3 buf6 = torch.ops.onednn.qlinear_pointwise(buf5, 0.005949148442596197, 0, constant6, constant4, constant3, constant1, 176.31645543014483, 100, False, 'none', [], '') assert_size_stride(buf6, (s0, 16), (16, 1)) del buf5 buf7 = reinterpret_tensor(buf4, (s0, 16), (16, 1)); del buf4 # reuse cpp_fused_dequantize_per_tensor_2(c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_long(s0)) return (buf7, ) ``` **TestPlan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_maxpool2d_linear_dynamic ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]

leslie-fang-intel · 2023-09-07T07:50:24Z

Hi @eellison, Could you kindly help to take a look of this PR?

ghstack-source-id: 81a5e6c Pull Request resolved: #108550

…upport" **Summary** This Diff enables dynamic batch size support for quantization use case in Inductor. Take the UT in this PR as example, after this PR, the generated code will have assumption of dynamic input batch size. ``` cpp_fused_quantize_per_tensor_0 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const float* in_ptr0, unsigned char* out_ptr0, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i1=static_cast<long>(0L); i1<static_cast<long>(3L); i1+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i2=static_cast<long>(0L); i2<static_cast<long>(static_cast<long>(ks1*ks1)); i2+=static_cast<long>(1L)) { auto tmp0 = in_ptr0[static_cast<long>(i2 + (i1*(static_cast<long>(ks1*ks1))) + (3L*i0*(static_cast<long>(ks1*ks1))))]; auto tmp1 = static_cast<float>(40.36037717834931); auto tmp2 = decltype(tmp0)(tmp0 * tmp1); auto tmp3 = std::nearbyint(tmp2); auto tmp4 = static_cast<float>(97.0); auto tmp5 = tmp3 + tmp4; auto tmp6 = static_cast<float>(0.0); auto tmp7 = max_propagate_nan(tmp5, tmp6); auto tmp8 = static_cast<float>(255.0); auto tmp9 = min_propagate_nan(tmp7, tmp8); auto tmp10 = static_cast<unsigned char>(tmp9); out_ptr0[static_cast<long>(i1 + (3L*i2) + (3L*i0*(static_cast<long>(ks1*ks1))))] = tmp10; } } } } } ''') cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, unsigned char* out_ptr1, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { for(long i1=static_cast<long>(0L); i1<static_cast<long>(16L); i1+=static_cast<long>(16L)) { { #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={at::vec::Vectorized<float>(0)}) float tmp_acc0 = 0; at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0); for(long i2=static_cast<long>(0L); i2<static_cast<long>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); i2+=static_cast<long>(1L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i1 + (16L*i0) + (16L*i2) + (16L*i0*(static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L))))) + (32L*i0*(at::native::div_floor_integer(ks1, 2L))))); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.010429476387798786)); auto tmp5 = tmp3 * tmp4; tmp_acc0_vec = tmp_acc0_vec + tmp5; } tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (16L*i0))); } } } } { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(1L)) { auto tmp0 = out_ptr0[static_cast<long>(i0)]; auto tmp1 = static_cast<float>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); auto tmp2 = tmp0 / tmp1; auto tmp3 = static_cast<float>(168.09128392896545); auto tmp4 = decltype(tmp2)(tmp2 * tmp3); auto tmp5 = std::nearbyint(tmp4); auto tmp6 = static_cast<float>(0.0); auto tmp7 = tmp5 + tmp6; auto tmp8 = max_propagate_nan(tmp7, tmp6); auto tmp9 = static_cast<float>(255.0); auto tmp10 = min_propagate_nan(tmp8, tmp9); auto tmp11 = static_cast<unsigned char>(tmp10); out_ptr1[static_cast<long>(i0)] = tmp11; } } } ''') cpp_fused_dequantize_per_tensor_2 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, const long ks0) { { for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(16L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i0)); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.0056716203689575195)); auto tmp5 = tmp3 * tmp4; tmp5.store(out_ptr0 + static_cast<long>(i0)); } } } ''') async_compile.wait(globals()) del async_compile def call(args): arg8_1, arg9_1, arg10_1 = args args.clear() s0 = arg8_1 s2 = arg9_1 assert_size_stride(arg10_1, (s0, 3, s2, s2), (3*(s2*s2), s2*s2, s2, 1)) buf0 = empty_strided((s0, 3, s2, s2), (3*(s2*s2), 1, 3*s2, 3), device='cpu', dtype=torch.uint8) cpp_fused_quantize_per_tensor_0(c_void_p(arg10_1.data_ptr()), c_void_p(buf0.data_ptr()), c_long(s0), c_long(s2)) del arg10_1 buf1 = torch.ops.onednn.qconv2d_pointwise(buf0, 0.024776775389909744, 97, constant5, constant2, constant3, constant0, [1, 1], [1, 1], [1, 1], 1, 95.88209060714476, 0, False, 'relu', [], '') assert_size_stride(buf1, (s0, 16, 1 + s2, 1 + s2), (16 + (16*(s2*s2)) + (32*s2), 1, 16 + (16*s2), 16)) del buf0 # Source Nodes: [quantize_per_tensor_default_2], Original ATen: [quantized_decomposed.quantize_per_tensor] buf2 = torch.ops.quantized.max_pool2d(buf1, [3, 3], [2, 2], [1, 1], [1, 1], False) del buf1 buf3 = buf2 assert_size_stride(buf3, (s0, 16, 1 + (s2 // 2), 1 + (s2 // 2)), (16 + (16*((s2 // 2)*(s2 // 2))) + (32*(s2 // 2)), 1, 16 + (16*(s2 // 2)), 16)) del buf2 buf4 = empty_strided((s0, 16, 1, 1), (16, 1, 16*s0, 16*s0), device='cpu', dtype=torch.float32) buf5 = empty_strided((s0, 16), (16, 1), device='cpu', dtype=torch.uint8) cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_long(s0), c_long(s2)) del buf3 buf6 = torch.ops.onednn.qlinear_pointwise(buf5, 0.005949148442596197, 0, constant6, constant4, constant3, constant1, 176.31645543014483, 100, False, 'none', [], '') assert_size_stride(buf6, (s0, 16), (16, 1)) del buf5 buf7 = reinterpret_tensor(buf4, (s0, 16), (16, 1)); del buf4 # reuse cpp_fused_dequantize_per_tensor_2(c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_long(s0)) return (buf7, ) ``` **TestPlan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_maxpool2d_linear_dynamic ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]

leslie-fang-intel · 2023-09-11T05:00:13Z

Hi @eellison, Could you kindly help to take a look of this PR?

ghstack-source-id: d43f4ae Pull Request resolved: #108550

…upport" **Summary** This Diff enables dynamic batch size support for quantization use case in Inductor. Take the UT in this PR as example, after this PR, the generated code will have assumption of dynamic input batch size. ``` cpp_fused_quantize_per_tensor_0 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const float* in_ptr0, unsigned char* out_ptr0, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i1=static_cast<long>(0L); i1<static_cast<long>(3L); i1+=static_cast<long>(1L)) { #pragma GCC ivdep for(long i2=static_cast<long>(0L); i2<static_cast<long>(static_cast<long>(ks1*ks1)); i2+=static_cast<long>(1L)) { auto tmp0 = in_ptr0[static_cast<long>(i2 + (i1*(static_cast<long>(ks1*ks1))) + (3L*i0*(static_cast<long>(ks1*ks1))))]; auto tmp1 = static_cast<float>(40.36037717834931); auto tmp2 = decltype(tmp0)(tmp0 * tmp1); auto tmp3 = std::nearbyint(tmp2); auto tmp4 = static_cast<float>(97.0); auto tmp5 = tmp3 + tmp4; auto tmp6 = static_cast<float>(0.0); auto tmp7 = max_propagate_nan(tmp5, tmp6); auto tmp8 = static_cast<float>(255.0); auto tmp9 = min_propagate_nan(tmp7, tmp8); auto tmp10 = static_cast<unsigned char>(tmp9); out_ptr0[static_cast<long>(i1 + (3L*i2) + (3L*i0*(static_cast<long>(ks1*ks1))))] = tmp10; } } } } } ''') cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, unsigned char* out_ptr1, const long ks0, const long ks1) { { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L)) { for(long i1=static_cast<long>(0L); i1<static_cast<long>(16L); i1+=static_cast<long>(16L)) { { #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={at::vec::Vectorized<float>(0)}) float tmp_acc0 = 0; at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0); for(long i2=static_cast<long>(0L); i2<static_cast<long>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); i2+=static_cast<long>(1L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i1 + (16L*i0) + (16L*i2) + (16L*i0*(static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L))))) + (32L*i0*(at::native::div_floor_integer(ks1, 2L))))); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(0.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.010429476387798786)); auto tmp5 = tmp3 * tmp4; tmp_acc0_vec = tmp_acc0_vec + tmp5; } tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (16L*i0))); } } } } { #pragma GCC ivdep for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(1L)) { auto tmp0 = out_ptr0[static_cast<long>(i0)]; auto tmp1 = static_cast<float>(1L + (static_cast<long>((at::native::div_floor_integer(ks1, 2L))*(at::native::div_floor_integer(ks1, 2L)))) + (2L*(at::native::div_floor_integer(ks1, 2L)))); auto tmp2 = tmp0 / tmp1; auto tmp3 = static_cast<float>(168.09128392896545); auto tmp4 = decltype(tmp2)(tmp2 * tmp3); auto tmp5 = std::nearbyint(tmp4); auto tmp6 = static_cast<float>(0.0); auto tmp7 = tmp5 + tmp6; auto tmp8 = max_propagate_nan(tmp7, tmp6); auto tmp9 = static_cast<float>(255.0); auto tmp10 = min_propagate_nan(tmp8, tmp9); auto tmp11 = static_cast<unsigned char>(tmp10); out_ptr1[static_cast<long>(i0)] = tmp11; } } } ''') cpp_fused_dequantize_per_tensor_2 = async_compile.cpp(''' #include "/tmp/torchinductor_root/ib/cibrnuq56cxamjj4krp4zpjvsirbmlolpbnmomodzyd46huzhdw7.h" extern "C" void kernel(const unsigned char* in_ptr0, float* out_ptr0, const long ks0) { { for(long i0=static_cast<long>(0L); i0<static_cast<long>(16L*ks0); i0+=static_cast<long>(16L)) { auto tmp0 = at::vec::Vectorized<uint8_t>::loadu_one_fourth(in_ptr0 + static_cast<long>(i0)); auto tmp1 = at::vec::convert_uint8_to_float(tmp0); auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0)); auto tmp3 = tmp1 - tmp2; auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(0.0056716203689575195)); auto tmp5 = tmp3 * tmp4; tmp5.store(out_ptr0 + static_cast<long>(i0)); } } } ''') async_compile.wait(globals()) del async_compile def call(args): arg8_1, arg9_1, arg10_1 = args args.clear() s0 = arg8_1 s2 = arg9_1 assert_size_stride(arg10_1, (s0, 3, s2, s2), (3*(s2*s2), s2*s2, s2, 1)) buf0 = empty_strided((s0, 3, s2, s2), (3*(s2*s2), 1, 3*s2, 3), device='cpu', dtype=torch.uint8) cpp_fused_quantize_per_tensor_0(c_void_p(arg10_1.data_ptr()), c_void_p(buf0.data_ptr()), c_long(s0), c_long(s2)) del arg10_1 buf1 = torch.ops.onednn.qconv2d_pointwise(buf0, 0.024776775389909744, 97, constant5, constant2, constant3, constant0, [1, 1], [1, 1], [1, 1], 1, 95.88209060714476, 0, False, 'relu', [], '') assert_size_stride(buf1, (s0, 16, 1 + s2, 1 + s2), (16 + (16*(s2*s2)) + (32*s2), 1, 16 + (16*s2), 16)) del buf0 # Source Nodes: [quantize_per_tensor_default_2], Original ATen: [quantized_decomposed.quantize_per_tensor] buf2 = torch.ops.quantized.max_pool2d(buf1, [3, 3], [2, 2], [1, 1], [1, 1], False) del buf1 buf3 = buf2 assert_size_stride(buf3, (s0, 16, 1 + (s2 // 2), 1 + (s2 // 2)), (16 + (16*((s2 // 2)*(s2 // 2))) + (32*(s2 // 2)), 1, 16 + (16*(s2 // 2)), 16)) del buf2 buf4 = empty_strided((s0, 16, 1, 1), (16, 1, 16*s0, 16*s0), device='cpu', dtype=torch.float32) buf5 = empty_strided((s0, 16), (16, 1), device='cpu', dtype=torch.uint8) cpp_fused_dequantize_per_tensor_mean_quantize_per_tensor_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_long(s0), c_long(s2)) del buf3 buf6 = torch.ops.onednn.qlinear_pointwise(buf5, 0.005949148442596197, 0, constant6, constant4, constant3, constant1, 176.31645543014483, 100, False, 'none', [], '') assert_size_stride(buf6, (s0, 16), (16, 1)) del buf5 buf7 = reinterpret_tensor(buf4, (s0, 16), (16, 1)); del buf4 # reuse cpp_fused_dequantize_per_tensor_2(c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_long(s0)) return (buf7, ) ``` **TestPlan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_maxpool2d_linear_dynamic ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]

leslie-fang-intel · 2023-09-19T08:24:45Z

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pytorchmergebot · 2023-09-19T08:26:34Z

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leslie-fang-intel · 2023-09-19T08:27:31Z

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ghstack-source-id: 3837eee Pull Request resolved: #108550

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leslie-fang-intel changed the title ~~[Quant][Inductor] Enable Quantization dynamic shape support~~ [Quant][Inductor] Enable Quantization dynamic batch size support Sep 7, 2023

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