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Merge branch 'main' into feature/add_randomSample_pyhon_api
2 parents 612b3e9 + 2286cf7 commit ae78feb

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‎include/infinicore/ops/mul.hpp‎

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#pragma once
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#include "../device.hpp"
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#include "common/op.hpp"
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namespace infinicore::op {
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class Mul {
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public:
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using schema = void (*)(Tensor, Tensor, Tensor);
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static void execute(Tensor c, Tensor a, Tensor b);
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static common::OpDispatcher<schema> &dispatcher();
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};
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Tensor mul(Tensor a, Tensor b);
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void mul_(Tensor c, Tensor a, Tensor b);
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} // namespace infinicore::op

‎python/infinicore/__init__.py‎

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from infinicore.ops.add import add
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from infinicore.ops.attention import attention
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from infinicore.ops.matmul import matmul
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from infinicore.ops.mul import mul
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from infinicore.ops.rearrange import rearrange
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from infinicore.tensor import (
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Tensor,
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"add",
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"attention",
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"matmul",
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"mul",
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"rearrange",
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"empty",
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"empty_like",

‎python/infinicore/ops/mul.py‎

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from infinicore.lib import _infinicore
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from infinicore.tensor import Tensor
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def mul(input, other, *, out=None):
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if out is None:
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return Tensor(_infinicore.mul(input._underlying, other._underlying))
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_infinicore.mul_(out._underlying, input._underlying, other._underlying)

‎src/infinicore/ops/mul/mul.cc‎

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#include "infinicore/ops/mul.hpp"
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namespace infinicore::op {
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common::OpDispatcher<Mul::schema> &Mul::dispatcher() {
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static common::OpDispatcher<Mul::schema> dispatcher_;
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return dispatcher_;
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};
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void Mul::execute(Tensor c, Tensor a, Tensor b) {
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dispatcher().lookup(context::getDevice().getType())(c, a, b);
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}
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Tensor mul(Tensor a, Tensor b) {
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auto c = Tensor::empty(a->shape(), a->dtype(), a->device());
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mul_(c, a, b);
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return c;
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}
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void mul_(Tensor c, Tensor a, Tensor b) {
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Mul::execute(c, a, b);
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}
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} // namespace infinicore::op

‎src/infinicore/ops/mul/mul_infiniop.cc‎

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#include "../../utils.hpp"
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#include "infinicore/common/hash.hpp"
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#include "infinicore/ops/common/cache.hpp"
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#include "infinicore/ops/mul.hpp"
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#include <infiniop.h>
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namespace infinicore::op::mul_impl::infiniop {
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thread_local common::OpCache<size_t, infiniopMulDescriptor_t> caches(
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100, // capacity
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[](infiniopMulDescriptor_t &desc) {
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if (desc != nullptr) {
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INFINICORE_CHECK_ERROR(infiniopDestroyMulDescriptor(desc));
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desc = nullptr;
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}
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});
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void calculate(Tensor c, Tensor a, Tensor b) {
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size_t seed = hash_combine(c, b, a);
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auto device_type = context::getDevice().getType();
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auto device_index = context::getDevice().getIndex();
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auto &cache = caches.getCache(device_type, device_index);
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auto desc_opt = cache.get(seed);
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infiniopMulDescriptor_t desc = nullptr;
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if (!desc_opt) {
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INFINICORE_CHECK_ERROR(infiniopCreateMulDescriptor(
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context::getInfiniopHandle(), &desc,
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c->desc(), a->desc(), b->desc()));
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cache.put(seed, desc);
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} else {
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desc = *desc_opt;
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}
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size_t workspace_size = 0;
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INFINICORE_CHECK_ERROR(infiniopGetMulWorkspaceSize(desc, &workspace_size));
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std::shared_ptr<Memory> workspace = context::allocateMemory(workspace_size);
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INFINICORE_CHECK_ERROR(infiniopMul(
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desc, workspace->data(), workspace_size,
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c->data(), a->data(), b->data(), context::getStream()));
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}
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static bool registered = []() {
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Mul::dispatcher().registerAll(&calculate, false);
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return true;
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}();
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} // namespace infinicore::op::mul_impl::infiniop

‎src/infinicore/pybind11/ops.hpp‎

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#include "ops/causal_softmax.hpp"
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#include "ops/matmul.hpp"
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#include "ops/random_sample.hpp"
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#include "ops/mul.hpp"
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#include "ops/rearrange.hpp"
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#include "ops/rms_norm.hpp"
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#include "ops/silu.hpp"
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bind_causal_softmax(m);
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bind_random_sample(m);
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bind_matmul(m);
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bind_mul(m);
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bind_rearrange(m);
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bind_rms_norm(m);
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bind_silu(m);

‎src/infinicore/pybind11/ops/mul.hpp‎

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#pragma once
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#include <pybind11/pybind11.h>
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#include "infinicore/ops/mul.hpp"
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namespace py = pybind11;
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namespace infinicore::ops {
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inline void bind_mul(py::module &m) {
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m.def("mul",
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&op::mul,
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py::arg("a"),
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py::arg("b"),
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R"doc(Element-wise multiplication of two tensors.)doc");
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m.def("mul_",
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&op::mul_,
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py::arg("c"),
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py::arg("a"),
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py::arg("b"),
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R"doc(In-place element-wise tensor multiplication.)doc");
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}
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} // namespace infinicore::ops

‎test/infinicore/ops/mul.py‎

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import sys
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import os
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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import torch
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import infinicore
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from framework.base import BaseOperatorTest, TensorSpec, TestCase
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from framework.runner import GenericTestRunner
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from framework.utils import is_broadcast
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# ==============================================================================
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# Operator-specific configuration
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# ==============================================================================
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# Test cases format: (shape, a_strides, b_strides, c_strides)
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_TEST_CASES_DATA = [
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((13, 4), None, None, None),
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((13, 4), (10, 1), (10, 1), (10, 1)),
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((13, 4), (0, 1), None, None),
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((13, 4, 4), None, None, None),
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((13, 4, 4), (20, 4, 1), (20, 4, 1), (20, 4, 1)),
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((13, 4, 4), (4, 0, 1), (0, 4, 1), None),
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((16, 5632), None, None, None),
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((16, 5632), (13312, 1), (13312, 1), (13312, 1)),
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]
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# Data types
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_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
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# Tolerance
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_TOLERANCE_MAP = {
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infinicore.float16: {"atol": 0, "rtol": 1e-2},
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infinicore.float32: {"atol": 0, "rtol": 1e-3},
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infinicore.bfloat16: {"atol": 0, "rtol": 5e-2},
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}
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def build_test_cases():
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test_cases = []
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for data in _TEST_CASES_DATA:
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shape = data[0]
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a_strides = data[1] if len(data) > 1 else None
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b_strides = data[2] if len(data) > 2 else None
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c_strides = data[3] if len(data) > 3 else None
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a_supports_inplace = not is_broadcast(a_strides)
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b_supports_inplace = not is_broadcast(b_strides)
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c_supports_inplace = not is_broadcast(c_strides)
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for dtype in _TENSOR_DTYPES:
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tolerance = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
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a_spec = TensorSpec.from_tensor(shape, a_strides, dtype)
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b_spec = TensorSpec.from_tensor(shape, b_strides, dtype)
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c_spec = TensorSpec.from_tensor(shape, c_strides, dtype)
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# Out-of-place (return value)
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test_cases.append(
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TestCase(
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inputs=[a_spec, b_spec],
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kwargs={},
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output_spec=None,
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comparison_target=None,
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tolerance=tolerance,
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description=f"Mul - OUT_OF_PLACE (dtype={dtype})",
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)
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)
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# With explicit output tensor (mul(a, b, out=c))
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if c_supports_inplace:
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test_cases.append(
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TestCase(
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inputs=[a_spec, b_spec],
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kwargs={},
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output_spec=c_spec,
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comparison_target="out",
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tolerance=tolerance,
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description=f"Mul - INPLACE(out) (dtype={dtype})",
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)
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)
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# In-place on first input (mul(a, b, out=a))
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if a_supports_inplace:
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test_cases.append(
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TestCase(
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inputs=[a_spec, b_spec],
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kwargs={"out": 0},
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output_spec=None,
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comparison_target=0,
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tolerance=tolerance,
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description=f"Mul - INPLACE(a) (dtype={dtype})",
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)
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)
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# In-place on second input (mul(a, b, out=b))
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if b_supports_inplace:
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test_cases.append(
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TestCase(
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inputs=[a_spec, b_spec],
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kwargs={"out": 1},
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output_spec=None,
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comparison_target=1,
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tolerance=tolerance,
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description=f"Mul - INPLACE(b) (dtype={dtype})",
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)
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)
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return test_cases
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_TEST_CASES = build_test_cases()
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class OpTest(BaseOperatorTest):
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"""Mul test with simplified test case parsing"""
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def __init__(self):
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super().__init__("Mul")
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def get_test_cases(self):
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return _TEST_CASES
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def torch_operator(self, a, b, out=None, **kwargs):
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return torch.mul(a, b, out=out)
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def infinicore_operator(self, a, b, out=None, **kwargs):
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try:
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return infinicore.mul(a, b, out=out)
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except AttributeError as exc:
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raise NotImplementedError("InfiniCore mul operator not available") from exc
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def main():
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"""Main entry point"""
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runner = GenericTestRunner(OpTest)
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runner.run_and_exit()
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if __name__ == "__main__":
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main()

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