mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +08:00
Lei Wang
bf8a6fc142
* [Refactor] Improve flash attention example and layout comparison logic - Removed unnecessary annotation for `lse_local_split` in the flash attention example to streamline the code. - Updated the handling of `lse_local_split` to utilize parallel processing for better performance. - Refactored kernel compilation and profiling logic to enhance clarity and maintainability in the flash attention example. - Added a condition in `FragmentNode::IsEqual` to handle broadcast cases, improving the robustness of layout comparisons. * lint fix * [Enhancement] Add support for shared memory scope in Fill operation - Introduced handling for `shared.dyn` and `shared` memory scopes in the Fill operation. - Implemented parallel operation and layout inference for improved performance in shared memory scenarios. - Updated thread loop partitioning and vectorization logic to accommodate new memory scope handling. * [Refactor] Remove deprecated decorator and enhance Cython kernel handling - Removed the deprecated decorator from the main module and added a new implementation in the utils module for better organization. - Introduced a pointer map in the Cython kernel adapter to manage pointer arguments, improving runtime shape resolution. - Updated the Cython kernel wrapper to utilize the new pointer map for handling kernel arguments. - Enhanced error checking in the tensor utility functions to ensure static shapes are enforced. - Added a new proxy module for buffer and tensor handling, streamlining the interface for TIR programs. * [Feature] Add matrix multiplication test and kernel implementation - Introduced a new test file `test_tilelang_language_ptr.py` that implements a matrix multiplication function using TileLang's primitives. - The `matmul_test` function defines a kernel for performing tile-level GEMM operations with customizable block sizes and data types. - Added a `run_matmul` function to compile and execute the kernel, along with a test function to validate the implementation. - Updated the `proxy.py` file to enhance type handling for buffer and tensor proxies, ensuring compatibility with TIR programs. - Minor formatting improvements in `deprecated.py` for better readability. * lint fix * [Refactor] Update tensor creation in matrix multiplication test - Replaced `T.Tensor.from_ptr` with `T.make_tensor` in `matmul_test` for improved clarity and consistency. - Updated imports in `__init__.py` to include `make_tensor`. - Added `make_tensor` function in `proxy.py` to streamline tensor creation from pointers. * [Refactor] Update tensor definitions across multiple files - Replaced instances of `T.Tensor` with updated tensor definitions in various benchmark and example files to enhance consistency and clarity. - Adjusted tensor shapes and types in functions related to matrix multiplication, attention mechanisms, and other operations. - Improved documentation in README and example files to reflect changes in tensor usage. * lint fix * [Refactor] Update tensor types in attention and matrix multiplication examples - Replaced instances of `T.Tensor` with `T.SharedTensor` and `T.FragmentTensor` in various attention and matrix multiplication functions to improve consistency and clarity. - Adjusted tensor definitions in benchmark and example files to align with the new tensor types. - Enhanced the overall structure and readability of the code by standardizing tensor usage across multiple files. * lint fix * [Refactor] Update tensor types in GEMM example and test files - Replaced instances of `T.Tensor` with `T.LocalTensor` and `T.Buffer` in the GEMM example and related test functions to improve consistency and clarity. - Enhanced the overall structure of the code by standardizing tensor usage across multiple files, aligning with recent updates in tensor definitions. * [Refactor] Update tensor usage in customize.py - Replaced instances of `T.Tensor` with `T.Buffer` in the `reshape` and `view` functions to enhance consistency with recent tensor definitions. - Improved code clarity by standardizing buffer usage across the file. * [Refactor] Update tensor types in test_tilelang_transform_annotate_device_regions.py - Replaced instances of `T.Tensor` with `T.Buffer` in the `before` and `expected` methods of the `TestAnnotateThreadExtent` and `TestAnnotateDeviceScope` classes to enhance consistency with recent tensor definitions. - Improved code clarity by standardizing buffer usage across the test file. * [Refactor] Update tensor types to SharedBuffer and FragmentBuffer - Replaced instances of `T.SharedTensor` and `T.FragmentTensor` with `T.SharedBuffer` and `T.FragmentBuffer` across multiple benchmark, example, and test files to enhance consistency with recent tensor definitions. - Improved code clarity and structure by standardizing buffer usage in attention and matrix multiplication functions. * [Refactor] Introduce Tensor alias for Buffer in proxy.py - Added a new alias `Tensor` for `Buffer` in `proxy.py` to facilitate JIT compilation, ensuring that inputs and outputs are mapped with `torch.Tensor`. - This change enhances clarity and consistency in tensor usage across the codebase.
296 lines
5.2 KiB
Python
296 lines
5.2 KiB
Python
from tilelang import tvm as tvm
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import tilelang.testing
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def matmul(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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accum_dtype,
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num_stages,
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threads,
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):
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor(A_shape, in_dtype),
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B: T.Tensor(B_shape, in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def run_gemm(
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M,
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N,
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K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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block_M,
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block_N,
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block_K,
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num_stages=3,
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num_threads=128,
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):
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program = matmul(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(program, out_idx=[2])
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profiler = kernel.get_profiler()
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
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def test_gemm_f16f16f16_nn():
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run_gemm(
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512,
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1024,
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768,
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False,
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False,
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"float16",
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"float16",
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"float16",
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128,
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256,
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32,
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2,
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)
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def test_gemm_f16f16f32_nn():
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run_gemm(
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512,
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1024,
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768,
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False,
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False,
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"float16",
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"float16",
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"float32",
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128,
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128,
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32,
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)
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def test_gemm_bf16bf16f32_nn():
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run_gemm(
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512,
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1024,
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768,
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False,
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False,
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"bfloat16",
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"bfloat16",
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"float32",
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128,
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128,
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32,
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)
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def test_gemm_f32f32f32_nn():
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run_gemm(
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512,
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1024,
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768,
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False,
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False,
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"float32",
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"float32",
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"float32",
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64,
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128,
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32,
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)
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def test_gemm_f16f16f16_tn():
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run_gemm(
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512,
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1024,
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768,
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True,
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False,
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"float16",
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"float16",
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"float16",
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128,
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256,
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32,
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2,
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)
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def test_gemm_f16f16f16_nt():
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run_gemm(
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512,
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1024,
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768,
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False,
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True,
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"float16",
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"float16",
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"float16",
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128,
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256,
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32,
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2,
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)
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def test_gemm_i8i8i32_nt():
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run_gemm(512, 1024, 768, False, True, "int8", "int8", "int32", 128, 128, 64)
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def test_gemm_i8i8i32_tn():
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run_gemm(512, 1024, 768, True, False, "int8", "int8", "int32", 128, 128, 64)
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def test_gemm_f64f64f64_nt():
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run_gemm(512, 512, 512, False, True, "float64", "float64", "float64", 64, 32, 16)
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def test_gemm_f32f32f32_nt():
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run_gemm(
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512,
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1024,
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768,
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False,
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True,
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"float32",
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"float32",
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"float32",
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64,
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128,
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32,
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)
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def test_gemm_f32f32f32_tn():
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run_gemm(
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512,
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1024,
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768,
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True,
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False,
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"float32",
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"float32",
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"float32",
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64,
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128,
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32,
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)
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def test_pad_aligned_f16f16f16_nn():
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run_gemm(
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512 - 8,
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1024 - 32,
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768 - 24,
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False,
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False,
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"float16",
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"float16",
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"float16",
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128,
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256,
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32,
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2,
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)
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def test_pad_f16f16f16_nn():
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run_gemm(
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512 - 9,
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1024 - 7,
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768 - 5,
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False,
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False,
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"float16",
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"float16",
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"float16",
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128,
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256,
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32,
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2,
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)
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def test_pad_f16f16f32_nn():
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run_gemm(
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512 + 19,
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1024 + 17,
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768 + 15,
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False,
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False,
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"float16",
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"float16",
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"float32",
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128,
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64,
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32,
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)
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if __name__ == "__main__":
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tilelang.testing.main()
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