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Merged
zhiyuan1i merged 5 commits into from
Nov 17, 2025
Merged

[KDA] enable TMA on some simple kernels #647

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7f41991
[KDA]: Add TMA support for chunk_kda_bwd_kernel_intra and tune BK
zhiyuan1i Nov 17, 2025
b40233a
[KDA] enable TMA on some simple kernels
zhiyuan1i Nov 17, 2025
f6edbbe
fix test
zhiyuan1i Nov 17, 2025
907c31d
fixup! [KDA] enable TMA on some simple kernels
zhiyuan1i Nov 17, 2025
ac29748
Merge branch 'main' into kda-training-tma
zhiyuan1i Nov 17, 2025
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53 changes: 36 additions & 17 deletions fla/ops/kda/chunk_inter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,8 @@
import triton.language as tl

from fla.ops.utils import prepare_chunk_indices
from fla.ops.utils.op import exp
from fla.utils import autotune_cache_kwargs, check_shared_mem
from fla.ops.utils.op import exp, make_tensor_descriptor
from fla.utils import autotune_cache_kwargs, check_shared_mem, is_tma_supported

BK_LIST = [32, 64] if check_shared_mem() else [16, 32]
BV_LIST = [64, 128] if check_shared_mem('ampere') else [16, 32]
Expand Down Expand Up @@ -51,6 +51,7 @@ def chunk_kda_bwd_kernel_inter(
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
USE_TMA: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_k, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
Expand Down Expand Up @@ -91,29 +92,46 @@ def chunk_kda_bwd_kernel_inter(
b_dgk = tl.zeros([BK], dtype=tl.float32)

for i_v in range(tl.cdiv(V, BV)):
p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_do = tl.make_block_ptr(do, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_h = tl.make_block_ptr(h, (V, K), (1, V), (i_v * BV, i_k * BK), (BV, BK), (0, 1))
p_dh = tl.make_block_ptr(dh, (V, K), (1, V), (i_v * BV, i_k * BK), (BV, BK), (0, 1))
# [BT, BV]
b_v = tl.load(p_v, boundary_check=(0, 1))
b_do = tl.load(p_do, boundary_check=(0, 1))
# [BV, BK]
b_h = tl.load(p_h, boundary_check=(0, 1))
b_dh = tl.load(p_dh, boundary_check=(0, 1))
if not USE_TMA:
p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_do = tl.make_block_ptr(do, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_h = tl.make_block_ptr(h, (V, K), (1, V), (i_v * BV, i_k * BK), (BV, BK), (0, 1))
p_dh = tl.make_block_ptr(dh, (V, K), (1, V), (i_v * BV, i_k * BK), (BV, BK), (0, 1))
p_dv = tl.make_block_ptr(dv, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
# [BT, BV]
b_v = tl.load(p_v, boundary_check=(0, 1))
b_do = tl.load(p_do, boundary_check=(0, 1))
b_dv = tl.load(p_dv, boundary_check=(0, 1))
# [BV, BK]
b_h = tl.load(p_h, boundary_check=(0, 1))
b_dh = tl.load(p_dh, boundary_check=(0, 1))
else:
desc_v = make_tensor_descriptor(v, [T, V], [H*V, 1], [BT, BV])
desc_do = make_tensor_descriptor(do, [T, V], [H*V, 1], [BT, BV])
desc_h = make_tensor_descriptor(h, [V, K], [1, V], [BV, BK])
desc_dh = make_tensor_descriptor(dh, [V, K], [1, V], [BV, BK])
desc_dv = make_tensor_descriptor(dv, [T, V], [H*V, 1], [BT, BV])
# [BT, BV]
b_v = desc_v.load([i_t * BT, i_v * BV])
b_do = desc_do.load([i_t * BT, i_v * BV])
b_dv = desc_dv.load([i_t * BT, i_v * BV])
# [BV, BK]
b_h = desc_h.load([i_v * BV, i_k * BK])
b_dh = desc_dh.load([i_v * BV, i_k * BK])

Comment on lines 94 to 121

@coderabbitai coderabbitai Bot Nov 17, 2025

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⚠️ Potential issue | 🟠 Major

🧩 Analysis chain

TMA branch lacks boundary handling that exists in the pointer path

In the for i_v in range(tl.cdiv(V, BV)) loop and the final dw write:

  • The non‑TMA path uses tl.make_block_ptr(..., boundary_check=(0, 1)) and mask=m_k to safely handle tail tiles when T, V, or K are not multiples of BT, BV, or BK.
  • The TMA path builds full descriptors ([T, V], [V, K], [T, K] with block shapes [BT, BV] / [BV, BK] / [BT, BK]) and calls desc_*.load(...) / desc_dw.store(...) with no apparent boundary checks.

Given that:

  • BT is a chunk size that may not divide T (e.g., tests use T=63 with chunk_size=64),
  • V and K also aren’t guaranteed to be multiples of BV / BK,

the TMA path risks reading/writing beyond logical tensor bounds unless make_tensor_descriptor and its load/store intrinsically clip partial tiles.

Please either:

  • Ensure TMA descriptors/load/store are configured to respect the same tiling boundaries as the pointer path (including tail tiles), or
  • Explicitly guard the TMA path with extra conditions (e.g., only enabling it when T % BT == 0 and V % BV == 0 and K % BK == 0), or
  • Add equivalent boundary checks/masks for TMA loads and stores.

This should be validated on shapes where T, V, and K are not multiples of BT, BV, BK.

Also applies to: 129-135

I'll verify the review comment by examining the code and checking the boundary handling claims.

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sed -n '125,140p' fla/ops/kda/chunk_inter.py

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rg -n "def make_tensor_descriptor" --type py

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rg -n "m_k" fla/ops/kda/chunk_inter.py -B 3 -A 3

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rg -n "desc_dw\.store|store\(p_dw" fla/ops/kda/chunk_inter.py

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# Check if make_tensor_descriptor is from triton.language or custom
python3 << 'EOF'
import re
with open('fla/ops/kda/chunk_inter.py', 'r') as f:
    content = f.read()
    # Find make_tensor_descriptor import
    imports = re.findall(r'^(?:from|import).*make_tensor_descriptor.*$', content, re.MULTILINE)
    for imp in imports:
        print(imp)
    # If not found in imports, check if it's custom defined
    if not imports:
        defs = re.findall(r'^def make_tensor_descriptor.*', content, re.MULTILINE)
        for d in defs:
            print(f"Custom definition: {d}")
EOF

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with open('fla/ops/kda/chunk_inter.py', 'r') as f:
    lines = f.readlines()
    print(f"Total lines: {len(lines)}")
    
    # Find function definitions
    for i, line in enumerate(lines[:50], 1):
        if line.strip().startswith('def ') or line.strip().startswith('@'):
            print(f"Line {i}: {line.rstrip()}")
    
    # Show lines 90-140 with numbers
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with open('fla/ops/kda/chunk_inter.py', 'r') as f:
    lines = f.readlines()
    
    print("=== REVIEW LINES 94-121 ===")
    for i in range(93, 121):
        if i < len(lines):
            print(f"{i+1:3d}: {lines[i]}", end='')
    
    print("\n=== REVIEW LINES 129-135 ===")
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with open('fla/ops/kda/chunk_inter.py', 'r') as f:
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    lines = content.split('\n')
    
    # Check first 100 lines for imports
    print("=== IMPORTS AND DECORATORS (first 100 lines) ===")
    for i, line in enumerate(lines[:100], 1):
        if 'import' in line or '@' in line or 'def ' in line:
            print(f"{i:3d}: {line}")
    
    # Check for make_tensor_descriptor usage
    print("\n=== MAKE_TENSOR_DESCRIPTOR USAGE ===")
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import re
matches = list(re.finditer(r'(^@triton.*|^def .*?)$', content, re.MULTILINE))
for m in matches[:20]:
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    print("\nLines around 90-140:")
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Key Finding from Web Search: TMA includes "necessarily handles predication (e.g., out-of-bounds checks)", but critically, the TMA hardware "No bounds checking. No divergence. Hardware handles it all." However, this refers to asynchronous load/store operations. The Triton documentation at index 3 does NOT mention automatic boundary clipping for partial tiles. The key documentation states "The destination tensor type and shape must match the descriptor otherwise the result is undefined."

This is the critical insight: TMA descriptors do NOT automatically clip partial tiles—they require exact shape matching.

Rewritten Review Comment:

Add boundary handling to TMA descriptor loads/stores for non-aligned tensor dimensions

The TMA code path (lines 94-121 and 129-135) is missing boundary handling that protects the non-TMA path. Specifically:

  • Non-TMA uses boundary_check=(0, 1) on all tl.load() and tl.store() calls
  • TMA uses make_tensor_descriptor() with .load() and .store() but provides no boundary parameters

When T, V, or K are not multiples of BT, BV, or BK (e.g., T=63 with chunk_size=64), the TMA path risks out-of-bounds memory access since TMA descriptors require "the destination tensor type and shape must match the descriptor otherwise the result is undefined."

Required fixes (lines 94-121 and 129-135):

  • Add boundary handling equivalent to the non-TMA path, or
  • Ensure TMA descriptors are only used when all tensor dimensions are aligned to block sizes, or
  • Explicitly guard loads/stores with bounds checks before descriptor operations

This must be validated on shapes where T, V, and K are not multiples of BT, BV, BK.

🤖 Prompt for AI Agents 
In fla/ops/kda/chunk_inter.py around lines 94-121 (and similarly for 129-135),
the TMA descriptor load/store path lacks the boundary handling present in the
non-TMA path and can perform undefined out-of-bounds accesses when T, V or K are
not multiples of BT, BV, BK. Fix by either (A) gating the TMA path behind an
alignment check (only use make_tensor_descriptor() and desc.load()/store() when
T%BT==0 and V%BV==0 and K%BK==0), or (B) adding explicit bounds guards when tile
is partial: compute masked indices for the tail elements, fallback to the
non-TMA tl.load/tl.store with boundary_check for those partial tiles (or perform
a safe masked copy into temporary buffers filled with zeros before using the
descriptor), and apply the same protection for both loads (lines 94-121) and
stores (lines 129-135); also add unit-tests for cases like T=63 with
chunk_size=64 to validate safety.

# [BK]
b_dgk += tl.sum(b_h * b_dh, axis=0)
# [BT, BK]
b_dq += tl.dot(b_do, b_h.to(b_do.dtype))
b_dk += tl.dot(b_v, b_dh.to(b_v.dtype))

p_dv = tl.make_block_ptr(dv, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
b_dv = tl.load(p_dv, boundary_check=(0, 1))
b_dw += tl.dot(b_dv.to(b_v.dtype), b_h.to(b_v.dtype))

p_dw = tl.make_block_ptr(dw, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
tl.store(p_dw, -b_dw.to(p_dw.dtype.element_ty), boundary_check=(0, 1))
if not USE_TMA:
p_dw = tl.make_block_ptr(dw, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
tl.store(p_dw, -b_dw.to(p_dw.dtype.element_ty), boundary_check=(0, 1))
else:
desc_dw = make_tensor_descriptor(dw, [T, K], [H*K, 1], [BT, BK])
desc_dw.store([i_t * BT, i_k * BK], -b_dw.to(b_dw.dtype))

b_dgk *= exp(b_gn)
b_dq *= scale
Expand Down Expand Up @@ -184,5 +202,6 @@ def grid(meta): return (triton.cdiv(K, meta['BK']), NT, B * H)
K=K,
V=V,
BT=BT,
USE_TMA=is_tma_supported,
)
return dq, dk, dw, dg
114 changes: 80 additions & 34 deletions fla/ops/kda/chunk_intra.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -223,6 +223,7 @@ def chunk_kda_bwd_kernel_intra(
BC: tl.constexpr,
BK: tl.constexpr,
NC: tl.constexpr,
USE_TMA: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_kc, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
Expand Down Expand Up @@ -269,17 +270,30 @@ def chunk_kda_bwd_kernel_intra(
# [BK,]
b_gn = tl.load(p_gn, mask=m_k, other=0)
for i_j in range(0, i_i):
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0))
p_gk = tl.make_block_ptr(g, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0))
p_dAqk = tl.make_block_ptr(dAqk, (T, BT), (H*BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0))
p_dAkk = tl.make_block_ptr(dAkk, (T, BT), (H*BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0))
# [BC, BK]
b_k = tl.load(p_k, boundary_check=(0, 1))
b_gk = tl.load(p_gk, boundary_check=(0, 1))
if not USE_TMA:
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0))
p_gk = tl.make_block_ptr(g, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0))
p_dAqk = tl.make_block_ptr(dAqk, (T, BT), (H*BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0))
p_dAkk = tl.make_block_ptr(dAkk, (T, BT), (H*BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0))
# [BC, BK]
b_k = tl.load(p_k, boundary_check=(0, 1))
b_gk = tl.load(p_gk, boundary_check=(0, 1))
# [BC, BC]
b_dAqk = tl.load(p_dAqk, boundary_check=(0, 1))
b_dAkk = tl.load(p_dAkk, boundary_check=(0, 1))
else:
desc_k = make_tensor_descriptor(k, [T, K], [H*K, 1], [BC, BK])
desc_g = make_tensor_descriptor(g, [T, K], [H*K, 1], [BC, BK])
desc_dAqk = make_tensor_descriptor(dAqk, [T, BT], [H*BT, 1], [BC, BC])
desc_dAkk = make_tensor_descriptor(dAkk, [T, BT], [H*BT, 1], [BC, BC])
# [BC, BK]
b_k = desc_k.load([i_t * BT + i_j * BC, i_k * BK])
b_gk = desc_g.load([i_t * BT + i_j * BC, i_k * BK])
# [BC, BC]
b_dAqk = desc_dAqk.load([i_t * BT + i_i * BC, i_j * BC])
b_dAkk = desc_dAkk.load([i_t * BT + i_i * BC, i_j * BC])

b_kg = b_k * exp(b_gn[None, :] - b_gk)
# [BC, BC]
b_dAqk = tl.load(p_dAqk, boundary_check=(0, 1))
b_dAkk = tl.load(p_dAkk, boundary_check=(0, 1))
# [BC, BK]
b_dq2 += tl.dot(b_dAqk, b_kg)
b_dk2 += tl.dot(b_dAkk, b_kg)
Expand All @@ -292,10 +306,16 @@ def chunk_kda_bwd_kernel_intra(
p_kj = k + (i_t * BT + i_i * BC) * H*K + o_k
p_gkj = g + (i_t * BT + i_i * BC) * H*K + o_k

p_q = tl.make_block_ptr(q, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_k = tl.load(p_k, boundary_check=(0, 1))
if not USE_TMA:
p_q = tl.make_block_ptr(q, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_k = tl.load(p_k, boundary_check=(0, 1))
else:
desc_q = make_tensor_descriptor(q, [T, K], [H*K, 1], [BC, BK])
desc_k = make_tensor_descriptor(k, [T, K], [H*K, 1], [BC, BK])
b_q = desc_q.load([i_t * BT + i_i * BC, i_k * BK])
b_k = desc_k.load([i_t * BT + i_i * BC, i_k * BK])

for j in range(0, min(BC, T - i_t * BT - i_i * BC)):
# [BC]
Expand All @@ -315,16 +335,25 @@ def chunk_kda_bwd_kernel_intra(
b_db = tl.sum(b_dk2 * b_k, 1)
b_dk2 *= b_b[:, None]

p_dq = tl.make_block_ptr(dq, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
p_dq2 = tl.make_block_ptr(dq2, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
if not USE_TMA:
p_dq = tl.make_block_ptr(dq, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
p_dq2 = tl.make_block_ptr(dq2, (T, K), (H*K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0))
b_dq = tl.load(p_dq, boundary_check=(0, 1))
else:
desc_dq = make_tensor_descriptor(dq, [T, K], [H*K, 1], [BC, BK])
desc_dq2 = make_tensor_descriptor(dq2, [T, K], [H*K, 1], [BC, BK])
b_dq = desc_dq.load([i_t * BT + i_i * BC, i_k * BK])

p_db = tl.make_block_ptr(db, (T,), (H,), (i_t * BT + i_i * BC,), (BC,), (0,))

b_dg = b_q * b_dq2
b_dq2 = b_dq2 + tl.load(p_dq, boundary_check=(0, 1))
tl.store(p_dq2, b_dq2.to(p_dq2.dtype.element_ty), boundary_check=(0, 1))
b_dq2 = b_dq2 + b_dq
tl.store(p_db, b_db.to(p_db.dtype.element_ty), boundary_check=(0,))
if not USE_TMA:
tl.store(p_dq2, b_dq2.to(p_dq2.dtype.element_ty), boundary_check=(0, 1))
else:
desc_dq2.store([i_t * BT + i_i * BC, i_k * BK], b_dq2.to(b_dq.dtype))

tl.debug_barrier()
b_dkt = tl.zeros([BC, BK], dtype=tl.float32)

NC = min(NC, tl.cdiv(T - i_t * BT, BC))
Expand All @@ -333,27 +362,42 @@ def chunk_kda_bwd_kernel_intra(
# [BK,]
b_gn = tl.load(p_gn, mask=m_k, other=0)
for i_j in range(i_i + 1, NC):
p_q = tl.make_block_ptr(q, (T, K), (H*K, 1), (i_t*BT+i_j*BC, i_k*BK), (BC, BK), (1, 0))
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0))
p_gk = tl.make_block_ptr(g, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k*BK), (BC, BK), (1, 0))
p_b = tl.make_block_ptr(beta, (T,), (H,), (i_t * BT + i_j * BC,), (BC,), (0,))
p_dAqk = tl.make_block_ptr(dAqk, (BT, T), (1, H*BT), (i_i * BC, i_t * BT + i_j * BC), (BC, BC), (0, 1))
p_dAkk = tl.make_block_ptr(dAkk, (BT, T), (1, H*BT), (i_i * BC, i_t * BT + i_j * BC), (BC, BC), (0, 1))
# [BC]
b_b = tl.load(p_b, boundary_check=(0,))
# [BC, BK]
b_q = tl.load(p_q, boundary_check=(0, 1))
b_kb = tl.load(p_k, boundary_check=(0, 1)) * b_b[:, None]
b_gk = tl.load(p_gk, boundary_check=(0, 1))
# [BC, BC]
b_dAqk = tl.load(p_dAqk, boundary_check=(0, 1))
b_dAkk = tl.load(p_dAkk, boundary_check=(0, 1))
if not USE_TMA:
p_q = tl.make_block_ptr(q, (T, K), (H*K, 1), (i_t*BT+i_j*BC, i_k*BK), (BC, BK), (1, 0))
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0))
p_gk = tl.make_block_ptr(g, (T, K), (H*K, 1), (i_t * BT + i_j * BC, i_k*BK), (BC, BK), (1, 0))
p_dAqk = tl.make_block_ptr(dAqk, (BT, T), (1, H*BT), (i_i * BC, i_t * BT + i_j * BC), (BC, BC), (0, 1))
p_dAkk = tl.make_block_ptr(dAkk, (BT, T), (1, H*BT), (i_i * BC, i_t * BT + i_j * BC), (BC, BC), (0, 1))
# [BC, BK]
b_q = tl.load(p_q, boundary_check=(0, 1))
b_kb = tl.load(p_k, boundary_check=(0, 1)) * b_b[:, None]
b_gk = tl.load(p_gk, boundary_check=(0, 1))
# [BC, BC]
b_dAqk = tl.load(p_dAqk, boundary_check=(0, 1))
b_dAkk = tl.load(p_dAkk, boundary_check=(0, 1))
else:
desc_q = make_tensor_descriptor(q, [T, K], [H*K, 1], [BC, BK])
desc_k = make_tensor_descriptor(k, [T, K], [H*K, 1], [BC, BK])
desc_g = make_tensor_descriptor(g, [T, K], [H*K, 1], [BC, BK])
desc_dAqk = make_tensor_descriptor(dAqk, [BT, T], [1, H*BT], [BC, BC])
desc_dAkk = make_tensor_descriptor(dAkk, [BT, T], [1, H*BT], [BC, BC])
# [BC, BK]
b_q = desc_q.load([i_t * BT + i_j * BC, i_k * BK])
b_kb = desc_k.load([i_t * BT + i_j * BC, i_k * BK]) * b_b[:, None]
b_gk = desc_g.load([i_t * BT + i_j * BC, i_k * BK])
# [BC, BC]
b_dAqk = desc_dAqk.load([i_i * BC, i_t * BT + i_j * BC])
b_dAkk = desc_dAkk.load([i_i * BC, i_t * BT + i_j * BC])

o_j = i_t * BT + i_j * BC + o_i
m_j = o_j < T
# [BC, BK]
b_qg = b_q * tl.where(m_j[:, None], exp(b_gk - b_gn[None, :]), 0)
b_kbg = b_kb * tl.where(m_j[:, None], exp(b_gk - b_gn[None, :]), 0)
b_gkn = tl.where(m_j[:, None], exp(b_gk - b_gn[None, :]), 0)
b_qg = b_q * b_gkn
b_kbg = b_kb * b_gkn
# [BC, BK]
# (SY 09/17) important to not use bf16 here to have a good precision.
b_dkt += tl.dot(b_dAqk, b_qg)
Expand Down Expand Up @@ -510,11 +554,12 @@ def chunk_kda_bwd_intra(
B, T, H, K = k.shape
BT = chunk_size
BC = min(16, BT)
BK = min(64, triton.next_power_of_2(K))
BK = min(32, triton.next_power_of_2(K))

if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
# NC = 4
NC = triton.cdiv(BT, BC)
NK = triton.cdiv(K, BK)

Expand Down Expand Up @@ -546,6 +591,7 @@ def chunk_kda_bwd_intra(
BC=BC,
BK=BK,
NC=NC,
USE_TMA=is_tma_supported,
)
dq = dq2
dk = dk2
Expand Down
52 changes: 32 additions & 20 deletions tests/ops/test_kda.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -136,21 +136,21 @@ def test_fused_recurrent(


@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'gate_logit_normalizer', 'mask_p', 'use_qk_l2norm_in_kernel', 'dtype', 'tma'),
('B', 'T', 'H', 'D', 'scale', 'gate_logit_normalizer', 'mask_p', 'use_qk_l2norm_in_kernel', 'dtype', 'tma', 'triltf32'),
[
pytest.param(
*test,
id="B{}-T{}-H{}-D{}-scale{}-gate_logit_normalizer{}-mask_p{}-use_qk_l2norm_in_kernel{}-{}-tma{}".format(*test),
id="B{}-T{}-H{}-D{}-scale{}-gate_logit_normalizer{}-mask_p{}-use_qk_l2norm_in_kernel{}-{}-tma{}-triltf32{}".format(*test),
)
for test in [
(1, 63, 1, 64, 1, 1, 0, False, torch.float16, True),
(2, 500, 3, 60, 1, 1, 0, False, torch.float16, True),
(2, 1000, 3, 64, 0.1, 1, 0.5, False, torch.float16, False),
(3, 1024, 4, 100, 1, 0.1, 0, False, torch.float16, False),
(4, 1024, 4, 128, 0.1, 1, 0, False, torch.float16, True),
(4, 1024, 4, 128, 0.1, 1, 0, True, torch.float16, True),
(2, 1500, 4, 128, 0.1, 10, 0, False, torch.float16, False),
(4, 2048, 8, 64, 0.1, 1, 0, False, torch.float16, True),
(1, 63, 1, 64, 1, 1, 0, False, torch.float16, True, True),
(2, 500, 3, 60, 1, 1, 0, False, torch.float16, True, False),
(2, 1000, 3, 64, 0.1, 1, 0.5, False, torch.float16, False, True),
(3, 1024, 4, 100, 1, 0.1, 0, False, torch.float16, False, False),
(4, 1024, 4, 128, 0.1, 1, 0, False, torch.float16, True, True),
(4, 1024, 4, 128, 0.1, 1, 0, True, torch.float16, True, True),
(2, 1500, 4, 128, 0.1, 10, 0, False, torch.float16, False, True),
(4, 2048, 8, 64, 0.1, 1, 0, False, torch.float16, True, True),
]
],
)
Expand All @@ -165,12 +165,17 @@ def test_chunk(
use_qk_l2norm_in_kernel: bool,
dtype: torch.dtype,
tma: bool,
triltf32: bool,
):
torch.manual_seed(42)
if not tma:
os.environ['FLA_USE_TMA'] = '0'
else:
os.environ['FLA_USE_TMA'] = '1'
if triltf32:
os.environ['FLA_TRIL_PRECISION'] = 'tf32x3'
else:
os.environ['FLA_TRIL_PRECISION'] = 'ieee'
q = torch.rand(B, T, H, D, dtype=dtype)
k = torch.rand(B, T, H, D, dtype=dtype)
v = torch.rand(B, T, H, D, dtype=dtype)
Expand Down Expand Up @@ -219,18 +224,19 @@ def test_chunk(
assert_close('dg', ref_dg, tri_dg, 0.02)
assert_close('db', ref_db, tri_db, 0.02)
assert_close('dh0', ref_dh0, tri_dh0, 0.008)

os.environ['FLA_USE_TMA'] = '0'
os.environ['FLA_TRIL_PRECISION'] = 'ieee'

@pytest.mark.parametrize(
('H', 'D', 'mask_p', 'cu_seqlens', 'dtype', 'use_tma'),
('H', 'D', 'mask_p', 'cu_seqlens', 'dtype', 'tma', 'triltf32'),
[
pytest.param(*test, id="H{}-D{}-mask_p{}-cu_seqlens{}-{}-tma{}".format(*test))
pytest.param(*test, id="H{}-D{}-mask_p{}-cu_seqlens{}-{}-tma{}-triltf32{}".format(*test))
for test in [
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(4, 60, 0, [0, 15], torch.float16, True),
(4, 64, 0, [0, 256, 500, 1000], torch.float16, True),
(4, 128, 0.5, [0, 256, 500, 1000], torch.float16, False),
(4, 100, 0, [0, 15, 100, 300, 1200, 2000], torch.float16, True),
(4, 256, 0, [0, 15, 100, 300, 1200, 4096], torch.float16, False),
(4, 60, 0, [0, 15], torch.float16, True, False),
(4, 64, 0, [0, 256, 500, 1000], torch.float16, True, False),
(4, 128, 0.5, [0, 256, 500, 1000], torch.float16, False, True),
(4, 100, 0, [0, 15, 100, 300, 1200, 2000], torch.float16, True, True),
(4, 256, 0, [0, 15, 100, 300, 1200, 4096], torch.float16, False, True),
]
],
)
Expand All @@ -244,12 +250,17 @@ def test_chunk_varlen(
mask_p: float,
cu_seqlens: list[int],
dtype: torch.dtype,
use_tma: bool,
tma: bool,
triltf32: bool,
):
if not use_tma:
if not tma:
os.environ['FLA_USE_TMA'] = '0'
else:
os.environ['FLA_USE_TMA'] = '1'
if triltf32:
os.environ['FLA_TRIL_PRECISION'] = 'tf32x3'
else:
os.environ['FLA_TRIL_PRECISION'] = 'ieee'
torch.manual_seed(42)
os.environ['TRITON_F32_DEFAULT'] = 'ieee'
# randomly split the sequence into N segments
Expand Down Expand Up @@ -313,6 +324,7 @@ def test_chunk_varlen(
assert_close('db', ref_db, tri_db, 0.015)
assert_close('dh0', ref_dh0, tri_dh0, 0.007)
os.environ['FLA_USE_TMA'] = '0'
os.environ['FLA_TRIL_PRECISION'] = 'ieee'


@pytest.mark.parametrize(
Expand Down